Measurements

<p>Marketing is witnessing a significant transformation as traditional identifiers and user-level tracking methods are increasingly deprecated. Moreover, the marketing journey is a perpetual cycle of discovery, learning, and feedback. Even with marketing innovation accounting for over <a href="https://www.gartner.com/en/newsroom/press-releases/gartner-says-marketing-innovation-makes-up-more-than-20--of-mark">20% of marketing budgets</a>, many marketers still grapple with the true definition of the phrase.</p>
<p>In this evolving landscape marketing experimentation emerges as an essential strategy. It equips you to measure the impact of your campaigns by and using the results to learn, iterate, and optimize.</p>
<p>But what consists of "experiments" in this context? What role do they play within the <a href="https://www.lifesight.io/blog/unified-marketing-measurement-guide">marketing measurement</a> toolkit?</p>
<p>In this blog post, we'll explore those questions, discuss the different forms and benefits of marketing experiments, address the challenges encountered, and make predictions.</p>
<h2>What Are Experiments in Marketing?</h2>
<p>Experiments, in marketing are structured approaches that enable teams to test and refine hypotheses iteratively within their marketing strategies. This testing can cover a range of elements, from messaging and channel selection to audience targeting and creative execution. Additionally, experiments can incorporate incrementality testing, such as geographical or split testing, to directly measure the additional impact of one variable over another.</p>
<p>That way, you can execute innovative campaigns and be confident of their success, as you're informed by evidence rather than intuition alone. Conducting marketing experiments also helps save costs, as you don't spend on things that don't work.</p>
<h2>Different Types of Marketing Experiments</h2>
<p>Although the following isn't exhaustive, here are the most common experiments performed by marketers:</p>
<h3>1) A/B testing</h3>
<p><a href="https://www.lifesight.io/blog/ecommerce-ab-testing">A/B testing</a>, or split testing, is a randomized controlled experiment where you test two versions of an element to see which version performs better. It's used in marketing, web development, and user experience (UX).</p>
<p>For example, different landing page layouts can be compared on websites to assess visitor behavior, such as how long they stay on the page or which buttons they click.</p>

![ab-testing-example-of-landing-pages](https://mighty-basketball-fd4aeed960.media.strapiapp.com/ab_testing_example_of_landing_pages_770976b606.webp)

<p><a href="https://upload.wikimedia.org/wikipedia/commons/2/2e/A-B_testing_example.png">source</a></p>
<p>Other A/B testing examples include call-to-action (CTA) buttons, email subject lines, and ad placements.</p>
<h3>2) Geo experiments</h3>
<p>Also known as Matched Market Test (MMT), <a href="https://www.lifesight.io/blog/geo-experiments-guide">geo experiments</a> enable you to analyze the actual impact of your ad campaign within a specific geographic market.</p>

![marketing-experiment-type-geo-experiments](https://mighty-basketball-fd4aeed960.media.strapiapp.com/marketing_experiment_type_geo_experiments_45643da258.webp)

<p><a href="https://www.lifesight.io/blog/geo-experiments-guide">source</a></p>
<p>It typically involves increasing or decreasing your ad spend in that area or even stopping your campaign altogether. You only then closely monitor the changes in sales, signups, downloads, or any other relevant KPIs within this market.</p>
<h3>3) Audience split testing</h3>
<p>It is a variation of A/B testing that involves dividing your audience into segments based on:</p>
<ul>
<li><strong>Psychography:</strong> Personality traits, attitudes, and interests</li>
<li><strong>Demography:</strong> Age, gender, and income level</li>
<li><strong>Behavior:</strong> Purchase history, brand interactions, and product usage rates</li>
</ul>
<p>Audience split testing can compare how two segments react to an entire product, feature set, or campaign rather than different variations of the same element (like an email subject line).</p>

![audience-split-testing-comparision-of-two-segments](https://mighty-basketball-fd4aeed960.media.strapiapp.com/audience_split_testing_comparision_of_two_segments_545ad59eb6.webp)

<p><a href="https://medium.com/dataxutech/a-b-testing-a-k-a-split-run-testing-in-ad-tech-why-when-what-8337f2c1a80d">source</a></p>
<p>For instance, it can help in product development by guiding the prioritization of diverse product features or offerings based on audience preferences.</p>
<h3>4) Multivariate testing</h3>
<p>This advanced testing method enables you to simultaneously test multiple variables or elements within a landing page, email, or any other marketing asset to determine which combination of changes produces the best outcome.</p>

![marketing-experiment-type-multivariate-testing](https://mighty-basketball-fd4aeed960.media.strapiapp.com/marketing_experiment_type_multivariate_testing_928ad07e54.webp)

<p><a href="https://blog.hubspot.com/blog/tabid/6307/bid/30556/the-critical-difference-between-a-b-and-multivariate-tests.aspx#what-is-Multivariate-testing">source</a></p>
<p>Unlike A/B testing, which compares the performance of two versions of a single variable, multivariate testing examines how different elements interact and the overall effect on user behavior or conversion rates.</p>
<h3>5) Split URL testing</h3>
<p>Also known as redirect testing, this involves testing separate URLs to see which one performs better. The testing variations include different page designs, navigational frameworks, or entirely different content approaches hosted on separate URLs.</p>

![split-url-testing](https://mighty-basketball-fd4aeed960.media.strapiapp.com/split_url_testing_4671b7e4ab.webp)

<p><a href="https://www.convert.com/blog/a-b-testing/ab-testing/">source</a></p>
<p>Instead of viewing a variation of the same webpage (as seen in A/B testing), the audience is directed to one of the two unique URLs in split URL testing.</p>
<h2>Scientific Method Applied in Marketing Experiments</h2>
<p>Conducting a marketing experiment requires a structured approach to produce desirable results. It comprises four stages:</p>
<h3>1) Hypothesis formulation</h3>
<p>First, identify a specific marketing problem or opportunity. For example, if you notice a poor-performing landing page on your site, define a hypothesis suggesting a potential solution or explanation.</p>
<p>It could be as straightforward as "switching the CTA button color from blue to red to boost the landing page's conversion rate."</p>
<h3>2) Experimentation</h3>
<p>Next, select an appropriate method (such as A/B testing) to test your marketing hypothesis. Define control and experimental groups and clarify the data you want to collect during the process.</p>
<p>Experiment by introducing different variations to respective groups; for example, some users see a blue CTA button, and others see a red one.</p>
<p>Ensure you test both landing page versions over a period of time and that the sample sizes are large enough to achieve statistical relevance.</p>
<h3>3) Observation</h3>
<p>Gather data from the experiment, such as the number of clicks on the CTA button, session durations, bounce rates, and other key metrics.</p>
<p>To determine differences in outcomes, use statistical methods like T-tests, Analysis of Variance (ANOVA), or regression analysis to compare the performance of the control group with that of the experimental group.</p>
<h3>4) Conclusion</h3>
<p>Analyze the data to determine if your hypothesis stands. If the red CTA button boosts conversions for the landing page, then the theory is validated. Make sure to adopt this change across all relevant marketing materials.</p>
<p>If the hypothesis is disproven, use the insights gained to refine it or explore new hypotheses. Lastly, document the process, findings, and actions taken. This will help you learn from each experiment and inform future marketing strategies.</p>
<h2>The Role of Experiments in the Measurement Toolkit</h2>
<p>Insights gained from experiments are valuable for your current campaign and informing your future marketing initiatives. By continuously iterating and improving your strategies based on what you learn from your experiments, you can enable growth for your team.</p>
<p>Let's explore the role experiments play in the measurement framework.</p>
<h3>1) Strategic integration with traditional tools</h3>
<p>Traditional measurement tools like multi-touch attribution (MTA) provide valuable insights into how various marketing touchpoints contribute to the customer's decision to convert.</p>
<p>However, these tools often rely on historical data and predetermined algorithms, which may not fully account for the changing dynamics of consumer behavior or the interactive effects of different channels.</p>
<p>Therefore, experiments, particularly incrementality tests, should not exist in isolation but rather be integrated with these traditional tools. This may involve technical setup for data sharing and alignment on KPIs.</p>
<p>For example, to assess the true effectiveness of your paid search <a href="https://www.lifesight.io/blog/digital-ad-campaign-optimization-strategies">ad campaign</a>, conduct an incrementality test, dividing your audience into a control group that does not see the ads and a test group that does.</p>
<p>If the test reveals a significant increase in conversions (e.g., sales, signups, demo trials, etc.) attributable directly to the paid search ads, far beyond what the MTA model initially indicated, it means the model underestimated the impact of paid search on driving conversions.</p>

![pan-wu-linkedin-post-on-incrementality](https://mighty-basketball-fd4aeed960.media.strapiapp.com/pan_wu_linkedin_post_on_incrementality_d9c1523d1c.webp)

<p><a href="https://www.linkedin.com/feed/update/urn:li:activity:7114602429685395456/">source</a></p>
<p>Therefore, you can use this opportunity to integrate more granular data and refine the parameters within your MTA model to capture customer behavior driven by ads.</p>
<p>You could incorporate additional touchpoints like video views, chatbot interactions, and social media interactions, adjust the attribution weight given to various marketing channels, or employ more sophisticated analytical techniques to understand the customer journey.</p>
<p>Alternatively, use this <a href="https://www.lifesight.io/resources/incrementality-testing-checklist">checklist to boost ROI with incrementality testing</a>.</p>
<h3>2) Validation of analytics data</h3>
<p>Analytics and measurement platforms provide a wealth of data, but without validation, there's always a risk of making decisions based on inaccurate or incomplete information.</p>
<p>Controlled experiments allow you to test, validate, and refine your hypotheses in a real-world setting. You can directly measure the effect of specific marketing activities, ensuring the data driving your decisions is accurate and actionable.</p>
<p>For example, if analytics suggest a high conversion rate from email marketing, an A/B test could validate this effect.</p>
<p>Create two versions of the email: one (Version A) remains unchanged as the control, and the other (Version B) has slight modifications in the CTA placement or offer.</p>
<p>After sending these versions to similar audience segments, the analytics insights are validated if the test confirms that Version A (the original high-performing email) drives higher conversions than expected.</p>
<h3>3) Calibration of Marketing Mix Models (MMMs)</h3>
<p>MMMs, by design, analyze historical data to deduce how different marketing variables contribute to sales volume over time.</p>
<p>However, without a clear understanding of causality, particularly whether a marketing activity genuinely drives sales or merely correlates with them, MMMs can misinterpret the effectiveness of different channels.</p>
<p>Incrementality tests, which involve controlled experiments like A/B testing, fill this gap by precisely isolating the effect of a single variable (e.g., an ad campaign) on outcomes. They do this by comparing a treated group (exposed to the campaign) against a control group (not exposed), providing reliable insights.</p>
<p>Integrating <a href="https://www.lifesight.io/blog/guide-to-incrementality-testing">incrementality testing</a> into MMM calibration involves employing the results from these tests to adjust the coefficients or weights assigned to marketing channels within the model.</p>
<p>Suppose an experiment shows that social media ads boost sales. In such a case, this insight can adjust the MMM, increasing the weight or influence attributed to social media ads in the model.</p>
<p>This recalibration ensures the model more accurately reflects the actual effectiveness of each marketing channel in driving sales, leading to smarter, more <a href="https://www.lifesight.io/resources/increase-ad-budget-strategically-with-incrementality-tests-and-geo-experiments">effective budget allocation and strategy development</a>. This process holds the promise of significantly improving your marketing outcomes.</p>
<h2>Benefits of Running Marketing Experiments</h2>
<p>Experimentation is important for the following reasons:</p>
<h3>1) Objective assessment of marketing strategies and tactics</h3>
<p>Running controlled experiments helps you base your marketing decisions on what truly works for your specific audience and market context rather than on assumptions or industry norms.</p>
<p>This objective approach also helps you make rational decisions that aren't overly influenced by your personal preferences or anecdotal evidence.</p>
<h3>2) Enhanced understanding of customer behavior and preferences</h3>
<p>Marketing experiments offer a direct window into the customer's world, revealing how they react to different messages, offers, and experiences. This insight is invaluable for tailoring marketing efforts to meet customer needs effectively.</p>
<p>By understanding which aspects of a campaign resonate with the audience, you fine-tune your messaging, design, and delivery channels to enhance engagement and conversion rates.</p>
<h3>3) Optimization of marketing spend by focusing on strategies that deliver measurable outcomes</h3>
<p>Trends are fleeting in this day and age, and what worked yesterday might not work tomorrow. Experiments help you stay in tune with the changes and allocate your budget toward campaigns and channels that have proven their worth in terms of engagement, conversions, and sales.</p>
<p>This optimization level maximizes the impact of the marketing spend and minimizes waste, ensuring that every dollar contributes to the bottom line.</p>
<h2>Common Challenges in Setting up and Running Effective Marketing Experiments</h2>
<p>The following three factors can adversely affect the success of your experiments:</p>
<h3>1) Not keeping bias at bay</h3>
<p>Biases can skew the results of a marketing experiment. In survey design, relying solely on multiple-choice questions may limit responses and reflect the surveyor's expectations rather than genuine customer feedback.</p>
<p>To counteract this, include open-ended questions that allow for spontaneous insights. Additionally, diverse sampling methods and blind testing should be considered to reduce bias further and ensure more credible outcomes.</p>
<h3>2) Not defining a clear hypothesis</h3>
<p>A clear, measurable hypothesis is crucial for predicting the outcome of marketing experiments. It should detail the anticipated effect on a specific metric rather than being a general statement.</p>
<p>For instance, rather than saying, "Feature X will improve user experience," specify, "Adding a CTA to our tax season landing page will increase signups by 60%."</p>
<p>Ensure your hypothesis is an objective statement and not a question. Then, it clearly outlines the expected result and how it will be measured.</p>
<p>Carlos Flavia, Professor of Marketing at the University of Zaragoza, emphasizes proving a significant link between two variables:</p>

![carlos-flavia-linkedin-post-on-a-significant-relation-between-two-variables](https://mighty-basketball-fd4aeed960.media.strapiapp.com/carlos_flavia_linkedin_post_on_a_significant_relation_between_two_variables_8a6f0d9c5e.webp)

<p><a href="https://www.linkedin.com/posts/activity-7143038461371994112-T7og?utm_source=share&amp;utm_medium=member_desktop">source</a></p>
<h3>3) Relying only on a single experiment result</h3>
<p>A standard and significant error in marketing research is basing conclusions on a single experiment. This approach risks drawing inaccurate deductions due to insufficient evidence. To identify genuine trends and eliminate outliers:</p>
<ul>
<li>Conduct your experiments multiple times</li>
<li>Spread these repetitions over some time</li>
<li>Ensure your results have enough data to be considered statistically reliable</li>
</ul>
<p>Consider this: if an outcome occurs once, it might be a fluke. If it happens twice, perhaps it's a coincidence. But three times? That suggests a trend. Repetitive testing helps observe consistent patterns and trends, leading to more accurate conclusions.</p>
<h2>The Future of Marketing Experiments</h2>
<ul>
<li><strong>AI-driven hypotheses generation: </strong>Since AI algorithms can sift through vast consumer datasets, they can uncover specific patterns, trends, and anomalies that human analysts might overlook. This information helps formulate more targeted hypotheses for marketing experiments tailored to individual or segment-specific needs and desires.</li>
</ul>
<ul>
<li><strong>Automated experiment platforms: </strong>These can handle many experiments simultaneously, automating repetitive tasks, such as sending surveys, collecting responses, and analyzing data. They also enable the testing of varied hypotheses across different segments and channels without overwhelming the team.</li>
</ul>
<ul>
<li><strong>Ethical AI and consumer privacy: </strong>You must prioritize transparency with consumers about how their data is used and ensure that consent is obtained in straightforward and understandable terms. Implement robust data security measures to protect consumer information from breaches and misuse. The use of <a href="https://www.lifesight.io/blog/ai-for-modern-marketers">AI technologies</a> ensures algorithms do not perpetuate biases or inequalities.</li>
</ul>
<h2>Marketing Works Best When Led by Evidence</h2>
<p>The critical function of experiments within a broad-based marketing measurement toolkit cannot be overstated. These tests serve as the cornerstone for marketers aiming to verify, fine-tune, and improve their strategies.</p>
<p>To benefit firsthand from the transformative impact of data-driven decision-making on your marketing outcomes, <a href="https://www.lifesight.io/demo">book a demo with Lifesight</a>, a unified marketing measurement platform.</p>
<p>Discover how we can empower your marketing experiments (and strategies along the way) with precision and scalability, driving unprecedented growth for your business.</p>

marketing-experiments-in-measurement.webp

Ever wonder how marketing experiments boost strategies? Learn about their various types, benefits, challenges, and future predictions in our blog.

The Important Role of Experiments in the Measurement Toolkit

Role of Marketing Experiments in Measurement 

<p>Are you ready to navigate the digital ad space like an expert in 2024? Media planning is critical to your marketing arsenal, dictating how, when, and where your ads will grab attention and drive conversation. With a substantial slice of marketing budgets dedicated to paid advertising, understanding the ins and outs of media planning is helpful; it&rsquo;s essential for crafting campaigns that follow your audience and stay with them.</p>
<p>Read on if you want to sharpen your media strategy to resonate in the ever-evolving digital landscape.</p>
<h2>What is Media Planning?</h2>
<p>Media planning is the strategic process of determining where, when, and how often an advertising message should be placed to achieve optimal engagement and ROI. It involves meticulous market research, setting ambitious yet achievable goals, judicious budget allocation, and selecting the perfect blend of media channels to broadcast your message.</p>
<p>This process is not linear but a cycle of continuous improvement driven by data and a deep understanding of consumer behavior.</p>
<h2>The Media Planning Process</h2>
<p>At its core, media planning starts with understanding your target audience and ends with measuring the success of your advertising efforts. Each step is interconnected, guiding you to make informed decisions that align with your marketing strategy.</p>
<h2>1) Research and Analysis</h2>
<p>The first step in media planning is to ensure you reach your target audience. Who are they? What are their interests, habits, and media consumption patterns? Understanding the audience is crucial because it informs every decision-making in the media planning process.</p>
<p>This deep dive into the audience's world involves analyzing demographics, psychographics, and behavioral data to paint a detailed picture of the ideal customer.</p>
<p>The next step is to understand the landscape in which they operate. Market research provides insights into the broader trends affecting the industry, competitors, consumer behaviors, and the effectiveness of various media channels.</p>
<p>This dual lens of market research and competitive analysis helps identify differentiation and strategic positioning opportunities.</p>
<h2>2) Setting Goals</h2>
<p>In the goal-setting phase of media planning, aligning with KPIs and focusing on harnessing data for precision marketing is vital. The crux is to align marketing objectives with core business goals, moving beyond vanity metrics that may look good on paper but don't drive business growth. At this juncture, marketing teams must step out of their silos and work in tandem with finance teams to determine which metrics are worth pursuing.</p>
<p>For example, a goal would be "to increase the customer acquisition rate by 20% within the next quarter through targeted multi-channel campaigns, utilizing data-driven insights to reduce the cost per acquisition by 15%." This goal provides a quantifiable target and reflects a synergy between marketing efforts and business outcomes.</p>
<p>If you want to set goals at the campaign level, expert marketer Brenden Delarua recommends identifying the north star of an ad campaign and working backward.</p>

![brenden-delarua-linkedin-post-on-setting-goals](https://mighty-basketball-fd4aeed960.media.strapiapp.com/brenden_delarua_linkedin_post_on_setting_goals_763a3c1968.webp)

<p>Once the campaign goals are laid out, you must align these objectives with your overall marketing strategy. For instance, if the broader marketing strategy is to establish a foothold in a new market, the media planning objectives might focus on building brand awareness and understanding within that specific demographic or geographic area.</p>
<h2>3) Selecting the Right Media Mix</h2>
<p>This stage is crucial for determining how to distribute your message across various channels, effectively engaging your audience, and achieving your campaign goals.</p>
<p>Here, you need to evaluate the plethora of available media channels thoroughly. This evaluation should consider several factors, including the channels' reach, the demographic makeup of target audiences, the context in which your ad will appear, and the overall compatibility with your campaign objectives.</p>
<p>For example, social media platforms can offer targeted reach and interactive engagement for digital-savvy audiences. At the same time, traditional channels like television or print may provide broader reach and high trust levels among certain demographic groups.</p>
<p>Also, the choice between digital and traditional channels should align with the campaign's goals, target audience preferences, and budget constraints, ensuring a strategic approach to media planning that maximizes the impact of both worlds.</p>
<h2>4) Budget Allocation</h2>
<p>In this stage, you determine how to distribute your advertising budget across various channels to maximize impact without overspending.</p>
<p>The allocation should reflect the priorities established during the objective-setting phase, with more resources directed towards channels that promise higher engagement and conversion rates for your specific audience.</p>
<p>Modern marketers run predictive MMM models to build optimal media plans with accurate forecasting. Some of the <a href="https://www.lifesight.io/blog/top-marketing-mix-modeling-tools">best MMM tools</a> offer features that can help you compare different scenarios with target KPIs and instantly update budgets on ad platforms.</p>

![budget-allocation-for-platforms](https://mighty-basketball-fd4aeed960.media.strapiapp.com/budget_allocation_for_platforms_27faae3086.webp)

<h2>5) Media Buying and Execution</h2>
<p>The essence of media buying lies in strategically acquiring ad spaces across various channels, ensuring that every dollar spent maximizes the brand&rsquo;s visibility and impact. Whether it's securing a spot on digital platforms or traditional media like television and billboards, the art of negotiation is key.</p>
<p>Effective media buying demands a deep understanding of market rates, keen negotiation skills, and an eye for the ideal placement. Here's a focused approach to master the buy:</p>
<ul>
<li><strong>Leverage data for negotiation:</strong> Utilize data to understand the value of different media channels for your specific audience. Armed with this information, you can negotiate the best rates aligning with Lifesight's KPIs, ensuring cost-effective reach and impact.</li>
</ul>
<ul>
<li><strong>Master the art of timing: </strong>Dive into the media landscape to identify when rates are most favorable. Planning your buys during these off-peak periods can significantly reduce costs without compromising audience reach.</li>
</ul>
<ul>
<li><strong>Placement precision: </strong>Placement is not just about reaching an audience; it's about reaching the right audience in the right context. Strategize placements to align with your audience's media consumption habits, ensuring your message resonates strongly and stands out.</li>
</ul>
<ul>
<li><strong>Rigorous rate review: </strong>Continuously analyze and benchmark your rates against industry standards and historical data to ensure competitive pricing for your ad placements.</li>
</ul>
<h2>6) Optimize campaigns</h2>
<p>Crafting compelling campaigns and creatives is only part of the job. To maximize effectiveness, you must continually test and tweak various elements like keywords, ad copy, visuals, and calls to action. Regular monitoring and performance analysis are essential to understanding what resonates with your audience.</p>
<h3>Tips to optimize campaigns and creatives:</h3>
<ul>
<li><strong>Employ a multi-touch attribution tool: </strong>The modern <a href="https://www.lifesight.io/blog/multi-touch-attribution-tools">MTA tools</a> help you understand which touchpoints contribute to conversions, enabling more efficient budget allocation.</li>
</ul>
<ul>
<li><strong>A/B testing: </strong>Always be testing. Whether it's new channels, different ad formats, or unique messaging, use a portion of your budget for experiments to find untapped opportunities.</li>
</ul>
<h2>Final Thoughts</h2>
<p>Looking toward the future, the trends in media planning are clear: the growing importance of data analytics and AI in crafting more personalized media strategies stands out. These technologies are not just buzzwords but powerful tools that can dissect vast amounts of data to reveal insights about audience behaviors, preferences, and trends.</p>
<p>Moreover, as consumer privacy concerns become more pronounced, adapting and innovating how we collect and utilize data will be crucial.</p>

media-planning-process.webp

Unlock the secrets of media planning in 2024. Learn the strategic process, from research to optimization, to maximize ROI in your advertising campaigns.

The Science Behind Media Planning 

Marketing Mix Modeling

<p>According to a <a href="https://www.gartner.co.uk/en/marketing/research/annual-cmo-spend-survey-research">Gartner survey</a>, CMOs invest over a quarter of their budgets in paid media. This spend is spread over social advertising (10.1%), search advertising (9.8%), and digital display advertising (9.3%). However, when leadership scrutinizes profitability and revenue more closely, sustained brand investment and the ability to tie the results to return on investment (ROI) are needed.</p>
<p>Also, accurate measurement and forecasting of marketing activities are complicated by increasing restrictions on data privacy, shifting consumer media consumption patterns, greater demand for on-the-go campaign changes, and the new availability of media options.</p>
<p><a href="https://www.lifesight.io/resources/case-study-mmm-drives-15-percent-incremental-ROI">Marketing Mix Modeling (MMM)</a> is an essential technique for CMOs to measure and evaluate upper-funnel activities that lead potential customers to the consideration phase but do not directly contribute to immediate sales. It determines the incremental impact associated with different marketing and promotional activities while controlling the external factors outside the brand's control.</p>
<p>For the marketing team, MMM can be used in the following ways: </p>
<ul>
<li>To show the overall incremental impact of marketing activities on the business</li>
<li>To predict outcomes of raising or lowering the marketing budget and the effect that will have on the overall budget</li>
<li>To suggest changes in the current marketing budget to improve performance</li>
</ul>
<p>A marketing mix modeling tool allows you to measure marketing effectiveness and predict the impact of future efforts, which are often focused on ad spend. The insights are presented in an easy-to-use dashboard to help you navigate the changing environments.</p>
<p>Let's discuss the ten best marketing mix modeling tools.</p>
<h2>1) Lifesight</h2>

![marketing-mix-modeling-tool-lifesight](https://mighty-basketball-fd4aeed960.media.strapiapp.com/marketing_mix_modeling_tool_lifesight_b8ab8ada52.webp)

<p><a href="https://www.lifesight.io/measure">Lifesight</a> is a unified marketing measurement tool that offers MMM, universal attribution, incremental testing, and causal inference under one roof. The tool is built for a modern marketing environment and leverages the power of AI and machine learning (ML) to deliver advanced marketing capabilities.</p>
<p>Focused on driving a collective marketing output, Lifesight helps businesses make data-driven decisions and optimize budgets and campaigns for higher ROI.</p>
<h3>Key features</h3>
<ul>
<li>Enables easy integration and segmentation of customer and marketing data from over 250 apps with zero coding.</li>
<li>Provides advanced forecasting and analytics to transform marketing data into actionable insights.</li>
<li>Gives a unified view of marketing performance across touchpoints.</li>
<li>Leverages AI-powered marketing attributions for granular optimization of marketing strategies.</li>
<li>Helps understand the causal impact of marketing activities on key business KPIs.</li>
<li>Presents AI-generated recommendations for optimal data-backed decision-making and budget optimization.</li>
<li>Offers media scenario planners, AI-generated recommendations, and tools for tracking the impact of marketing decisions across the marketing funnel.</li>
<li>Offers advanced customer analytics, cohort analysis, and predictive customer analytics for insights into marketing campaign effectiveness.</li>
<li>Allows multiple media scenarios configuration for optimized outcomes and scaling marketing budget across channels</li>
</ul>
<p><strong>Pricing: </strong>Custom pricing</p>
<h3>Why choose Lifesight for marketing mix modeling?</h3>
<p>Lifesight is not just a tool that offers marketing mix modeling but goes beyond that and incorporates multi-touch attribution (MTA), incremental testing, and causal inference into a single platform. It is highly recommended for businesses looking to streamline their marketing mixes, bring them on one platform, and get a holistic view of their marketing efforts.</p>
<h2>2) Northbeam</h2>

![marketing-mix-modeling-tool-northbeam](https://mighty-basketball-fd4aeed960.media.strapiapp.com/marketing_mix_modeling_tool_northbeam_217ede94e0.webp)

<p>Northbeam is a marketing intelligence platform that leverages ML to offer data-driven marketing strategies. An advanced suite of features helps marketers understand customer behavior, optimize strategies, and maximize return on ad spending.</p>
<h3>Key Features</h3>
<ul>
<li>Customizable attribution models and real-time conversion data to help attribute the impact of touchpoints accurately</li>
<li>MMM+ solution to forecast revenue and optimize budget mix across channels</li>
<li>Creative analytics capabilities to identify winning formulas to help iterate and pause ads and scale ad accounts</li>
<li>Frequent data updates for agile decision-making and responsiveness to changing market conditions</li>
<li>Offers first-time MER (Marketing Efficiency Ratio) and CAC (Customer Acquisition Cost) analytics.</li>
</ul>
<p><strong>Pricing: </strong>They offer 3 plans:</p>
<ul>
<li>Starter: $1000/month for early-stage brands with up to 1 million page views per month</li>
<li>Professional: $2500/ month for growing brands in need of a higher level of services</li>
<li>Enterprise: Custom Pricing</li>
</ul>
<h3>Why choose Northbeam for marketing mix modeling?</h3>
<p>Northbeam is suitable for mid-sized to large businesses with an established marketing team because of its affordability and the requirement for significant data to operate accurately. It is an excellent tool to have by your side, as it offers good accuracy and analytics to optimize your strategy. However, it is expensive compared to other tools available in the market and users report experiencing a decline in support services.</p>
<h2>3) Adtriba</h2>

![marketing-mix-modeling-tool-adtriba](https://mighty-basketball-fd4aeed960.media.strapiapp.com/marketing_mix_modeling_tool_adtriba_a279159d87.webp)

<p>Adtriba is a marketing measurement and optimization platform that helps understand the true impact of marketing activities across channels. The tool offers a data-driven approach to marketing decisions and provides a holistic view of campaign performance for better budget allocation. Adtriba offers Adtriba Sphere for MMM and Adtriba Core for MTA and unified marketing measurement.</p>
<h3>Key features</h3>
<ul>
<li>Automates data collection from both online and offline sources, eliminating manual data entry</li>
<li>Provides deeper insights using ML and advanced statistical models to identify trends and correlations</li>
<li>Offers actionable recommendations for budget allocation and strategic alignment for optimizing marketing efforts.</li>
</ul>
<ul>
<li>Friendly user interface with easy navigation</li>
<li>Easy to set and use immediately for customer insights</li>
</ul>
<p><strong>Pricing:</strong> Custom pricing</p>
<h3>Why choose Adtriba for marketing mix modeling?</h3>
<p>Adtriba is suitable for growing businesses with a marketing spend of about 30k/month and seeking deep analytics and actionable insights to improve their ROI. occasionally, AI-powered evaluation feature delivers wrong information on graphs and heavy reliance on analytics makes implementation challenging for marketers.</p>
<h2>4) Recast</h2>

![marketing-mix-modeling-tool-recast](https://mighty-basketball-fd4aeed960.media.strapiapp.com/marketing_mix_modeling_tool_recast_9cf84337d6.webp)

<p>Recast is a marketing mix modeling tool that helps optimize channel mix, forecast campaign reliability, and eliminate marketing spend wastage. The privacy-friendly platform refreshes weekly to measure incrementality and help understand marketing impact through every media channel.</p>
<h3>Key features</h3>
<ul>
<li>Offers fully verifiable outcomes for informed decision-making</li>
<li>Navigates through privacy controls like iOS14.5, ad blockers, and Intelligent Tracking Prevention</li>
<li>Goal tracker automatically measures performance against set metrics and the probability of meeting goals.</li>
</ul>
<ul>
<li>Weekly reports for incremental impact.</li>
</ul>
<p><strong>Pricing: </strong>Custom pricing</p>
<h3>Why choose Recast for marketing mix modeling?</h3>
<p>It is suitable for modern marketers who demand weekly insights into marketing mix performance to drive ROI and need quick, customizable attribution models.</p>
<h2>5) Rockerbox</h2>

![marketing-mix-modeling-tool-rockerbox](https://mighty-basketball-fd4aeed960.media.strapiapp.com/marketing_mix_modeling_tool_rockerbox_03743ec7bc.webp)

<p>Rockerbox is a marketing platform empowering data-driven decision-making with its centralized data approach. The tool offers actionable insights to enable businesses to make prudent decisions about marketing investments.</p>
<h3>Key features</h3>
<ul>
<li>Leverages historical data for forecasting future ad spending and revenue</li>
<li>Optimizes campaigns and helps identify areas for adjusting budget allocation</li>
<li>Offers easy data transfer to the data warehouse (Snowflake, Redshift, BigQuery) for further analysis</li>
<li>Identifies redundant retargeting impressions and focuses on the ones generating incremental customers for optimizing ad spend</li>
</ul>
<ul>
<li>Imports UTM parameters to track print media results</li>
</ul>
<p><strong>Pricing: </strong>Custom pricing</p>
<h3>Who is Rockerbox's marketing mix modeling tool for?</h3>
<p>It is suitable for B2C marketers needing daily performance monitoring, customer journey analysis, and social media ad optimization. However, users complain about occasional dashboard breakdown experiences, and obtaining data for actionable insights is time-consuming.</p>
<h2>6) Cassandra</h2>

![marketing-mix-modeling-tool-cassandra](https://mighty-basketball-fd4aeed960.media.strapiapp.com/marketing_mix_modeling_tool_cassandra_39dc9c1a51.webp)

<p>Cassandra is a marketing mix modeling tool designed for non-technical teams and businesses to optimize their marketing media mix. Using ML technology, it analyzes the data to create personalized media plans to maximize ROI. It follows a three-step process: discover wasteful campaigns, identify overspends, and simulate the best media plan for driving optimal results.</p>
<h3>Key features</h3>
<ul>
<li>Enables building a marketing mix model in three weeks, fast implementation, and immediate access to actionable insights</li>
<li>Offers budget allocator to simulate and predict the most effective plan, ensuring optimal resource allocation</li>
<li>Provides frequent model updates to guarantee insight relevancy and help businesses stay agile to market changes</li>
</ul>
<ul>
<li>Easy and quick setup with rapid access to results</li>
<li>User-friendly interface, making it accessible to non-technical users</li>
<li>No-code platform</li>
</ul>
<p><strong>Pricing: </strong>They offer 4 plans:</p>
<ul>
<li><strong>Starter:</strong> Free</li>
<li><strong>Self-service:</strong> $1100/month</li>
<li><strong>Managed service: </strong>$3300/month</li>
<li><strong>Enterprise: </strong>Custom pricing</li>
</ul>
<h3>Why choose Cassandra for marketing mix modeling?</h3>
<p>It is best suited for non-technical marketers looking for an easy-to-use MMM solution to maximize their marketing ROI. However, users complain about too many updates that cause disruptions and a lack of in-depth insights and interpretations of graphs.</p>
<h2>7) Sellforte</h2>

![marketing-mix-modeling-tool-sellforte](https://mighty-basketball-fd4aeed960.media.strapiapp.com/marketing_mix_modeling_tool_sellforte_d6cff6109a.webp)

<p>Sellforte is marketing mix modeling software that offers marketing performance measurement and revenue forecasting for e-commerce, retail, and agencies. It empowers businesses to gain valuable insights from their marketing data, optimizes campaigns, and drives data-backed decision-making</p>
<h3>Key Features</h3>
<ul>
<li>Offers industry-specific solutions like eCom, DTC brands, and specialty retail and help them optimize marketing spending</li>
<li>Provides almost real-time access to insights for optimizing marketing efforts using rich media data</li>
<li>Utilizes rich statistical techniques like Bayesian Inference for accurately measuring marketing effectiveness and predicting outcomes</li>
</ul>
<ul>
<li>Cloud-based platform with no coding or infrastructure requirement</li>
<li>Intuitive and user-friendly interface.</li>
</ul>
<p><strong>Pricing: </strong>They offer 4 plans:</p>
<ul>
<li>Digital: $990/ month for digital marketers</li>
<li>Global: $1990/ month for digital marketing teams</li>
<li>Offline: $2490/ month for local eCom/D2C marketing teams</li>
<li>Enterprise: Custom pricing</li>
</ul>
<h3>Why choose Sellforte for marketing mix modeling?</h3>
<p>Sellforte emphasizes catering to eCom and retail industries of all sizes with features relevant to the sector, such as category uplifts. Those seeking extensive data science expertise and requiring a user-friendly marketing mix modeling tool can check out this solution. However, it lacks the functionality to evaluate promotions at the retailer level.</p>
<h2>8) Mass Analytics</h2>

![marketing-mix-modeling-tool-mass-analytics](https://mighty-basketball-fd4aeed960.media.strapiapp.com/marketing_mix_modeling_tool_mass_analytics_91d8c09626.webp)

<p>Mass Analytics is an MMM and media measurement solution that offers a wide range of managed services and software to help identify revenue drivers and optimize budgets.</p>
<h3>Key features</h3>
<ul>
<li>MassTer software for comprehensive marketing mix modeling with advanced analytics</li>
<li>MassFeeds data preparation software for integrating data from various sources into a unified dataset for analysis</li>
<li>MassTer Insights optimization software to streamline data preparation processes using automation features.</li>
</ul>
<ul>
<li>Good customer support services with 1:1 training in the beginning and assistance throughout the project timeline</li>
<li>A visually appealing interface</li>
</ul>
<p><strong>Pricing: </strong>Custom pricing</p>
<h3>Why choose Mass Analytics for marketing mix modeling?</h3>
<p>With an emphasis on advanced analytics and data-driven insights, it provides tools for computing marketing ROI and running simulations for maximum efficiency in MMM campaigns. However, the automatic model generated by MassTer is not often suitable for user requirements and visualization is not comprehensive, which restricts communication of the insights derived from the tool.</p>
<h2>What Should You Look for in a Marketing Mix Modeling Tool?</h2>
<p>When choosing a <strong>marketing mix modeling tool,</strong> consider the following factors to ensure the tool meets your needs and helps drive higher ROI on your marketing spending.</p>
<ul>
<li>Data integration capabilities and segmentation options</li>
<li>A comprehensive marketing measurement model, including MMM, <a href="https://www.lifesight.io/blog/guide-to-incrementality-testing">incrementality testing</a>, and causal inference</li>
<li>Customization to fit your business objectives, like setting up different attribution models, adjusting the accounts for market trends, seasonality, and other factors</li>
<li>AI integration for recommendations and forecasting. Campaign planning and comparing different marketing scenarios to find the optimal fit</li>
<li>Easy-to-use UI with interactive dashboard and data visualization</li>
<li>Scalability for efficient handling of large and complex data volumes and ability to conduct analyses without disruption</li>
<li>Reliable customer support services</li>
<li>Cost-effectiveness</li>
<li>Seamless integration capability for easy data integration</li>
</ul>
<p>In a privacy-first era, navigating the complexities of marketing measurement needs an intuitive, intelligent tool. Lifesight offers a <a href="https://www.lifesight.io/blog/unified-marketing-measurement-guide">unified marketing</a> mix model to empower you with cutting-edge marketing measurement techniques like universal attribution, MMM, incrementality testing, and causal AI.</p>
<p><a href="https://www.lifesight.io/demo">Request a free demo</a> today to learn how Lifesight revolutionizes your marketing mix modeling techniques and drives higher marketing ROI.</p>

top-marketing-mix-modeling-tools.webp

Explore the top marketing mix modeling tools for accurate ROI measurement. From Lifesight to Northbeam, find the best fit for your business needs!

Top Marketing Mix Modeling Tools for 2024

<p>In an era where the digital footprint of the average person exceeds <a href="https://www.trtworld.com/life/people-spend-daily-average-of-seven-hours-online-worldwide-54765">6 hours </a>a day, the art and science of media buying have become indispensable for marketers. Gone are the days of simply choosing the perfect billboard spot or the prime-time TV slot.</p>
<p>These changes have driven media buying into a new era in which precision targeting and ad customization are both perks and musts. As we delve deeper into this complicated digital ecosystem, marketers' task becomes clear: grasp the complexities of modern media buying and harness its power to attract the ever-changing online customer.</p>
<h2>What is Media Buying?</h2>
<p>Media buying is the process of purchasing advertising space to promote your products or services. It's where the planning meets the road; after all, even the most meticulously crafted marketing strategy needs a vehicle to reach its audience.</p>
<p>The primary goal here is straightforward: to secure the most advantageous spots for your ads at the best possible price. Whether it's a prime-time television slot, a standout position on a popular website, or a captivating billboard on a busy street, the right placement ensures your message resonates with your target audience effectively.</p>
<h2>Types of Media Buying</h2><br>
<h3>Direct buying</h3>
<p>Direct buying refers to the purchase of ad spaces directly from a publisher without using automated systems or intermediaries. This method involves negotiating directly with the website, platform, or media outlet where you want your ad to appear. It often includes buying specific slots for a set period, and ensuring your ads run in preferred locations at predetermined times.</p>
<h4>Advantages:</h4>
<ul>
<li><strong>Guaranteed placement:</strong> You know exactly where and when your ads will appear, providing a level of certainty and control over your ad environment.</li>
<li><strong>Customized deals:</strong> Allows for customized agreements, including preferred pricing, specific ad placements, and unique sponsorship opportunities not available through automated platforms.</li>
<li><strong>Stronger publisher relationships:</strong> Direct interaction fosters a stronger relationship with publishers, which can benefit future negotiations and custom ad opportunities.</li>
</ul>
<h3>Programmatic buying</h3>
<p>Programmatic buying automates the buying and selling of online ad space using software and algorithms. This method utilizes data insights and technology to decide which ads to buy and how much to pay for them, all in real-time. It encompasses various buying mechanisms, including real-time bidding (RTB), private marketplaces (PMPs), and guaranteed deals.</p>
<h4>Advantages:</h4>
<ul>
<li><strong>Efficiency and scale: </strong>Automates the ad-buying process, making it possible to manage multiple ad exchanges and data exchange platforms simultaneously.</li>
<li><strong>Targeting and personalization: </strong>Leverages user data for highly targeted advertising, ensuring ads reach the right audience at the right time.</li>
<li><strong>Cost-effectiveness:</strong> Often provides a more cost-effective way to purchase ads due to the algorithmic determination of ad prices and the ability to buy ad impressions individually.</li>
</ul>
<p>Programmatic buying strips away much of the manual effort involved in media buying, though it demands a solid understanding of technology and trust in automated systems.</p>
<h2>Media Buying by Channel</h2>
<p>Broadly, we can categorize media buying by channel into two main types: traditional and digital. Each has its unique set of strategies, platforms, and outcomes.</p>
<table>
<tbody>
<tr>
<td>
<p><strong>Feature</strong></p>
</td>
<td>
<p><strong>Traditional Media Buying</strong></p>
</td>
<td>
<p><strong>Digital Media Buying</strong></p>
</td>
</tr>
<tr>
<td>
<p>Platforms</p>
</td>
<td>
<p>TV, radio, newspapers, billboards, magazines</p>
</td>
<td>
<p>Websites, social media, search engines, programatic</p>
</td>
</tr>
<tr>
<td>
<p>Targeting</p>
</td>
<td>
<p>Broad demographic groups (age, gender, location)</p>
</td>
<td>
<p>Highly specific targeting (interests, behaviors, detailed demographics)</p>
<br /><br /></td>
</tr>
<tr>
<td>
<p>Cost</p>
</td>
<td>
<p>Often requires a significant budget for prime placements</p>
</td>
<td>
<p>More flexible, with options for smaller budgets; costs can vary widely based on format and targeting</p>
</td>
</tr>
<tr>
<td>
<p>Measurement</p>
</td>
<td>
<p>Measured with estimated viewership, circulation figures, ratings</p>
</td>
<td>
<p>Real-time data analytics, click-through rates, impressions, conversions</p>
</td>
</tr>
<tr>
<td>
<p>Purchasing</p>
</td>
<td>
<p>Fixed schedules and spots purchased in advance</p>
</td>
<td>
<p>Real-time bidding, allowing for instant ad placements</p>
</td>
</tr>
<tr>
<td>
<p>Adaptability</p>
</td>
<td>
<p>Changes and optimizations can be challenging and slow to implement</p>
</td>
<td>
<p>Ads can be quickly adjusted, tested, and optimized in real time</p>
</td>
</tr>
<tr>
<td>
<p>Engagement</p>
</td>
<td>
<p>Passive engagement; viewers may see or hear ads without active interaction</p>
</td>
<td>
<p>Interactive engagement; viewers can click, share, or directly engage with ads</p>
</td>
</tr>
<tr>
<td>
<p>ROI Measurement</p>
</td>
<td>
<p>Indirect and often challenging to attribute directly to sales</p>
</td>
<td>
<p>Direct attribution to actions, allowing for precise ROI calculation</p>
</td>
</tr>
<tr>
<td>
<p>Reach</p>
</td>
<td>
<p>Can achieve broad reach but with less precision</p>
</td>
<td>
<p>Global reach with the ability to finely tune the audience</p>
</td>
</tr>
</tbody>
</table>
<h2>3 Steps for Effective Digital Media Buying</h2><br/>
<h3>1) Media planning</h3>
<p>Media buying doesn't operate in isolation. It's one half of a duo, with media planning playing an indispensable role as its counterpart. Think of media planning as the architect, designing the blueprint of a campaign. It involves understanding your audience deeply, defining clear objectives, smartly allocating your budget, and selecting the right channels to carry your message.</p>
<p>Media planning determines who your ads should speak to, what message they should convey, and when this conversation should happen. Media buying, then, is the builder, turning those plans into reality by purchasing the spaces where those ads will live.</p>
<h3>2) Set up your campaign</h3>
<p>Whether you're looking for extensive reach, detailed targeting, or programmatic efficiency, there's a digital media buying platform that fits the bill. The specifics of this phase differ based on the media buying platform in question.</p>
<p>Commonly, when utilizing a demand-side platform (DSP), you'll enter various campaign specifics, including the type of campaign, creative materials, budget, intended audiences, and approaches to bidding. After finalizing these inputs, the next steps are to initiate the campaign and anticipate outcomes.</p>
<p>If you are opting for a direct buying route typically involves a joint effort between your team and the account manager from the publishing side.</p>
<h3>3) Monitor outcomes and make adjustments</h3>
<p>One of the most critical and sometimes daunting aspects of rolling out an ad campaign is tracking its progress.</p>
<p>From the moment your campaign goes live, you begin to collect valuable data regarding its performance. It's crucial at this juncture not to jump to conclusions prematurely. Accumulating sufficient data for a comprehensive analysis is key before considering any strategic shifts.</p>
<p>Here a media buyer's role is to optimize the campaign's success while adhering to budgetary constraints. Therefore, should an ad underperform as per the set benchmarks, making timely adjustments throughout the campaign's duration is not just advisable but necessary.</p>
<p>For example, consider a plant-based snack brand initiating an advertising campaign on social media. They decide to test both carousel ads and a single-image ad. Within the initial days, data reveals the carousel ads are drawing more engagement and conversions compared to the single image ad.</p>
<p><strong>Pro tip:</strong> Use modern <a href="https://www.lifesight.io/blog/multi-touch-attribution-tools">multi-touch attribution tools</a> for daily campaign optimization.</p>
<h2>Best Digital Media Buying Platforms</h2>
<p>Whether you're looking for extensive reach, detailed targeting, or programmatic efficiency, there's a digital media buying platform that fits the bill.</p>
<h3>1) Google Ads</h3>

![google-ads-digital-media-buying-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/google_ads_digital_media_buying_platform_2555170270.webp)

<p>Google Ads stands as the colossus of digital advertising, offering access to the largest search engine and a network of websites worldwide. It enables advertisers to reach potential customers as they search for specific terms, browse websites, or use mobile apps. With its wide range of targeting options, from keyword and demographic to location and behavior, Google Ads ensures your message reaches the right audience at the right time.</p>
<h3>2) Meta Ads Manager</h3>

![meta-ads-manager-digital-media-buying-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/meta_ads_manager_digital_media_buying_platform_a035a5ab9c.webp)

<p>Meta Ads Manager provides unparalleled access to the extensive user base of Facebook and Instagram. It's renowned for its detailed targeting capabilities, including interests, behaviors, and detailed demographics. The platform also offers a variety of ad formats, from standard image and video ads to interactive carousel and story ads, making it a versatile choice for brands aiming to increase their visibility and engagement.</p>
<h3>3) X Ads</h3>

![x-ads-digital-media-buying-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/x_ads_digital_media_buying_platform_570f7403c6.webp)

<p>X ads formerly known as Twitter ads allow brands to tap into real-time conversations and engage with a highly active user base. Its targeting capabilities include keyword, interest, and demographic options, making it ideal for timely and event-driven campaigns. X's unique ad formats, such as promoted tweets and hashtags, offer creative ways to increase visibility and engagement.</p>
<h3>4) Snapchat Ads</h3>

![snapchat-ads-digital-media-buying-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/snapchat_ads_digital_media_buying_platform_df6a00770d.webp)

<p>Snapchat Ads cater to a younger demographic with engaging, ephemeral content. Its ad manager supports various ad formats tailored to the platform's unique user experience, including Snap Ads, Story Ads, and Filters. Snapchat's AR lenses provide a particularly innovative way to create interactive brand experiences.</p>
<h3>5) LinkedIn Ads</h3>

![linkedin-ads-digital-media-buying-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/linkedin_ads_digital_media_buying_platform_2f1c5fbc4d.webp)

<p>For B2B marketers, LinkedIn Ads is a premier platform, offering access to a professional audience. Its targeting options are tailored to reach users based on job titles, industry, company size, and more, making it highly effective for lead generation and professional branding.</p>
<h3>6) Pinterest Ads</h3>

![pinterest-ads-digital-media-buying-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/pinterest_ads_digital_media_buying_platform_ef0ee636c8.webp)

<p>Pinterest Ads are ideal for brands aiming to reach an audience planning future purchases. With options like promoted pins and shopping ads, Pinterest is especially effective for lifestyle, home decor, fashion, and food-related industries.</p>
<h3>7) Google Display & Video 360</h3>

![google-display-video-360-digital-media-buying-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/google_display_video_360_digital_media_buying_platform_f73e590169.webp)

<p>DV360 is a comprehensive platform offering advanced features for buying display, video, TV, and audio ads across the internet. It integrates seamlessly with other Google services, providing access to extensive inventory and allowing for detailed audience targeting. DV360 excels in data integration, campaign management, and performance analysis, making it a powerful tool for advertisers seeking extensive reach with precise targeting capabilities.</p>
<h3>8) Amazon DSP</h3>

![amazon-dsp-digital-media-buying-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/amazon_dsp_digital_media_buying_platform_81a5c97725.webp)

<p>Amazon's Demand Side Platform (DSP) allows advertisers to programmatically buy display, video, and audio ads both on and off Amazon's platforms. What sets Amazon DSP apart is its access to Amazon's vast consumer shopping data, enabling highly targeted advertising based on actual shopping and browsing behaviors. It's particularly effective for advertisers aiming to influence purchase decisions and measure the direct impact of their ads on sales.</p>
<h3>9) The Trade Desk</h3>

![the-trade-desk-digital-media-buying-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/the_trade_desk_digital_media_buying_platform_580098d279.webp)

<p>The Trade Desk is a global programmatic advertising platform known for its flexibility, extensive inventory access, and advanced targeting options. It supports a wide range of ad formats across digital media, including social, mobile, video, and connected TV. With a strong focus on data-driven decision-making, The Trade Desk offers sophisticated tools for optimization and real-time bidding, catering to advertisers looking for efficiency and scale.</p>
<h3>10) Adobe Advertising Cloud</h3>

![adobe-advertising-cloud-digital-media-buying-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/adobe_advertising_cloud_digital_media_buying_platform_56d01574af.webp)

<p>Part of Adobe's Digital Marketing Cloud, Adobe Advertising Cloud offers a unified platform for managing ads across traditional TV and digital formats. It emphasizes transparency and control, allowing advertisers to plan, buy, and measure their advertising efforts across channels from a single interface. Its integration with Adobe Analytics and Audience Manager enhances its targeting and optimization capabilities.</p>
<h3>11) Verizon Media</h3>

![verizon-media-digital-media-buying-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/verizon_media_digital_media_buying_platform_c72a404405.webp)

<p>Verizon Media offers a suite of advertising tools and solutions across its portfolio of brands and networks. It provides access to diverse inventory, including Yahoo, AOL, and other Verizon-owned platforms. Verizon Media is recognized for its strong mobile and video advertising performance, with advanced solutions for targeting, ad delivery, and measurement.</p>
<h3>12) Basis</h3>

![basis-digital-media-buying-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/basis_digital_media_buying_platform_c17762e27e.webp)

<p>Basis stands out for its comprehensive media buying and campaign management capabilities, emphasizing ease of use and efficiency. It supports direct, programmatic, search, and social media buying from a single platform, integrating tools for planning, buying, analytics, and business intelligence. Its user-friendly interface and automated workflows make it accessible for teams of all sizes.</p>
<h3>13) AdRoll</h3>

![adroll-digital-media-buying-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/adroll_digital_media_buying_platform_27e575f683.webp)

<p>AdRoll specializes in retargeting, helping advertisers to re-engage website visitors across the web, social media, and email. It's designed to work for businesses of all sizes, offering solutions to increase brand awareness, conversions, and customer loyalty. AdRoll's platform uses AI to optimize ad campaigns, delivering personalized ad experiences to users based on their online behavior.</p>
<h3>14) Criteo</h3>

![criteo-digital-media-buying-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/criteo_digital_media_buying_platform_d7c5e8cb0e.webp)

<p>Criteo is best known for its dynamic retargeting technology, which personalizes online display ads to users who have previously visited the advertiser's website. It leverages large-scale data to understand user intent, delivering relevant ads that drive high conversion rates. Criteo's platform is designed for e-commerce companies looking to boost sales through targeted advertising.</p>
<h3>15) Adcolony</h3>

![adcolony-digital-media-buying-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/adcolony_digital_media_buying_platform_7c6ad7ac10.webp)

<p>Adcolony is a mobile video advertising platform known for its high-quality video ads optimized for mobile apps. It provides advertisers with in-app advertising solutions that focus on user engagement and interactivity. Adcolony's technology ensures ads are delivered seamlessly without disrupting the user experience, making it ideal for brands looking to capture the attention of mobile users.</p>
<h3>16) AdCritter</h3>

![adcritter-digital-media-buying-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/adcritter_digital_media_buying_platform_e481e8ecef.webp)

<p>AdCritter is a platform designed to simplify the programmatic advertising process for small to medium-sized businesses, making advanced advertising technologies accessible to companies without the need for large advertising teams or budgets. It provides a user-friendly interface that allows advertisers to create, launch, and manage TV and digital ad campaigns across a multitude of channels, including web, mobile, social media, and Connected TV (CTV).</p>
<h2>Future of Media Buying</h2>
<p>By the time we reach the late 2020s and early 2030s, social media giants, with their vast user bases and extensive data repositories, are expected to be at the forefront of shaping the advertising sector. Amazon, Facebook, and Google, as leading platforms in the market, will likely become prime spaces for company advertisements.</p>
<p>Also, an increased emphasis on programmatic buying highlights a shift towards more efficient, data-driven decisions, moving away from traditional negotiation processes. Meanwhile, the rise of Connected TV (CTV) and Over-the-Top (OTT) advertising reflects a significant shift in viewership from traditional TV to online platforms, offering targeted, interactive ad opportunities.</p>
<p>Adapting to changes, experimenting with new strategies, and leveraging data for informed decision-making are all crucial for staying ahead.</p>

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Explore the evolving landscape of media buying in the digital era. Learn how to navigate changes, target audiences effectively, and optimize campaigns for success.

What is Media Buying? Step by Step Guide to Media Buying

<p>Measuring and analyzing the effectiveness of marketing campaigns is challenging for marketers owing to the complexities of omnichannel marketing strategies, the choice of the audience to deprecate third-party cookies, and limited data that make it hard to quantify the impact of marketing activities on business.</p>
<p>This becomes even more challenging with multiple marketers working on different campaigns on different channels in silos.</p>
<p>This is where unified marketing measurement, or UMM, saves your marketing efforts.</p>
<p>A <a href="https://www.lifesight.io/blog/unified-marketing-measurement-guide">unified marketing measurement</a> approach involves employing multiple measurement methods to find out the true effectiveness of your marketing. To find the truth when there isn't a single source, a unified approach uses both deterministic and probabilistic testing methods together. It breaks silos leading to precise budgeting decisions, not possible with single methodologies like MTA, resulting in a <a href="https://www.thinkwithgoogle.com/_qs/documents/13385/TwGxOP_Unified_Marketing_Measurement.pdf">40%</a> upliftment.</p>
<p>This article will discuss the best UMM tools in 2024 to help consolidate your marketing efforts and generate accurate measuring metrics for you.</p>
<h2>Top unified marketing measurement tools</h2>
<h2>1) Lifesight</h2>

![unified-marketing-measurement-tool-lifesight](https://mighty-basketball-fd4aeed960.media.strapiapp.com/unified_marketing_measurement_tool_lifesight_067bcf3744.webp)

<p><a href="https://www.lifesight.io/">Lifesight</a> is a comprehensive, unified marketing measurement tool designed to provide marketers with insights into the effectiveness of their omnichannel marketing efforts. At its core, Lifesight employs a <a href="https://www.lifesight.io/blog/causal-ai">causal-based measurement </a>solution, moving beyond mere correlation to unveil the real impact of your campaigns.</p>
<p>This AI-powered platform combines the precision of <a href="https://www.lifesight.io/resources/case-study-mta-drives-stellar-40-percent-quarterly-growth">multi-touch attribution</a>, the strategic foresight of <a href="https://www.lifesight.io/resources/case-study-mmm-drives-15-percent-incremental-ROI">marketing mix modeling</a>, and the definitive insight of <a href="https://www.lifesight.io/resources/it-to-find-true-value-of-marketing-campaigns">incrementality testing</a> to provide holistic actionable marketing insights.</p>
<h3>Key Features</h3>
<ul>
<li>Multi-channel data integrations, including online and offline channels into a single platform for comprehensive analysis</li>
<li>Provides a holistic view of the customer journey with interaction and engagement tracking across all touchpoints</li>
<li>Offers server-side tracking and advanced attribution modeling capabilities for improving marketing performance, retargeting, and accurately attributing conversions</li>
<li>AI-generated marketing plans, forecasts, recommendations, and insights for optimal decision-making and understanding the impact on business KPIs</li>
<li>Comprehensive tracking of marketing impact and analysis to identify patterns, trends, and correlations to uncover optimization opportunities</li>
<li>Real-time monitoring of campaign performance and optimization for maximizing ROI</li>
<li>250+ no-code integrations for smooth data flow from CRM systems, marketing tools, data warehouses, and ad platforms without coding.</li>
</ul>
<p><strong>Pricing: </strong>Custom Pricing</p>
<h3>Why choose Lifesight for unified marketing measurement?</h3>
<p>Lifesight offers a fully integrated platform that requires no data scientists or analysts to navigate, ensuring that actionable insights are within easy reach for all marketers. Using advanced forecasting and analytics, Lifesight enables marketers to make informed decisions. It provides channel-level budget recommendations, optimization, and resource allocation insights to achieve ad efficiency.</p>
<h2>2) Adtriba</h2>

![unified-marketing-measurement-tool-adtriba](https://mighty-basketball-fd4aeed960.media.strapiapp.com/unified_marketing_measurement_tool_adtriba_ee6fa631b2.webp)

<p><a href="https://www.adtriba.com/">Adtriba</a> is a marketing analytics software that helps businesses optimize their marketing strategies. The two solutions, Adtriba Core and Adtriba Sphere, holistically evaluates marketing performance and advertising efforts across various channels.</p>
<p>Adtriba aims to deliver precise and detailed analysis of customer's journey using machine learning algorithms.</p>
<h3>Key Features</h3>
<ul>
<li>Offers multi-touch attribution modeling, providing insights on different marketing touch points contribution to the customer journey</li>
<li>Automated data collection consolidating marketing data from both offline and online channels</li>
<li>Leverages predictive analytics to forecast future performance and identify optimation opportunities</li>
<li>Insights like comparison of converting and non-converting customer journeys and fractional conversion for an accurate value of marketing efforts</li>
<li>Campaign-level evaluation of channels for smart budget allocation</li>
</ul>
<p><strong>Pricing:</strong> Custom pricing</p>
<h3>Why choose Adtriba for unified marketing measurement?</h3>
<p>With easy-to-use features and in-depth reports, Adtriba is ideal for organizations looking to understand how ads drive sales and conversions in real-time. However, the ease of implementation presents a challenge, as incorporating it into marketing solutions can be difficult due to its heavy reliance on analytics.</p>
<h2>3) Kantar-Blackwood Seven</h2>

![unified-marketing-measurement-tool-kantar-blackwood-seven](https://mighty-basketball-fd4aeed960.media.strapiapp.com/unified_marketing_measurement_tool_kantar_blackwood_seven_7cae186cc2.webp)

<p>Kantar, an analytics and brand solution, acquired <a href="https://blackwoodseven.com/company/">Blackwood Seven</a>, an AI-powered marketing ROI measurement and optimization company, creating an analytics platform combining both.</p>
<p>The platform enables the deployment of different marketing models simultaneously under its Hamilton AI platform to forecast and analyze the business impact of marketing activities.</p>
<h3>Key Features</h3>
<ul>
<li>Helps quantify marketing and media investments across all channels</li>
<li>Predicts and optimizes the impact of marketing on sales</li>
<li>Helps in understanding the factors impacting churn and how to prevent them</li>
<li>Delivers strategic insights into activities driving the brand and how to maximize that impact</li>
<li>Evaluates media efficacy based on the publisher, time, channel, and custom levels</li>
</ul>
<p><strong>Pricing: </strong>Custom Pricing</p>
<h3>Why choose Kantar Blackwood Seven for unified marketing measurement?</h3>
<p>Kantar Blackwood Seven combines research and analytics with AI predictive modeling to forecast marketing performance accurately and prevent churn rates. It is ideal for businesses that need frequent budget evaluations, simplifying budgeting for both offline and online marketing effortlessly.</p>
<p>However, it's important to note that the initial configuration of Kantar Blackwood Seven requires technical expertise, which might be a barrier for some companies. Additionally, the platform does not offer continuous chat or call support for companies outside the US, which could impact the level of immediate assistance available to international clients.</p>
<h2>Few other vendors who are offering multiple measurement methods:</h2>
<h2>4) Liftlab</h2>

![unified-marketing-measurement-tool-liftlab](https://mighty-basketball-fd4aeed960.media.strapiapp.com/unified_marketing_measurement_tool_liftlab_f03affe1dd.webp)

<p><a href="https://www.liftlab.com/">Liftlab</a> is a marketing effectiveness platform offering real-time insights to help businesses optimize marketing performance and drive revenue. It provides a comprehensive view of marketing profitability and combines media experimentation with economic modeling to predict optimal spending levels and maximize performance.</p>
<h3>Key features</h3>
<ul>
<li>Provides real-time view of marketing profit and loss for optimization and informed decision-making</li>
<li>Predictive modeling to estimate marginal profitability of marketing for budget allocation optimization</li>
<li>Combines MMM results with ongoing testing findings for optimization strategies</li>
<li>Elasticity modeling for insights into potential performance at different spending levels</li>
</ul>
<p><strong>Pricing: </strong>Custom Pricing</p>
<h3>Why choose Liftlab for unified marketing measurement?</h3>
<p>Liftlab analyzes your marketing spending based on profit and loss from different activities. Optimize your budget allocation and drive revenue with instant access to campaign performance metrics.</p>
<h2>5) Aryma Labs</h2>

![unified-marketing-measurement-tool-aryma-labs](https://mighty-basketball-fd4aeed960.media.strapiapp.com/unified_marketing_measurement_tool_aryma_labs_d6238080c5.webp)

<p><a href="https://www.arymalabs.com/">Aryma Labs</a> offers marketing measurement and attribution solutions to drive marketing efficiency. The tool uses MMM modeling, causal inference, and experimentation to optimize marketing performance and ROI.</p>
<h3>Key features</h3>
<ul>
<li>Captures long-term effect of marketing and provides a comprehensive view of the cumulative effect on business</li>
<li>Incorporates dynamic beats for adjustments according to market changes to ensure model relevancy over time</li>
<li>Offers domain-specific MMM model to ensure relevant marketing insights</li>
<li>Capability to model a wide range of KPIs to measure and optimize marketing for specific goals</li>
</ul>
<p><strong>Pricing: </strong>They offer three plans:</p>
<ul>
<li>Single KPI starting at $10K</li>
<li>Single KPI with frequent updates starting at $20K</li>
<li>Multiple KPI with frequent updates: Custom Pricing</li>
</ul>
<h3>Why choose Aryma Labs for unified marketing measurement?</h3>
<p>The customized marketing measurement solutions by Aryma Labs provide accurate and actionable insights to improve marketing ROI.</p>
<h2>6) Cassandra</h2>

![unified-marketing-measurement-tool-cassandra](https://mighty-basketball-fd4aeed960.media.strapiapp.com/unified_marketing_measurement_tool_cassandra_9b49db6d50.webp)

<p><a href="https://www.cassandra.app/">Cassandra</a> is designed for non-technical teams to help them optimize their marketing strategies and maximize ROI. The tool uses an ML-based MMM model to simplify the complex media planning and budgeting process.</p>
<p>It helps you analyze wasteful campaigns in-depth, identify overspending channels, and run simulations to find the most effective media plan.</p>
<h3>Key features</h3>
<ul>
<li>Leverages advanced ML algorithms to analyze vast datasets and create tailored media plans for maximizing ROI</li>
<li>Helps identify wasteful campaigns not driving incremental sales and reallocates resources</li>
<li>Identifies channels with diminishing returns, pinpointing areas of overspending and underspending for better budget allocation</li>
</ul>
<p><strong>Pricing: </strong>They offer 4 plans:</p>
<ul>
<li><strong>Starter:</strong> Free</li>
<li><strong>Self-service:</strong> $1100/month</li>
<li><strong>Managed service: </strong>$3300/month</li>
<li><strong>Enterprise: </strong>Custom pricing</li>
</ul>
<h3>Why choose Cassandra for unified marketing measurement?</h3>
<p>Cassandra uses ML to expedite the MMM modeling process, reducing reliance on technical expertise. It enables the non-technical teams to uncover insights and make data-driven campaign decisions, boasting a clear and easy-to-use UI that appeals to users without a technical background.</p>
<p>The platform facilitates faster implementation of MMM projects and presents results with easy-to-interpret, visually appealing graphics. While its graphs are aesthetically pleasing, there is a noted need for more descriptive interpretations to aid users in understanding the data more intuitively.</p>
<h3>7) Objective Platform</h3>

![unified-marketing-measurement-tool-objective-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/unified_marketing_measurement_tool_objective_platform_e70e4e50ee.webp)

<p><a href="https://www.objectiveplatform.com/">Objective platform</a> is a UMM solution provider that aims to enhance the impact and accountability of marketing efforts. It helps optimize channels and campaigns to increase marketing impact, improve team alignment, and quantify investment value.</p>
<p>Objective Platform streamlines and simplifies measuring marketing efforts to measure investment value using AI to automate data from 200+ sources.</p>
<h3>Key features</h3>
<ul>
<li>Offers tools and dashboards to facilitate data-driven decision-making for optimizing marketing strategies</li>
<li>Helps align teams to work on similar KPIs for better control over marketing operations</li>
<li>Maximizes marketing impact by helping in achieving targets or the highest ROI from the fixed budget</li>
<li>Goes beyond channel-specific metrics to provide optimization insights and evaluate the true impact of investments</li>
</ul>
<p><strong>Pricing: </strong>Custom Pricing</p>
<h3>Why choose Objective Platform for unified marketing measurement?</h3>
<p>Objective Platform offers UMM models and goes beyond channel-specific metrics to view marketing performance comprehensively.</p>
<h2>Final thoughts</h2>
<p>Marketers adopt the UMM approach to improve the effectiveness of marketing efforts across channels and optimize campaigns in real-time to optimize resource allocation and drive tangible business impact.</p>
<p>Lifesight offers cutting-edge capabilities for driving marketing success in the privacy-first era. It provides unified measurement and optimization to navigate the modern marketing landscape and achieve value for your marketing spending. By collating data from various channels and customer touchpoints, Lifesight gives you a holistic understanding of your marketing performance.</p>
<p><a href="https://www.lifesight.io/demo">Book a demo</a> today to learn more about Lifesight's unified marketing measurement.</p>

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Here are the best UMM tools in 2024 to consolidate your marketing efforts and generate accurate measuring metrics for you. 

Best Unified Marketing Measurement Tools in 2024

<p>The biggest challenge for a marketer managing multiple ad campaigns across channels is getting a holistic view of campaign performance. Ad performance data received at the right time can help drive timely action and course correction in campaign strategy and ensure optimum return on investment (ROI) for every dollar spent.</p>
<p>This blog discusses the intricacies of optimizing marketing ad spend, and provides actionable strategies to maximize ROI.</p>
<h2>Why Optimize Ad Spend?</h2>
<p>Marketers strive hard to capture eyeballs as more and more businesses decide to increase their digital ad spend budgets. As attention spans dwindle, attracting and retaining consumer interest is a constant challenge, often aggravated by limited budgets and demanding business needs.</p>
<p>In such a scenario, optimizing ad spends can help identify high-value channels and maximize ROI for the company. The process involves analyzing data and changing and improving different aspects of the ads such as creative and copy, targeting the right audience, and/or trying out different ads, bidding, and keyword strategies.</p>
<h2>Top 4 Ad Spend Optimization Strategies</h2>
<p>Depending on the type of insights that need to be derived, three ad spend optimization strategies can be utilized: A/B testing, lift testing, and marketing mix modeling. Here's an in-depth look at each of these:</p>
<h2>1) A/B testing</h2>
<p>A/B testing is the most straightforward and preferred approach to ad spend optimization. Companies widely use it for optimizing websites (over <a href="https://www.notifyvisitors.com/blog/ab-testing-statistics/#:~:text=More%20than%2080%20%25%20of%20companies%20are%20using%20AB%20tests%20on%20their%20websites.%20Also%2C%20more%20than%2060%20%25%20are%20using%20ab%20testing%20on%20landing%20pages%20and%2059%20%25%20of%20the%20companies%20use%20ab%20testing%20on%20PPC%20ads.">80%), </a>landing pages (over 60%), and for Pay Per Click (PPC) ads (59%).</p>
<p>A/B testing involves testing different variables and determining which ones drive the maximum impact.</p>
<p>The "A" and "B" in A/B Testing refer to the two versions being tested: The control (A) and the variant (B).</p>
<p>For example, a digital marketing agency running a Facebook ad campaign for a client promoting a new product will create two versions of the ad:</p>
<ul>
<li>Version A: Known as the Control ad, it could present the product image with a simple headline and call-to-action (CTA)</li>
<li>Version B: Known as the Variant, this could have a different product image, a more compelling headline, with an added "limited-time offer" incentive in the CTA</li>
</ul>
<p>The agency will run both ad versions simultaneously and target them at different audience sets. If data shows that version B is having better click-through rates (CTR) and conversions, then the agency can implement necessary changes to the ads and optimize ad spends accordingly.</p>
<p>Implementing A/B testing in a structured manner involves the following:</p>
<h3>1) Selecting platforms</h3>
<p>There are several A/B testing platforms, such as Google Optimize, Optimizely, and Omniconvert, that help run tests and provide relevant data. Deciding on the right platform should be based on its ability to continuously respond to changes in marketing elements being tested, such as email subject lines, website elements, or digital ads.</p>
<h3>2) Defining test groups</h3>
<p>The next step is mapping and identifying test candidates for the control group (A) and variant group (B) and minimizing bias.</p>
<p>Consider demographics, behavior, geographic location, etc. and, based on the A/B testing tool being used, define the sample size along with the test duration. A <a href="https://cxl.com/ab-test-calculator/">sample size calculator</a> can also ease the process.</p>
<h3>3) Implementing tests</h3>
<p>The next step is to run the respective campaign variants. Ensure the variations are correctly depicted for each treatment group and the process runs without technical glitches. Establishing protocols and procedures ensures consistency and reliability throughout the test.</p>
<p>For example, when running out of email campaign variations, ensure that emails are sent turn-by-turn to test groups under the same conditions and at the same time. Moreover, the testing process should be monitored closely to promptly identify and respond to the patterns that might lead to errors or disagreements.</p>
<h3>4) Measuring and analyzing results</h3>
<p>Measuring and analyzing is the most critical part of A/B testing and generally a statistical significance of 95% indicates a clear winner.</p>
<p>However, what if the test does not show much difference in results between the two variants? Then it means the variables did not significantly affect the results, rendering the test inconclusive.</p>
<p>The next step is to use the failed data to create a new interaction for another test.</p>
<h3>5) Interpreting A/B testing results</h3>
<p>While the implementation phase focuses on executing the planned tests, the interpretation phase shifts the focus toward analyzing the data collected and deriving actionable insights.</p>
<p>Compare the various performance metrics, such as conversion rate, and click-through rate (CTR).</p>
<p>Also look at:</p>
<p><strong>Cost Per Acquisition (CPA) of each variant</strong>, which will help evaluate efficiency in acquiring new customers or leads. A lower CPA indicates that the variation is more cost-effective in generating conversions, maximizing ad spend's ROI.</p>
<p><strong>Evaluating Return on Ad Spend (ROAS) is also crucial. </strong>Higher ROAS indicates that the variation generates more revenue than the ad spend, resulting in higher overall profitability.</p>
<h2>2) Lift test</h2>
<p>Lift test is a form of A/B testing. It is used to measure the impact of a specific campaign or ad by comparing the behavior of a group that was exposed (treatment) against a similar group that was not (control).</p>
<p>It generally includes geo experiments and audience split tests.</p>
<p><a href="https://www.lifesight.io/blog/geo-experiments-guide">Geo experiments</a>: This type of lift test involves selecting specific geographic areas to receive a particular advertising campaign, while comparable areas do not. By comparing sales or conversion data from these different regions, marketers can infer the incremental impact of the campaign attributable to the advertising efforts.</p>
<p>For instance, a beverage company might launch a new ad campaign in the Midwest but not in the Northeast. If sales increase in the Midwest relative to the Northeast beyond typical regional variance, the campaign can be credited for that lift.</p>
<p><strong>Audience split tests: </strong>Similar to geo experiments but focusing on demographics rather than location, the audience split tests segment the target market into different groups based on attributes like age, interests, or behaviors. One segment is exposed to the campaign, while the other serves as the control. The resulting performance data allows marketers to fine-tune their strategies to the segments that are most responsive to their campaigns.</p>
<p>Implementing these types of lift tests enables more precise measurement of a campaign's effectiveness. With Lifesight's Incrementality Testing feature, you can gain a deeper understanding of how your campaigns drive incremental conversions and revenue, ensuring you're not just spending efficiently, but also effectively.</p>
<h2>3) Marketing Mix Modeling (MMM)</h2>
<p><a href="https://www.lifesight.io/blog/principles-of-marketing-mix-modeling">Marketing Mix Modeling</a> is an analytical technique used to understand the impact of various marketing activities on business outcomes. It provides a comprehensive approach to analyzing the effectiveness of marketing strategies across different channels and touchpoints. Let's look at the process of implementing MMM in more detail:</p>
<h3>Gathering historical data</h3>
<p>Historical data includes data from different sources, such as sales records, advertising spending, pricing information, and market share data. This forms the foundation for analyzing past marketing activities and their impact on business objectives.</p>
<p>This data can show trends, patterns, and relationships between marketing inputs and performance metrics over a season. To get accurate insights from MMM, the historical data quality must be good, accurate, and complete.</p>
<h3>Building or choosing a model</h3>
<p>Once historical data is collected, the next step is to build or choose a suitable model for MMM based on business objectives, data characteristics, and analytical requirements.</p>
<p>Some commonly used models are regression analysis, time series analysis, econometric models, and machine learning (ML) algorithms. Some also build custom models or leverage pre-existing models developed by industry experts or third-party providers.</p>
<p>However, while creating custom models, it is essential to capture the complex relationships between marketing inputs and business outcomes and provide accurate predictions.</p>
<p><a href="https://www.lifesight.io/blog/marketing-mix-modeling-and-budget-optimizer">Lifesight's MMM</a> employs advanced ML for precise insights into ad channel effectiveness, offering granular insights, accurate measurement, and strategic spending prioritization. Together with the <a href="https://www.lifesight.io/resources/optimizing-budget-allocation-with-marketing-mix-modeling">budget optimizer tool</a>, it ensures every dollar yields maximum ROI by removing the guesswork.</p>
<h3>Calibrating with lift test data</h3>
<p>Lift test data provides a reliable benchmark for evaluating MMM's predictive power. Aligning the model's parameters with the lift test results ensures that the model reflects real-world dynamics and provides reliable projections for future campaigns. This adds credibility and helps build confidence in the model's ability to guide strategic decision-making and optimize the ad budget.</p>
<h3>Utilizing MMM for budget allocation</h3>
<p>To use MMM for budget allocation, analyze the performance metrics derived from the MMM analysis. Identify the channels that contribute most effectively to KPIs, such as sales revenue, conversion rates, or ROI.</p>
<p>It is then easy to <a href="https://www.lifesight.io/resources/optimizing-budget-allocation-with-marketing-mix-modeling">allocate your budget</a> to platforms that demonstrate high efficacy in driving desired results. Keep the marginal returns of additional investments in check while doing so.</p>
<p>It is also important to continuously monitor and adjust <strong>ad spend</strong> distribution based on the market scenario, consumer behavior trends, and external factors.</p>
<h2>4) Multi-Touch Attribution (MTA)</h2>
<p><a href="https://www.lifesight.io/blog/multi-touch-attribution-models-and-examples">Multi-Touch Attribution (MTA) models</a> play a crucial role in optimizing ad spend by providing granular insights at both the campaign and creative levels. Unlike traditional last-click attribution models, MTA takes into account all the touchpoints a customer interacts with before converting. This nuanced view allows marketers to understand which specific campaigns and creative elements contribute most significantly to conversions and sales.</p>
<p>For example, by <a href="https://www.lifesight.io/resources/mta-implementation-checklist">implementing MTA models</a>, you can determine if a video ad on social media or a display banner on a news site is more influential in the customer journey. You can then allocate more budget to the higher-performing creatives, optimizing your spend for maximum effectiveness.</p>
<p>With Lifesight, you can not only track which campaigns are driving the best results but also drill down to see how individual creatives are performing. This level of detail empowers you to invest wisely in the campaigns and creatives that truly move the needle, ensuring that your marketing dollars are spent both efficiently and effectively.</p>
<h2>Final Thoughts</h2>
<p>Today's omnichannel marketing, optimizing ad spend isn't just about cutting costs. It's about investing smartly to weave the strongest threads of consumer connection. Whether it's the sharp precision of A/B testing, the discerning insights from lift tests, or the overarching strategy enabled by Marketing Mix Modeling, the ultimate goal remains constant: to maximize ROI and drive sustainable growth.</p>
<p>Ready to see the difference a unified approach can make? <a href="https://www.lifesight.io/demo">Book a free demo</a> with Lifesight today and steer your campaigns towards unmatched success.</p>

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Learn top ad spend optimization strategies for marketers managing multiple campaigns. Maximize ROI with A/B testing, lift tests, MMM, and MTA.

Ways To Optimize Marketing Ad Spend: MMM, MTA, AB Tests & Lift Tests

Top 4 Ways To Optimize Marketing Ad Spend: MMM, MTA, AB Tests & Lift Tests

<p>We are witnessing a period where the marketing industry is going through some seismic shifts with new consumer-focused privacy technologies and changing rules, which limits data tracking and usage. Traditional attribution models are losing accuracy due to signal loss. Furthermore, the complexity and opacity of the digital ecosystem have contributed to fraud and mistrust, with various "black boxes" that hinder transparency.</p>
<p>Yet, <a href="https://www.bazaarvoice.com/blog/best-practices-marketing-attribution/">41% of marketers</a> still use the last-touch attribution model for online channels, which can misrepresent the true efficacy of the entire marketing journey. Even more telling is that <a href="https://cmosurvey.org/wp-content/uploads/sites/15/2019/02/The_CMO_Survey-Highlights-and_Insights_Report-Feb-2019.pdf">64% of businesses</a> lack the quantitative tools necessary for demonstrating the impact of their marketing spend on company performance.</p>
<p>To combat these challenges, brands and marketers must adopt a unified marketing measurement approach that paints a holistic picture of their marketing efforts.</p>
<h2>What is unified marketing measurement?</h2>
<p>A unified marketing measurement approach refers to the use of multiple measurement methodologies to measure your marketing effectiveness. In the absence of a single source of truth, a unified approach combines both deterministic and probabilistic measurement methodologies to triangulate the truth.</p>
<p>This can include methods like surveys, marketing mix modeling (MMM), incrementality testing, last-click attribution, or multi-touch attribution (MTA). While methods like surveys and last-click attribution have historically provided valuable insights, newer, more advanced techniques offer enhanced accuracy and a better understanding of measurement data.</p>
<h3>What is triangulation in marketing measurement?</h3>
<p>In marketing measurement, triangulation refers to the practice of using any three measurement methodologies to provide a more complete picture that gets closer to the "truth." The &ldquo;truth&rdquo; here refers to measuring the true effectiveness of your marketing.</p>
<p>It includes the use of marketing mix modeling, multi-touch attribution, and incrementality testing to triangulate the truth of marketing effectiveness. With this integration, marketers can see the direct as well as indirect impacts of their campaigns, follow customers through all of their interactions, and make smart decisions based on both short-term actions and long-term strategy planning.</p>
<p>This allows the decision-makers (CMOs, CFOs, etc) to not only measure but also predict how their marketing strategies will impact their main business goals with UMM.</p>
<p>The below image illustrates how we approach triangulation in marketing</p>

![how-we-approach-triangulation-in-marketing](https://mighty-basketball-fd4aeed960.media.strapiapp.com/how_we_approach_triangulation_in_marketing_b388894a7f.webp)

<p>If you want to listen to a few experts talking about this triangulation approach then here's a podcast by the Google marketing platform on how attribution, experiments, and MMM are the future of media effectiveness measurement.</p>

<a href="https://marketingplatformacademy.withgoogle.com/events/gmp-partner-podcast-attribution-experiments-mmm">![google-marketing-platform-podcast-attribution-experiments-mmm](https://mighty-basketball-fd4aeed960.media.strapiapp.com/google_marketing_platform_podcast_attribution_experiments_mmm_9e2b30e3cc.webp)</a>

<h2>The three core pillars of unified marketing measurement</h2>
<p>The three pillars of unified marketing measurement (UMM) are essential components that together provide a holistic view of marketing effectiveness. Let's examine each of these in detail.</p>
<h3>1) Marketing mix modeling (MMM): The strategic component</h3>
<p>MMM involves building statistical models that capture the sales relationship to marketing spending to determine how different marketing mix elements contribute to sales volume.</p>
<p>This methodology uses regression analysis to quantify the incremental impact of different marketing mix variables while controlling for various internal (advertising, promotions, etc.) and external factors (seasonality, competition, etc.).</p>
<p>Marketers should use MMM to set overarching goals for various marketing channels. Also, the decision-making at this stage must be based on strategic KPIs, which include incremental KPIs like incremental ROAS/CPA.</p>
<p>These metrics provide insights into the overall effectiveness of marketing channels and strategies, ensuring that spend and effort are aligned with business objectives.</p>
<h3>2) Universal attribution (UA): The operational component</h3>
<p>Universal attribution transcends traditional customer journey assessments by integrating signal-based and aggregate methodologies, offering a panoramic view of marketing performance.</p>
<p>Typically, it merges the granular insights of Multi-Touch Attribution (MTA) with the macro-level analysis of Marketing Mix Modeling (MMM). This dual approach ensures every customer interaction, from digital engagements to offline experiences, is accounted for, to deliver actionable insights.</p>
<p>While using universal attribution marketers focus shifts to the day-to-day optimization of marketing efforts. Here's how universal attribution aids marketers in the decision-making process:</p>
<ul>
<li><strong>Campaigns:</strong> At this level, campaigns are designed and executed to achieve the strategic objectives set at the channel level. Decisions about campaigns are typically based on a mix of strategic KPIs (from the MMM) and more immediate performance metrics.</li>
</ul>
<ul>
<li><strong>Ad Groups:</strong> Within each campaign, there are ad groups that contain sets of ads targeted to more segmented audiences or thematic groupings within the campaign's broader target. Decisions at the ad group level focus on performance metrics that are more immediate and specific than at the campaign level.</li>
</ul>
<ul>
<li><strong>Ads/Creatives:</strong> At the most granular operational level, individual ads or creatives are evaluated. Here, decision-making is based on direct performance metrics such as conversions, Return On Ad Spend (ROAS), Cost Per Acquisition (CPA), and the lift (incrementality) that each ad or creative contributes. These metrics allow for tactical adjustments to optimize the performance of individual ads.</li>
</ul>
<h3>3) Incrementality testing: The validating component</h3>
<p>Within the UMM framework, incrementality testing is designed to measure the additional value generated by specific marketing activities, beyond what would have occurred without those activities. This approach helps marketers identify the true effectiveness of their campaigns by isolating and evaluating the incremental impact of marketing spend on consumer behavior and sales outcomes.</p>
<p>The essence of incrementality testing lies in its ability to address a fundamental question: <em>&ldquo;Did this marketing activity drive additional outcomes that wouldn't have happened otherwise?"</em></p>
<p>It plays a crucial role across both levels, validating the assumptions made based on MMM and UA insights. It ensures that decisions are not just data-informed but also incrementally effective, adding real value to the business.</p>
<h3>The value of geo experiments and causal inference in measuring marketing effectiveness</h3>
<p><a href="https://www.lifesight.io/blog/geo-experiments-guide">Geo experiments</a> are a powerful method for conducting incrementality testing, where different geographic areas are exposed to different marketing treatments (or a control group with no treatment) to measure the differential impact.</p>
<p>This approach is particularly valuable for large-scale campaigns where marketers seek to understand the broader, aggregate effect of marketing efforts across different regions. By comparing performance metrics such as sales or conversion rates between the test and control groups, marketers can directly attribute differences to the marketing intervention, accounting for regional variations in consumer behavior.</p>
<p>On the other hand, <a href="https://help.lifesight.io/en/articles/8957098-what-is-causal-inference">causal inference</a> takes incrementality testing a step further by applying statistical methods to infer the cause-and-effect relationships between marketing activities and outcomes. It moves beyond mere correlation, enabling marketers to establish a deeper understanding of how specific actions influence consumer decisions.</p>
<p>Causal inference techniques, such as counterfactual reasoning and structural equation modeling, help in isolating the effect of individual marketing tactics, and controlling for external factors that might also affect the outcomes.</p>
<p>This comprehensive approach not only quantifies the direct impact of marketing activities but also illuminates the pathways through which these activities influence consumer behavior, guiding strategic decisions and optimizing marketing performance for sustained growth.</p>
<h2>3 ways to leverage unified marketing measurement to increase your brand's profit</h2>
<p>Unified Marketing Measurement (UMM) stands as a cornerstone in modern marketing, offering actionable insights that drive strategic decisions and enhance profitability. Here's how UMM transforms key marketing activities:</p>
<h3>1) Scenario planning and forecasting</h3>
<p>UMM infuses scenario planning and forecasting with precision, enabling marketers to craft robust strategies based on predictive analytics and incrementality. This capability allows for nimble adaptation to market dynamics, ensuring that marketing efforts are not only responsive but also proactive.</p>
<p>The foresight gained through UMM facilitates efficient campaign planning, minimizes risk by preempting market shifts, and optimizes resource allocation to capture emerging opportunities.</p>
<h3>2) Budget and campaign optimization</h3>
<p>At the heart of UMM's value proposition is its ability to refine budget allocation and campaign strategies with a focus on incremental gains. This optimization process is rooted in a deep analysis of data across all marketing channels, ensuring that investments are directed towards efforts with the highest return.</p>
<p>The outcome is a streamlined marketing spend that maximizes incremental revenue and boosts return on ad spend (ROAS), translating into a more efficient and effective marketing operation.</p>
<h3>3) Marketing impact assessment</h3>
<p>UMM transcends traditional metrics, providing a clear view of marketing's contribution to key business objectives. This analytical approach dispenses with superficial metrics in favor of deep insights that quantify the genuine influence of marketing activities on revenue growth and customer engagement.</p>
<p>By doing so, UMM empowers marketers to eliminate waste by discontinuing underperforming channels and doubling down on strategies proven to yield tangible results. The result is a marketing strategy backed by empirical evidence, capable of demonstrating ROI with unparalleled clarity.</p>
<h2>Tips for Transitioning into a Unified Measurement Approach</h2>
<p>For marketers looking to adopt or transition to a unified measurement approach, here are some actionable tips:</p>
<ul>
<li>Start Small: Begin by integrating two methodologies, such as MTA and incrementality testing, before gradually adding others into the mix. This phased approach helps in managing complexity.</li>
<li>Prioritize Data Integrity: Ensure that your data collection, storage, and processing practices are robust, compliant with regulations, and capable of supporting integrated analyses.</li>
<li>Leverage Technology: Utilize advanced analytics platforms that can handle multiple measurement methodologies and offer integrated insights.</li>
<li>Cultivate Expertise: Invest in training for your team or collaborate with external experts who can navigate the complexities of a unified measurement framework.</li>
</ul>
<h2>The path ahead</h2>
<p>By leveraging UMM, businesses can optimize cross-channel performance, unlock deeper customer insights for superior personalization, make agile decisions with real-time data, and conduct a comprehensive analysis of the entire marketing funnel. The result is a more strategic allocation of marketing spend, improved ROI, and ultimately, a stronger connection with customers.</p>
<p><a href="https://www.lifesight.io/demo">Book a demo</a> with us today and discover how Lifesight can transform your marketing strategies, optimize your performance across channels, and help you make decisions with confidence.</p>

unified-marketing-measurement-guide.webp

Explore how Unified Marketing Measurement (UMM) tackles modern marketing challenges, offering actionable insights to drive strategic decisions and enhance profitability.

A Guide To Unified Marketing Measurement 

Attribution

<p>Artificial intelligence (AI) technologies have progressed rapidly over the recent years. We are witnessing a measurable shift from traditional rule-based systems to modern machine learning (ML) algorithms. More than understanding the significance of correlations in data, knowing the underlying cause-and-effect relationships has become vital. This realization has led to the growth of causal artificial intelligence (AI).</p>
<p>To illustrate, consider identifying the impact of changing ad placement on sales. Isolating the variables will help identify the specific factors driving customer conversions. This is precisely where causal AI holds immense promise.</p>
<p>Continue reading as we learn more about causal AI and how it promises to revitalize your marketing strategy.</p>
<h2>The mechanics of causal AI</h2>
<p>Causal AI operates on the principles of <a href="https://help.lifesight.io/en/articles/8957098-what-is-causal-inference">causal inference</a> and counterfactual reasoning. These aspects enable it to go beyond identifying correlations to understanding the drivers behind observed phenomena.</p>
<p>Let us take a closer look to learn more about each principle.</p>
<p><strong>Causal inference</strong> estimates the causal effect of one variable on another within a system. Unlike correlation, which merely highlights statistical associations between variables, causal inference uncovers the direct impact of one variable's changes on another. At the same time, it accounts for potential confounding factors.</p>
<p>Consider the example of a company targeting young adults with a marketing campaign, dividing them into two groups: one exposed to the campaign (treatment group) and one not (control group). The company can infer the campaign's impact on young adult sales by comparing sales data between the two groups.</p>
<p><strong>Counterfactual reasoning</strong> involves imagining alternative scenarios to assess the impact of different actions. This aspect of counterfactual reasoning helps causal AI simulate different outcomes and pinpoint the specific factors driving observed changes.</p>
<p>Think of it as a time when you want to simulate scenarios where different marketing strategies were implemented to understand the potential outcomes. For example, you may analyze how sales would have differed if you had launched a different advertising campaign.</p>
<h2>Causal AI vs. Correlational AI</h2>
<p>Here are the key differences between causal AI and correlational AI:</p>
<table>
<tbody>
<tr>
<td>
<p><strong>Aspect</strong></p>
</td>
<td>
<p><strong>Causal AI</strong></p>
</td>
<td>
<p><strong>Correlational AI</strong></p>
</td>
</tr>
<tr>
<td>
<p>Objective</p>
</td>
<td>
<p>Uncover cause-and-effect relationships</p>
</td>
<td>
<p>Identify statistical associations between variables</p>
</td>
</tr>
<tr>
<td>
<p>Focus</p>
</td>
<td>
<p>Causal inference</p>
</td>
<td>
<p>Correlation analysis</p>
</td>
</tr>
<tr>
<td>
<p>Approach</p>
</td>
<td>
<p>Considers interventions and counterfactual scenarios</p>
</td>
<td>
<p>Analyzes patterns and relationships in data</p>
</td>
</tr>
<tr>
<td>
<p>Interpretation</p>
</td>
<td>
<p>Emphasizes understanding the mechanism of action</p>
</td>
<td>
<p>Highlights statistical dependencies</p>
</td>
</tr>
<tr>
<td>
<p>Actionability</p>
</td>
<td>
<p>Facilitates decision-making based on causal insights</p>
</td>
<td>
<p>Provides insights for further investigation</p>
</td>
</tr>
<tr>
<td>
<p>Use Cases</p>
</td>
<td>
<p>Optimization of marketing strategies, policy-making</p>
</td>
<td>
<p>Exploratory data analysis, trend identification</p>
</td>
</tr>
<tr>
<td>
<p>Data Requirements</p>
</td>
<td>
<p>Requires consideration of potential confounders</p>
</td>
<td>
<p>Relies on large datasets for robust correlation</p>
</td>
</tr>
</tbody>
</table>
<p>Continue reading: <a href="https://www.lifesight.io/blog/correlation-vs-causation"><em>Correlation in Marketing vs Causation: Understanding the Difference</em></a></p>
<h2>Applications of causal AI in various industries</h2>
<p>Causal AI's capabilities extend beyond theoretical concepts with practical applications across industries.</p>
<h3>Healthcare</h3>
<p>Causal AI uncovers the causal factors influencing patient outcomes. The information enables healthcare providers to develop personalized treatment plans. Detailed analysis of patient data and medical histories influences the decision-making process.</p>
<p>Pharmaceutical companies use causal AI to identify the relationships between molecular pathways and disease progression. This information helps prioritize promising drug candidates, optimize clinical trial designs, and bring new treatments to market more efficiently.</p>
<h3>Finance</h3>
<p>Financial institutions use causal AI to assess creditworthiness and detect fraudulent activities. Causal AI enhances risk management strategies by identifying the factors contributing to financial risks.</p>
<p>Hedge funds and investment firms use causal AI to unearth investment opportunities and predict market movements. They make better investment decisions and generate higher client returns through optimized trading strategies.</p>
<h3>Marketing</h3>
<p>Causal AI accurately attributes sales and conversions to specific marketing channels and campaigns. Marketers use causal AI to <a href="https://www.lifesight.io/blog/measure-casual-impact-in-marketing">identify the causal impact</a> of each marketing touchpoint on customer behavior. Optimized campaign strategies also enable effective allocation of marketing budgets.</p>
<p>Analyzing customer data and interactions helps marketers identify high-value customers and personalize marketing strategies. A maximized customer lifetime value drives increased revenue for better profitability.</p>
<h2>The benefits of causal AI in marketing</h2><br/>
<h3>1) Enhanced decision-making</h3>
<p>Priorities for your marketing team can range from understanding optimal timing for product launches to adjusting messaging to resonate with specific audience segments.</p>
<p>Causal AI generates actionable insights and enables data-driven decisions by understanding the underlying cause-and-effect relationships driving consumer behavior.</p>
<p>Imagine knowing what tactics are influencing conversions to yield the desired results. Modern marketers use causal AI to allocate marketing resources accurately and optimize real-time campaign performance.</p>
<h3>2) Attribution modeling</h3>
<p>Causal AI accurately attributes credit to each marketing touchpoint based on its true causal impact on customer behavior while traditional attribution models rely on simplistic rules or heuristics.</p>
<p>Moreover, causal AI goes a step ahead by considering the complex connection of marketing channels and customer interactions.</p>
<p>When you have a deeper understanding of the causal pathways that lead to desired outcomes, it will lead to an optimized marketing mix and a more accurate budget allocation for maximized return on investment (ROI).</p>
<h3>3) Marketing mix modeling</h3>
<p>Causal AI offers a holistic view of how marketing activities interact and influence overall performance by analyzing historical data and controlled experiments to quantify the causal impact of each marketing channel and campaign on business metrics like customer acquisition and revenue.</p>
<p>Let's understand the benefits of causal AI in a real-world use case with the help of <a href="https://www.lifesight.io/platform">Lifesight's unified marketing measurements platform</a>.</p>
<p>Lifesight offers tools that uncover the truth behind your marketing performance in the privacy-first era. Users gain invaluable insights to optimize strategies by measuring the causal impact of marketing activities on business KPIs. Lifesight's continuous testing and calibration processes ensure improved measurement accuracy.</p>
<p>Furthermore, Lifesight helps make swift and optimal action through AI-generated recommendations, forecasting, and AI-powered attribution and provides granular insights for optimization.</p>
<p>With the ability to plan, forecast, and compare multiple marketing scenarios, uncovering opportunities and understanding the impact on business KPIs becomes seamless.</p>
<h2>Challenges and considerations in implementing causal AI</h2>
<ul>
<li><strong>Data quality: </strong>These challenges can arise from missing values, incomplete datasets, measurement errors, and biases. Addressing these challenges thoroughly requires imputation techniques for missing values and rigorous validation processes to detect and correct errors.</li>
</ul>
<ul>
<li><strong>Complexity: </strong>Causal relationships have numerous interconnected variables, making isolating and identifying the true causal factors challenging. Addressing this challenge involves using sophisticated algorithms that handle complex interactions among variables.</li>
</ul>
<ul>
<li><strong>Expert analysis: </strong>AI algorithms uncover data patterns and correlations. However, expert human judgment helps contextualize findings and validate causal inferences. Data scientists and domain experts must collaborate cohesively to ensure causal models reflect real-world phenomena accurately.</li>
</ul>
<h2>The Future of Causal AI</h2>
<p>While it will continue offering deeper insights into the intricate cause-and-effect relationships, the potential challenges discussed above will continue to pose a problem. However, the good news is causal AI will continue to evolve as technology advances and datasets become more extensive and complex.</p>
<p>Potential advancements on the horizon include the following:</p>
<ul>
<li>Enhanced predictive capabilities</li>
<li>Refined <a href="https://www.lifesight.io/newsroom/uncover-the-why-behind-the-buy-with-causal-inference">causal inference</a> techniques</li>
<li>Advanced integration with emerging technologies like ML and automation</li>
</ul>
<p>Lifesight stands at the forefront of this change through its advanced causal AI solutions to tackle the ever-evolving challenges marketers face. Lifesight's measurement and optimization solutions extract actionable insights that drive revenue-enhancing operations.</p>
<h2>Experience the transformative potential of causal AI with Lifesight</h2>
<p>Lifesight's causal AI solutions leverage observational data and advanced techniques to move beyond conventional number-crunching and provide clear insights into causal relationships. Refining your strategies will become seamless with advanced distinguishing factors highlighted between mere correlations and genuine causality.</p>
<p>Book a <a href="https://www.lifesight.io/demo">demo</a> now!</p>
<h2>FAQs</h2><br/>
<h3>1) What are causal AI use cases and examples?</h3>
<p>Causal AI examples and use cases span across industries and applications. In marketing, causal AI helps with attribution modeling to accurately attribute conversions to specific marketing touchpoints. In healthcare, causal AI helps personalize treatment plans by understanding the causal relationships between medical interventions and patient outcomes.</p>
<h3>2) Causal AI vs. generative AI: Which is better?</h3>
<p>Causal AI focuses on understanding cause-and-effect relationships within data, enabling more accurate predictions. Generative AI, on the other hand, generates new data instances and outputs based on learned patterns. While both have their merits, causal AI is preferable when understanding causality and making predictions based on causal relationships are crucial.</p>
<h3>3) What are causal networks in AI?</h3>
<p>Causal networks in AI are graphical models that represent causal relationships between variables in a system. These networks use directed edges to indicate the direction of causality. It allows for the inference of causal effects and the prediction of outcomes based on interventions or variable changes.</p>

causal-ai.webp

Dive into the world of Causal AI and discover its significance in understanding cause-and-effect relationships. Explore applications across industries and learn how it revolutionizes marketing strategies.

What is Causal AI and Why Does it Matter?

Analytics

<p>Data is a critical aspect of any decision-making process. But how often do you manage to leverage it effectively? This is especially true when considering existing concerns like data scarcity, privacy, and regulatory issues. Enter synthetic data which refers to artificially created data that mimics real-world datasets while preserving privacy and confidentiality.</p>
<p>When it comes to marketing, where privacy concerns may constrain access to real-world consumer data, synthetic data offers a viable solution for generating realistic yet privacy-preserving data for analysis and experimentation.</p>
<p>Continue reading to know how synthetic data will redefine the way marketers analyze data and enhance the decision-making process.</p>
<p>This article looks at the meaning of synthetic data and ways to use it effectively.</p>
<h2>How is synthetic data generated using algorithms and models?</h2>
<p>Synthetic data generation uses advanced algorithms and models that produce data mimicking real-world datasets. These methods use techniques like generative models and statistical approaches. These practices help in creating synthetic data that exhibit similar statistical properties and patterns to authentic data.</p>
<p>The four synthetic data generation approaches with algorithms and models are:</p>
<h3>1) Generative adversarial networks (GANs)</h3>
<p>GANs comprise two neural networks: a generator and a discriminator. The synthetic data generator produces samples that are indistinguishable from actual data. On the other hand, the discriminator learns to differentiate between natural and synthetic data.</p>
<p>GANs use an adversarial training process to iteratively refine the generator's ability to generate realistic data by competing against the discriminator. GANs excel in capturing complex data distributions and generating high-quality synthetic data. For example, you can generate everything from images to structured data.</p>
<h3>2) Variational autoencoders (VAEs)</h3>
<p>VAEs learn the underlying distribution of input data. They consist of an encoder network that maps input data into a latent space. A decoder network reconstructs the input data from the latent space.</p>
<p>Sampling from the learned latent space helps VAEs generate new data points that follow the learned distribution. VAEs offer the advantage of providing a probabilistic framework for generating synthetic data while enabling data interpolation and exploration.</p>
<h3>3) Statistical approaches</h3>
<p>Statistical methods, like<a href="https://aws.amazon.com/what-is/monte-carlo-simulation/"> Monte Carlo simulations</a> and bootstrapping techniques, also help with synthetic data generation. It involves generating random samples from probability distributions to simulate scenarios.</p>
<p>Conversely, bootstrapping involves resampling from the observed data with replacement to generate synthetic data with similar statistical properties.</p>
<h3>4) Hybrid and domain-specific approaches</h3>
<p>Some synthetic data generation methods combine multiple techniques, like combining generative models with statistical approaches for improved performance.</p>
<p>Additionally, domain-specific algorithms can cater to the unique requirements of specific industries and applications to provide more accurate and relevant information for various synthetic data use cases.</p>
<h2>Differences between synthetic data and real-world data</h2>
<p>Synthetic data and real-world data serve distinct purposes and possess inherent differences. These distinctions impact their applicability and utility in various contexts.</p>
<p>Here's a comparative table highlighting their key characteristics.</p>
<table>
<tbody>
<tr>
<td>
<p><strong>Aspect</strong></p>
</td>
<td>
<p><strong>Synthetic Data</strong></p>
</td>
<td>
<p><strong>Real-World Data</strong></p>
</td>
</tr>
<tr>
<td>
<p>Origin</p>
</td>
<td>
<p>Generated artificially</p>
</td>
<td>
<p>Collected from authentic sources</p>
</td>
</tr>
<tr>
<td>
<p>Variability and Complexity</p>
</td>
<td>
<p>May not capture all nuances</p>
</td>
<td>
<p>Reflects inherent complexity and variability</p>
</td>
</tr>
<tr>
<td>
<p>Privacy and Confidentiality</p>
</td>
<td>
<p>Designed to preserve privacy</p>
</td>
<td>
<p>May contain sensitive information</p>
</td>
</tr>
<tr>
<td>
<p>Generalization and Bias</p>
</td>
<td>
<p>May exhibit biases inherent in algorithms</p>
</td>
<td>
<p>Reflects biases from data collection methods</p>
</td>
</tr>
<tr>
<td>
<p>Scalability and Cost</p>
</td>
<td>
<p>Cost-effective and scalable</p>
</td>
<td>
<p>Costly and resource-intensive</p>
</td>
</tr>
</tbody>
</table>
<h2>The role of synthetic data in marketing analytics</h2>
<p>Synthetic data is transforming marketing analytics by ensuring privacy and enabling risk-free experimentation. It addresses data privacy concerns effectively, a critical issue when personal customer information is involved in <a href="https://newsroom.ibm.com/2021-07-28-IBM-Report-Cost-of-a-Data-Breach-Hits-Record-High-During-Pandemic">44% of data breaches</a>. By mimicking real consumer behavior without using actual customer data, it allows marketers to analyze and gain insights while complying with privacy regulations. This data not only enriches marketing datasets by adding diversity but also enhances understanding of consumer segments and behaviors.</p>
<p>Furthermore, synthetic data is invaluable for training predictive models, offering a reliable alternative to real-world data that might be limited or biased. It supports testing different marketing strategies in simulated environments, helping predict outcomes, and adjusting campaigns for better performance. Its compatibility across various platforms aids in creating personalized marketing efforts seamlessly.</p>
<p>By leveraging synthetic data, marketers can adapt strategies based on the latest consumer trends and market dynamics, optimizing engagement and conversion rates. This crisp approach to using synthetic data underscores its importance in developing effective, privacy-compliant marketing strategies.</p>
<h2>Use cases in marketing mix modeling and incrementality testing</h2>
<p><a href="https://www.lifesight.io/blog/marketing-mix-modeling-importance">Marketing mix modeling</a> (MMM) and incrementality testing are integral to marketing analytics as they help assess the effectiveness of marketing strategies. The diverse applications and use cases of MMM in improving marketing performance include:</p>
<h3>Attribution modeling</h3>
<p>Is accurately <a href="https://techbootcamps.utexas.edu/blog/attribution-modeling/">attributing sales or conversions</a> to marketing channels a problem your organization faces? This is a common concern, especially when we look at multi-channel environments and cross-device behaviors. Marketing mix modeling helps overcome the <a href="https://www.lifesight.io/blog/last-touch-vs-multi-touch-attribution">attribution modeling</a> issue by analyzing historical data and including advertising spend, promotional activities, and external influences to quantify the impact of each component on overall performance.</p>
<h3>Budget allocation</h3>
<p>Proper budget allocation for different marketing campaigns can be challenging due to reasons like overlapping touchpoints, uncertainties with ROI, and limited resources. Marketing mix modeling guides decision-making by identifying the channels and strategies contributing to key performance indicators (KPIs) like customer acquisition cost (CAC) and lifetime value of a customer (LTV).</p>
<h3>Campaign optimization</h3>
<p>Compare the outcomes between synthetic test data and control groups to test the <a href="https://www.lifesight.io/budget-campaign-optimization">incremental impact of marketing campaigns</a>. Incrementality testing helps through randomized control trials (RCTs) or quasi-experimental designs for <a href="https://www.lifesight.io/blog/digital-ad-campaign-optimization-strategies">campaign optimization</a>. Such interventions isolate the causal effect of specific marketing interventions on consumer behavior and business outcomes.</p>
<h3>Channel performance analysis</h3>
<p>Analyze the performance of each channel to evaluate their effectiveness in driving desired outcomes. Use the following KPIs to identify high-performing channels: customer acquisition cost (CAC), return on ad spend (ROAS), and customer lifetime value (CLV).</p>
<h3>Seasonality and trend analysis</h3>
<p><a href="https://www.lifesight.io/blog/seasonality-in-marketing-mix-modeling">Seasonality in marketing mix modeling</a> allows the modern marketer to understand the relationship between seasonal trends, marketing activities, and sales. Analyze the root cause behind seasonal peaks using an automated and unified marketing measurement platform such as <a href="https://www.lifesight.io/">Lifesight</a>.</p>
<h3>Product portfolio management</h3>
<p>Incrementality testing examines the incremental impact of new product launches or changes in pricing strategies. Controlled experiments or A/B tests help gauge consumer response to product innovations or pricing changes. Insights from this testing help guide data-driven decisions regarding product portfolio management.</p>
<h3>Long-term strategic planning</h3>
<p>Keep tabs on future trends to adapt your marketing strategies. Marketing mix modeling helps extrapolate historical trends and conduct scenario analysis to promote robust strategies that align with your marketing objectives.</p>
<h2>The advantage of using synthetic data as a control in marketing experiments</h2>
<p>Synthetic data generates realistic yet artificial datasets that enhance the reliability of research efforts. Let's understand the three benefits on offer and how they hold promise for your marketing campaigns.</p>
<h3>1) Standardizes and controls the environment</h3>
<p>Control variables and replicate experimental conditions accurately with synthetic data. A standardized process eliminates confounding factors to create a reliable foundation for conducting experiments.</p>
<p>For example, imagine if you want to test the effectiveness of email marketing strategies across demographic segments. Generating synthetic data that accurately reflects the demographic profiles ensures a controlled environment for your experiments</p>
<h3>2) Enables large-scale experiments</h3>
<p>Synthetic data helps with large-scale experimentation through the flexibility to generate vast quantities of data quickly and cost-effectively. Scalability allows you to test multiple hypotheses and gather significant insights without the constraints of limited sample sizes.</p>
<p>For example, a retail company uses synthetic data to simulate customer interactions across sales channels for enhanced analysis of the impact of marketing initiatives on revenue.</p>
<h3>3) Mitigates privacy concerns</h3>
<p>Synthetic data overcomes privacy concerns through privacy-preserving alternatives that mimic the underlying patterns and distributions of real data without compromising individuals' privacy.</p>
<p>Consider the example of a healthcare company conducting market research on patient preferences for a new medical device. They can use synthetic data to simulate patient demographics and treatment outcomes without accessing actual patient records.</p>
<h2>Challenges of Market Matching and Synthetic Data's Solution</h2>
<p>Here are some common challenges associated with market matching.</p>
<ul>
<li><strong>Difficulty in finding comparable control groups: </strong>It can be a struggle to identify suitable control groups that accurately reflect the characteristics and behaviors of the target audience. Access to comparable control groups will gauge the effectiveness of your marketing strategies and attribute outcomes to specific interventions.</li>
</ul>
<ul>
<li><strong>Risk of selection bias and confounding variables: </strong>Market matching techniques based solely on observable characteristics may introduce selection bias. Furthermore, they cannot adequately account for confounding variables influencing consumer behavior. Incorrect comparisons can lead to flawed conclusions.</li>
</ul>
<ul>
<li><strong>Limited causal inference: </strong>A campaign cannot yield desired results if marketing teams fail to establish causal inference that determines the true impact of marketing interventions on consumer outcomes. Without randomized control groups or rigorous experimental designs, it can be a struggle to attribute changes in results to specific marketing activities.</li>
</ul>
<p>Now that we know the challenges, let's examine how synthetic data helps overcome them.</p>
<ul>
<li><strong>Enhances data augmentation and representation:</strong> Synthetic data provides a controlled environment for generating datasets that resemble real-world scenarios to augment existing datasets with additional variables that improve the granularity of analyses.</li>
</ul>
<ul>
<li><strong>Mitigates bias and confounding variables:</strong> Synthetic data generation algorithms replicate the distribution of relevant characteristics and demographics in the synthetic datasets. Use this data to control confounding variables for a more accurate comparison between treatment and control groups and reduce the likelihood of biased results.</li>
</ul>
<ul>
<li><strong>Facilitates controlled experiments and causal inference: </strong>If you're looking to assess the impact of marketing campaigns on consumer behavior, synthetic data simulates randomized control groups and manipulates variables of interest.</li>
</ul>
<h2>Synthetic data and marketing mix modeling</h2>
<p>In <a href="https://www.lifesight.io/blog/principles-of-marketing-mix-modeling">marketing mix modeling (MMM)</a>, synthetic data addresses key challenges and enhances the effectiveness of analytical processes. Synthetic data supports data augmentation efforts to supplement existing datasets with additional variables. The result is enriched datasets with improved model representativeness and enhanced accuracy of forecasting.</p>
<p>However, using synthetic data also presents potential risks of overfitting. The model may learn from noise in the data rather than genuine patterns. Evaluate the performance of your data models and validate their output against real-world observations.</p>
<h2>Conclusion</h2>
<p>Synthetic data overcomes issues like data scarcity and privacy to mitigate challenges associated with data-driven decision-making and presents challenges such as potential biases and risks of overfitting. Harness the power of synthetic data to gain deeper insights into your target audience and improve your forecasting accuracy.</p>
<h2>FAQs</h2><br/>
<h3>1) What is synthetic data in AI?</h3>
<p>Synthetic data in AI refers to artificially generated data that trains and tests AI models. There are several benefits on offer like resembling real-world data while preserving privacy.</p>
<h3>2) What is synthetic data in machine learning?</h3>
<p>Synthetic data in machine learning is artificially created data that mirrors real-world datasets. Such datasets train machine learning models and address data scarcity and privacy issues.</p>
<h3>3) What is a synthetic data example?</h3>
<p>An example of synthetic data is generating fake customer profiles with demographic information for market analysis.</p>
<h3>4) Is synthetic data from generative models ready for image recognition?</h3>
<p>Synthetic data generated from generative models like GANs can be suitable for image recognition tasks, provided that the models produce realistic and diverse images that capture relevant features.</p>
<h3>5) What is synthetic test data?</h3>
<p>Synthetic test data is artificially generated data explicitly used for testing purposes. Synthetic data finds common applications in training machine learning models and conducting research experiments.</p>

what-is-synthetic-data.webp

Discover how synthetic data revolutionizes marketing analytics, addressing privacy concerns and enabling risk-free experimentation. Learn about its generation, use cases, and impact on marketing mix modeling.

What is Synthetic Data: Everything You Need to Know

Incrementality

geo-experiments-guide.webp

Learn the step-by-step strategies, case studies, and best practices to conduct effective geo experiments that lead to profitable growth for your business.

A Step-By-Step Guide For Mastering Geo Experiments

Trends & Insights

<p>Believe it or not, marketing has the most to gain from AI of all business functions.</p>
<p>AI in marketing enhances the core marketing activities for each stage of the customer's purchasing journey &ndash; understanding your audience's needs, matching them to products, personalizing campaigns and services, and converting leads to customers.</p>
<p>McKinsey's research suggests that <a href="https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/ai-powered-marketing-and-sales-reach-new-heights-with-generative-ai"> 90%</a> of commercial leaders expect to use generative AI solutions over the next two years. Forward-thinking marketing leaders and CMOs are already considering (and implementing) AI to connect with and serve their customers.</p>
<p>Here, we outline the different ways in which AI is reshaping marketing and the suggestive path forward.</p>
<h2>1) Data enrichment and synthesis to deliver the best first-party data to marketing channels</h2>
<p>The <a href="https://www.montecarlodata.com/blog-data-quality-survey"> State of Data Quality Survey</a>, 2023, suggests that 25% or more of the company revenue is impacted due to poor data quality.</p>
<p>Use AI-based <a href="https://www.lifesight.io/blog/consumer-data-enrichment"> consumer data enrichment</a> to improve data quality as AI-powered data platforms merge external and internal data sources to derive quality conclusions. They augment existing data sets and add more value to the data for better understanding and deeper analysis.</p>
<p>Modern marketers use data platforms leveraging data enrichment to get a 360-degree overview of customer needs, segment target audiences, and personalization.</p>
<p>Let's understand these use cases with examples.</p>
<p>Suppose you're running an eCommerce brand with omnichannel marketing strategies. You have rich 1st party data sources but cannot derive valuable insights that inform your marketing strategies and ad campaigns.</p>
<h3>Use case 1: Using a first-party identity graph for enriched customer profiles</h3>
<p>Leveraging a first-party identity graph significantly enhances the quality of first-party data available to marketing channels. This graph stores a user's identifiers, such as names, contacts, and marketing identifiers like Facebook Pixel IDs and Google Click IDs.&nbsp;</p>
<p>It enriches these identities with external identifiers and links anonymous to known identifiers, enabling marketers to track anonymous customer profiles more effectively. This, in turn, improves match rates for ad channels, facilitating better-targeted retargeting campaigns and triggering more precise marketing automation flows.</p>
<h3>Use case 2: Setting up CAPI</h3>
<p>Incorporating a <a href="https://www.lifesight.io/blog/guide-to-conversions-api">conversion API</a> (CAPI) represents a transformative approach to managing conversion data. Unlike traditional client-side methods like web pixels, CAPI operates server-side, offering a direct channel to send event data from your servers to third-party advertising platforms.&nbsp;</p>
<p>This method not only enhances the flexibility and reliability of tracking conversions but also ensures complete ownership and control over the conversion data. Through CAPI, marketers can significantly improve the accuracy and efficiency of their ad targeting and measurement, leading to more effective marketing strategies.</p>
<h3>Use case 3: Segment target audiences</h3>
<p>Leverage data platforms to enrich data through real-time processing and segmentation based on the audience's demography, behavior, geography, and preferences. Build segment-specific ad campaigns to target your audience in a personalized manner to improve <a href="https://www.lifesight.io/blog/drive-engagement-strategy-using-customer-data">user engagement</a> and ad retargeting.</p>
<h3>Use case 4: Hyper-personalize communications</h3>
<p>Use data enrichment and synthesis to access real-world audience data points like preferences, dislikes, favorite social channels, and lifestyles. Personalize your communications based on these insights to build a special relationship with your audience and boost conversion rates.</p>
<p><a href="https://www.lifesight.io/connect">Lifesight</a>'s customer data platform seamlessly integrates marketing, business, and customer data, enabling a more granular analysis. Lifesight offers,</p>
<ul>
<li>AI-generated segments to dive deeper into customer behavior and make informed marketing decisions</li>
<li>Predictive behavior modeling to automatically predict customer behavior like LTV, purchases, churn rate, and more</li>
</ul>
<h2>2) AI tools for marketing content and creatives at scale</h2>
<p>By now, you must've used generative AI marketing tools like ChatGPT, Bard, or Jasper to streamline content production, boost campaign creativity, and deliver personalized customer experiences.</p>
<p>According to <a href="https://www.thedrum.com/news/2024/01/03/2024-experts-discuss-evolving-role-generative-ai-marketing"> Zara Kerwood</a>, Senior Director of Creative Technology at George P. Johnson (UK and Nordics), 'leveraging AI for marketing campaigns must not be about showing off the tech itself, but to enhance customer experiences.'</p>
<p>Here's an example of how the <a href="https://rasa.io/wp-content/uploads/2020/08/rasaio_AMA-Case-Study.pdf">American Marketing Association (AMA) used AI</a> to create personalized newsletter content for improved customer experience.</p>
<ul>
<li>They created custom articles for each newsletter subscriber, merged their internal content featuring at the top, and individualized subject lines.</li>
<li>The AMA team was able to save time on creating newsletter content and increase their send frequency simultaneously using AI.</li>
</ul>
<p>The result?</p>
<ul>
<li>42% monthly subscriber engagement rate</li>
<li>AMA.org became the most clicked news source within the newsletter, significantly boosting website traffic.</li>
</ul>
<p>Alongside this, another practical use case of AI is in creating unique and stunning creatives for ad campaigns.</p>
<p>A study by <a href="https://iab.it/wp-content/uploads/2022/03/Kantar.pdf">KANTAR</a> on using AI creative testing to meet client demands suggests that premium-quality creatives can boost an ad campaign's performance 12x, leading to a 30% increase in ROI.</p>
<p>As per <a href="https://www.superside.com/blog/ai-design-projects">Superside</a>, using Midjourney image generation to replace traditional stock images in their client's ads allowed the team to develop a unique style while moving fast and maintaining brand consistency.</p>
<p>The result was 70% design time saved to create 114 ad variations.</p>
<h2>3) Machine learning in advertising</h2>
<p>Advertisers are using machine learning products to save time and generate better results for ad campaigns. The two most popular products have been Google Performance Max and Meta's Advantage+ Shopping campaigns.</p>
<h3>The advantages of using Performance Max campaigns include:</h3>
<ul>
<li>Improved performance: Machine learning optimizes ad delivery and targeting to help businesses achieve better results compared to managing campaigns manually.</li>
<li>Efficient use of resources: They find the most effective combination of bids and targeting to meet your performance goals and get more value from your advertising spend.</li>
<li>Simplified campaign management: As machine learning takes the guesswork out of campaign management to optimize delivery and targeting, you save time and resources that would otherwise be spent on manual optimization.</li>
<li>Increased flexibility: You can use them for a variety of purposes including driving leads and sales or increasing website traffic.</li>
<li>Create new assets at scale: Google AI generates new images and text assets for cross-channel campaign optimization.</li>
<li>Enhance existing assets: Use AI-powered image editing in Google Ads to test your image variations, share previews of all assets, and simplify the creative review workflow for your teams.</li>
</ul>
<p>According to a case study by <a href="https://www.goinflow.com/blog/performance-max-case-study/">Inflow</a>, when KEH Camera consolidated its Smart Shopping Campaigns with Performance Max, the results outperformed its Smart Shopping Campaigns significantly.</p>
<p>In comparison to the first quarter of 2022 (Smart Shopping Campaigns), in the first quarter of 2023 (Pmax), KEH was able to achieve:</p>
<ul>
<li>76.3% increase in ad revenue</li>
<li>44.1% increase in transactions</li>
</ul>
<p>All automated ad products by Meta are unified under Meta Advantage. Meta released Advantage+ Shopping campaigns as a rival to Google's Performance Max. Advantage+ Shopping campaigns use machine learning to help businesses reach their targeted audience more efficiently without lengthy setup time.</p>
<h3>The benefits of using Meta's Advantage+ Shopping campaigns are:</h3>
<ul>
<li>Quick and easy setup</li>
<li>Use machine learning to build agile and effective ad combinations</li>
<li>Target new and current customers with personalized recommendations</li>
<li>Automates/ combines all audiences, reducing overlapping</li>
<li>Tests 150 creative combinations to determine the best-performing ad</li>
</ul>
<p>Lifestyle brand <a href="https://business.instagram.com/success/homesick?locale=en_GB">Homesick</a> ran Shops ads with Advantage+ Shopping campaigns on Instagram and Facebook. They saw an 18% decrease in cost per purchase and a 27% higher return on ad spend when selecting "website and shop" vs selecting "website" only.</p>
<h2>4) Predictive modeling and insights to increase campaign effectiveness</h2>
<p>Predictive modeling helps marketers keep up with the ever-evolving consumer and market trends and make informed marketing decisions and strategies.</p>
<p>Each model is tailored to predict a specific outcome such as the chances of success of a new product launch, the consumer's likelihood to purchase, or the impact of a marketing campaign on ROI.</p>
<p>A case in point is how <a href="https://marketingplatform.google.com/about/resources/loreal-taiwan-uses-predictive-insights-to-reach-the-right-customers/">L'Oréal used predictive analytics</a> (Google Marketing Platform and Google Cloud) to predict which customers are more likely to purchase offline (in stores). They then reached those customers with online advertising campaigns.</p>
<p>Before this, even though customers discovered and interacted with the brand digitally, the majority of sales occurred offline. This fragmented journey, which led to offline sales, made it challenging for L'Oréal to connect its digital marketing investment to the ROI.</p>
<p>With predictive modeling to analyze this data, they found that there's a 14-day time lag between people visiting the L'Oréal website and going to the store to make the purchase. These insights helped them build a customized ML model to predict people who are likely to visit the store to purchase in the next two weeks.</p>
<p><a href="https://www.lifesight.io">Lifesight</a>'s unified marketing measurement platform lets you measure the true causal impact of marketing with incrementality testing and Marketing Mix Modeling (MMM).</p>
<ul>
<li>Design and run experiments with an incrementality testing suite</li>
<li>Create optimal media plans with accurate forecasts and optimize marketing budgets with <a href="https://www.lifesight.io/marketing-mix-modeling"> MMM</a></li>
<li>Get a unified view of campaign performance, leverage data-driven attribution models on customer journeys</li>
<li>Predict outcomes accurately and automatically identify the cause-and-effect relationship between influencing factors</li>
</ul>

![platform-spend-vs-total-revenue](https://mighty-basketball-fd4aeed960.media.strapiapp.com/platform_spend_vs_total_revenue_f58ac64c9f.webp)

<h2>The Way Forward</h2>
<p>When looking at introducing AI in marketing, we recommend the following four actions:</p>
<ul>
<li><strong>Audit the commercial activities:</strong> Within your marketing department, explore how you could start implementing AI use cases. This decision could be based on the challenges your team faces.</li>
<li><strong>Build a cross-functional team:</strong> Form a commercial team (for example that includes data, marketing, and sales) to test the applicability of commercial use cases.</li>
<li><strong>Identify the low-hanging fruit:</strong> What are the low-cost, low-risk, and high-impact areas for application?</li>
<li><strong>Launch some AI experiments: </strong>Start with a few pilot use cases, track results, and refine the approach before broader implementation.</li>
</ul>
<p>Lifesight is a unified marketing measurement platform that helps marketers make data-driven decisions using marketing mix modeling, incrementality testing, and causal AI. The three areas where you could start using AI and predictive modeling to supercharge your marketing performance are:</p>
<ul>
<ul>
<li><strong>Budget campaign optimization:</strong> Optimize your ad spend and campaigns on a granular level to reduce your cost-per-acquisition (CPA) and drive higher return on ad spend (ROAS) to drive business KPIs using <a href="https://www.lifesight.io/budget-campaign-optimization">budget optimization</a>.</li>
<li><strong>Media planning and forecasting:</strong> Lifesight's <a href="https://www.lifesight.io/media-scenario-planner">incrementality-powered media planning and forecasting tool</a> helps you build optimal marketing scenarios without lengthy planning to maximize your ROI without the manual grunt work.</li>
<li><strong>Marketing impact assessment: </strong>Track the performance of your decisions on critical business KPIs and avoid wasted media spend through incrementality intelligence using <a href="https://www.lifesight.io/marketing-impact-assessment">marketing impact assessment</a>. </li>
</ul>
</ul>
<p>To implement AI-powered marketing measurements, book a <a href="https://www.lifesight.io/demo">demo with Lifesight</a>.</p>

4 Ways Modern Marketers Can Start Their AI Journey

4 Ways Modern Marketers Can Start Their AI Journey.webp

Discover the transformative power of AI in marketing with Lifesight's comprehensive guide. Explore data enrichment, content creation, machine learning in advertising, and predictive modeling to optimize your marketing strategies.

<p>In 2023, businesses alone witnessed a staggering <a href="https://www.madfestlondon.com/insights/why-100-billion-in-advertising-waste-is-about-attention">$100 billion in media spending</a> evaporate due to inadequate measurement practices. As we move into 2024, the significance of marketing measurement becomes even clearer. It is not only a strategic necessity but also a crucial tool in navigating through a sea of data and channels.</p>
<p>This article aims to outline the evolution and predict key trends that will define marketing measurement in 2024. Drawing on the latest industry insights and our expert analysis, we aim to offer a roadmap for marketers seeking to optimize their campaigns and prove ROI in the new norms of a privacy-first, AI-driven marketplace.</p>
<h2>Trend 1: Shift from Third-party to First-party Data</h2>
<p>Marketers are now operating in a post-cookie world, where the use of first-party data and privacy-compliant methods has become the gold standard. This evolution is not just a response to regulatory pressures; it reflects a maturing industry that prioritizes consumer trust and transparent data stewardship.</p>
<p>Not long ago, marketers heavily relied on data from external platforms to understand customer behaviors and preferences. This data, often collected without direct interaction with the consumer, has been essential for creating personalized marketing campaigns. Nevertheless, this approach is changing rapidly.</p>
<p>A recent report by Twilio indicated that <a href="https://investors.twilio.com/news/news-details/2022/Twilio-Report-Shows-Consumers-Want-Personalization-But-Dont-Trust-Brands-With-Their-Data/default.aspx">43% of business leaders</a> are already shifting their focus towards first-party data due to its capacity to deliver better privacy protections for customers.&nbsp;</p>
<p>This direct data comes from interactions such as website visits, app usage, or email engagement, and is voluntarily provided by customers. With this data in hand, marketers can tailor experiences more effectively, enhancing customer satisfaction and trust. Moreover, first-party data respects user privacy, aligning with the increasing global demands for data protection.</p>
<p>Adapting to this new norm requires robust systems like Customer Data Platforms (CDP) that can handle the complexities of gathering and utilizing first-party data. They help organize customer information from various sources, providing a comprehensive view of the customer journey.</p>
<p>Another compelling case for this shift is the success story of Domino&rsquo;s Mexico, which, upon implementing a Customer Data Platform (CDP) to manage first-party data, saw a <a href="https://segment.com/customers/dominos/">65% decrease in cost per acquisition</a> and a 700% increase in return on ad spending. This underscores the tangible benefits that first-party data management can bring to businesses in terms of efficiency and effectiveness.</p>
<p>These statistics not only illustrate the urgency with which companies are approaching the shift but also the benefits they stand to gain.</p>
<h2>Trend 2: Rise of Unified Marketing Measurement Platforms</h2>
<p>With the dawn of 2024, the marketing industry is undergoing a revolutionary shift towards Unified Marketing Measurement (UMM) platforms. This shift is monumental because it represents the merging of several attribution methodologies into a single, cohesive framework.</p>

![Rise of Unified Marketing Measurement Platforms](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Rise_of_Unified_Marketing_Measurement_Platforms_dddbe07be1.webp)

<p>UMM is a comprehensive approach that combines the robustness of marketing mix modeling (MMM), the precision of multi-touch attribution (MTA), the clarity of incrementality experiments, and the predictive power of causal AI to provide a comprehensive view of marketing efforts.</p>
<p>Now, let's analyze the objectives, requirements, strengths, and weaknesses of these measurement approaches.</p>
<table>
<tbody>
<tr>
<td>
<p><strong>Methodology</strong></p>
</td>
<td>
<p><strong>Objective</strong></p>
</td>
<td>
<p><strong>Required Data</strong></p>
</td>
<td>
<p><strong>Strengths</strong></p>
</td>
<td>
<p><strong>Weaknesses</strong></p>
</td>
</tr>
<tr>
<td>
<p><strong>MTA (Multi-Touch Attribution)</strong></p>
</td>
<td>
<p>Day-to-day optimization of online campaigns and tactical adjustments.</p>
</td>
<td>
<p>Granular user-level data from digital touchpoints.</p>
</td>
<td>
<p>Near real-time performance tracking and allocation of value to customer interactions.</p>
</td>
<td>
<p>Often lacks offline media insights and may require third-party cookies.</p>
</td>
</tr>
<tr>
<td>
<p><strong>MMM (Marketing Mix Modeling)</strong></p>
</td>
<td>
<p>Strategic allocation across channels for budget planning and assessing marketing's overall impact.</p>
</td>
<td>
<p>Aggregated KPI and media data on a weekly/daily basis.</p>
</td>
<td>
<p>Accounts for external factors and measures all media activities holistically.</p>
</td>
<td>
<p>Can lack the granularity needed for digital optimization and typically has a delayed feedback loop.</p>
</td>
</tr>
<tr>
<td>
<p><strong>Incrementality Testing</strong></p>
</td>
<td>
<p>Determining the incremental impact of marketing campaigns or creatives.</p>
</td>
<td>
<p>Detailed data from controlled experiments.</p>
</td>
<td>
<p>Highly detailed and accurate measurement with clear causal links.</p>
</td>
<td>
<p>Requires extensive planning, time, and resources; influenced by experimental design and external factors.</p>
</td>
</tr>
<tr>
<td>
<p><strong>Causal Inference </strong></p>
</td>
<td>
<p>Strategic decision-making is based on understanding causal relationships to predict outcomes.</p>
</td>
<td>
<p>All available data from digital and non-digital sources, experiments, and existing analyses.</p>
</td>
<td>
<p>Offers predictive insights into marketing outcomes and can operate without the need for direct experiments.</p>
</td>
<td>
<p>A complex implementation may require a sophisticated understanding of AI models.</p>
</td>
</tr>
</tbody>
</table>
<h2>Trend 3: Increased Transparency in Measurement</h2>
<p>This trend is gaining momentum due to the necessity for brands to establish trust with consumers and to navigate the complexities of a fragmented media environment with increased confidence. Stringent data privacy regulations such as GDPR and CCPA have acted as catalysts, reinforcing the importance of transparency. These regulations require marketers to communicate clearly with consumers about data usage, which in turn affects the measurement tools and strategies they employ.</p>
<p>Brands are now more aware of the ethical implications of their data practices, leading to a shift towards using more transparent measurement platforms.</p>
<p>The enhanced transparency in measurement is directly connected to improved marketing effectiveness. When marketers understand the 'why' and 'how' behind the numbers, they can make more informed decisions that lead to better outcomes.</p>
<h2>Trend 4: Privacy-First Approach: Shifting from Deterministic to Probabilistic Measurement</h2>
<p>The fourth crucial trend in marketing measurement for 2024 is the definitive shift from deterministic to probabilistic measurement models. This change is driven by a privacy-first approach that has become paramount in the industry.</p>
<p>The adoption of a privacy-first strategy has accelerated due to increased consumer awareness and the implementation of strict privacy laws worldwide. In response, marketers are increasingly moving away from deterministic methods, which rely on specific and identifiable user data, toward probabilistic approaches that prioritize user anonymity and data protection.</p>
<p>Probabilistic measurement utilizes algorithms and statistical models to estimate the probability of marketing touchpoints influencing consumer behavior. Unlike deterministic methods that track direct interactions using identifiable data, probabilistic models aggregate data patterns to make informed predictions, thereby preserving user privacy.</p>
<p>This trend emphasizes the balance between maintaining measurement accuracy and respecting user privacy. Probabilistic models have advanced to the point where they can provide insights that are almost as precise as deterministic models, without compromising the privacy of individual data.</p>
<p>With the phase-out of third-party cookies and identifiers, the marketing world is adapting to a future without cookies. Probabilistic measurement is well-suited to this new reality because it does not depend on cookies to track user behavior across the web.</p>
<p>While the shift brings numerous benefits, it also presents challenges, such as the need for larger data sets and more sophisticated analysis techniques. Furthermore, the industry must continue to educate stakeholders about the value and effectiveness of probabilistic models to gain wider acceptance.</p>
<h2>Trend 5: Rise of an Experimentation Culture</h2>
<p>This trend indicates a significant change in how businesses approach marketing strategies, emphasizing learning and adapting through continuous testing.</p>
<p>Marketers are increasingly embracing an experimental mindset, where the "test and learn" approach becomes essential to the development of marketing strategies. This culture of experimentation is rooted in the scientific method, relying on hypotheses, controlled tests, and measurable outcomes to guide decisions.</p>
<p>Experimentation fuels innovation by enabling marketers to test new tactics on a smaller scale before implementing them widely. This approach not only mitigates risk but also encourages creativity. Marketers are less constrained by the fear of failure and more motivated by the potential for discovery and improvement.</p>
<p>Central to the culture of experimentation is the use of <a href="https://www.lifesight.io/blog/guide-to-incrementality-testing">incrementality tests</a> and <a href="https://www.lifesight.io/blog/ecommerce-ab-testing">A/B testing </a>frameworks. These methods provide clear insights into effective strategies by comparing the performance of various marketing tactics against a control group.</p>
<p>The rise of an experimentation culture solidifies a data-driven decision-making ethos, where the abundance of data gathered from experiments informs and enhances marketing strategies, resulting in campaigns that are not only effective but also highly efficient.</p>
<h2>Bonus trend: Causal AI-based decision-making</h2>
<p>The causal AI market is anticipated to reach a value of <a href="https://www.polarismarketresearch.com/industry-analysis/causal-ai-market/request-for-sample?utm_source=Ojaswini_Linkedin_25++sep&amp;utm_medium=+Causal+AI+Market&amp;utm_campaign=social">$543.73 million</a>, with the rise of research and development (R&amp;D) activities being a key factor in this expansion.&nbsp;</p>
<p>Many prominent companies are pouring resources into Causal AI, including Uber, McKinsey, and Netflix. Causal AI was included in the latest Hype Cycle for Emerging Technologies by industry analysis firm Gartner.</p>
<p>Why?</p>
<p>Because Causal AI helps businesses make more informed decisions. Instead of guessing or assuming the reasons why a marketing campaign worked well, Causal AI can determine the actual causes behind its success. This means that businesses can focus on what truly matters and make decisions that result in better outcomes.</p>
<p>For example, if a company observes an increase in sales following a specific ad campaign, Causal AI can assist in determining whether the campaign directly caused the increase or if other factors were involved. This deep understanding can guide them in planning their next steps.</p>
<p>Causal AI can predict the consequences of different choices before they are made. This is like having the ability to see into the future. Companies can utilize it to experiment with ideas in the virtual world, anticipate potential outcomes, and then select the optimal choice.</p>
<p>Typically, businesses must conduct real-world tests to determine the viability of their strategies, a process that can be both risky and costly. However, with Causal AI, they can conduct these tests in a simulated environment, which can save a significant amount of time and money.</p>
<p>All of this means that businesses can plan more effectively. They can have more confidence in their strategies because they are based on a clearer understanding of cause and effect.</p>
<h2>Final Thoughts</h2>
<p>As we move forward, marketers must embrace these new measurement methods. They offer a pathway to more effective, responsible, and customer-centric marketing strategies. With Lifesight, you gain access to advanced analytics, privacy-compliant data handling, and a suite of tools that are in line with the latest trends in marketing measurement.</p>
<p><a href="https://www.lifesight.io/demo">Book a demo</a> now!</p>

marketing-measurement-trends.webp

Unlock the future of marketing with insights on marketing measurement trends for 2024. Learn how to navigate a privacy-first landscape efficiently.

Marketing Measurement Trends & Prediction for 2024

<p>Imagine if the Marlboro Man campaign had to be reconfigured in today's time. Instead of the Marlboro Man figure, there'd probably be a digital ad campaign with a custom AI avatar of him.</p>
<p>Despite the campaign's popularity back in the 1960s, in 2024, it will need relevant keywords, proper channel budget allocation, and running ads on the right channel to gain the audience's attention.</p>
<p>This is how far we've come in the digital advertising and marketing landscape. An ad today requires attention from the search engine, social media, and prospects, birthing the need for ad campaign optimization.</p>
<p>Ad campaign optimization lets you enhance creatives, landing pages, targeting, bidding, and other factors to achieve your campaign objectives. But to know if the campaign is effective, you must know the campaign optimization techniques.</p>
<p>You see, optimizing an ad isn't about making it search-engine ready; rather about using the right campaign optimization tools with the right knowledge and metrics in mind.</p>
<p>So take this blog as your go-to guide for "how to optimize ad campaigns?" Here are the 11 key strategies to optimize your digital ad campaign.</p>
<h2>1) Collecting and analyzing data</h2>
<p>With Google's response to a cookieless future with its "<a href="https://developers.google.com/privacy-sandbox">The Privacy Sandbox</a>", ways of collecting data are changing. Chrome will disable third-party cookies for 1% of users from Q1 2024. So relying on third-party cookies no longer works.</p>
<h3><strong>How do you collect data then?</strong></h3>
<p>With the third-party cookies phasing out, redirect your data collection efforts toward privacy-friendly ways to collect data--first-party cookies being the most basic one.</p>
<p>While the methods of data collection vary, a few direct ways to collect data include:</p>
<ul>
<li>Website Sign-ups</li>
<li>Newsletters</li>
<li>Surveys</li>
<li>Interviews</li>
<li>Social media (through QnA, polls, etc)</li>
</ul>
<p>Use Google Analytics 4 to collect data.</p>
<p>Now that you have laid the groundwork for informed marketing decisions, it's time to analyze this data.</p>
<h3><strong>How to analyze data?</strong></h3>
<p>The Big Data Analytics market is projected to reach <a href="https://www.prnewswire.com/news-releases/the-global-big-data-analytics-market-2027-a-105-billion-opportunity-assessment-301014418.html">105.08 billion dollars</a> by 2027. The direct implication of this is that more and more businesses will use tools to analyze data (there's no reason why they shouldn't).</p>
<p>Today, tools to analyze data also,</p>
<ul>
<li>Let you deep-dive into custom behavior</li>
<li>Set and measure campaign goals</li>
<li>Perform all data-related queries in a no-code manner</li>
</ul>
<p>Use <a href="https://www.lifesight.io/connect">Lifesight's Connect</a> to integrate, segment, and activate your data from a single source of truth using integrations and data tools.</p>
<h2>2) Create effective ad designs and ad copy</h2>
<p>An effective copy meets your consumer or prospect right where they are.</p>
<p>It must be a compelling visual attention-grabbing copy to elicit a specific emotion along with:</p>
<ul>
<li>Catches consumer's attention</li>
<li>Solves a problem</li>
<li>Educates/entertains the reader</li>
<li>Builds advocacy</li>
<li>Is visually appealing</li>
</ul>
<p><strong><em>Remember:</em></strong><em> You're selling to a human being at the end of the day.</em></p>
<h2>3) Optimize your ad landing page</h2>
<p>A poorly designed landing page does more harm than good. Your team spends time designing the page, writing the copy, adding visuals, and optimizing it, only for the time and effort to go to waste.</p>
<p>Imagine the user clicks on your ad. The user then gets redirected to your webpage. Let's look at two ad landing page examples.</p>
<p>If your landing page ad looks like this, bingo!</p>

![ad-landing-page-example-1](https://mighty-basketball-fd4aeed960.media.strapiapp.com/ad_landing_page_example_1_e987cf8035.webp)

<p><a href="https://www.getresponse.com/?a=V3vpwMTrHn&amp;gclid=CjwKCAiAqY6tBhAtEiwAHeRopc1e1D19hmCIkJy27t3xGPmaILRWVTuU5Yn9_hw-KIwAzhzXT2u1xBoCtvcQAvD_BwE">Source</a></p>
<p>This page has a clear and concise copy, easy navigation and exactly tells the user what they can expect.</p>
<p>Now imagine landing on a page such as this.</p>

![ad-landing-page-example-2](https://mighty-basketball-fd4aeed960.media.strapiapp.com/ad_landing_page_example_2_fc6d70ef7b.webp)

<p><a href="https://www.cruise.com/">Source</a></p>
<p>The page speaks a hundred things all at once, making it confusing for the user to gauge the core messaging.</p>
<p>An optimized landing page ad is clutter-free, easy to navigate, and has valuable content and relevant keywords.</p>
<h2>4) Choose relevant keywords</h2>
<p>The first step to choosing relevant keywords is to know all the consumer intents&ndash;the top four being informational, navigational, commercial, and transactional.</p>
<p>So if your campaign targets top-funnel consumers, use informational keywords. Similarly, choose navigational and commercial investigation intent types for consumers in the middle funnel and transactional for those at the bottom.</p>
<p>Once you have the types of consumer intent, create a keyword list after considering search volume, competition, and cost per click for each keyword (we'll cover bidding strategy later in this article). Use Google Ads Keyword Planner, SEMrush, or any other keyword research tool.</p>
<p>After this, choose the type of <a href="https://support.google.com/google-ads/answer/2497941?hl=en">audience segment</a> for your campaign (affinity, custom, life-events, in-market, website, and app visitors) and Google Ads will show ads to people who are likely in the selected categories.</p>
<p>![campaign-types-and-its-selected-segments](https://mighty-basketball-fd4aeed960.media.strapiapp.com/campaign_types_and_its_selected_segments_4e6e182e2a.webp)</p>
<p>Flag each segment for the campaign based on its type. The ad will only reach audiences belonging to the selected segment types.</p>
<p>Analyze the campaign performance and update the keyword list or segment types based on the performance.</p>
<p><strong><em>Remember: </em></strong><em>Also add a negative keywords list to your campaign so that your ad doesn't show up on irrelevant queries.</em></p>
<h2>5) Contextual retargeting</h2>
<p>Contextual retargeting is on the rise as we bid farewell to cookie-based targeting.</p>
<p>Through contextual retargeting, you pull back the bounced users to your website by advertising on the sites they visit (basically you show up where they consume content). For example, if you're a gym wear brand, contextual targeting places your ad on web pages that talk about workouts and exercises. Although the contextual retargeting audiences differ for each brand, the chances of pulling back the bounced users are still higher this way.</p>
<p>To build a contextually targeted campaign:</p>
<ul>
<li>Define your ad group, and include branded keywords in keyword-targeted ad groups</li>
<li>Generate keywords for each ad group (Avoid repeating the keywords in the same ad group)</li>
<li>Set Ad group bids</li>
<li>Add negative keywords so that the ads don't appear on irrelevant searches</li>
<li>Optimize after a few ways and check if any ad groups require bidding adjustments.</li>
</ul>
<h2>6) Budget allocation</h2>
<p>With precise budget allocation, there's no space for any what-ifs as you already would know, and control your funds effectively across all media channels.</p>
<p>For that, you need answers to - "How do I get an accurate forecast of budget performance?" and, "What budget allocation can be changed then?" along with prescient insights on the campaign's performance drivers, seasonal trends, price shifts, advertising and media selection, etc.</p>
<p>Use Lifesight's <a href="https://www.lifesight.io/marketing-mix-modeling">Marketing Mix Modelling Solution</a> to give you an accurate forecast budget based on your past paid marketing.</p>
<p>Equipped with this data, your marketing team knows the channels that give the highest ROI. Optimize your marketing spend, build optimal media plans with accurate forecasting, compare different scenarios with target KPIs, and update budgets in real-time on ad platforms with marketing mix modeling.</p>
<h2>7) Perform A/B tests on creatives and landing pages</h2>
<p>No matter how well-planned and goal-oriented a digital ad campaign is, there's no way to predict the audience's reaction to it. Hence, segment audiences into smaller groups and conduct ab testing on these groups with different campaign versions.</p>
<p>For Google Ads users, run the experiment version in eight steps.</p>

![create-ab-native-experiment-test-steps](https://mighty-basketball-fd4aeed960.media.strapiapp.com/create_ab_native_experiment_test_steps_aea9aa8dda.webp)

<p><a href="https://support.google.com/admanager/answer/7661678?hl=en">Source</a></p>
<p>Google recommends running the experiment for two days as it's sufficient for the system to gather data for analysis. Then analyze the following metrics:</p>
<ul>
<li>Net revenue generated from Ad impressions</li>
<li>Ad revenue per thousand Ad impressions</li>
<li>Ad CTR</li>
<li>Percentage of ads returned as compared to ads requested</li>
</ul>
<p>Analyzing these metrics will give you clarity on whether you should go with the experiment campaign style or not.</p>
<p>Additional things to consider while conducting A/B testing:</p>
<ul>
<li>Modify only one thing at a time so that it's easy to track its impact on the performance</li>
<li>Don't change the entire structure of the ad copy (for example, a copy must have a CTA at the end)</li>
<li>Test different variations of the landing page or the creatives, at different times</li>
</ul>
<h2>8) Exclude site categories and underperforming placements</h2>
<p>Just as Google Ads lets you choose sites or videos where you want to show your ads, it also lets you exclude sites that may not be good for your brand in eight simple steps.</p>

![Exclude site categories and underperforming placements](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Exclude_site_categories_and_underperforming_placements_937f9b3fa7.webp)

<p><a href="https://support.google.com/google-ads/answer/7331110?hl=en#:~:text=Just%20as%20you%20can%20target,t%20appropriate%20for%20your%20brand.">Source</a></p>
<p>Ensure your ads appear on the sites and domains that are a good fit for your brand, to drive more traffic to your website and also keep your brand position intact. Define what underperforming placements mean to your business and drop sites in the exclusion list after data collection and analysis.</p>
<h2>9) Use frequency capping</h2>
<p>Frequency capping limits the number of times your ad appears to a user.</p>
<p>Using it differs for display and video campaigns.</p>
<p>For display campaigns:</p>
<ul>
<li>Let Google Ads optimize the number of times your ad shows to users or manually manage the number of impressions on the campaign or ad.</li>
<li>Google automatically uses third-party cookies, in case they're unavailable, go for <a href="https://support.google.com/admanager/answer/10650804?hl=en#:~:text=The%20%E2%80%9CNetwork%20Settings%E2%80%9D%20tab%20is,cases%20such%20as%20frequency%20capping.">first-party cookies</a> to approximate impressions.</li>
</ul>
<p>As for a video campaign, set a limit on your videos for time intervals depending on their views, impressions, or both.</p>
<p><strong><em>Note</em></strong><em>: If a video of a campaign is part of multiple video campaigns, the frequency cap of that campaign considers impressions and views count from all those other campaigns.</em></p>
<p>Decide the frequency of an ad after knowing the number of unique viewers and average impressions per user (in the last 7 days, 14 days, and 30 days). Once the campaign reaches the cap, Google will stop showing it to the user.</p>
<p>After knowing how frequently you want your ad to show and on which platform, you can make further bid adjustments.</p>
<h2>10) Make bid adjustments</h2>
<p>How do you improve the campaign performance when it does not drive expected results?</p>
<p>For <a href="https://support.google.com/google-ads/answer/2732132?hl=en">Google Ad users</a>, adjust a bid i.e. tell Google Ads platform that you want to modify the frequency of your ads based on search parameters. Bid less or more depending on the clicks from specific devices or locations.</p>
<p>For example, the historical data tells you that clicks from computers as compared to mobile devices have generated more CTR (click-through rate) on a campaign. To pull in more of these customers, increase your bid by up to 900%.</p>
<p>The other hidden treasure is to use Google Ads's portfolio bid strategy, an AI-driven bid strategy that groups multiple campaigns, ad groups, and keywords to set bids. Quickly analyze and change bids of all campaigns falling under a single portfolio!</p>
<p>Miles McNair, a Google Ads expert's recent LinkedIn post talks about how Google Ads's portfolio build strategies guarantee less wasted ad spend.</p>

![Miles McNair on Linkedin about hidden optimization in google ads](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Miles_Mc_Nair_on_Linkedin_about_hidden_optimization_in_google_ads_283dd2953c.webp)

<p><a href="https://www.linkedin.com/posts/milesmcnair_there-is-a-hidden-optimization-in-google-activity-7152692956678791168-IVbq?utm_source=share&amp;utm_medium=member_desktop">Source</a></p>
<p>If you run Meta ad campaigns, refer to <a href="https://en-gb.facebook.com/business/help/1619591734742116?id=2196356200683573">this page</a> for more details.</p>
<h2>11) Optimize your ads for mobile</h2>
<p>Analyze your search terms report to recognize the keywords that perform better on mobile phones. Use this information to make mobile bid adjustments. Increase the visibility of your ad to increase your chances of reaching your target audience.</p>
<p>Here are some tips to optimize your ads for mobile devices:</p>
<p>Use the right ad assets - Once you have the performing set of keywords, use the right ad assets (business phone number, address, or specific links to pages on your website). These assets make your ad more appealing to your customers. Moreover, Google Adwords doesn't ask you to pay any extra charges for using these ad assets.</p>
<p>Rotate multiple ad variations - When you have more than one ad in your ad group, Google Ads starts showing better-performing ones by default. Have 2-4 different ads with varying headlines and description text.</p>
<p>Your landing page should match your ad - Your landing page should mirror the CTA and text in your ad to provide a clear path to take that action (filling out a form, signing up on your app, and purchasing a product). Feature promotions and offers prominently on your landing page.</p>
<h2>Implement, Measure, and Optimize Your Digital Ad Campaigns</h2>
<p>If there's one thing to mention about optimizing a digital ad campaign today, it would be to pay attention to changes in the digital landscape and continuously adapt your strategy to remain relevant. Let your data guide you, but don't forget the value of user experience.</p>
<p>Lifesight is a unified measurement platform that lets marketers measure and optimize ad campaigns for sustained success.</p>
<p><br /><a href="https://www.lifesight.io/demo">Book your demo</a> to learn more about how Lifesight helps you collect and analyze data.</p>

Key Strategies to Optimize Your Digital Ad Campaign

Key Strategies to Optimize Your Digital Ad Campaign.webp

Top strategies to optimize digital ad campaigns and boost ROI with expert insights on targeting, creatives, and analytics. 

Optimize Digital Ad Campaign: 11 Key Strategies for Success

Omnichannel

7 Advanced Data Strategies for High-Value Omnichannel Brands

7 Advanced Data Strategies for High-Value Omnichannel Brands.webp

Learn data-driven eCommerce strategy in 7 steps. Maximize profitability and customer engagement with effective data collection, governance, and analysis.

multi-touch-attribution-tools.webp

Take a look at the best multi-touch attribution tools for 2024 to optimize your marketing efforts. Find the perfect tool for your business to boost your ROI.

Best Multi-touch Attribution Tools for 2024

<p>When it comes to marketing attribution, two predominant models emerge last-touch and multi-touch attribution. Both offer unique insights into consumer behavior and campaign effectiveness, but understanding their differences is crucial for optimizing your marketing strategy.</p>
<p>This need for sophisticated understanding becomes more crucial as we navigate the challenges of a privacy-first online world. Stringent privacy regulations like GDPR and CCPA, along with ad blockers and changes in <a href="https://www.lifesight.io/blog/strategies-for-third-party-cookie-deprecation">cookie tracking</a>, have posed hurdles to traditional attribution models.</p>
<p>These developments impact how marketers collect and analyze data, leading to potential inaccuracies in attributing sales or conversions to the right channels. Whether you're a seasoned marketer or new to the field, understanding these models is key to attributing success accurately and optimizing your marketing strategies effectively.</p>
<h2>What is Touch-Based Attribution in Marketing?</h2>
<p>Attribution in marketing refers to the process of identifying and assigning credit to the various marketing touchpoints that a consumer encounters along their journey towards making a purchase or completing a desired action. This process is crucial in understanding which elements of your marketing strategy are most effective and how they contribute to the end goal.</p>
<h2>What is Last Touch Attribution?</h2>
<p>Last Touch Attribution is a marketing attribution model where the entire credit for a conversion (such as a sale or lead generation) is attributed to the final touchpoint or interaction that a customer had with the brand before taking the desired action. This model assumes that the most recent interaction is the most significant in the customer's decision-making process.</p>
<p>For instance, if a customer first discovers a product through a social media ad, then reads a blog post about it, but finally clicks on a search engine ad before making a purchase, the Last Touch Attribution model would assign full credit for the conversion to the search engine ad.</p>
<h3>Pros & Cons of Last Touch Attribution</h3>
<table>
<tbody>
<tr>
<td>
<p><strong>Aspect</strong></p>
</td>
<td>
<p><strong>Pros</strong></p>
</td>
<td>
<p><strong>Cons</strong></p>
</td>
</tr>
<tr>
<td>
<p>Simplicity and Implementation</p>
</td>
<td>
<p>It just tracks the final interaction, making it easy to understand and use.</p>
</td>
<td>
<p>Neglecting all touchpoints before the final engagement distorts the customer journey.</p>
</td>
</tr>
<tr>
<td>
<p>Effectiveness</p>
</td>
<td>
<p>Can work in short sales cycles or impulse buys where the last interaction matters most.</p>
</td>
<td>
<p>Not ideal for long sales cycles because the last encounter may not reflect the complete decision-making process.</p>
</td>
</tr>
<tr>
<td>
<p>Conversion Optimization</p>
</td>
<td>
<p>Helps identify channels or campaigns that close sales or convert, delivering rapid optimization insights.</p>
</td>
<td>
<p>Last-touch data can misallocate the marketing budget by underinvesting in early or middle-stage channels.</p>
</td>
</tr>
<tr>
<td>
<p>Marketing Strategy Alignment</p>
</td>
<td>
<p>Popular among marketers who are new to attribution modeling due to its ease of use.</p>
</td>
<td>
<p>Neglects upper-funnel marketing like content and social media, which boosts brand visibility and engagement early in the customer journey.</p>
</td>
</tr>
</tbody>
</table>
<h2>Multi-Touch Attribution</h2>
<p>Multi-Touch Attribution (MTA) is a sophisticated approach in marketing analytics that evaluates the impact of each touchpoint in a customer's journey towards a conversion. Unlike traditional models that assign credit to just one interaction (such as the first or last click), multi-touch attribution recognizes that modern customer journeys are complex and involve multiple interactions across various channels.</p>
<p>The goal of multi-touch attribution is to accurately distribute credit to each marketing touchpoint (like an ad click, an email open, or a social media engagement), thereby providing a more nuanced understanding of what's working in a marketing strategy. This information is crucial for marketers looking to optimize their campaigns, allocate budgets effectively, and improve overall marketing ROI.</p>
<h3>Exploring Various Multi-Touch Models</h3>
<ol>
<li><strong>Linear Model: </strong>This model assigns equal credit to each touchpoint in the customer journey. It's straightforward and assumes that all interactions are equally important in driving a conversion.</li>
<li><strong>Time Decay Model: </strong>Here, more credit is given to touchpoints that occur closer to the time of conversion. This model is based on the rationale that the interactions just before conversion play a more significant role in the decision-making process.</li>
<li><strong>U-Shaped (Position-Based) Model: </strong>The U-Shaped model attributes more credit to the first and last touchpoints (usually 40% each), and the remaining 20% is distributed across other touchpoints. This model recognizes the importance of the initial engagement and the final conversion action.</li>
<li><strong>W-Shaped Model: </strong>An extension of the U-Shaped model, it assigns significant credit to three key touchpoints: the first interaction, the lead creation, and the final conversion, recognizing these as critical stages in the customer journey.</li>
<li><strong>Custom or Algorithmic Model:</strong> This model uses algorithms and machine learning to dynamically assign credit to each touchpoint based on how likely they are to contribute to a conversion. It's tailored to the specific nuances of a business&rsquo;s marketing strategy and customer behavior.</li>
</ol>
<p><a href="https://www.linkedin.com/in/aimeemeester/">Aimee Meester</a>, CEO and Chief Marketing Aficionado at Madison Taylor Marketing recommends using multi-touch attribution models to know the impact of social media on the marketing funnel</p>

![Aimee Meester Explains the impact of social mediaon marketing funnel](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Aimee_Meester_Explains_the_impact_of_social_mediaon_marketing_funnel_1b2d1ffd54.webp)

<p><a href="https://www.linkedin.com/posts/aimeemeester_marketingmetrics-attributionmodels-roi-activity-7130982853269491713-6xzW/?utm_source=share&utm_medium=member_desktop">Source</a></p>
<h2>Situational Usage: When to Use Each Model?</h2>
<p>Now let's explore the scenarios where you can use these models to your advantage.</p>
<h3>Last Touch Attribution:</h3>
<ul>
<li>Best used when the sales cycle is short and straightforward.</li>
<li>Ideal for businesses with a dominant marketing channel that consistently drives conversions.</li>
<li>Useful in scenarios where budget and resources for complex analytics are limited.</li>
</ul>
<h3>Multi-Touch Attribution:</h3>
<ul>
<li>Essential for long sales cycles involving multiple touchpoints.</li>
<li>Crucial for <a href="https://www.lifesight.io/blog/steps-to-develop-omnichannel-strategy">omnichannel marketing strategies</a> where understanding the impact of each channel is necessary for optimization.</li>
<li>Beneficial in scenarios where marketers aim to understand the customer journey comprehensively and allocate budgets effectively across multiple channels.</li>
</ul>
<p>Check out this <a href="https://www.lifesight.io/resources/case-study-mta-drives-stellar-40-percent-quarterly-growth">case study</a> to learn how a major online plant store used Lifesight&rsquo;s Multi-Touch Attribution to track their marketing results, make better campaign investments, and learn more about how their customers behave.&nbsp;</p>
<p>An eco-friendly online nursery that had a presence in digital markets. It had all the right buzzwords to stay in the minds of customers. But their growth made us wonder which marketing platforms really increased sales.&nbsp;</p>

![Case Study on MTA](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Case_Study_on_MTA_5daca1200f.webp)

<p>The brand used Multi-Touch Attribution to figure out their customers' journeys and make their marketing strategies work better. This included everything from Google Ads to partnerships with influencers.</p>
<p><a href="https://www.linkedin.com/in/carla-penn-kahn-0384b0a/">Carla Penn-Kahn</a>, co-founder of Profit Peak, suggests that omnichannel and eCommerce businesses embrace three essential trends to achieve profitability in 2024.</p>
<ol>
<li>Profit-driven bidding.</li>
<li>Multi-touch attribution.</li>
<li>Marketing Mix Modeling.</li>
</ol>

![Carla Penn-Kahn explains three essential trends to achieve profitability in 2024](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Carla_Penn_Kahn_explains_three_essential_trends_to_achieve_profitability_in_2024_0f045c9eb1.webp)

<p><a href="https://www.linkedin.com/posts/carla-penn-kahn-0384b0a_digital-marketing-in-2024-will-be-redefined-activity-7142723255731113987-0vMS?utm_source=share&utm_medium=member_desktop">Source</a></p>
<h2>Endgame</h2>
<p>Attribution models serve as the navigational compass that guides marketers through the complex journey of understanding customer interactions and the effectiveness of their marketing efforts. The ideal model sheds light on which touchpoints are most influential, helping in allocating resources efficiently and enhancing the return on investment.</p>
<p>Lifesight's platform, powered by AI and machine learning, revolutionizes Multi-Touch Attribution by integrating comprehensive customer journey data from online + offline channels, ensuring a complete view for accurate analysis &mdash; we call it Universal Attribution. Additionally, our predictive analytics capabilities enable strategic future planning by forecasting customer behaviors based on current data touchpoints.</p>
<p>Are you ready to transform your approach to <a href="https://www.lifesight.io/blog/ecommerce-marketing-attribution">marketing attribution</a> and decision-making? <a href="https://www.lifesight.io/demo">Book a demo</a> today and unlock the full potential of your marketing strategies.</p>

last-touch-vs-multi-touch-attribution.webp

Learn the differences between last-touch and multi-touch attribution models in marketing. Learn how to optimize your strategy for better campaign effectiveness.

Last Touch vs Multi-Touch Attribution: Choosing the Right Model

Understanding Attribution Models: Last Touch vs Multi-Touch Attribution

<p>Understanding the subtle differences between correlation and causation is pivotal in marketing analytics.</p>
<p>Consider a scenario where you launch a Christmas campaign and subsequently witness a surge in sales. This situation might initially seem like a clear case of causation - the campaign (cause) directly leads to increased sales (effect). However, it's crucial to delve deeper to ascertain whether this relationship is purely causal.</p>
<p>However, the picture often gets more complex. Other factors, such as a seasonal increase in shopping or significant discounts, could also contribute to this sales uptick. This scenario typifies correlation, where multiple interconnected variables collectively influence the final outcome.</p>
<p>Marketers who understand the difference between correlation and causation can better discover the actual drivers of a campaign's performance.</p>
<p>Correlation and causation are cornerstone concepts in strategic marketing decision-making, essential for analyzing buying patterns, targeting consumer interests, and formulating <a href="https://www.lifesight.io/blog/drive-engagement-strategy-using-customer-data">customer engagement strategies.</a></p>
<p>In this article, we discuss the difference between causation and correlation and the best practices to get the most out of them.</p>
<h2>What is Correlation?</h2>
<p>Correlation, as observed in methodologies like Marketing Mix Modeling (MMM), explores the relationship between different factors and outcomes without relying exclusively on customer data. While traditional MMM takes a statistical route to uncover relationships between various internal and external factors and their collective influence on specific outcomes, it doesn't inherently establish cause and effect. However, the model can be enhanced to approach causality when calibrated with incrementality data.</p>
<p>Incrementality data provides insights into the actual lift that marketing efforts contribute, beyond what would have occurred without the specific campaign. By integrating incrementality insights, MMM can be transformed into a more causal model, allowing marketers to attribute outcomes to specific actions with greater confidence.</p>
<p>For example, in the above scenario, the correlation approach would show that A is not the only reason behind the outcome B for the same store. There is another factor called C, which defines seasonality. That means Instagram sales of a brand increased in February because of running more ads and the increased shopping tendency of the customers due to Valentine&rsquo;s Day.</p>
<p>Clearly, with a correlation approach, you cannot hold a single factor responsible for an outcome. Consider other relevant factors like recession, inflation, political stability, seasonality, and similar statistically relevant trends that directly or indirectly influence a buyer&rsquo;s purchase decision.</p>
<h2>What is Causation?</h2>
<p>For omni-channel marketing campaigns with multiple touchpoints, it is difficult to figure out what caused the outcome of a marketing campaign. With causation, marketers predict the &ldquo;why&rdquo; or root cause behind specific campaign outcomes.</p>
<p>Marketing leaders use the <a href="https://www.lifesight.io/blog/measure-casual-impact-in-marketing">concept of causation</a> to understand the direct influences of different marketing initiatives and channels on the overall ROI of an organization.</p>
<p>For example, a retail store noticed a sudden surge in Instagram sales in February. If the outcome (B) is an increase in sales through Instagram, the causation approach will show you that the reason (A) behind this outcome is an increase in Instagram ads budget.</p>
<h2>Correlation vs. Causation - What are the differences?</h2>
<p>If two variables have a cause and effect or causal relationship, in that case, causation and correlation are synonymous. However, when a third variable is present, it is not always true that there is a directionality issue or possibility of a chain reaction.</p>
<p><a href="https://www.linkedin.com/in/jayacunzo/">Jay Acunzo</a>, co-founder at Creator Kitchen, shares a real-life dilemma of marketers deciding between causation and correlation.</p>

![real-life dilemma of marketers deciding between causation and correlation post by Jay Acunzo](https://mighty-basketball-fd4aeed960.media.strapiapp.com/real_life_dilemma_of_marketers_deciding_between_causation_and_correlation_post_by_Jay_Acunzo_59a9dc7606.webp)

<p><a href="https://twitter.com/jayacunzo/status/1138102397669167105">Source</a></p>
<p>Below are the scenarios where correlation and causation are not similar and the concept of correlation vs. causation comes into the picture:</p>
<ul>
<li>When there is a third variable, it is difficult to estimate the impact of each variable on the outcome. A third variable influences the first two variables distinctively, and it is impossible to establish a cause-and-effect relationship between the outcome and the three variables with causation.</li>
</ul>
<ul>
<li>The cause is generally the independent variable and the effect is the dependent variable. But in some instances, there are directionality issues between these two variables, and hence, causation fails in these scenarios.</li>
</ul>
<ul>
<li>There are situations where multiple variables develop a chain reaction among each other, and there is no single way to define a cause-and-effect relationship. In these cases, marketers conclude that those variables are correlated.</li>
</ul>
<h2>How Causality Unlocks Decision-Making in Marketing?</h2>
<p>Marketing leaders ultimately seek to answer the &ldquo;Why&rdquo; and &ldquo;How&rdquo; behind strategies, concepts, and campaigns.</p>
<ul>
<li>Why has this campaign tanked, and how do I improve its performance in the next quarter?</li>
<li>Why did this campaign perform well, and how can I replicate success?</li>
</ul>
<p>While correlation cannot answer your questions definitively, causality will.</p>
<p>Here&rsquo;s how causality simplifies decision-making for marketing leaders.</p>
<h3>Predict hypothetical situations to improve strategy</h3>
<p>With causality, there is no room for guesswork. If you allocate an extra 10% of your budget to your LinkedIn campaign, you know the possible outcomes.</p>
<p>It is no longer a trial and error process where you increase the budget across all channels and observe that only LinkedIn gives you the desired ROI, and other channels like Facebook, Instagram, and Google Ads could be doing better.</p>
<p>Rather, causality helps you find answers to:</p>
<ul>
<li>Which marketing channel contributes to conversion, and in what proportion?</li>
<li>What are the possible changes expected in the overall campaign ROI when you cut the budget from one channel and allocate it to another?</li>
</ul>
<p><a href="https://www.linkedin.com/in/rohitmah/">Rohit Maheswaran</a>, the co-founder at Lifesight, explains the untapped potential of causal marketing in his LinkedIn post.</p>

![Rohit Maheswaran explains the untapped potential of causal marketing in his LinkedIn post](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Rohit_Maheswaran_explains_the_untapped_potential_of_causal_marketing_in_his_Linked_In_post_eff78b5615.webp)

<p><a href="https://www.linkedin.com/posts/rohitmah_decisionmaking-correlationvscausality-marketing-activity-7136036228155535360-JVUb/?utm_source=share&amp;utm_medium=member_desktop">Source</a></p>
<h3>Cut down expenses on non-incremental value campaigns</h3>
<p>The predictions from causality help you identify and remove ineffective campaigns and double down on investment for successful campaigns, contributing significantly to the ROI.</p>
<p>That means your GTM strategy will no longer be a compilation of &ldquo;if this happens, that will happen&rdquo;. It will be about pricing, promotion, product, and marketing strategies that list clear outcomes and help you make solid decisions on the next steps.</p>
<p>You will have answers to:</p>
<ul>
<li>Which campaign is most profitable, and how will its performance change if you change messaging?</li>
<li>How is promotion and customer loyalty related? If you remove influencer marketing from the promotional strategy and add a loyalty bonus, how will that affect overall sales?</li>
</ul>
<h3>Develop a marketing roadmap that is informed and free of assumptions</h3>
<p>An informed and predictable marketing roadmap will develop a better GTM strategy. With causal marketing, you have all the information about product positioning, pricing, and channel effectiveness.</p>
<p>For example, causal marketing helps you answer:</p>
<ul>
<li>How will a new product launch impact the performance of existing products?</li>
<li>How do purchasing patterns change if you experiment with pricing models?</li>
</ul>
<p>With all this information, marketers build customized marketing roadmap that can be iterated as required.</p>
<h2>Best Practices for Marketers to Implement Causal Marketing</h2>
<p>Causal marketing is the collection of in-depth market and advertising research to learn more about the anonymous users engaging with your ads or website, understand their customer journeys, and map a significant cause to all the campaign outcomes.</p>
<p>While causal marketing helps you optimize your existing campaigns to achieve better outcomes, it is a long process to drill down which factors cause which outcomes.</p>
<p>Here are some best practices for marketers to perform causal marketing strategies efficiently.</p>
<h3>1) Choose your variables carefully</h3>
<p>Picking the correct set of variables is crucial in causal marketing. There should be one dependent and one independent variable, and make sure to define both distinctively so there is no confusion or possibility of correlation.</p>
<p>List the confounding and control variables. The confounding variables are those that may indirectly impact your marketing experiments. The control variables are benchmarks to compare your results.</p>
<p>Suppose you are running both advertisement and email campaigns simultaneously, and the outcome is increased sales.</p>
<p>With causal marketing, you will discover which of these campaigns drives more outstanding sales.</p>
<p>But make sure to list all the other variables, like seasonality, decline in the inflation rate, and discount percentage for both channels, that might influence your increasing sales.</p>
<h3>2) Incorporate Incrementality Intelligence Using Experiments</h3>
<p>Utilize experimental designs to assess the incremental impact of marketing efforts. This involves setting up tests, such as <a href="https://www.lifesight.io/best-shopify-apps/ab-testing">A/B testing</a>, where you modify one independent variable at a time and observe the resulting changes.</p>
<p>Tools like Google Analytics, Google Ads, and Google Search Console can facilitate these experiments. Monitor how these changes affect outcomes and use the data patterns to shape future organic and paid marketing strategies.</p>
<h3>3) Leverage Causal Inference</h3>
<p>Apply causal inference methods to more accurately predict outcomes based on your marketing interventions. This approach goes beyond correlation, allowing you to understand the direct impact of specific actions on your marketing goals. Use causal inference to analyze the effectiveness of different channels and tactics, and make informed decisions on where to allocate resources for the maximum return.</p>
<h3>4) Build a controlled environment</h3>
<p>When conducting a causal marketing experiment, use a controlled environment. This means conducting surveys and interviews and collecting the latest trends and stats to understand the industry standards.</p>
<p>Another great way to develop controlled environments is through A/B tests. Keep changing one independent variable at a time in your marketing software (like Google Analytics, Google Ads, Google Search Console, etc.) to find out how the outcomes are changing. Use these data patterns to plan your next organic and paid marketing strategy.</p>
<h2>Conclusion</h2>
<p>Both causal and correlation marketing are crucial for marketers. CMOs and marketing leaders are responsible for presenting the effectiveness of their marketing initiatives to the board members and interpreting the underlying causes of the success (or failure) of various campaigns.</p>
<p>By learning causal vs. correlation marketing approaches, drawing meaningful conclusions to establish the effectiveness of a marketing campaign becomes easier.</p>
<p>Conducting causal experiments manually is next to impossible as it involves analyzing large datasets. Marketers may require months and even years to identify the underlying reasons behind a campaign&rsquo;s performance.</p>
<p>But with Lifesight, getting a transparent overview of all marketing channels in a single dashboard, comparing different attribution models, and finally unlocking the reasons behind how a campaign influences your overall ROI is much easier.</p>
<p>Want to get started with casual marketing with Lifesight?</p>
<p><a href="https://www.lifesight.io/demo">Request a demo</a></p>

Correlation in Marketing vs Causation Understanding the Difference

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Learn the difference between correlation and causation in marketing analytics. Discover how to leverage causal marketing for better ROI. Book a demo now!

Causal vs Correlation Marketing: Unveiling Campaign Insights

Correlation in Marketing vs Causation: Understanding the Difference 

<p>Marketing decision-making is a complex and nuanced process, often challenging due to the multitude of factors that influence each decision. It's not easy to find your way through different types of data, figure out what market trends mean, and understand how customers act.</p>
<p>When marketers don't have enough data, they often have to deal with the risk of operating in the dark. This tendency to overlook the 'intelligence' aspect can lead to decisions that are not fully informed or grounded in the realities of the market and customer needs.</p>
<p>To combat this, it's crucial for marketers to embrace a more analytical and data-driven approach, shedding light on the shadows of uncertainty and assumption. With the right set of marketing intelligence tools, visualizing the performance of your marketing campaigns and decoding the underlying patterns is not rocket science anymore.</p>
<p>In this guide, we will take you through all the concepts and frameworks related to marketing intelligence to plan your marketing initiatives better.</p>
<h2>The Pillars of Marketing Intelligence</h2>
<p>Marketing intelligence helps you answer questions like - who engaged with your store, how they engage, and the possible next steps to optimize your marketing campaigns.</p>
<p>Marketing intelligence is a vast concept with no tool to help with end-to-end marketing visualization. You must know the structure and components of marketing intelligence to identify the right set of tools.</p>
<h3>Structure of marketing intelligence</h3>
<p>Marketing intelligence is the compilation of four different types of intelligence:</p>
<ul>
<li><strong>Market intelligence -</strong>refers to all the specific information about a particular industry and its standard metrics. When a brand enters a new market, it is essential to set benchmarks and KPIs depending on the market standards and opportunities. Market intelligence reveals the scope of success and potential risks</li>
</ul>
<ul>
<li><strong>Customer intelligence - </strong>focuses on understanding the target audiences in a particular industry, inside out. Customer intelligence shares insights like client demographics, typical purchase patterns, and common pain points, giving you a fair idea of what to expect from potential customers.</li>
</ul>
<ul>
<li><strong>Competitive intelligence - </strong>gathers data on leading players and competitors in the same market. These datasets can be competitors' strategies, their roadmaps, their organic data, and so on.</li>
</ul>
<ul>
<li><strong>Product intelligence - </strong>focuses on existing products in a market, their features, how they solve customers' pain points, what features they are lacking, and so on. These insights help product teams understand what sort of product they want to develop to satisfy potential customers' needs.</li>
</ul>
<ul>
<li><strong>Pricing Intelligence - </strong>analyzes the pricing strategies of products and services in the market. It looks at how different pricing models affect customer behavior, market demand, and competitive positioning. This intelligence helps businesses in setting competitive yet profitable pricing strategies, ensuring they stay relevant and appealing to their target audience while maintaining healthy profit margins.</li>
</ul>
<h3>Components of marketing intelligence</h3>
<p>The significant components of any marketing intelligence tool are the data collector, analyzer, visualizer, and execution.</p>
<ul>
<li><strong>Data collector - </strong>The data analyzer identifies data points that align with your business goals and integrates with these data sources to collect different data types like marketing, <a href="https://www.lifesight.io/glossary/conversion">conversion</a>, and non-marketing data. You need these datasets to establish your marketing campaigns' KPIs and track data better.</li>
</ul>
<ul>
<li><strong>Data analyzer - </strong>The built-in data analyzer performs a simple yet meaningful analysis of all collected data to identify the underlying patterns and how these datasets are correlated. For small businesses with limited datasets, simple data analysis is fairly easy using spreadsheets. For large enterprises, since a large volume of data is involved in the mix, you need advanced machine learning algorithms to perform regression analysis.</li>
</ul>
<ul>
<li><strong>Data visualizer - </strong>Analyzing a large data set is insufficient for decision-making. Marketing intelligence tools have a built-in data visualization dashboard showing real-time data patterns. With data visualization, marketers develop a concrete idea of how their marketing initiatives are performing across multiple touchpoints to</li>
</ul>
<ul>
<li><strong>Execution - </strong>The execution element in a marketing intelligence tool is related to decision-making. It is about clearly showing the current marketing scenario and planning future steps.</li>
</ul>
<h2>The Role of Technology in Marketing Intelligence</h2>
<p>Technology is the foundation of modern marketing intelligence initiatives. Marketing intelligence is slowly shifting its focus towards strategic decision-making, and technology is the critical driver for this shift as it makes marketing insights actionable.</p>
<p>Here is how technology plays a crucial role in marketing intelligence:</p>
<p><strong>Artificial Intelligence (AI) and Machine Learning (ML) - </strong>AI redefines different aspects of a business, and there are no exceptions when it comes to market intelligence. The integration of AI in marketing intelligence helps brands:</p>
<ul>
<li>Process qualitative insights from a large volume of data through sophisticated natural language processing</li>
<li>Use predictive analytics to process structured and unstructured data and uncover the underlying patterns between data attributes</li>
<li>Forecast future data trends and next steps based on historical analysis of past data</li>
</ul>
<p>With increasing digital data footprints, the volume of data available on the internet is inexplicable. The power of big data lies in analyzing this large volume of unstructured datasets and converting them into actionable steps. The primary application of big data in marketing intelligence is creating sample attributes that brands leverage to understand target customers' behavioral patterns.</p>
<h2>Gathering and Analyzing Market Data</h2>
<p>One of the significant steps in the marketing intelligence process is data collection. Knowing what data to collect and manage makes a lot of difference in the marketing intelligence process.</p>
<p>Here are some of the most effective data-collection strategies for businesses:</p>
<ul>
<li><strong>Surveys - </strong>Surveys include a series of questions related to customers' experience with a brand, purchase barriers, objections, and so on. The answers to these questions will help marketers derive critical insights regarding your campaign messaging and marketing mix.</li>
</ul>
<ul>
<li><strong>Social listening - </strong>Tracking the sentiments of social followers and community members through their posts, comments, and engagements helps you gauge their perception of your brand. Social media marketing tools like Hootsuite gather all this information to help you make conclusions related to target audience's emotions, and accordingly, decision-making becomes more accessible.</li>
</ul>
<ul>
<li><strong>First-party user data - </strong><a href="https://www.lifesight.io/blog/first-party-data-and-role-of-customer-data-platforms">First-party data</a> involves data related to online consumer behavior, like user interactions, purchase history, scrolling data, and saved items. These datasets give you an overview of users' interests and browsing behavior to plan your marketing campaigns efficiently.</li>
</ul>
<ul>
<li><strong>Competitor research - </strong>Analyzing your competitor data also helps in marketing decision-making. Analyzing competitors' marketing, pricing, distribution, and product strategies often helps a brand uncover valuable insights that later contribute to its marketing initiatives.</li>
</ul>
<h2>Consumer Insights and Behavior Analysis</h2>
<p>Once you have the necessary datasets, the next step is to analyze them to derive critical insights.</p>
<p>Some of the methods to analyze collected data using marketing intelligence tools are:</p>
<ul>
<li><strong>Statistical analysis - </strong>The purpose is to decode what the collected datasets tell you about the customers. While quantitative data can be analyzed directly for statistical significance, qualitative data requires segregation and assigning themes.</li>
</ul>
<ul>
<li><strong>Exploratory analysis - </strong>Behind a marketing intelligence campaign, there should be a distinctive set of questions that marketers want to address. For example - why is conversion not increasing? Why was there a dip in organic impressions last month?</li>
</ul>
<p>In exploratory analysis, marketers develop hypotheses based on these questions and identify data patterns that might help them find the answers.</p>
<h2>Integrating Marketing Intelligence into your Organizational Strategy</h2>
<p>After you analyze all the datasets, integrate the insights into your marketing strategy. This process involves two steps - compiling all your findings and preparing the next steps.</p>
<ul>
<li><strong>Compiling all your findings - </strong>Create a detailed report involving charts, numbers, and a proper explanation of all your findings. The goal is when a marketer comes across this dashboard, they will be able to conclude easily.</li>
</ul>
<ul>
<li><strong>Preparing the next steps - </strong>If marketing intelligence helps you derive clear conclusions from the collected data, planning the next step is straightforward. All you have to do is convert the findings into actionable steps. For example, if the marketing intelligence dashboard shows that Facebook is the most poorly performing marketing channel, the actionable step will be to reduce Facebook's marketing budget.</li>
</ul>
<p>However, if the marketing intelligence report doesn't provide all the necessary information to plan a roadmap, you will need to collect more details or perform data analysis again.</p>
<h2>Ethical Considerations in Marketing Intelligence</h2>
<p>Marketing intelligence is entirely dependent on data. But how you collect these datasets makes a lot of difference. Your data sources must be ethical, and you must ensure that you have users' consent when using first-party user data. Brands like <a href="https://www.apple.com/legal/privacy/en-ww/">Apple</a> have solid data policies related to third-party provider integrations when collecting user data.</p>
<p>If you are also using a third-party marketing intelligence tool, you must be aware of the following data protection frameworks:</p>
<ul>
<li><strong>General Data Protection Regulation (GDPR) - </strong>This framework explains how brands should handle confidential data of the EU member countries. GDPR went into effect on May 25, 2018.</li>
</ul>
<ul>
<li><strong>Health Insurance Portability and Accountability Act (HIPAA) - </strong>This US regulatory framework is focused on protecting medical data and applies to healthcare service providers and healthcare brands.</li>
</ul>
<ul>
<li><strong>California Privacy Rights Act (CPRA) - </strong>CRPA has been in effect since January 1, 2020. It adds a layer of data protection for all businesses dealing with sensitive data related to California residents.</li>
</ul>
<h2>Overcoming Challenges in Marketing Intelligence</h2>
<p>Even seasoned marketers don't have it all figured out when marketing intelligence is questioned. Marketing intelligence is associated with multiple challenges that you must address:</p>
<h3>Challenge 1: Collecting competitor data</h3>
<p>There are multiple small, medium, and big competitors for a new brand starting. It is a challenge to collect appropriate data for each competitor. Even though you are using a marketing intelligence tool, some of the concerning factors are:</p>
<ul>
<li>The volume and variety of the data</li>
<li>Confusion related to which data points to select</li>
<li>Limited access to competitor platforms</li>
</ul>
<p><strong>Solution</strong></p>
<p>Use an AI-powered data intelligence tool that collects and streamlines competitor data collection processes based on pre-defined instructions. For example, tools like SEMRush Ahrefs help marketers collect critical competitor data related to a brand's organic performance.</p>

![paid competitor analysis through SEMRUSH](https://mighty-basketball-fd4aeed960.media.strapiapp.com/paid_competitor_analysis_through_SEMRUSH_7d81b87260.webp)

<p><a href="https://www.semrush.com/kb/873-analyze-competitors-advertising-strategy">Source</a></p>
<h3>Challenge 2: Turning intelligence findings into actionable steps</h3>
<p>Despite using an intuitive marketing intelligence dashboard, it is not always easy to turn the findings of the intelligence tool into actionable next steps. This is mainly because the insights you get from the marketing intelligence tool are diverse. For example, if you get insights around websites, paid campaigns, and social platforms - how will you prioritize which platform to target first?</p>
<p><strong>Solution</strong></p>
<p>Invest in an all-in-one marketing measurement tool like <a href="https://www.lifesight.io/attribution">Lifesight </a>to help you derive valuable insights from marketing dashboards along with the level of prioritization. Additionally, collaborate with analysts who have prior experience in decoding such patterns.</p>
<h2>Make the most of Marketing Campaigns with Lifesight</h2>
<p>Choosing the right marketing intelligence tool is a pivotal decision that significantly influences the success of your marketing campaigns. Lifesight stands out as an essential tool in this realm, offering a host of features and benefits designed to enhance your marketing strategies effectively.</p>
<p>Lifesight's automated marketing intelligence platform excels in:</p>
<ol>
<li><strong>Increasing budget efficiency with improved Return on Ad Spend (ROAS): </strong>Lifesight provides deep insights into campaign performance, enabling marketers to allocate budgets more effectively. By analyzing and identifying the most profitable channels and strategies, Lifesight helps in optimizing <a href="https://www.lifesight.io/blog/return-on-ad-spend-strategy">ROAS</a>, ensuring that every dollar spent contributes meaningfully to the overall campaign success.</li>
<li><strong>Understanding factors that drive incremental profits:</strong> Lifesight goes beyond surface-level analytics to delve into what really drives profits in your marketing endeavors. Through detailed analysis, it uncovers the underlying factors contributing to incremental profits, allowing businesses to focus on strategies that deliver tangible financial returns.</li>
<li><strong>Providing intelligent recommendations for simplified Decision-Making:</strong> The platform offers actionable recommendations based on data-driven insights, simplifying the decision-making process for marketers. These intelligent suggestions are tailored to align with your specific campaign goals, ensuring that each decision is informed and strategic.</li>
</ol>
<p>Ready to try out Lifesight's unified marketing measurement platform?</p>
<p><a href="https://www.lifesight.io/demo">Schedule a demo</a>!</p>

A Guide To Marketing Intelligence For A Modern Marketer A Guide To Marketing Intelligence For A Modern Marketer 

A Guide To Marketing Intelligence For A Modern Marketer.webp

Unlatch the power of marketing intelligence with our comprehensive guide. Learn how to gather data, analyze insights, and optimize campaigns effectively.

A Guide To Marketing Intelligence For A Modern Marketer 

How Can Brands And Agencies Prepare For The End Of Third-Party Cookies

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Find the impact of Google's third-party cookie phase-out on marketers and the strategies for brands and agencies to navigate this transition effectively.

Preparing Brands & Agencies for the Third-Party Cookie Era's End

How Can Brands And Agencies Prepare For The End Of Third-Party Cookies?

<p>Marketing Mix Modeling (MMM) and Media Mix Modeling (MxM) are often mistakenly used interchangeably. However, there are distinct differences between the two. If you are here, you likely understand that media mix and marketing mix models deal with different types of marketing data.</p>
<p>Media Mix Modeling analyzes the effectiveness of different media channels within the marketing mix to optimize the advertising spend and maximize ROI. On the other hand, Marketing Mix Modeling measures the impact of various marketing elements, including external factors like political scenarios, inflation, recession, seasonality, and natural disasters, to understand their effect on sales and marketing ROI.</p>
<p> This article is all set to explain you about Marketing Mix Modeling vs Media Mix Modeling  (and some similarities) between the two MMMs.</p>
<h2>Understanding Marketing Mix Modeling</h2>
<p><a href="https://www.lifesight.io/blog/accuracy-validation-and-calibration-of-marketing-mix-modeling">Marketing mix models</a> identify the impact of different marketing initiatives on the overall ROI. It is a holistic approach focusing on external factors (political, environmental, economic, financial) and analyzing the extent to which these factors influence the performance of a marketing campaign.</p>
<p>A marketing mix model applies statistical methods like correlation trend analysis to understand how the internal and external variables are associated with impacting the sales or ROI of a brand.</p>
<p>In the image below, Gabriele Franco, founder of Cassandra, discusses only marketing mix modeling. He explains what problems B2B brands encounter in measuring campaign effectiveness and why MMM is the easiest solution available:</p>

![Linkedin post on marketing mix modeling by gabriele franco](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Linkedin_post_on_marketing_mix_modeling_by_gabriele_franco_437a522053.webp)

<p><a href="https://www.linkedin.com/posts/gabriele-franco-hybrida_simple-guide-to-get-marketing-mix-modeling-activity-7140651403772944384-MmsC/?utm_source=share&amp;utm_medium=member_desktop">Source</a></p>
<h3>Components of Marketing Mix Modeling</h3>
<p>The components of a marketing mix model are as follows:</p>
<ul>
<li><strong>Product - </strong>The item a brand offers. Success hinges on understanding the customer's needs and differentiating your product from competitors.</li>
</ul>
<ul>
<li><strong>Place - </strong>This refers to distribution channels or locations where the product is sold, not just the marketing channel being analyzed.</li>
</ul>
<ul>
<li><strong>Promotion - </strong>This involves all promotional activities and their collective impact on ROI.</li>
</ul>
<ul>
<li><strong>Price - </strong>A crucial element that influences the success of marketing campaigns based on how pricing strategies affect customer behavior.</li>
</ul>
<h3>Data types used in marketing mix modeling</h3>
<p>The data inputs required in marketing mix modeling are segregated into three categories:</p>
<ul>
<li><strong>KPI-based data - </strong>Sales over time, product category sales, loyalty program data, and geographical sales data.</li>
</ul>
<ul>
<li><strong>Marketing data - </strong>promotional data, campaign budget vs spends, channel-specific campaign spending, organic spending.</li>
</ul>
<ul>
<li><strong>External data - </strong>Seasonal trends, discounts data, changing market trends.</li>
</ul>
<h2>Deep Dive into Media Mix Modeling</h2>
<p>Media Mix Modeling is a focused approach that zeroes in on the performance of individual media channels like television, social media, and advertising platforms. It leverages historical data to offer insights into campaign performance and future potential.</p>
<p>Bilal Adham, Group Vice President at DP World, explains why major players like Facebook Amazon are slowly shifting to media mix modeling in the below image:</p>

![major players like Facebook Amazon are slowly shifting to media mix modeling post by bilal adham](https://mighty-basketball-fd4aeed960.media.strapiapp.com/major_players_like_Facebook_Amazon_are_slowly_shifting_to_media_mix_modeling_post_by_bilal_adham_18b8cdd3f4.webp)

<p><a href="https://twitter.com/bilaladham/status/1701501008084390031">Source</a></p>
<h3>Drivers of Media Mix Modeling</h3>
<p>Brands segregate their media mix modeling initiatives and outcomes depending on the following drivers:</p>
<ul>
<li><strong>Base drivers - </strong>Outcomes from existing brand recognition without paid marketing</li>
</ul>
<ul>
<li><strong>Incremental drivers - </strong>Results from paid marketing activities.</li>
</ul>
<ul>
<li><strong>Other drivers - </strong>Significant outcomes not classified under base or incremental drivers.</li>
</ul>
<h3>Data types used in Media Mix Modeling</h3>
<p>Media Mix Modeling is focused on channel-specific data, including</p>
<ul>
<li><strong>Sales data </strong>- Conversion metrics across different platforms.</li>
</ul>
<ul>
<li><strong>Customer data - </strong>Demographic information and buying habits.</li>
</ul>
<ul>
<li><strong>Spend data - </strong>media spend breakdown for multiple marketing channels.</li>
</ul>
<h2>Marketing Mix Modeling vs Media Mix Modeling - Comparative Analysis</h2>
<p>While both models aim to optimize marketing resource allocation, they differ significantly in approach and data utilization:</p>
<p>But that's not all. The approaches and data types used in media mix modeling and <a href="https://www.lifesight.io/blog/marketing-mix-modeling-and-media-buying-insights">marketing mix modeling</a> are largely different. Here's a comparative analysis of both:</p><table>
<tbody>
<tr>
<td>
<p><strong>Factors</strong></p>
</td>
<td>
<p><strong>Marketing mix modeling</strong></p>
</td>
<td>
<p><strong>Media mix modeling</strong></p>
</td>
</tr>
<tr>
<td>
<p>Goal</p>
</td>
<td>
<p>To offer a holistic overview of all factors (internal and external) affecting the marketing performance of a business.</p>
</td>
<td>
<p>To identify channel-specific factors on a deeper level that optimize the advertising spend of a campaign to achieve desired results.</p>
</td>
</tr>
<tr>
<td>
<p>Scope</p>
</td>
<td>
<p>Focused on category-level performance measurement, MMM helps marketers develop an integrated overview of the market category as a whole and not just a brand-specific overview.</p>
</td>
<td>
<p>Brand-level overview of the marketing mix. This approach focuses on data-driven insights from one or more media channels to measure the impact of advertising.</p>
</td>
</tr>
<tr>
<td>
<p>Data types</p>
</td>
<td>
<p>KPI-based data, marketing data, and non-marketing external factors.</p>
</td>
<td>
<p>Sales data, customer data, and campaign spending data.</p>
</td>
</tr>
<tr>
<td>
<p>Key question</p>
</td>
<td>
<p>Which internal and external factors are the key driving factors in the sales revenue generated from advertising campaigns?</p>
</td>
<td>
<p>How do you achieve the maximum impact from your advertising campaigns?</p>
</td>
</tr>
</tbody>
</table>
<p>Ultimately, it all comes down to your goal and requirements when deciding between the marketing and media mix models.</p>
<p>The marketing mix model is beneficial when you want to:</p>
<ul>
<li>Evaluate the efficacy of various marketing channels, both online and offline.</li>
<li>Determine optimal spending per channel.</li>
<li>Expand reach economically.</li>
</ul>
<p>Media mix modeling is best suited when you want to:</p>
<ul>
<li>Investigate a specific channel's contribution to overall ROI.</li>
<li>Gain a detailed performance overview for targeted marketing initiatives.</li>
</ul>
<h2>Marketing Mix Modeling Use Case </h2>
<p>Here is Marketing Mix Modeling Use Case of an FMCG brand and its strategic shift to MMM led to a <a href="https://www.lifesight.io/resources/case-study-mmm-drives-15-percent-incremental-ROI">15% increase in marketing ROI</a>.</p>
<p><strong>Problems</strong></p>
<ul>
<li>Identifying the influence of specific marketing channels like TV commercials, online ads, radio ads, and print media on overall ROI</li>
<li>Complicated data sources integrations leading to a time-consuming process</li>
<li>Inability to allocate budgets effectively, leading to identifying the contribution of each channel in overall sales growth</li>
</ul>
<p><strong>Solutions</strong></p>
<ul>
<li>Performing regression analysis on different data types like sales, marketing, spending, and competitor actions to build the MMM.</li>
<li>Conducting scenario analysis with MMM to get comprehensive insights into budgeting and improving marketing decision-making.</li>
</ul>
<p><strong>Results</strong></p>
<ul>
<li>Increased ROI by 15%</li>
</ul>
<h2>Use Case for Media Mix Modeling</h2>
<p>Suppose you run an ecommerce store selling luxury bags. Currently, your target marketing channels are:</p>
<ul>
<li>Facebook, Google, TikTok, and Instagram advertising</li>
<li>Organic social media promotions</li>
<li>Television advertisements</li>
</ul>
<p>You have implemented <a href="https://www.lifesight.io/glossary/media-mix-modeling">media mix modeling</a> to get data-driven insights into which of these channels contribute the most to your overall sales ROI.</p>
<p>Here are the possible insights a media mix modeling will derive in the case of the ecommerce store:</p>
<ul>
<li>How many products were purchased from each media channel?</li>
<li>What is the ROI derived from each media channel for a particular timeframe?</li>
<li>What is the optimal level of spend associated with each marketing channel for a specific timeframe?</li>
<li>What should be the ad spend from each channel in the coming year based on historical performance?</li>
</ul>
<p>To get these insights, a media mix modeling will demand the following data inputs from the ecommerce store:</p>
<ul>
<li>No purchases over the defined timeframe</li>
<li>Ideal customer profile involving the demographic data of each customer</li>
<li>Spend breakdown for each marketing channel</li>
</ul>
<h2>Start your Marketing Mix Modeling Journey with Lifesight</h2>
<p>Now that you have a clear idea about marketing mix modeling vs media mix modeling, it is time to try both approaches.</p>
<p>Starting with marketing mix modeling is a smarter idea, as you need a holistic overview of your marketing initiatives before diving deeper into a specific channel.</p>
<p>The perfect tool to begin your association with marketing mix modeling is <a href="https://www.lifesight.io/marketing-mix-modelling">Lifesight</a> which helps brands:</p>
<ul>
<li>Get a clear overview of the channel-wise performance of multiple campaigns and recommendations on the next steps</li>
<li>Forecast on upcoming campaign performance based on historical data analysis</li>
<li>Create customized MMM that fulfill your business goals and KPIs</li>
</ul>
<p>Want to cut down unnecessary ad budgets and predict future outcomes accurately?</p>
<p><a href="https://www.lifesight.io/demo">Book a demo!</a></p>

Marketing Mix Modeling vs. Media Mix Modeling - Differences and Use Cases

Marketing Mix Modeling vs. Media Mix Modeling - Differences and Use Cases.webp

Find the difference between Marketing Mix Modeling and Media Mix Modeling. Understand their goals, and data types, and determine which is best for your business.

Marketing Mix Modeling vs Media Mix Modeling Differences & Use Cases

Marketing Mix Modeling vs Media Mix Modeling - Differences and Use Cases

<p>Organic marketing takes time to show its impact on the overall ROI. To drive sales faster, businesses must have a data-driven digital advertising strategy.</p>
<p>But, most marketers struggle with how to approach ad campaigns to achieve marketing optimization. Which digital channels to prioritize? How much of the budget do you allocate to each channel?</p>
<p>In this do more with less resources economy, as a marketer, the last thing you want to do is drive ad campaigns based on assumptions and miss out on significant ROI opportunities. You must follow a systematic and analytics-driven process for conversion rate optimization to increase ad revenue.</p>
<p>Here are some of the tried and tested tips to optimize your marketing strategy and help you make informed decisions about ad budget and channels.</p>
<h2>What is Marketing Optimization?</h2>
<p>Marketing optimization is the process of continuously improving and maximizing the efficiency of marketing activities across all channels to enhance ROI.</p>
<p>According to <a href="https://www.statista.com/topics/2495/b2b-marketing/">Statista</a>, investment in advertising is following an upward trend in the B2B industry. By 2024, the overall ad spend amount in the US is expected to reach 37.7 billion U.S. dollars, up from 32 billion U.S. dollars in 2022.</p>
<p>While B2B spending on marketing technology keeps growing, there"s hardly any scope of errors in your marketing budgets.</p>
<p>The appropriate marketing optimization strategies will give you the insights you need to earn positive ROI from your ad campaigns.</p>
<h2>Top 10 Marketing Optimization Strategies to Explore in 2024</h2>
<p>Marketing optimization strategies vary based on industry types, target channels, locations, and the intent of target audiences. Nevertheless, here are the <a href="https://www.lifesight.io/blog/conversion-rate-optimization-strategies">conversion rate optimization strategies</a> that work with slight adjustments.</p>
<h2>Audience Targeting and Segmentation</h2>
<p><strong>What is it?</strong></p>
<p>Businesses target a vast group of audiences through their paid ad campaigns. To ensure that your ad messaging matches the intents of the audiences, segment your audiences into different sub-groups.</p>
<p>Some of the popular factors to base your audience segmentation are:</p>
<ul>
<li>Demography - location, age, occupation</li>
<li>Behavior - purchase history, saved items</li>
<li>Psychographic - beliefs, attitudes, personality types, values</li>
<li>Customized - search-term, website, mobile app</li>
</ul>
<p>To build a data-rich audience segment, consider leveraging analytics tools to combine these factors into sub-groups.</p>
<p><strong>Why do you need it?</strong></p>
<p>Seth Godin, a seasoned marketing expert, explained in a recent interview with <a href="https://www.inc.com/john-brandon/seth-godin-says-this-is-best-way-to-avoid-mediocre-marketing.html">Inc</a> why segmentation matters in B2B ad campaigns - <em>"We worry about disappointing, missing, offending or otherwise leaving behind someone who might become our customer".</em></p>
<p>Audience segmentation helps marketers to:</p>
<ul>
<li>Personalize their ad collaterals and messaging based on the needs of the target audiences</li>
<li>Define target audiences" needs better to boost conversion rates</li>
<li>Strengthen customer loyalty by building relationships</li>
<li>Generate more leads to fill the lead pipeline</li>
</ul>
<p><strong>Best practices</strong></p>
<ul>
<li>Your audience should be precisely defined. That means continuously researching and documenting detailed insights about each segment. The more specific information you have about the target audiences, the more personalized and effective your ad messaging will be.</li>
<li>While starting with a single channel is a good idea, explore and analyze performance across all major ad channels like Google Ads, Facebook Ads, LinkedIn Ads, Instagram Ads, YouTube ads, etc. Budget allocation depends on your audiences" presence or level of engagement, but using cross-channel communication strategies will increase the chances of conversion.</li>
</ul>
<h2>Multi-touch attribution models</h2>
<p><strong>What is it?</strong></p>
<p>Multi-touch attribution models measure the impact of different touchpoints on the customer journey to identify the contribution of each touchpoint in converting a lead into a customer.</p>
<p>They help businesses with critical insights about customer journeys to optimize the marketing process.</p>
<p>Some of the common types of multi-touch attribution models are:</p>
<ul>
<li>First-touch attribution model where the first marketing touchpoint is considered the most significant one</li>
</ul>
<p>Daniel McCarthy, a marketing professor at Emory University, shows a real-life first-touch attribution model:</p>

![real-life first-touch-attribution-model-linkedin-post-by-daniel-mccarthy](https://mighty-basketball-fd4aeed960.media.strapiapp.com/real_life_first_touch_attribution_model_linkedin_post_by_daniel_mccarthy_49ba2c5c3e.webp)

<p><a href="https://twitter.com/d_mccar/status/1442286132733956103">Source</a></p>
<ul>
<li>Last-touch attribution model where the final touchpoint is given the most credibility</li>
<li>Linear attribution model where all touchpoints are considered to create equal impact</li>
</ul>
<p>Additionally, for complex customer journeys, marketers use other attribution models like - U-shaped, W-shaped, and Time Decay.</p>
<p><strong>Why do you need it?</strong></p>
<ul>
<li>Cross-channel marketing is confusing as marketers often struggle to identify the most and least profitable marketing channels. With multi-touch attribution, each channel or publisher gets credits based on their contribution towards the overall ROI from a campaign. This helps marketers to make informed marketing optimization decisions.</li>
<li>Multi-touch attribution also gives marketers an idea of what works and doesn"t to run successful paid marketing campaigns. With the appropriate model, you will have a detailed idea of which ad messaging creative created the most value in a customer journey.</li>
</ul>
<p><strong>Best practices</strong></p>
<ul>
<li>Integrate all marketing touchpoints with your attribution model, including both online and offline channels. Assess all touchpoints objectively, without ignoring any based on assumptions</li>
<li>Incorporate offline touchpoints into your attribution analysis. For example, if a customer engages with your brand at an offline event before visiting your website, that event becomes a key touchpoint in their journey.</li>
<li>To unify touchpoints, use universal attribution tools like <a href="https://www.lifesight.io/attribution">Lifesight</a>, which can automatically capture event costs and integrate them with online data, giving weighted ROI credits to offline channels to accurately allocate budgets.</li>
</ul>
<h2>Marketing Mix Modeling (MMM)</h2>
<p><strong>What is it?</strong></p>
<p>Marketing mix modeling is identifying the impacts of various marketing initiatives on an organization's overall ROI. Unlike attribution models that capitalize on first-party consumer data, MMM analyzes how external factors like seasonality, political aspects, recession, inflation, and natural disasters are correlated with the overall marketing ROI.</p>
<p><strong>Why do you need it?</strong></p>
<p>Brands like Apple and Google have announced significant privacy upgrades that would limit advertisers' ability to track activity across apps.In 2024, more brands will start applying techniques like media mix modeling as it provides a holistic view of the marketing ecosystem and its impact on channel ROI.</p>
<p><strong>Best practices</strong></p>
<ul>
<li>Don"t opt for manual MMM, as feeding the model with all necessary datasets may take months. Instead, <a href="https://www.lifesight.io/marketing-mix-modelling">automate the marketing mix modeling </a>process with tools that integrate easily with your data sources and auto-calibrate the models to provide accurate insights.</li>
<li>Data quality makes a lot of difference for MMM. Use high-quality and granular conversion, ad spend, marketing, non-marketing, and brand data to train your marketing mix models. Focus on the accuracy and consistency of these datasets and keep cleansing them regularly to improve the quality of outputs.</li>
</ul>
<h2>Incrementality Testing and Experimentation</h2>
<p><strong>What is it?</strong></p>
<p>Incrementality Testing aims to determine the direct impact of a given marketing activity by isolating it and assessing changes in desired outcomes. It includes looking at a test group that is shown the campaign and a control group that is not.</p>
<p>Some common modes of incrementality testing are A/B testing, multivariate testing, and geo-match marketing.</p>
<p><strong>Why do you need it?</strong></p>
<ul>
<li><a href="https://www.lifesight.io/incrementality-testing">Incrementality testing</a> assists in marketing optimization by enabling precise ad spend adjustments and providing insights into the incremental impact of advertisements.</li>
<li>Understand the effectiveness of marketing channels and the adequacy of your ads in driving conversions.</li>
</ul>
<p><strong>Best practices</strong></p>
<ul>
<li>Clearly define your goals for incrementality testing to ensure its success. Choose the approach that aligns with your objectives.</li>
<li>Remarketing campaigns are popular in DTC, and incrementality testing is a game-changer in those scenarios. Since incrementality testing identifies the actual value of your campaigns, brands leverage these insights to adjust their remarketing campaign messaging.</li>
<li>Utilize tools like <a href="https://www.lifesight.io/cdp">Lifesight</a> for seamless data integration and advanced customer segmentation, avoiding manual testing complexities.</li>
</ul>
<h2>Measuring and Reporting</h2>
<p><strong>What is it?</strong></p>
<p>Tracking and presenting all your campaign metrics in a visually appealing dashboard is crucial for stakeholders to get the gist of your budget and marketing performance at a glance.</p>
<p>A well-structured campaign dashboard or report helps marketers derive critical insights from each active campaign to make actionable decisions.</p>
<p>A typical marketing report looks like this:</p>

![typical-marketing-report-looks-like-this](https://mighty-basketball-fd4aeed960.media.strapiapp.com/typical_marketing_report_looks_like_this_694219e29a.webp)

<p><a href="https://www.powermyanalytics.com/reports/ppc/">Source</a></p>
<p><strong>Why do you need it?</strong></p>
<ul>
<li>Provides you easy access to data to track campaign performance in a single dashboard</li>
<li>Visualized data gives you an understanding of historical data patterns to predict the future tendencies of audiences</li>
<li>Real-time marketing reports paint a transparent picture of how your campaign KPIs are changing different independent variables like time</li>
</ul>
<p><strong>Best practices</strong></p>
<ul>
<li>Break down data, and instead of using a large volume of datasets, try to use as much granular data as possible.</li>
<li>While data is the foundation of each marketing report, it is not always sufficient. Make sure to include a conclusion based on the data insights and the next steps in the report for your and other stakeholders" convenience.</li>
<li>Focus on meaningful metrics and avoid vanity metrics. For example, click impressions are important, but more critical metrics are <a href="https://www.lifesight.io/glossary/conversion">conversion</a>, ad spending, click-to-conversion rate, and campaign ROI. Your marketing report must have detailed analyses of these metrics.</li>
</ul>
<h2>Conversion Rate Optimization (CRO)</h2>
<p><strong>What is it?</strong></p>
<p>CRO is the process of optimizing all digital touchpoints, not only websites, based on potential user intent to optimize conversions.</p>
<p><strong>Why do you need it?</strong></p>
<ul>
<li>CRO helps you optimize your budget by converting your website visitors into paid customers. If done correctly, this strategy significantly reduces your conversion acquisition costs</li>
<li>CRO gives you access to valuable datasets like user behavior, scrolling depth, heatmaps, bounce rate, and clicks. Marketers are free to use these datasets to improve website conversion in the future</li>
</ul>
<p><strong>Best practices</strong></p>
<ul>
<li>Implement contextual CTAs based on user intent rather than generic messages.</li>
<li>Your top-performing pages should have personalized messaging that has worked in the past. The more it resonates with your target audiences, the higher the conversion possibilities.</li>
</ul>
<h2>Creative Optimization</h2>
<p><strong>What is it?</strong></p>
<p>Your ad creatives are standing at the forefront of your ad campaigns. It all comes down to how personalized the ad creatives are and whether they encourage your prospects to click. Creative optimization is about optimizing ad creatives based on messaging and design so that potential users find them irresistible to ignore.</p>
<p><strong>Why do you need it?</strong></p>
<p>Compelling creatives that accurately convey your offer are essential&mdash;they are the primary engagement point for users with your brand.</p>
<p>Here is an ad creative from Paperflite that we absolutely loved:</p>

![ad-creative-from-Paperflite](https://mighty-basketball-fd4aeed960.media.strapiapp.com/ad_creative_from_Paperflite_366726631f.webp)

<p><a href="https://www.linkedin.com/company/paperflite/posts/?feedView=ads">Source</a></p>
<p><strong>Best practices</strong></p>
<ul>
<li>Consistently A/B tests your creatives to identify the one that gets the most clicks and modify your messaging and designs accordingly.</li>
<li>Add a clear CTA that solidifies your offer.</li>
<li>Experiment with various creative formats, like videos and animations, to capture user attention.</li>
</ul>
<h2>Channel Optimization</h2>
<p><strong>What is it?</strong></p>
<p>Channel optimization involves tailoring your marketing efforts across different platforms to maximize ROI and user engagement.</p>
<p><strong>Why do you need it?</strong></p>
<ul>
<li>Channel optimization helps you understand where the customers are engaging the most with your brand</li>
<li>With channel optimization, you will identify the top-performing ad channels and making budgeting decisions will be easier</li>
</ul>
<p><strong>Best practices</strong></p>
<ul>
<li>Use real-time attribution tools to track how your ad campaigns are performing in real-time.</li>
<li>Map your buyer journey and align your ad creatives accordingly.</li>
<li>Explore your target audiences" most favorite channels and invest more in them to get maximum attention and engagement.</li>
</ul>
<h2>Lifecycle Marketing</h2>
<p><strong>What is it?</strong></p>
<p>Lifecycle marketing is about fostering meaningful interactions throughout the customer's journey to nurture leads into loyal customers.</p>
<p><strong>Why do you need it?</strong></p>
<ul>
<li>Lifecycle marketing assists brands in creating more loyal customers by encouraging them to repeat purchases. Since lifecycle marketing gives you granular insights into the behaviors of existing customers, winning the trust of existing customers becomes simpler</li>
<li>With lifecycle marketing, you will derive data-driven insights about your customers, their behaviors, and the various active touchpoints. Use these insights to improve your other marketing efforts, like content organic social</li>
</ul>
<p><strong>Best practices</strong></p>
<ul>
<li>Be as detailed as possible while building a customer profile. Combine the insights from multiple data points to identify the top lifecycle stages of your target customers.</li>
<li>Manual customer profile creation is next to impossible when you can access multiple data points. Use the right tool that auto-generates your customer profile to help you adjust your campaigns.</li>
</ul>
<h2>Real-Time Bidding (RTB)</h2>
<p><strong>What is it?</strong></p>
<p>RTB is part of programmatic media buying that involves instantaneous ad auctioning for targeted ad placements.</p>
<p><strong>Why do you need it?</strong></p>
<ul>
<li>For paid marketers, RTB helps you create targeted buying opportunities. It lets you focus more on creating relevant advertisements by refining targeting whenever required</li>
</ul>
<ul>
<li>RTB relies heavily on the data associated with real-time audiences. As a result, the relevancy of the advertisements increases, leading to more growth opportunities</li>
</ul>
<p><strong>Best practices</strong></p>
<ul>
<li>Experiment with multiple ad formats like audio, static, video, native, and social, and keep the A/B testing on until you find out the most suitable ad type for your brand</li>
</ul>
<ul>
<li>Keep tracking the two KPIs - Bid Floors and Blocklist to monitor the minimum bid that the publisher will accept and the URLs that you don"t want your ad to associate with.</li>
</ul>
<h2>Conclusion</h2>
<p>Marketing optimization is an integral aspect of any successful paid marketing campaign. With strategies like multi-touch attribution, marketing mix modeling, audience segmentation, incrementality testing, and reporting, generating leads from your ad campaigns becomes fairly straightforward.</p>
<p>These conversion rate optimization strategies help you get a clear overview of your campaigns so you make decisions regarding their effectiveness and budget allocation.</p>

Top 10 Marketing Optimization Strategies to Improve Marketing ROI

Top 10 Marketing Optimization Strategies to Improve Marketing ROI.webp

Find the latest marketing optimization strategies for 2024 to enhance ROI. Learn about audience targeting, multi-touch attribution, and more for effective ad campaigns.

Top 10 Marketing Optimization Strategies 2024: Tips & Trends

popular-social-media-platforms.webp

Learn the meteoric rise of social media platforms from 2019 to 2023. Uncover key insights, top visiting countries, and user demographics in this in-depth analysis.

10 Popular Social Media Platforms: Growth and Analysis,2024

10 Most Popular Social Media Platforms: Growth and Visits Analysis, 2024

<p>A budget proposal is the first thing a client will see and the last thing they will like to read. So, how do you make sure you prepare it well?</p>
<p>Every new year, two things are spoken about a lot:</p>
<ul>
<li>Taking up a new gym membership</li>
<li>Getting approvals for your marketing budgets</li>
</ul>
<p>Well, we ain't the people to talk more about the first one. But, we can surely help you succeed in the second one.</p>
<p>As an agency, you are primarily responsible for a set of goals that the client decides on. This makes asking for more budgets a little complicated as you become more of an executioner than a strategist.</p>
<p>Agencies often find themselves in a tight spot, balancing client expectations with the hard reality of limited funds. The challenge isn't just about getting more money; it's about proving that every dollar spent is a wise investment. It's a world where "spend less, gain more" is the new mantra, and justifying every penny is crucial.</p>
<p>But fear not!</p>
<p>In this guide, we will dive into everything that will help you win more budgets - from the right tools and strategies to the approaches you can take to conquer them.</p>
<h2>Building the Foundation: Get the Data-In</h2>
<p>Before you jump into preparing a pitch, build nuance in your understanding.</p>
<p>As an agency, you have the means to understand what is shaping a particular industry. Utilize this to the fullest by documenting everything you can to build depth in your pitch. To help you understand the needs of your clients better, we advise you to understand three things in depth:</p>
<h3>What does your client help solve?</h3>
<p>We are not talking about your client's product here, instead focus more on the problem the business solves. Get to the core of the problem and then branch out the solutions your client offers.</p>
<p>And don&rsquo;t stop at that, always keep updating it by constantly asking your clients about the improvements in their products/services.</p>
<h3>Who are your clients helping?</h3>
<p>We can&rsquo;t stress this enough. Getting to know your clients&rsquo; customers is extremely effective in charting out the right strategy for you. This is an exercise that needs to be performed in conjunction with your client. Complement that with your research by talking to your client's customers frequently.</p>
<p>A well-defined customer profiling document will help you identify what problems customers have, where they are, what they like, and more insights. This is crucial for crafting the right campaigns that get in those dollars.</p>
<h3>How is the industry shaping up?</h3>
<p>As an agency, you will have access to a group of clients in the same industry. Utilize this data to identify trends and patterns. Be aware of how the industry is growing, what is the average spend in this industry, what are other players doing in marketing, what channels are they using, and how is the ROI on those channels.</p>
<p>Getting such a sense of direction will help you understand what you need to do and what you don&rsquo;t.</p>
<h2>Collating the Data: Studying the Past</h2>
<p>The foundation will give you a nuanced understanding of all the fronts of your client&rsquo;s business. However, this only gives you the power to &ldquo;tell&rdquo;. To get buy-in from your clients you also need to bring the data on what has worked in the past. This data helps in two ways:</p>
<ul>
<li>Help you identify the gaps in your strategy</li>
<li>Invoke confidence among your clients</li>
</ul>
<p>This begs the question: How can you consolidate all this data into a single place?</p>
<p>There are two answers to this.</p>
<p>One is by setting up a manual process of tracking metrics like budgets every month, spends across channels, and ROI on these channels. This system fails to help you derive ROI out of awareness campaigns or offline spends that you know helped. Merely going by the gut or saying, &ldquo;<em>We found most brands doing this and seeing success</em>&rdquo;, will make you less likely to get those budgets.</p>
<p>This is where the second method comes in.</p>
<p>Using a tool like Lifesight can help you consolidate all the data in a single place and help you identify the right metrics across ROAS, conversions, CAC, total revenue, and more.</p>
<p>The Marketing Mix Modeling feature also helps you understand how your campaigns influence the bottom line and thereby helps you back your demand for budgets with data.</p>

![Marketing Budget Strategies for Agencies - Collating the Data.png](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Marketing_Budget_Strategies_for_Agencies_Collating_the_Data_040b0eab6e.png)

<h2>Initiate Planning: Identifying the Channels</h2>
<p>Once you have access to all the data, you need to start figuring out what channels you are going to invest in and how much would each channel need to get a good ROI.</p>
<p>Now here is where things get interesting.</p>
<p>Most agencies look at the data and take a calculated guess based on that. The chances of success then get lopsided. If competitor A is investing $10,000 in Facebook, does that mean you will have to invest too?&nbsp;</p>
<p>Though Facebook might be the channel where your intended audience is active, what are the other channels that are driving revenue for your clients?</p>
<p>These questions become pertinent to creating a solid data-backed strategy for asking for more budgets.</p>
<p>So how do you answer these questions?</p>
<p>If you thought about attribution then you are right my friend! But mere last-touch attribution is not going to do the trick here. As we said before, marketing is a nuanced game.</p>
<p>If your customers are coming across you on different channels, you cannot put all your money on the channel where the conversion happens. At the same time, you need to question the impact of every touchpoint of the customer.</p>
<p>This is where universal attribution will help.</p>
<p>Using Universal Attribution, you can understand what channels your customers are engaging with you. This will help you develop a customer journey and showcase how interactions are happening across channels.</p>
<p>If you are using Lifesight&rsquo;s Universal Attribution model, you can also look at how every channel is bringing in revenue and the weightage of each channel. This will help you prove specific ROI coming from each campaign and channel thereby making your pitch more trustworthy.</p>

![Marketing Budget Strategies for Agencies - Identifying the Channels.png](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Marketing_Budget_Strategies_for_Agencies_Identifying_the_Channels_f44807dbd2.png)

<p>For instance, if customers are majorly learning about the brand on Facebook, checking the website, seeing a TikTok ad, and then making a purchase, Lifesight can map out this entire journey on an order level, with the weighted distribution of revenue from each touchpoint.</p>
<p>Such data-backed analysis will help you provide clear insights into how your marketing is functioning and driving revenue. This will build trust among your clients to understand the whole picture better.</p>
<h2>Putting the Money: How to Allocate the Right Budgets?</h2>
<p>Identifying the channels and the revenue attribution is a major step in winning over your clients. But then the next question they would ask is &ldquo;How much money do we need to invest in these channels to get the best returns?&rdquo;</p>
<p>Usually, most agencies fix the budgets in proportion to the revenues brought by every channel. But attribution is like giving a direction, it won&rsquo;t help you answer any questions on numbers. Moreover, budget allocation is a whole different ball game.</p>
<p>With the world moving to a more dynamic, omnichannel way of buying, pinpointing what works is difficult for an agency. And making decisions based on the gut is not wise enough when you are dealing with thousands or millions of dollars.</p>
<p>The last year for instance saw spending patterns change during BFCM as compared to 2022. It&rsquo;s very hard to predict such trends. You can only do that if you have been studying all the data that influences a purchase and making decisions based on that.</p>
<p>Agencies cannot do it, but a machine learning model trained on shopping data having myriad internal and external factors can.&nbsp;</p>
<p>With brands sitting on huge data and AI being more accessible a former tool called Marketing Mix Modeling (MMM) has come to the forefront to help you achieve just that.</p>
<p>While attribution gives you direction as to what channels and campaigns people are engaging with, MMM gives you depth as to how much money you can invest in those channels to get the best returns possible.</p>
<p>The output generated from an MMM model is developed based on the internal data you feed it - spends on channels, and revenue on a day-to-day basis. A good MMM model also factors in external dependencies like seasonal trends, market fluctuations, etc.</p>
<p>Furthermore, MMM is best suited for omnichannel as it can help you pinpoint the exact budgets required for online channels - Facebook, Google, Twitter, and offline - events, print, OOH, and radio.</p>
<p>For instance, Lifesight&rsquo;s MMM model helps agencies direct the right budgets to every channel based on the marketing and sales information fed to it. With an accuracy of up to 98%, agencies can get the desired return for their investment on these channels.</p>
<p>You can also track non-revenue metrics like conversions, awareness, reach, installs, store visits, registrations, admissions, and subscriptions to gauge the effectiveness of your awareness campaigns.&nbsp;</p>
<p>The best part?</p>
<p>The budget optimizer tool helps you understand how much of the budget needs to be spent on what channels to attain the ROAS set by you. Such a tool will help you showcase the effectiveness of the strategies and campaigns you plan using the data owned by the brands, instead of following trends.</p>

![Marketing Budget Strategies for Agencies - Allocate the Right Budgets.png](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Marketing_Budget_Strategies_for_Agencies_Allocate_the_Right_Budgets_009f83e054.png)

<p>This adds more weight to your pitch and puts more conviction in your campaigns and channel strategy.</p>
<p>Additionally, Lifesight allows you to test different scenarios to understand how they impact the goal you plan to achieve. All you have to do is add your goals and target KPIs, and you can chart out scenarios for different outcomes. This will help you plan better and showcase how tweaks in the strategy can impact the bottom line.</p>
<h2>Talking Strategies: Balancing Short-Term Goals with Long-Term Goals</h2>
<p>Marketing as a function is always asked for results. In most scenarios, your clients expect you to get them quick wins and they are not wrong in their expectations.</p>
<p>Let me explain.</p>
<p>When a brand hires an agency for their marketing, they usually make this decision by having multiple conversations with you and other people who have worked with you. They expect to take a chance and look to prove themselves right.</p>
<p>This is usually done when agencies deliver some quick wins to the brands.</p>
<p>To deliver such quick wins, you as an agency have to map out what are the short-term goals in the first three months with the action plan to achieve them - what campaigns you are going to run on what channels, what is the expected ROI and how will that be proven.</p>
<p>Does that mean you have to be short-sighted? No.</p>
<p>While you set up trust in the first few months, you also need to get a deep understanding of the brand&rsquo;s long-term goals. And by the long term, you need not have a 5-year strategy, a one to two-year strategy works best.</p>
<p>Map out the long-term strategy with milestones set for every month - have them clearly defined and seek out what resources you may need to achieve them. These could be time from internal subject matter experts, investment in other tools, or the like.</p>
<h2>Filling the Last Gap: Going Beyond Data</h2>
<p>Tools like Universal Attribution and MMM are based on the data you provide them. The decisions made by these tools help you optimize your current marketing spend to get the best results. However, the output of these tools is built from historical data generated.</p>
<p>Though these tools are robust, they won&rsquo;t point you to new channels, campaigns, or strategies. This is where your human analysis comes into the picture.</p>
<p>As you seek for more budgets, do not directly invest the whole of it into existing channels. Split the budget to serve existing goals. The 70/20/10 rule is one of the best ways to do that.</p>
<p>According to this rule, you split the entire budget into three buckets - 70%, 20%, and 10%. Here&rsquo;s how you can use them in your marketing:</p>
<h3>70% budget on what&rsquo;s working</h3>
<p>This budget is allocated to the channels and campaigns that are already working and generating positive results for the brand. The output delivered by tools like MTA and MMM is the best use case here.</p>
<h3>20% budget on innovation</h3>
<p>While you continue optimizing your successful campaigns, it is also equally important to understand how you can notch it up. Setting some budget aside for trying some innovative campaigns can help you break the boundaries and expand your horizons.</p>
<h3>10% budget on experiments</h3>
<p>With marketing continuously evolving, it is pertinent for you to test different channels or different campaigns that have not been used before. For instance, Meta&rsquo;s Threads can be a good channel for you to experiment and see how can it perform for your clients. You can also test how you can expand your client&rsquo;s geographical reach by using Geo experiments.</p>
<p>But the thing with experiments is that the results are not certain. You cannot really make out whether the experiment is going to prove your hypothesis right or not. Secondly, measuring these experiments is hard.</p>
<p>Suppose you run a geo experiment to test a new region and see positive results in your paid campaign, can you really conclude that the cause was the new audience targeting?</p>
<p>What if there are other factors?</p>
<p>As it&rsquo;s said, correlation is not always causation. However, you can measure causation.</p>
<p>With an experimentation platform like Lifesight, you can run multiple experiments like Geo experiments and split testing. The platform helps you design and run experiments by letting you choose your control variables and test variables. Furthermore, if you are testing the impact on a metric, the platform will also validate what should be your control and test variants so that you get the best results.</p>

![Marketing Budget Strategies for Agencies - Going Beyond Data.png](https://mighty-basketball-fd4aeed960.media.strapiapp.com/Marketing_Budget_Strategies_for_Agencies_Going_Beyond_Data_1ac393ae4a.png)

<p>However, the best part about such tests is that you can study both the correlation and causation factors of your experiments. Lifesight lets you ask questions about your experiments and data so that you can clearly understand what were the driving factors of the results and finally accurately answer the question - &ldquo;<em>Did this experiment actually drive impact?</em>&rdquo;</p>
<p>If you are using MMM, the platform also goes ahead and augments the experiment data to the MMM model, so that your model is enhanced with newer findings around your marketing data. This continuous enhancement keeps your MMM model accuracy soaring so that your budgeting decisions are always backed with the most recent data.</p>
<p>As the last piece of advice, we suggest you ensure these experiments are properly documented and reported to your clients at a fixed cadence. Clearly mention what you are trying to achieve through these experiments and upon completion share your learnings with your clients.</p>
<h2>The Best Tip: Over-Communicate</h2>
<p>If you think about it, budgets are always diverted towards strategies that seem to be the most reliable. In that sense, your primary job as an agency would depend more on how reliable you as a partner can be.</p>
<p>Building reliability can act like an unending leverage that will always put the right budgets in your sack. But building this reliability takes constant communication with your clients. An agency that constantly documents its work, builds reports, and shares it with its clients regularly, always seems to have the best share of the budget pie.</p>
<p>I get it, this might come across as time-consuming. But with a unified marketing measurement tool like Lifesight, you can showcase the whole of your marketing regularly so that your agency is seen as a trusted partner in the success of your clients.</p>
<h2>Quick Recap: How to win more budgets?</h2>
<ul>
<li>Build a solid foundation of your client&rsquo;s services, customers, and their industry.</li>
<li>Get all the historical data you can get your hands on.</li>
<li>Identify what channels have been working using MTA.</li>
<li>Use MMM to get clarity on how much money you need to invest in each channel.</li>
<li>Balance your strategy by including a good mix of short-term and long-term goals.</li>
<li>Always split the budget to have some room for innovation and experimentation.</li>
<li>Have a system in place to measure the effectiveness of your experiments and understand the causality.</li>
<li>Use feedback loops from the experiments you run to feed your marketing decision engine like Lifesight.</li>
<li>Build reliability among stakeholders by periodically communicating the performance of your marketing.</li>
</ul>
<h2>Summing it up</h2>
<p>In conclusion, winning more budgets as an agency hinges on a smart, data-informed approach. With technology helping more marketers give a true sense of their strategies, tools like Lifesight are becoming the go-to for building trust among clients and showcasing expertise. With the boundaries blurring between technology and other fields, it is about time for agencies to get into the data game and help their clients achieve certainty even during uncertain times.</p>
<p><em>You can see Lifesight in action and understand how it can help you win more budgets by </em><a href="https://www.lifesight.io/demo?utm_source=blog&amp;utm_medium=website&amp;utm_campaign=how+to+win+budgets"><em>setting up a demo</em></a><em>.</em></p>

Marketing Budget Strategies for Agencies.png

Discover 7 powerful strategies to boost agency budgets in 2024. Learn key tactics for securing more funding and maximizing marketing success.

Marketing Budget Strategies for Agencies to Win more Budgets

7 Strategies to Help Agencies Win more Budgets in 2024

<p>On January 4th, a new chapter in digital browsing began as Google started phasing out third-party cookies for 30 million users, a significant first step in a broader plan to eliminate them in Chrome by the third quarter of 2024.</p>
<p>As users, we've enjoyed the benefits of personalized web experiences, appreciating the tailored content and relevant suggestions that make our online journey more enjoyable and efficient. Among these, third-party cookies have been pivotal, yet they come with privacy trade-offs that can no longer be overlooked.</p>
<p>In this blog, we'll delve into what Google Tracking Protection is, why it's important, and how it marks a new chapter in the balancing act between digital convenience and user privacy. Understanding this shift is crucial, not just for tech enthusiasts but for anyone who browses the internet and cares about how their digital footprint is managed.</p>
<h2>The Role and Impact of Third-Party Cookies</h2>
<p>Third-party cookies have long been the backbone of online tracking, silently working behind the scenes every time we browse the web. But what exactly are they?</p>
<p>Upon visiting a website, cookies are deposited onto your browser. The cookies generated by the website you are currently browsing are referred to as "first-party cookies" Nevertheless, numerous websites incorporate components from external sources, such as advertisements or social network widgets. These elements also can store cookies on your browser, commonly referred to as "third-party cookies".</p>
<p>They are mainly used for cross-site tracking and online advertising. Moreover, these cookies also track our online movements across different websites, compiling a detailed profile of our interests, preferences, and even locations. Now this data has become a goldmine for advertisers seeking to deliver more personalized digital experiences. But as you might guess, it raises significant privacy concerns.</p>
<p>With an explosion in data gathering and processing, new privacy regulations were necessary and hence the General Data Protection Regulation (GDPR) was introduced in 2018. Its primary goals were to strengthen the protection of personal data, unify data privacy laws across the European Union, and give individuals more control over their personal information.</p>
<p>Soon the importance and timeliness of GDPR were made more apparent by the Facebook and Equifax data breaches. These high-profile incidents highlighted the vulnerabilities in data security and the potential consequences of mishandling personal data. They brought widespread public attention to the issues of privacy and data protection, emphasizing the need for stringent regulations.</p>
<p>It's this growing unease about privacy that has led to a reevaluation of third-party cookies' role in our digital lives, setting the stage for new solutions like Google Tracking Protection.</p>
<h2>Google's Pivot to Privacy: Introducing Tracking Protection</h2>
<p>Google's response to the rising privacy concerns is a significant pivot towards enhanced user privacy, epitomized by the introduction of Tracking Protection. This feature is a key component of Google's ambitious <a href="https://developers.google.com/privacy-sandbox/overview">Privacy Sandbox initiative</a>. The initiative aims to redefine the balance between delivering personalized web experiences and safeguarding user privacy. But how does Tracking Protection fit into this?</p>
<p>Tracking Protection is essentially Google's way of phasing out the traditional third-party cookie model. Instead of allowing various websites to place their cookies on your browser, thereby tracking your activities across the web, Tracking Protection limits this capability. It functions by blocking third-party cookies by default, effectively putting a barrier to the kind of widespread tracking we've become accustomed to.</p>
<p>The beauty of Tracking Protection lies in its approach. While it restricts third-party cookies, it doesn't completely eradicate the concept of understanding user preferences for a personalized online experience.</p>
<p>With Google's <a href="https://developers.google.com/privacy-sandbox/relevance/topics#:~:text=API%20and%20implementations.-,What%20is%20the%20Topics%20API%3F,the%20sites%20a%20user%20visits.">Topics API</a>, the browser keeps track of things that the user seems to be interested in based on how they browse. This information is saved locally on the device of the user. The Topics API can then let API callers, like ad tech platforms, see what topics a user is interested in without giving away any other details about what the user is browsing.</p>
<p>Here's a simplified example that shows how the Topics API could help an ad tech platform choose the right ad.</p>

![simplified-example-about-how-the-topics-api-could-help-an-ad-tech-platform](https://mighty-basketball-fd4aeed960.media.strapiapp.com/simplified_example_about_how_the_topics_api_could_help_an_ad_tech_platform_ddab3e0c12.webp)

<p><a href="https://developers.google.com/privacy-sandbox/relevance/topics">Source</a></p>
<h2>Impact of Tracking Protection</h2>
<p>With Tracking Protection, your online behavior is less transparent to countless advertisers and data brokers. This means a decrease in the strangely accurate targeted ads that are based on your browsing history, offering a sense of privacy that's been gradually diminishing in the modern digital age. It also gives you more control over your data, aligning with the growing demand for transparency and consent in data collection.</p>
<p>On the flip side, the digital advertising landscape faces a seismic shift. Advertisers and marketers, long reliant on the detailed user profiles built from third-party cookies, will need to adapt to this new, more <a href="https://www.lifesight.io/connect">privacy-focused environment</a>.</p>
<p>While Tracking Protection doesn't eliminate the concept of targeted advertising, it forces a move towards less invasive methods, such as contextual advertising based on the content being viewed rather than the user's browsing history.</p>
<p>Deirdre Bosa, TechCheck Anchor at CNBC accurately explains in this video what this means for Google's ad dominance.</p>

<iframe width="560" height="315" src="https://www.youtube.com/embed/oU7MMyxzHoQ?si=bHPyll8xwPDOHTqb" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

<p>However, the implementation of Google's Tracking Protection doesn't come without its set of challenges and implications for the future of online privacy and digital advertising.</p>
<h2>Challenges and Future Outlook</h2>
<p>One of the foremost technical hurdles is the shift in infrastructure and strategy required for advertisers and publishers. With third-party cookies becoming obsolete, these entities must adapt to new technologies and methods like contextual advertising or <a href="https://www.lifesight.io/blog/first-party-data-and-role-of-customer-data-platforms">first-party data collection</a>. This transition demands not only technological upgrades but also a strategic rethinking of how to engage and understand audiences effectively in a privacy-first world.</p>
<p>There's also a regulatory dimension to this shift. As governments around the world tighten data privacy regulations (like GDPR in Europe and CCPA in California), companies must navigate an increasingly complex legal landscape. Compliance with these varying regulations, while also adapting to new technologies like Tracking Protection, adds another layer of complexity for businesses operating online.</p>
<p>Looking forward, the trajectory seems to be set towards a more privacy-conscious online world. Users are increasingly aware of their digital rights and demand more control over their personal data. This shift is likely to continue, with technologies evolving to provide more secure and private browsing experiences while still catering to the need for personalized content and ads.</p>
<p>For digital advertising, the future is about balance and innovation. Advertisers will likely explore new methods that respect user privacy while still delivering effective campaigns. This could include a greater reliance on machine learning algorithms to <a href="https://www.lifesight.io/analyze">analyze marketing impact</a> without infringing on individual privacy, or an increased use of first-party data where users directly provide information to the sites they trust.</p>
<p>Digital Marketing Consultant, Aakash Goyal recommends adopting privacy-first solutions and implementing enhanced conversions for web objectives:</p>
<ul>
<li>Google Tag Manager</li>
<li>Google Tag</li>
<li>Google Ads API</li>
</ul>

![linkedin-post-by-aakash-goyal-on-adopting-privacy-first-solutions-and-implementing-enhanced-conversions](https://mighty-basketball-fd4aeed960.media.strapiapp.com/linkedin_post_by_aakash_goyal_on_adopting_privacy_first_solutions_and_implementing_enhanced_conversions_0affba0936.webp)

<p><a href="https://www.linkedin.com/feed/update/urn:li:activity:7148962919530045440?updateEntityUrn=urn%3Ali%3Afs_feedUpdate%3A%28V2%2Curn%3Ali%3Aactivity%3A7148962919530045440%29">Source</a></p>
<h2>Final Thoughts</h2>
<p>While this shift poses technical and regulatory challenges, particularly for advertisers accustomed to the old ways of third-party cookies, it also opens the door to innovation and a reimagined digital ecosystem.</p>
<p>As we move forward, the balance between effective advertising and respect for user privacy will be key. Tracking Protection isn't just a feature; it's a signpost pointing towards a future where privacy is a fundamental part of the digital experience, heralding a new era for users and businesses alike. This journey towards a more privacy-respecting web is not just necessary; it's inevitable.</p>

google-tracking-protection.webp

Find the impact of Google's Tracking Protection on online privacy and advertising. Learn about the challenges and future outlook in a privacy-first digital world.

What Is Google Tracking Protection?

<p>It goes without saying that data-driven marketing strategies are critical for brands to effectively reach and engage with customers. Successfully leveraging different data types, like zero party, first-party, second-party, and third party data, enables more personalized and impactful marketing.</p>
<p>A <a href="https://images.forbes.com/forbesinsights/StudyPDFs/Turn-DataDrivenCustomerCentric-REPORT.pdf">report from Forbes</a> revealed that data-driven marketing strategies can increase ROI by as much as 5-8 times their campaign spend. This remarkable <a href="https://www.lifesight.io/glossary/return-on-investment">return on investment</a> highlights the immense power of leveraging the right kind of data in the right context. In this blog, we'll explore how each type of data &ndash; zero, first, second, and third &ndash; plays a unique role in shaping marketing strategies.</p>
<h2>Understanding Different Data Types</h2>
<p>Leveraging different consumer data types represents a vital capability for brands seeking to effectively reach, engage, and build loyalty with their audiences. From declared zero party insights to trusted first-party intelligence, rich second-party perspectives to broad third party market views, each data source offers unique applications for powering marketing strategies and personalization in our privacy-first, consumer-centric age.</p>
<h3>1) Zero Party Data: The Direct Consumer Insight</h3>
<p><a href="https://www.lifesight.io/blog/zero-party-data">Zero party data</a> refers to information that customers intentionally and proactively choose to share with a brand. This direct declaration of preferences, interests, needs, and other personal insights can be gathered through voluntary participation in surveys, feedback forms, profile questionnaires, and interactive quizzes.</p>
<p>In the context of measurement, especially in <a href="https://www.lifesight.io/blog/guide-to-incrementality-testing">Incrementality Testing</a>, zero party data is crucial. It offers direct insights into customer preferences and behaviors, aiding in the assessment of the direct impact of specific marketing initiatives.</p>
<p>Enabling zero party data collection fosters value-driven, mutually beneficial relationships where consumers offer transparency into their wants and behaviors in exchange for more personalized experiences. Consider an apparel brand conducting an optional Style Finder survey to capture color palette preferences, favored fabrics, typical usage occasions, and other stylistic insights from subscribers. These zero party declarations can then inform highly tailored recommendations and advertisements that reflect individual tastes.</p>
<p>Progressive brands are also incentivizing zero party data sharing by offering rewards like members-only discounts, early sale access, bonus loyalty points, or gated content in return for completed profiles. These opt-in programs increase enrollment while amplifying consumer voices for improved products, services, and messaging relevance.</p>
<p>They also offer compliant data collection that avoids infringing on consumer privacy. Adidas adiClub Membership Programme represents one example, granting users gear giveaways and special events access based on their activity log sharing.</p>

![adiClub-membership-programme-landing-page](https://mighty-basketball-fd4aeed960.media.strapiapp.com/adi_Club_membership_programme_landing_page_355c904e25.webp)

<p>Such creative mutual value exchange establishes the consumer foundation so integral for next-generation data strategies.</p>
<p>Suprio Guha Thakurta, an experienced business strategist and ex-chief Strategy Officer at The Economist in a <a href="https://www.linkedin.com/posts/suprioguhathakurta_first-party-data-vs-zero-party-data-do-activity-7105808012128206848-5WQg?utm_source=share&amp;utm_medium=member_desktop">recent post</a> discussed the differences between first-party and zero party data in the context of the declining use of third party cookies, exacerbated by Apple's App Tracking Transparency initiative.</p>

![first-party-data-vs-zero-party-data](https://mighty-basketball-fd4aeed960.media.strapiapp.com/first_party_data_vs_zero_party_data_1f429fa932.webp)

<h3>2) First Party Data: The Gold Standard</h3>
<p>First party data constitutes the behavioral, transactional, and descriptive information a brand collects through direct owned-channel interactions across digital properties like websites, mobile apps, and online communities as well as brick-and-mortar store networks. This can range from purchase histories, website activities, <a href="https://www.lifesight.io/blog/dtc-customer-loyalty-programs">loyalty programs</a>, opt-in mailing lists, ratings and reviews, and CRM data to social media engagement, geolocation check-ins, user-generated content including imagery, and more.</p>
<p>This data is pivotal in <a href="https://www.lifesight.io/blog/multi-touch-attribution-models-and-examples">Multi-Touch Attribution</a> (MTA) and <a href="https://www.lifesight.io/blog/principles-of-marketing-mix-modeling">Marketing Mix Modeling</a> (MMM). It provides detailed insights into customer journey touchpoints and the effectiveness of various marketing channels, thus enhancing the precision of marketing effectiveness measurement.</p>

<p>Robust first-party data assets enable advanced audience segmentation for highly targeted, personalized ad campaigns and customer experiences. Fashion merchandisers can segment mobile app users based on browsing behaviors to promote recently viewed items. Alternatively, hospitality brands may customize hotel landing pages with recommended local attractions based on a visitor's metro area.</p>
<p>While first-party data delivers incredibly rich, precise consumer perspectives, privacy remains imperative. Allowing customers transparency through clear opt-in consent and privacy policies helps secure trust that maintains data access over the long term.</p>
<p>A recent study indicates that <a href="https://searchengineland.com/cdps-very-important-for-getting-value-out-of-customer-data-say-58-of-marketers-surveyed-343309">58% of marketers</a> believe <a href="https://www.lifesight.io/cdp">CDPs</a> are very important for getting value out of customer data by offering a unified view of customer interactions from various sources. They ensure data accuracy, aid in real-time processing, and enhance personalization.</p>
<h3>3) Second Party Data: Strategic Partnerships</h3>
<p>Second-party data refers to consumer intelligence acquired from strategic brand partners through co-marketing initiatives, publisher partnerships, mailing list-sharing agreements, or aggregated data platforms.</p>
<p>Unlike third party data providers, second-party data partners directly own original audience insights from their own branded assets and engaged communities. By contractual data exchange or licensed access, second-party assets can expand their reach while preserving security and privacy standards.</p>
<p>For example, an eco-conscious fashion retailer could partner with a like-minded organic restaurant chain to promote sustainability programs. They can measure program success and exchange segment analytics to inform future initiatives by accessing each other's customer emails under agreed terms. Similarly, consumer tech brands frequently amplify launch campaigns by sharing early adopter segments.</p>
<p>In a measurement-focused approach, this data enriches Multi-Touch Attribution (MTA) and Marketing Mix Modeling (MMM) analyses. It broadens the understanding of customer behaviors and market trends, offering a more expansive view of marketing effectiveness across various channels and partner ecosystems.</p>
<p>When combined with a brand's first party data strategy, second-party data introduces supplemental perspectives that provide a more three-dimensional understanding of shared target audiences. These collaborative data alliances allow for larger brand positioning and strategy optimization based on expanded channel insights.</p>
<h3>4) Third Party Data: The Broad Perspective</h3>
<p>Third party data refers to information compiled and aggregated by external data brokers who source broad consumer intelligence across public government sources, credit agencies, published surveys, website behavioral tracking, location data providers, and more. As third party data providers have no direct relationship with the consumers or brands using this data, the insights tend to be more generalized despite the large scale.</p>
<p>Still, third party consumer data often powers market analysis, competitive intelligence, and audience discovery initiatives. It allows brands to spot emerging trends, size up untapped customer segments beyond their owned channels, or activate new geographies.</p>
<p>For example, franchise brands use third party insights to evaluate optimal expansion locations, while publishers lean on third party psychographic data to improve readership models and ad relevancy.</p>
<p>However, data privacy legislation like GDPR and CCPA has severely restricted access to more sensitive data fields around geographic, financial, health, political leanings, and other categories. Paired with the rising consumer wariness of uncontrolled data sharing, third party sets have declined in both breadth and specificity compared to just a few years ago. Their application requires careful compliance and ethical balancing today.</p>
<p>While third parties still support expanded perspectives when applied judicially, direct zero and first-party data now represent the foremost drivers of customer intelligence capability. Tomorrow's brands must champion transparent, value-based data relationships that respect consumers as equal partners if they hope to thrive through relevant messaging and experiences that build trust.</p>
<p>Mikko Piippo is a digital analytics and marketing expert who provides practical tips on leveraging data to drive growth. In a <a href="https://www.linkedin.com/posts/mikkopiippo_data-digitalanalytics-activity-7011960294289600512-piRk?utm_source=share&amp;utm_medium=member_desktop">recent post</a>, he summarizes the key data types: zero party data directly shared by consumers, first-party data collected from a company's customers, second-party data shared between partner companies, and third party data sourced externally. His advice is to focus on zero and first-party data to build trusted consumer relationships that power personalization.</p>

![linkedin-post-by-mikko-piipco-on-data-types](https://mighty-basketball-fd4aeed960.media.strapiapp.com/linkedin_post_by_mikko_piipco_on_data_types_00696bb476.webp)

<h2>Data Types in Action: Real-World Applications</h2><br/>
<h3>1) Enhancing Personalization with Zero and First Party Data</h3>
<p>Direct zero and first party data represent the most valuable asset for hyper-personalizing marketing campaigns and customer experiences. As privacy regulations evolve, brands must adapt their personalization strategies. Rather than relying solely on third party data, savvy marketers are focused on collecting zero and first-party data directly from customers. This transparently obtained data powers hyper-personalized experiences.</p>
<p>For example, online retailer Andie uses their Fit Quiz to gather zero party data like style and size preferences.</p>

![online-retailer-andie-uses-their-fit-quiz-to-gather-zero-party-data](https://mighty-basketball-fd4aeed960.media.strapiapp.com/online_retailer_andie_uses_their_fit_quiz_to_gather_zero_party_data_57205dbcd1.webp)

<p>They combine this with first-party data from their ecommerce site in a CDP. This powers automated email flows with tailored product recommendations. The results? A <a href="https://www.digioh.com/blog/zero-party-data-vs-first-party-data-personalization#:~:text=Zero%2Dparty%20data%20allows%20you,a%2020%25%20increase%20in%20sales">343% conversion rate boost</a> for Andie.</p>
<p>The key is consent - customers explicitly provide the data and understand how it will be used. This fosters trust while enabling true one-to-one personalization. Reduce generic messaging and drive higher engagement and conversions with zero and first-party data.</p>
<h3>2) Leveraging Second Party Data for Niche Targeting</h3>
<p>Second-party data helps brands expand their reach within niche target groups outside their owned channels. Take California wine producer Winc, which traded email lists with upscale hotel chain Four Seasons based on hospitality guest spending analytics. This exclusive wine club member access supplemented Winc's existing base to penetrate a highly relevant yet difficult-to-access customer segment with specialized vineyard partnerships.</p>
<p>Meanwhile, Verizon shared cellular plan subscriber segments with Walt Disney Parks to better target families for seasonal deals leveraging one brand's strength with parents and the other's appeal among kids and teenagers. Such exclusive second-party data swaps can unlock valuable new micro-segments.</p>
<h3>3) Utilizing Third Party Data for Market Expansion</h3>
<p>While less targeted than directly sourced data, third party sets still deliver broader market perspectives that support expansion initiatives. Consider fast-casual restaurant chain Chipotle, which turns towards third party psychographic and behavioral data to help enter the European market. By profiling key consumer habits and food preferences based on aggregated panel and survey data, they tailored new store designs, ingredient sourcing, and their marketing campaigns to align with each regional market's expectations.</p>
<p>That said, heavy reliance on broad third party intelligence over time poses risks. This highlights why directly collected zero and first-party data must anchor personalization efforts, with only supplemental support from third party sources to segment their customer needs and preferences. Compared to untargeted emails, segmentation helps marketers send more relevant emails, which increases <a href="https://www.constantcontact.com/blog/email-marketing-research-and-statistics/">open rates by 30%</a> and click-through rates by 50%. An estimated <a href="https://www.litmus.com/wp-content/uploads/pdf/2021-state-of-email-report.pdf?utm_campaign=wc-2021-10-state_of_email-get_more_followup&amp;utm_source=email&amp;mkt_tok=ODY0LVZQWi0xMzEAAAGJ1Hl-ksK6RPt0strs8XAmztD-RJB1s30-HCs5IrJWzUr1WRH1dr_5qnxkYJPA2cmjCpRzAIR7j3BaCoBH4GYgczspReMfCJ-Fw0jqFj3Srw">64% of firms</a> employ segmentation in their email marketing. The most successful brands focus less budget on massive but imprecise data acquisition and segmentation, and more on building genuine customer relationships through owned channels.</p>
<h2>Integrating Data for Comprehensive Marketing Strategies</h2>
<p>Nowadays savvy marketers integrate zero, first, and second-party data assets to inform larger strategies rooted in rich customer intelligence. This comprehensive approach delivers more relevant experiences and communications compared to any single data type alone.</p>
<p>Strategic integration enables both a wide lens and microscope view from customer journey analytics revealing cross-channel behavioral patterns to campaign personalization optimized for micro-segment tastes. Brands must ingrain responsible data usage across teams through governance frameworks that embed privacy and compliance by design. Marketers should champion value-based data relationships where consenting customers get rewarded for transparency with more personalized value in return.</p>
<p>With strong first party data pillars and zero party co-created insights in place, secondary sources can layer on supplemental perspectives. However, third party data reliance should be minimized due to imprecise targeting and rising regulations. Approaching data strategies as an ongoing journey instead of a one-time project allows brands to responsively adapt to consumer feedback and legislative environments.</p>
<p>Brands that will thrive are those investing in customer intelligence platforms that ethically unify data sources into powerful assets. They also cultivate organizational data literacy through training to drive informed decision-making. With a comprehensive view of the customer and an ingrained culture of data responsibility, brands can build genuine loyalty through relevant messaging and experiences that respect consumer needs.</p>
<h2>Epilogue</h2>
<p>In closing, first-party intelligence remains the foremost asset for personalization while zero party insights directly anchor experiences to consumer needs. Combined they fuel relevant messaging and lifetime engagement. Yet second-party partnerships can fill gaps unlocking valuable niche audiences. More broadly, judiciously applied third party data identifies growth opportunities to quantify market potential.</p>
<p>Still, brands must champion responsible data practices respecting consumer privacy and evolving regulations. As new data applications arise, transparency through updated privacy notices and consent processes maintains trust.</p>
<p>Future-focused marketing stacks like <a href="https://www.lifesight.io/connect">Lifesight</a> allow seamless, ethical data orchestration - unifying zero, first, and second-party assets while minimizing third party dependencies. This comprehensive approach makes every touchpoint count through campaign relevance rooted in customer intelligence. <a href="https://www.lifesight.io/demo">Request a demo today</a> to transform data into better consumer experiences driving measurable impact.</p>

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