{"id":15905,"date":"2023-02-06T22:18:22","date_gmt":"2023-02-06T21:18:22","guid":{"rendered":"https:\/\/coreanalytics.stageweb.builders\/2023\/02\/06\/calibrating-a-marketing-mix-model\/"},"modified":"2026-10-07T11:54:03","modified_gmt":"2026-10-07T10:54:03","slug":"calibrating-a-marketing-mix-model","status":"publish","type":"post","link":"https:\/\/coreanalytics.it\/en\/2023\/02\/06\/calibrating-a-marketing-mix-model\/","title":{"rendered":"Calibrating a Marketing Mix Model"},"content":{"rendered":"\t\t
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\n\t\t\t\t\tHow to Calibrate MMM with Incrementality Tests\t\t\t<\/h2>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\tThe Decision Crossroads: 1.4x or 2.5x ROI? Which Number Do You Present to the Board? \t\t\t<\/h6>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Imagine this scenario: six months ago, you invested in a geo-lift test on TV that measured an incremental ROI of 2.5x. Today, you receive the first refresh of your annual Marketing Mix Modeling (MMM), and the same channel shows an ROI of 1.4x. Of course, the creative has changed, and the media mix is different, but such a profound divergence cannot simply be ignored when you need to justify investments to the Board. <\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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What is the correct number to present? The strategic answer is: <\/span>neither alone; you should have integrated them<\/b>.<\/span><\/p><\/blockquote>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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This is precisely the purpose of <\/span>advanced MMM calibration with incrementality data<\/b>: an approach that combines the holistic view of Marketing Mix Modeling with the causal robustness of controlled experiments, becoming the state-of-the-art in measurement for 2026.<\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\tThe Invisible Problem: Why Traditional Historical Data Is No Longer Enough\t\t\t<\/h2>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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To guide a brand toward sustainable growth, you cannot rely solely on simple correlations:<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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A classic MMM estimates the effect of each channel on sales using the historical covariance between investments and results. It’s a powerful tool, but it has the inherent limitation of not being able to absolutely isolate the true cause. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\n\t\t\t\t\t\t\t<\/span>\t<\/span>\t\t\t\tThe Labyrinth of Overlapping Channels<\/span><\/span>\n\t\t\t<\/span>\n\t\t\t<\/h5>\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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If every time a TV campaign launches, the paid social team increases spending to amplify awareness, the traditional model struggles to separate the true contribution of the two channels. This is the classic problem of multicollinearity, which results in unstable estimates where small variations in input data produce macro-variations in the estimated coefficients. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Incrementality tests (like geo-lifts) are designed to break this uncertainty. By scientifically isolating a group of geographic areas where TV is activated and a control group where it is not, they measure the difference in sales causally attributable to the channel. This is not a statistical estimate based on observational data, but a true experimental measurement. <\/p>

However, tests alone have an obvious commercial limitation: they are costly, require weeks of setup, and cannot cover the entire media plan simultaneously. A brand cannot afford continuous geo-lifts for every single lever every year.
However, tests alone have an obvious commercial limitation: they are costly, require weeks of setup, and cannot cover the entire media plan simultaneously. A brand cannot afford continuous geo-lifts for every single lever every year. <\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\tThe Core Analytics Formula: Combining Certainty with a Holistic View\t\t\t<\/h2>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The idea behind our calibration is elegant: use the scientific results of incrementality tests as “informed starting points” (<\/span>priors<\/span><\/i>) within the MMM’s statistical model.<\/span><\/span><\/p>

By incorporating the test results directly into the model’s estimation, the system will combine experimental data with evidence from historical data. The result? The <\/span>MMM inherits the causal certainty<\/b> of the test (<\/span>causal grounding<\/span><\/i>), while the <\/span>test gains cross-channel consistency<\/b> and the holistic view of the entire marketing model.<\/span><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\tThe Core Analytics Process: 4 Phases at a Glance\t\t\t<\/h2>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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We have standardized the operational flow to guarantee maximum methodological accuracy without wasting your team’s time:<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\n\tThe Baseline Model (Historical Data)<\/span>\n\t\n \n <\/g><\/svg> <\/span>\n \t\t<\/span>\n\t<\/span>\n<\/h4>\n
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We estimate a first MMM based exclusively on past data history, obtaining an unbiased initial benchmark.<\/p>\t<\/div>\n<\/div>\n

\n\tTargeted Incrementality Tests<\/span>\n\t\n \n <\/g><\/svg> <\/span>\n \t\t<\/span>\n\t<\/span>\n<\/h4>\n
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We conduct geo-lift tests focused exclusively on channels with the highest budgets or where the baseline model shows greater statistical uncertainty, optimizing resources where they are truly needed.<\/p>\t<\/div>\n<\/div>\n

\n\tTranslation into Statistical Parameters<\/span>\n\t\n \n <\/g><\/svg> <\/span>\n \t\t<\/span>\n\t<\/span>\n<\/h4>\n
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We convert the ROI evidence from field tests into precise mathematical probability distributions (statistical priors) perfectly digestible by the model's architecture.<\/p>\t<\/div>\n<\/div>\n

\n\tValidation and Compromise Rate<\/span>\n\t\n \n <\/g><\/svg> <\/span>\n \t\t<\/span>\n\t<\/span>\n<\/h4>\n
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We re-estimate the definitive integrated model. If historical data and tests converge, the estimate is 100% solid. If they diverge, the system activates a preventive check on data quality and anomalies before delivery. Every number is backed by independent cross-validation. <\/p>\t<\/div>\n<\/div>\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\tThe 3 Errors in Tests that Core Analytics Neutralizes for You\t\t\t<\/h2>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Calibrating the Marketing Mix is not automatic: a poorly designed experiment risks polluting the entire model. Here are the three real risks we eliminate at the root to protect your budget decisions: <\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\n\tThe Test that Erases History (Unbalanced Model)<\/span>\n\t\n \n <\/g><\/svg> <\/span>\n \t\t<\/span>\n\t<\/span>\n<\/h4>\n
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If an experiment carries too much weight, it crushes the brand's historical data, forcing the model to ignore the past and only replicate the latest result.<\/p>

The Core Analytics Solution:<\/strong> We scientifically weigh the importance of history and experiment based on the actual quality and reliability of the two signals.<\/p>\t<\/div>\n<\/div>\n

\n\tFalse Seasonality (Temporal Copy-Paste)<\/span>\n\t\n \n <\/g><\/svg> <\/span>\n \t\t<\/span>\n\t<\/span>\n<\/h4>\n
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The results of a geo-lift depend on the specific context in which it was conducted (period, creative, competition). An excellent ROI measured in summer cannot be blindly applied to winter. <\/p>

The Core Analytics Solution:<\/strong> We isolate effects within the model through precise temporal sub-periods or hierarchical priors to respect real business cycles.<\/p>\t<\/div>\n<\/div>\n

\n\tThe Flawed Experiment (Contaminated Data)<\/span>\n\t\n \n <\/g><\/svg> <\/span>\n \t\t<\/span>\n\t<\/span>\n<\/h4>\n
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Tests with few control areas or altered by external factors (such as spill-over contamination or parallel digital campaigns) generate distorted numbers. Calibrating the Marketing Mix with a flawed test is worse than not doing it at all. <\/p>

The Core Analytics Solution:<\/strong> We apply a rigorous audit of the test's statistical design before incorporating it into the system, discarding weak or compromised signals.<\/p>\t<\/div>\n<\/div>\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\tHow to Get Started: 3 Tailored Solutions for Your Goals\t\t\t<\/h2>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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This level of precision requires the ability to invest in regular tests, a team ready to manage advanced data flows, and a planning horizon of at least 12-18 months to maximize return on investment. Core Analytics has developed three flexible access options: <\/span><\/b><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\n\t\t\t\t\t\t\t<\/span>\t<\/span>\t\t\t\tGeo-lift Entry-Level<\/span><\/span>\n\t\t\t<\/span>\n\t\t\t<\/h5>\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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A product dedicated to mid-size brands that want to measure real incrementality on a priority media channel before moving to a full MMM.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\n\t\t\t\t\t\t\t<\/span>\t<\/span>\t\t\t\tCore Coffee<\/span><\/span>\n\t\t\t<\/span>\n\t\t\t<\/h5>\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Two hours of on-demand consulting dedicated to auditing your current MMM (even if developed with another partner) to evaluate the opportunity and feasibility of calibration.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\n\t\t\t\t\t\t\t<\/span>\t<\/span>\t\t\t\tFull-Stack Learning Infrastructure<\/span><\/span>\n\t\t\t<\/span>\n\t\t\t<\/h5>\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Transforms static reports into a dynamic integrated system where each new test becomes the starting point for the next model, reducing uncertainty and optimizing budgets in real-time.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Stop buying simple, passive annual reports. Start investing in a true corporate continuous learning infrastructure. <\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\tWant to adopt a solid measurement framework?\t\t\t<\/h3>\n\t\t\t

Book a slot and discuss it with an expert from our team.<\/span><\/p><\/div>\n\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\nBook Your Slot<\/span>\n<\/path><\/path><\/path><\/g><\/path><\/path><\/path><\/g><\/svg>\n<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

How to Calibrate MMM with Incrementality Tests The Decision Crossroads: 1.4x or 2.5x ROI? Which Number Do You Present to the Board? Imagine this scenario: six months ago, you invested in a geo-lift test on TV that measured an incremental ROI of 2.5x. Today, you receive the first refresh of your annual Marketing Mix Modeling […]<\/p>\n","protected":false},"author":1,"featured_media":15906,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[17,19],"tags":[86,89,92],"class_list":["post-15905","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-economy","category-research","tag-analysis-2","tag-corporate-2","tag-statistics-2"],"yoast_head":"\nCalibrating a Marketing Mix Model - Core Analytics<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/coreanalytics.it\/en\/2023\/02\/06\/calibrating-a-marketing-mix-model\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Calibrating a Marketing Mix Model - Core Analytics\" \/>\n<meta property=\"og:description\" content=\"How to Calibrate MMM with Incrementality Tests The Decision Crossroads: 1.4x or 2.5x ROI? Which Number Do You Present to the Board? Imagine this scenario: six months ago, you invested in a geo-lift test on TV that measured an incremental ROI of 2.5x. 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