{"id":11639,"date":"2026-03-09T14:18:33","date_gmt":"2026-03-09T08:48:33","guid":{"rendered":"https:\/\/easyinsights.ai\/blog\/?p=11639"},"modified":"2026-03-10T10:53:19","modified_gmt":"2026-03-10T05:23:19","slug":"mmm-incrementality-models-limitations-reliable-performance-insights","status":"publish","type":"post","link":"https:\/\/easyinsights.ai\/blog\/mmm-incrementality-models-limitations-reliable-performance-insights\/","title":{"rendered":"Why MMM and Incrementality Models Keep Lying &#8211; and How to Get Reliable Performance Insights"},"content":{"rendered":"<span class=\"span-reading-time rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\"> 6<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span>\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"536\" src=\"https:\/\/easyinsights.ai\/easyinsights_wordpress\/wp-content\/uploads\/2026\/03\/1200x628-15-1024x536.jpg\" alt=\"MMM and Incrementality Models Keep Lying\" class=\"wp-image-11649\" srcset=\"https:\/\/easyinsights.ai\/easyinsights_wordpress\/wp-content\/uploads\/2026\/03\/1200x628-15-1024x536.jpg 1024w, https:\/\/easyinsights.ai\/easyinsights_wordpress\/wp-content\/uploads\/2026\/03\/1200x628-15-300x157.jpg 300w, https:\/\/easyinsights.ai\/easyinsights_wordpress\/wp-content\/uploads\/2026\/03\/1200x628-15-768x402.jpg 768w, https:\/\/easyinsights.ai\/easyinsights_wordpress\/wp-content\/uploads\/2026\/03\/1200x628-15.jpg 1200w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>If your MMM model says Meta drives 10% of revenue, while your incrementality test claims 40%, one of them is lying &#8211; possibly both.<\/p>\n\n\n\n<p>Today, CMOs and performance leaders are making million-dollar media budget decisions using marketing measurement models that rarely agree. MMM, incrementality testing, platform attribution, and GA4 all tell different stories &#8211; leaving teams confused, not confident.<\/p>\n\n\n\n<p>In this blog, we\u2019ll discuss why MMM and incrementality models often mislead, where their assumptions break down in a privacy-first, cookieless world, and what actually works to get reliable performance marketing insights based on clean, real conversion data &#8211; not modelled guesswork.<\/p>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_81 ez-toc-wrap-center counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Jump ahead to:<\/p>\n<span class=\"ez-toc-title-toggle\"><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"#\" data-href=\"https:\/\/easyinsights.ai\/blog\/mmm-incrementality-models-limitations-reliable-performance-insights\/#Why_Measurement_Became_So_Broken\" >Why Measurement Became So Broken<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"#\" data-href=\"https:\/\/easyinsights.ai\/blog\/mmm-incrementality-models-limitations-reliable-performance-insights\/#Where_MMM_Breaks_Down\" >Where MMM Breaks Down<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"#\" data-href=\"https:\/\/easyinsights.ai\/blog\/mmm-incrementality-models-limitations-reliable-performance-insights\/#What_Reliable_Performance_Measurement_Actually_Looks_Like\" >What Reliable Performance Measurement Actually Looks Like<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"#\" data-href=\"https:\/\/easyinsights.ai\/blog\/mmm-incrementality-models-limitations-reliable-performance-insights\/#How_to_Use_MMM_and_Incrementality_Correctly\" >How to Use MMM and Incrementality Correctly<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"#\" data-href=\"https:\/\/easyinsights.ai\/blog\/mmm-incrementality-models-limitations-reliable-performance-insights\/#Where_EasyInsights_Fit_In\" >Where EasyInsights Fit In&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"#\" data-href=\"https:\/\/easyinsights.ai\/blog\/mmm-incrementality-models-limitations-reliable-performance-insights\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Why_Measurement_Became_So_Broken\"><\/span>Why Measurement Became So Broken<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Marketing measurement was already imperfect &#8211; but recent changes in how data is collected and shared have made it significantly worse. At its core, today\u2019s measurement challenges stem from three major forces: the loss of cookies, black-box platforms, and fragmented data across systems.<\/p>\n\n\n\n<p><strong>1. Cookies Are Disappearing &#8211; and With Them, Cross-Site Tracking<\/strong><\/p>\n\n\n\n<p>For decades, marketers relied on third-party cookies to track users across websites, tie touchpoints together, and understand journeys from awareness to conversion. But third-party cookies are being blocked across major browsers &#8211; and even first-party identifiers are being restricted by privacy rules and platform changes. As a result:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Audience tracking and retargeting lose precision.<br><\/li>\n\n\n\n<li>Marketers can\u2019t consistently follow a user\u2019s journey from site to site.<br><\/li>\n\n\n\n<li>Traditional attribution models become fragmented or inaccurate.&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>Without these persistent identifiers, the foundational data that measurement models depend on is weakened &#8211; forcing many teams to rely on probabilistic or incomplete signals instead of clear user paths.<a href=\"https:\/\/www.mckinsey.com\/capabilities\/growth-marketing-and-sales\/our-insights\/as-the-cookie-crumbles-three-strategies-for-advertisers-to-thrive?utm_source=chatgpt.com\"> Source<\/a><\/p>\n\n\n\n<p><strong>2. The Platform Black Box Problem (Meta, Google &amp; Others)<\/strong><\/p>\n\n\n\n<p>Major ad platforms like Meta and Google control their own ecosystems &#8211; from how data is collected to how results are reported. These systems offer limited transparency into:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>How they define and count conversions<br><\/li>\n\n\n\n<li>How their algorithms optimize delivery<br><\/li>\n\n\n\n<li>How they attribute value across channels<\/li>\n<\/ul>\n\n\n\n<p>Because each platform uses its own logic and reporting standards, the same campaign can show very different performance depending on where you look. This creates <strong>multiple &#8220;truths&#8221;<\/strong> instead of one reliable source, making cross-channel comparisons inherently inconsistent.&nbsp;<\/p>\n\n\n\n<p><strong>3. Fragmented Data Across Platforms, Analytics Tools &amp; CRM<\/strong><\/p>\n\n\n\n<p>Today\u2019s marketing ecosystem spans dozens of tools:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Ad platforms (Meta, Google, etc.)<br><\/li>\n\n\n\n<li>Analytics systems (GA4)<br><\/li>\n\n\n\n<li>CRM and backend systems (HubSpot)<br><\/li>\n\n\n\n<li>Offline conversions (store visits, call centres, POS)<\/li>\n<\/ul>\n\n\n\n<p><strong>Also Read:<\/strong><a href=\"https:\/\/easyinsights.ai\/blog\/marketing-mix-modelling-a-comprehensive-guide\/\"><strong> Marketing Mix Modelling: A Comprehensive Guide<\/strong><\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Where_MMM_Breaks_Down\"><\/span><strong>Where MMM Breaks Down<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Despite those aspirations, the practical limitations of MMM are significant &#8211; especially in today\u2019s privacy-centric, fast-paced landscape.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"683\" height=\"1024\" src=\"https:\/\/easyinsights.ai\/easyinsights_wordpress\/wp-content\/uploads\/2026\/03\/ChatGPT-Image-Mar-9-2026-02_14_21-PM-683x1024.png\" alt=\"ChatGPT Image Mar , , PM\" class=\"wp-image-11641\" style=\"width:436px;height:auto\" srcset=\"https:\/\/easyinsights.ai\/easyinsights_wordpress\/wp-content\/uploads\/2026\/03\/ChatGPT-Image-Mar-9-2026-02_14_21-PM-683x1024.png 683w, https:\/\/easyinsights.ai\/easyinsights_wordpress\/wp-content\/uploads\/2026\/03\/ChatGPT-Image-Mar-9-2026-02_14_21-PM-200x300.png 200w, https:\/\/easyinsights.ai\/easyinsights_wordpress\/wp-content\/uploads\/2026\/03\/ChatGPT-Image-Mar-9-2026-02_14_21-PM-768x1152.png 768w, https:\/\/easyinsights.ai\/easyinsights_wordpress\/wp-content\/uploads\/2026\/03\/ChatGPT-Image-Mar-9-2026-02_14_21-PM.png 1024w\" sizes=\"(max-width: 683px) 100vw, 683px\" \/><\/figure><\/div>\n\n\n<p><strong>1. Heavy Reliance on Historical, Aggregated Data<br><\/strong>Traditional MMM can only work with past performance data &#8211; typically 18-36+ months &#8211; so it assumes historical channel effectiveness will repeat in the future. This is risky when consumer behavior, platforms, or market conditions change rapidly.\u00a0<\/p>\n\n\n\n<p><strong>2. Assumes Stable Relationships That No Longer Exist<\/strong><strong><br><\/strong>Because MMM models depend on statistical assumptions about how spending affects outcomes, shifts in channel mechanics (e.g., <a href=\"https:\/\/easyinsights.ai\/blog\/att-loss-of-idfa-and-skadnetwork-reshaped-digital-marketing\/\">Apple ATT<\/a>, evolving Google algorithms) can make those assumptions outdated &#8211; leading to misleading channel elasticities.<\/p>\n\n\n\n<p><strong>3. Sensitive to Time Windows &amp; Seasonality Assumptions<\/strong><strong><br><\/strong>MMM works on aggregated intervals (weekly\/monthly), and its seasonal adjustments are assumptions &#8211; not always accurate reflections of real customer patterns. This makes results highly dependent on how analysts choose and preprocess time and seasonality data.&nbsp;<\/p>\n\n\n\n<p><strong>4. Data Sparsity &amp; Quality Issues Distort Results<\/strong><strong><br><\/strong>When historical data is incomplete, inconsistent, or inconsistent across channels, MMM outputs can be unreliable. Without sufficient data variability, models struggle to separate noise from real effects.&nbsp;<\/p>\n\n\n\n<p><strong>5. Slow Feedback Loops &#8211; Not Built for Real-Time Optimization<\/strong><strong><br><\/strong>MMM is traditionally built quarterly or annually, meaning insights often arrive <em>after<\/em> decisions must be made &#8211; too slow for real-time optimization or tactical campaign adjustments. Unlike incrementality tests or real-time attribution, MMM <em>cannot<\/em> provide live feedback.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_Reliable_Performance_Measurement_Actually_Looks_Like\"><\/span>What Reliable Performance Measurement Actually Looks Like<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Modern performance measurement isn\u2019t about switching models every quarter. It\u2019s about fixing the foundation your entire growth stack relies on.<\/p>\n\n\n\n<p>When measurement is reliable, every team, marketing, analytics, and finance works off the same version of reality.<\/p>\n\n\n\n<p class=\"has-medium-font-size\"><strong>Server-Side, First-Party Data Collection<\/strong><\/p>\n\n\n\n<p><a href=\"https:\/\/easyinsights.ai\/blog\/server-side-vs-client-side-tracking\/\">Client-side tracking<\/a> is no longer dependable. Browsers block it. Users opt out. Platforms receive partial signals.<\/p>\n\n\n\n<p>A modern measurement setup moves data collection server-side, using first-party data you control. This ensures:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Higher data accuracy and durability<\/li>\n\n\n\n<li>Better match rates across ad platforms<\/li>\n\n\n\n<li>Resilience against <a href=\"https:\/\/easyinsights.ai\/blog\/first-party-data-the-answer-to-third-party-cookie-loss\/\">cookie loss<\/a> and browser restrictions<\/li>\n<\/ul>\n\n\n\n<p class=\"has-medium-font-size\"><strong>Deduplicated, Event-Level Conversions<\/strong><\/p>\n\n\n\n<p>If the same purchase is counted three times across tools, Reliable measurement means:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Each conversion has a single, unique event ID<\/li>\n\n\n\n<li>Web, server, and CRM events are deduplicated<\/li>\n\n\n\n<li>Platforms receive clean, non-inflated conversion signals<\/li>\n<\/ul>\n\n\n\n<p>This allows optimization to happen on real business outcomes, not duplicated noise.<\/p>\n\n\n\n<p class=\"has-medium-font-size\"><strong>Consistent Signals Sent to Ad Platforms<\/strong><\/p>\n\n\n\n<p>When Google, Meta, and analytics tools receive different versions of the same conversion, optimization breaks. A reliable setup ensures:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The same event definitions are sent everywhere<\/li>\n\n\n\n<li>Consistent timestamps, values, and identifiers<\/li>\n\n\n\n<li>Platforms learn from the same truth, not conflicting inputs<\/li>\n<\/ul>\n\n\n\n<p>This is how you stabilize learning phases, control CPA, and scale predictably.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"How_to_Use_MMM_and_Incrementality_Correctly\"><\/span>How to Use MMM and Incrementality Correctly<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>MMM and incrementality tests are powerful-but only when they\u2019re treated as supporting instruments, not as the final authority on performance. They guide direction. They do not run your campaigns.<\/p>\n\n\n\n<p class=\"has-medium-font-size\"><strong>1. MMM: For Strategic Direction, Not Daily Decisions<\/strong><\/p>\n\n\n\n<p>Marketing Mix Modeling works best at a macro level, where noise averages out, and long-term patterns emerge.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-text-color has-background has-link-color has-fixed-layout\" style=\"color:#2c01ca;background-color:#e2dff4\"><tbody><tr><td><strong>Use MMM for<\/strong><\/td><td><strong>Do not use MMM for<\/strong><\/td><\/tr><tr><td>Understanding long-term channel contribution<\/td><td>Creative-level decisions<\/td><\/tr><tr><td>Identifying diminishing returns at higher spend levels<\/td><td>Weekly bid or budget changes<\/td><\/tr><tr><td>Setting budget guardrails across channels and regions<\/td><td>Real-time optimization<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><strong>Relevant data sources:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Clean historical spend data (Meta, Google, offline channels)<\/li>\n\n\n\n<li>Revenue from a single source of truth (warehouse \/ CRM)<\/li>\n\n\n\n<li>Macro variables (holidays, demand shifts)<\/li>\n<\/ul>\n\n\n\n<p>MMM should inform where to invest, not how to optimize today.<\/p>\n\n\n\n<p class=\"has-medium-font-size\"><strong>2. Incrementality: For Hypothesis Validation<\/strong><\/p>\n\n\n\n<p>Incrementality testing answers a very specific question: <strong>\u201cWhat would have happened if we didn\u2019t do this?\u201d<\/strong><\/p>\n\n\n\n<p>It is most effective when used surgically, not continuously.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-text-color has-background has-link-color has-fixed-layout\" style=\"color:#2c01ca;background-color:#e2dff4\"><tbody><tr><td><strong>Use incrementality for<\/strong><\/td><td><strong>Do not use incrementality for<\/strong><\/td><\/tr><tr><td>Validating whether a channel is truly incremental<\/td><td>Always-on optimization<\/td><\/tr><tr><td>Testing assumptions (e.g. brand vs performance overlap)<\/td><td>Comparing creatives or audiences daily<\/td><\/tr><tr><td>Measuring lift for specific channels or tactics<\/td><td>Declaring a channel \u201cdead\u201d based on one test<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Incrementality tells you if something works, not how to scale it sustainably.<\/p>\n\n\n\n<p class=\"has-medium-font-size\"><strong>3. What Actually Drives Day-to-Day Optimization<\/strong><\/p>\n\n\n\n<p>Daily performance is not driven by models. It\u2019s driven by clean, trusted signals.<\/p>\n\n\n\n<p><strong>Day-to-day optimization should rely on:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Accurate, deduplicated conversion events<br><\/li>\n\n\n\n<li>Stable event definitions<br><\/li>\n\n\n\n<li>Consistent signals sent to all platforms<\/li>\n<\/ul>\n\n\n\n<p>When platforms receive clean data, their algorithms do the heavy lifting, bidding, targeting, pacing, and creative learning.<\/p>\n\n\n\n<p><strong>Relevant data sources:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/easyinsights.ai\/blog\/server-side-tracking-for-better-conversion-tracking\/\">Server-side first-party<\/a> events<br><\/li>\n\n\n\n<li>Deduplicated conversion pipelines<br><\/li>\n\n\n\n<li>Ad platform APIs (Meta CAPI, Google Ads conversions)<br><\/li>\n\n\n\n<li>Analytics layer for monitoring, not re-attribution<\/li>\n<\/ul>\n\n\n\n<p><strong>The Right Hierarchy<\/strong><\/p>\n\n\n\n<p>Think of measurement like this:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Data foundation<\/strong> \u2192 Runs optimization<\/li>\n\n\n\n<li><strong>Incrementality<\/strong> \u2192 Validates assumptions<\/li>\n\n\n\n<li><strong>MMM<\/strong> \u2192 Sets strategic boundaries<\/li>\n<\/ul>\n\n\n\n<p>When you reverse this order, growth slows, and teams start debating numbers instead of scaling performance.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Where_EasyInsights_Fit_In\"><\/span>Where EasyInsights Fit In&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p><a href=\"https:\/\/easyinsights.ai\/\">EasyInsights<\/a> performs as the measurement layer between your ads and your brand&#8217;s results. They don\u2019t change your marketing strategy &#8211; they make sure the data feeding your models is actually correct.<\/p>\n\n\n\n<p><strong>What this ensures:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>You track real-user conversions, not inflated numbers<\/li>\n\n\n\n<li>The same conversion isn\u2019t counted twice across platforms<\/li>\n\n\n\n<li>Important actions aren\u2019t missed due to tracking gaps or<a href=\"https:\/\/easyinsights.ai\/blog\/losing-signals-and-how-to-recover-with-first-party-data\/\"> signal loss<\/a><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>When MMM and incrementality models fail, the problem is rarely the model\u2014it\u2019s the data. Poor signals, missing events, and duplicate tracking often lead to misleading insights.<\/p>\n\n\n\n<p>EasyInsights performs as the measurement layer that ensures your analytics stack starts with trustworthy data by:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Capturing accurate first-party events across browsers and servers, reducing signal loss.<\/li>\n\n\n\n<li>Sending clean, deduplicated events to ad platforms and analytics tools.<\/li>\n\n\n\n<li>Unifying data from CRM, websites, and ad platforms to give every conversion a clear source and value.<\/li>\n<\/ul>\n\n\n\n<p><strong><a href=\"https:\/\/easyinsights.ai\/book-demo\">Book a demo<\/a> now to understand how these models keep performing!<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>If your MMM model says Meta drives 10% of revenue, while your incrementality test claims 40%, one of&hellip;<\/p>\n","protected":false},"author":15,"featured_media":11649,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[59],"tags":[145,409,36,408,346],"class_list":["post-11639","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tracking","tag-marketing","tag-marketing-mix-modelling","tag-marketing-optimization","tag-mmm","tag-tracking"],"aioseo_notices":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.3.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Why MMM and Incrementality Models Keep Lying - and How to Get Reliable Performance Insights<\/title>\n<meta name=\"description\" content=\"Why MMM and Incrementality Models fail, and how better data collection and attribution can help marketers get reliable performance insights.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/easyinsights.ai\/blog\/mmm-incrementality-models-limitations-reliable-performance-insights\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Why MMM and Incrementality Models Keep Lying - and How to Get Reliable Performance Insights\" \/>\n<meta property=\"og:description\" content=\"Why MMM and Incrementality Models fail, and how better data collection and attribution can help marketers get reliable performance insights.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/easyinsights.ai\/blog\/mmm-incrementality-models-limitations-reliable-performance-insights\/\" \/>\n<meta property=\"og:site_name\" content=\"EasyInsights\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/EasyInsightsai-522100504893809\" \/>\n<meta property=\"article:published_time\" content=\"2026-03-09T08:48:33+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-10T05:23:19+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/easyinsights.ai\/easyinsights_wordpress\/wp-content\/uploads\/2026\/03\/1200x628-15.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Sonali Arya\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@easy_insights\" \/>\n<meta name=\"twitter:site\" content=\"@easy_insights\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Sonali Arya\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/easyinsights.ai\/blog\/mmm-incrementality-models-limitations-reliable-performance-insights\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/easyinsights.ai\/blog\/mmm-incrementality-models-limitations-reliable-performance-insights\/\"},\"author\":{\"name\":\"Sonali Arya\",\"@id\":\"https:\/\/easyinsights.ai\/blog\/#\/schema\/person\/bdf9cbd774a6ca2fea1b575b60d2793e\"},\"headline\":\"Why MMM and Incrementality Models Keep Lying &#8211; 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