--- title: "Marketing Mix Modeling (MMM) Agency & Google Meridian" description: "Measure the real impact of each media channel with Marketing Mix Modeling. Google Meridian experts, data pipelines and actionable strategic recommendations." url: https://edgeangel.co/en/marketing-activation/agence-mmm-google-meridian lang: en --- > Site index for agents, read it before exploring further : https://edgeangel.co/llms.txt > Canonical HTML page, which also carries the diagrams and visuals : https://edgeangel.co/en/marketing-activation/agence-mmm-google-meridian # Marketing Mix Modeling Measure the real impact of every euro invested in media Analytics attribution and ad platform reporting tell different stories, and neither is complete. Marketing Mix Modeling is the only approach that measures the causal impact of each media channel, including those that tracking cannot capture. EdgeAngel deploys Google Meridian, Google's open source framework, to give you a reliable view of what truly drives sales and optimize your budget allocation. [Let's talk about your media mix](https://edgeangel.co/en/marketing-activation/agence-mmm-google-meridian#contact) [Discover our services](https://edgeangel.co/en/marketing-activation/agence-mmm-google-meridian#services-nav) Google Partner Open Source Privacy-Safe The Performance Iceberg Iceberg What attribution sees Tracked clicks ~30% of reality What MMM reveals Delayed effect Media Adstock View-through Impressions ROAS Saturation Perf ceilings Synergies Combined effects Brand Lift SEO Volume Impact Baseline Organic demand Real impact revealed ~70% increment ## Why Marketing Mix Modeling has become essential The Performance Iceberg Iceberg What attribution sees Tracked clicks ~30% of reality What MMM reveals Delayed effect Media Adstock View-through Impressions ROAS Saturation Perf ceilings Synergies Combined effects Brand Lift SEO Volume Impact Baseline Organic demand Real impact revealed ~70% increment MMM answers a question that digital attribution can no longer solve alone: **When attribution is no longer enough** consent constraints (GDPR, ePrivacy), the end of third-party cookies and browser restrictions (ITP, ETP) make attribution increasingly incomplete. MMM works on aggregated data. No cookies, no tags, no consent dependency. Privacy-safe by design. **Measuring what clicks can't see** your Meta, YouTube, Demand Gen or TV campaigns generate demand that converts elsewhere, later. Last-click attribution systematically underestimates them. MMM models delayed effects (adstock) and diminishing returns (saturation) to reveal the true contribution of each channel. **Deciding on facts, not platform reports** should you invest more in Meta? Is buying brand keywords in SEA actually profitable? MMM answers with response curves per channel and confidence intervals. You make decisions based on causality, not self-reporting. **Accessible thanks to Google Meridian and AI** what used to require data scientist teams and six-figure budgets is now accessible thanks to open source frameworks and AI. We make MMM operational for companies investing from a few hundred thousand euros per year in media. [Explorer nos services](https://edgeangel.co/en/marketing-activation/agence-mmm-google-meridian#services-nav) The Performance Iceberg Iceberg What attribution sees Tracked clicks ~30% of reality What MMM reveals Delayed effect Media Adstock View-through Impressions ROAS Saturation Perf ceilings Synergies Combined effects Brand Lift SEO Volume Impact Baseline Organic demand Real impact revealed ~70% increment ### Nos Certifications ## They trust us ## Our Marketing Mix Modeling Services From raw data to strategic decisions: end-to-end support across the entire MMM value chain. 01 / 03 - 01 ### Data engineering: the foundation of a reliable MMM A model is never better than the data feeding it. Our Modern Data Stack expertise ensures clean, structured and comprehensive data. - Automated pipelines: we aggregate all your sources (ad platforms, CRM, analytics, back-office) into a centralized Data Warehouse (BigQuery). Every pipeline is orchestrated, monitored and documented. - Cleaning and structuring: raw data from ad platforms is never directly usable. We harmonize it to create a dataset that meets Google Meridian requirements: weekly granularity, spend, impressions, business KPIs, geographic breakdown. - Relevant control variables: Google Query Volume (industry search trends), seasonality, promotions, competitive events. Each variable is selected for its causal role in the model, not just to improve R², but to avoid confusing a natural demand spike with a campaign effect. - Our competitive advantage: we don't build a data infrastructure from scratch for MMM. We leverage the stack we already deploy for our clients (BigQuery, Airbyte, Dataform). MMM-ready data is a natural byproduct of a solid data architecture. - 02 ### Modeling with Google Meridian We use Google Meridian, Google's open source framework based on Bayesian causal inference, to build a model calibrated to your business reality. - A model, not a black box: Meridian is a Bayesian hierarchical model developed by Google Research. It models delayed effects (adstock), saturation (Hill curves), geographic variations and seasonality. Each channel has its own dynamics. And everything is transparent, auditable, open source. - Calibration through business knowledge: we inject your reality into the model: expected ROI per channel, business seasonality, pricing strategy, promo effects. An MMM without business calibration is a theoretical exercise. We ask the right questions because we know the field. - Incremental tests to refine the MMM: we design geo-experiments: cutting a channel in a geographic area for a few weeks to observe a measurable shock. These experiments directly calibrate Meridian's Bayesian priors and significantly improve result accuracy. - Validation rigor: MCMC convergence (R-hat), prior/posterior analysis, baseline testing, goodness of fit. We don't deliver a model without validating it. You receive a report with confidence intervals and an honest explanation of what the model can, and cannot, tell you. - 03 ### From analysis to action: strategic recommendations The model is not the end. It's the beginning of real strategic thinking. We translate results into concrete decisions. - ROI per channel, with its degree of certainty: for each channel, the model produces an incremental ROI with its Bayesian distribution. Not a single number, but an honest range. You know what each euro really generates and with what reliability. - Budget optimization (+20 to 50% efficiency): at constant or flexible budget, the model identifies saturation zones and under-invested channels. Our clients see a 20 to 50% improvement in their media mix efficiency after reallocation. - Scaling top-of-funnel with confidence: Meta Ads, Demand Gen, YouTube, awareness... these channels often seem unprofitable in a last-click attribution reading. MMM reveals their real contribution. You know what to accelerate, without flying blind. - Answering tough questions: should you buy brand keywords in SEA? Do Advantage+ campaigns really generate new sales? Does Demand Gen have a real impact? MMM provides factual answers where attribution fails. - Iterative roadmap: an MMM is not a one-shot. We define a plan of incremental tests and model updates to improve accuracy over time. The goal: a living strategic steering tool. 01 ### Data engineering: the foundation of a reliable MMM A model is never better than the data feeding it. Our Modern Data Stack expertise ensures clean, structured and comprehensive data. - Automated pipelines: we aggregate all your sources (ad platforms, CRM, analytics, back-office) into a centralized Data Warehouse (BigQuery). Every pipeline is orchestrated, monitored and documented. - Cleaning and structuring: raw data from ad platforms is never directly usable. We harmonize it to create a dataset that meets Google Meridian requirements: weekly granularity, spend, impressions, business KPIs, geographic breakdown. - Relevant control variables: Google Query Volume (industry search trends), seasonality, promotions, competitive events. Each variable is selected for its causal role in the model, not just to improve R², but to avoid confusing a natural demand spike with a campaign effect. - Our competitive advantage: we don't build a data infrastructure from scratch for MMM. We leverage the stack we already deploy for our clients (BigQuery, Airbyte, Dataform). MMM-ready data is a natural byproduct of a solid data architecture. ## Our conviction: attribution steers daily operations, MMM directs the strategy For a long time, web analytics dictated budgets. Today, analyzing the customer journey solely through cookies systematically undervalues the impact of top-of-funnel (Meta, Demand Gen, YouTube) and pushes to over-invest in retargeting at the expense of pure acquisition. This is not a GA4 configuration problem. It's a paradigm problem: attribution was designed for a world where every conversion left a complete digital trace. That world no longer exists. We believe modern marketing measurement relies on a complementary triptych: **attribution** for daily steering and smart bidding, **incrementality tests** for causal proof in the field, and **MMM** for strategic allocation. Thanks to the open source Google Meridian framework and our full mastery of the data chain, we make MMM accessible and operational. Not in six months, not only for multinationals, but in a few weeks, for companies that have real questions about their mix. Our difference: we're not a pure-play data science shop disconnected from operational reality. We build the pipelines, we know the ad platforms, we understand Smart Bidding, and we know what a CAC is. The MMM we deliver integrates into your daily marketing management. We practice what we preach. ## The team deploying your MMM - ### Olivier Chubilleau CEO & Data Engineering Expert Olivier defines the strategic vision for MMM projects at EdgeAngel. With 10 years of experience at the intersection of Data and Marketing, he brings the business knowledge essential to model calibration and leads the most complex projects. - ### Pauline Guibert Analytics Engineering Expert Pauline bridges analytics and MMM. Her expertise in data modeling and data marketing ensures that pipelines feed the Meridian model with reliable, structured data perfectly mapped to business challenges. - ### Mathieu Lima Lead Data Engineer Mathieu ensures data quality at the model's input. His mastery of data collection (GTM, sGTM, Firebase) and data infrastructure (BigQuery, Airbyte) guarantees robust pipelines that feed Meridian with clean and comprehensive data. ## Ready to accelerate your growth ? Let's discuss your data and marketing challenges. We'll respond within 24 hours. ### Paul Schmitt Consulting Director "Our goal is to make your data actionable to generate concrete value, quickly." [Or call us directly: +33 1 84 16 42 20](tel:+33184164220) ## Frequently Asked Questions about MMM Marketing Mix Modeling (MMM) is a statistical method that measures the causal impact of each marketing channel on your sales, without individual tracking or cookies. Specifically, from aggregated data (media spend, sales by week and geographic area, context variables), a Bayesian regression model (here Google Meridian) decomposes each variation of your KPIs between your media channels, seasonality, promotions and organic baseline demand. It models the delayed effect of campaigns (a TV ad doesn't convert on the same day) and diminishing returns (beyond a certain budget, marginal impact decreases). Result: ROI per channel, response curves, and a factual basis for optimizing your mix, with zero cookie dependency. Because digital attribution is reaching its structural limits, and open source frameworks like Google Meridian have made MMM accessible. MMM has existed since the 1960s, but was eclipsed for 20 years by click-based attribution. Three phenomena are putting it back at center stage: (1) the end of third-party cookies and strengthening of consent reduce attribution coverage, (2) top-of-funnel channels (Meta, YouTube, TV) generate effects that clicks don't capture, (3) open source frameworks (Google Meridian released in 2025, Meta Robyn) and cloud have divided implementation costs by 10. What was reserved for very large advertisers is now within reach for mid-market companies. These are three complementary tools operating at different time scales. Attribution (GA4, data-driven) steers daily operations. It feeds Smart Bidding and real-time performance tracking. Its limitation: it depends on tracking, doesn't see delayed effects, and misses channels without clicks. MMM takes a broader view. It measures the causal contribution of each channel over the medium-long term, including what attribution can't see. Incremental tests (geo-experiments) create a controlled shock: cutting a channel in a geographic area for a few weeks and observing the impact. It's the gold standard for causal validation. Results calibrate the MMM and improve its accuracy. Attribution for daily operations, MMM for strategy, incremental tests for proof. Google Meridian is the most advanced open source MMM framework on the market, developed by Google's research teams. We adopted it because it's transparent, auditable and aligns with our philosophy: no magic, just rigor. It natively integrates Reach & Frequency data (YouTube), geographic granularity (geo-level model), and Google Query Volumes as an industry demand signal. Its Bayesian approach allows injecting business knowledge via priors, which makes the difference between a theoretical model and a reliable decision tool. As a Google Partner, we master integration with the Google ecosystem (Ads, BigQuery, YouTube R&F). A first usable model is typically delivered in 6 to 10 weeks. The timeline breaks down as follows: 2-3 weeks for data collection, cleaning and structuring (a critical, often underestimated phase); 2-3 weeks for modeling, calibration and diagnostics; 1-2 weeks for result analysis and recommendations. The limiting factor is almost always data: availability of historical data (ideally 2 years minimum on a weekly basis), quality of ad platform exports, CRM access. Our pipeline expertise (BigQuery, Airbyte, Dataform) significantly accelerates this phase. MMM starts providing relevant answers when you invest more than 100K per year in media, spread across multiple channels. The determining criterion is not so much volume as mix complexity. As soon as you invest across multiple channels simultaneously, including top-of-funnel (Social, YouTube, Display), and have doubts about attribution, MMM delivers value. What also matters: channel diversity, available historical data (2 years weekly minimum), and ideally geographic granularity. Five years ago, the entry ticket was much higher. Open source frameworks and AI have changed the game. No. sGTM and MMM are complementary, they address different problems. sGTM improves collection quality: it partially bypasses blockers, extends cookie duration, feeds Meta CAPI and Google Enhanced Conversions. It's an excellent technical lever for improving attribution and smart bidding reliability. But sGTM doesn't change the fundamental limitations of attribution: it doesn't measure non-clicked campaigns, doesn't model delayed effects, and remains dependent on consent. MMM operates at a different level, on aggregated data, without tags or cookies. However, a well-configured sGTM produces cleaner attribution data, and this cleaner data can serve as more reliable priors for MMM. The two reinforce each other. Results are measured across three concrete axes. Decision clarity: you know which channel truly generates value, with confidence intervals. Not a single self-declared number from a platform, but an honest Bayesian distribution. Budget optimization: our clients see on average a 20 to 50% improvement in their media mix efficiency after reallocation. Concretely, more results for the same investment, or the same results with a reduced budget. Confidence to scale: channels like Meta Ads awareness, YouTube or Demand Gen, which seem unprofitable in last-click attribution, often reveal a much higher incremental ROI than classic dashboards suggest. MMM gives you the confidence needed to accelerate on these channels. [Contactez nos experts](https://edgeangel.co/en/marketing-activation/agence-mmm-google-meridian#contact) ## Discover our other expertise A complete suite of tools to master your data [ ### Google Ads Impression, spend and conversion data integrated into the MMM pipeline. Learn more ](https://edgeangel.co/en/performance-acquisition/google-ads)[ ### Google Analytics GA4 attribution steers daily operations, MMM directs the strategy. Learn more ](https://edgeangel.co/en/data-foundations/google-analytics)[ ### Data Studio Visualization of MMM results and steering dashboards. Learn more ](https://edgeangel.co/en/marketing-activation/data-studio)[ ### Meta Ads Reach, frequency and Social spend to model the top of funnel. Learn more ](https://edgeangel.co/en/performance-acquisition/meta-ads) ```json {"@context":"https://schema.org","@type":"Organization","name":"EdgeAngel","url":"https://edgeangel.co","logo":"https://edgeangel.co/assets/brand/edgeangel-logo.svg","sameAs":["https://www.linkedin.com/company/edgeangel"],"contactPoint":{"@type":"ContactPoint","email":"hello@edgeangel.co","telephone":"+33 1 84 16 42 20","contactType":"customer service"}} {"@context":"https://schema.org","@type":"Service","name":"Marketing Mix Modeling (MMM) Agency & Google Meridian","description":"Measure the real impact of each media channel with Marketing Mix Modeling. Google Meridian experts, data pipelines and actionable strategic recommendations.","provider":{"@type":"Organization","name":"EdgeAngel","url":"https://edgeangel.co"},"areaServed":"France","url":"https://edgeangel.co/en/marketing-activation/agence-mmm-google-meridian"} {"@context":"https://schema.org","@type":"FAQPage","mainEntity":[{"@type":"Question","name":"What is Marketing Mix Modeling?","acceptedAnswer":{"@type":"Answer","text":"Marketing Mix Modeling (MMM) is a statistical method that measures the causal impact of each marketing channel on your sales, without individual tracking or cookies. Specifically, from aggregated data (media spend, sales by week and geographic area, context variables), a Bayesian regression model (here Google Meridian) decomposes each variation of your KPIs between your media channels, seasonality, promotions and organic baseline demand. It models the delayed effect of campaigns (a TV ad doesn't convert on the same day) and diminishing returns (beyond a certain budget, marginal impact decreases). Result: ROI per channel, response curves, and a factual basis for optimizing your mix, with zero cookie dependency."}},{"@type":"Question","name":"Why is MMM making a strong comeback today?","acceptedAnswer":{"@type":"Answer","text":"Because digital attribution is reaching its structural limits, and open source frameworks like Google Meridian have made MMM accessible. MMM has existed since the 1960s, but was eclipsed for 20 years by click-based attribution. Three phenomena are putting it back at center stage: (1) the end of third-party cookies and strengthening of consent reduce attribution coverage, (2) top-of-funnel channels (Meta, YouTube, TV) generate effects that clicks don't capture, (3) open source frameworks (Google Meridian released in 2025, Meta Robyn) and cloud have divided implementation costs by 10. What was reserved for very large advertisers is now within reach for mid-market companies."}},{"@type":"Question","name":"What's the difference between attribution, MMM and incremental tests?","acceptedAnswer":{"@type":"Answer","text":"These are three complementary tools operating at different time scales. Attribution (GA4, data-driven) steers daily operations. It feeds Smart Bidding and real-time performance tracking. Its limitation: it depends on tracking, doesn't see delayed effects, and misses channels without clicks. MMM takes a broader view. It measures the causal contribution of each channel over the medium-long term, including what attribution can't see. Incremental tests (geo-experiments) create a controlled shock: cutting a channel in a geographic area for a few weeks and observing the impact. It's the gold standard for causal validation. Results calibrate the MMM and improve its accuracy. Attribution for daily operations, MMM for strategy, incremental tests for proof."}},{"@type":"Question","name":"Why does EdgeAngel use Google Meridian?","acceptedAnswer":{"@type":"Answer","text":"Google Meridian is the most advanced open source MMM framework on the market, developed by Google's research teams. We adopted it because it's transparent, auditable and aligns with our philosophy: no magic, just rigor. It natively integrates Reach & Frequency data (YouTube), geographic granularity (geo-level model), and Google Query Volumes as an industry demand signal. Its Bayesian approach allows injecting business knowledge via priors, which makes the difference between a theoretical model and a reliable decision tool. As a Google Partner, we master integration with the Google ecosystem (Ads, BigQuery, YouTube R&F)."}},{"@type":"Question","name":"How long does it take to set up an MMM?","acceptedAnswer":{"@type":"Answer","text":"A first usable model is typically delivered in 6 to 10 weeks. The timeline breaks down as follows: 2-3 weeks for data collection, cleaning and structuring (a critical, often underestimated phase); 2-3 weeks for modeling, calibration and diagnostics; 1-2 weeks for result analysis and recommendations. The limiting factor is almost always data: availability of historical data (ideally 2 years minimum on a weekly basis), quality of ad platform exports, CRM access. Our pipeline expertise (BigQuery, Airbyte, Dataform) significantly accelerates this phase."}},{"@type":"Question","name":"What minimum media budget for a relevant MMM?","acceptedAnswer":{"@type":"Answer","text":"MMM starts providing relevant answers when you invest more than 100K per year in media, spread across multiple channels. The determining criterion is not so much volume as mix complexity. As soon as you invest across multiple channels simultaneously, including top-of-funnel (Social, YouTube, Display), and have doubts about attribution, MMM delivers value. What also matters: channel diversity, available historical data (2 years weekly minimum), and ideally geographic granularity. Five years ago, the entry ticket was much higher. Open source frameworks and AI have changed the game."}},{"@type":"Question","name":"Does server-side tagging (sGTM) replace MMM?","acceptedAnswer":{"@type":"Answer","text":"No. sGTM and MMM are complementary, they address different problems. sGTM improves collection quality: it partially bypasses blockers, extends cookie duration, feeds Meta CAPI and Google Enhanced Conversions. It's an excellent technical lever for improving attribution and smart bidding reliability. But sGTM doesn't change the fundamental limitations of attribution: it doesn't measure non-clicked campaigns, doesn't model delayed effects, and remains dependent on consent. MMM operates at a different level, on aggregated data, without tags or cookies. However, a well-configured sGTM produces cleaner attribution data, and this cleaner data can serve as more reliable priors for MMM. The two reinforce each other."}},{"@type":"Question","name":"What concrete results can you expect from an MMM?","acceptedAnswer":{"@type":"Answer","text":"Results are measured across three concrete axes. Decision clarity: you know which channel truly generates value, with confidence intervals. Not a single self-declared number from a platform, but an honest Bayesian distribution. Budget optimization: our clients see on average a 20 to 50% improvement in their media mix efficiency after reallocation. Concretely, more results for the same investment, or the same results with a reduced budget. Confidence to scale: channels like Meta Ads awareness, YouTube or Demand Gen, which seem unprofitable in last-click attribution, often reveal a much higher incremental ROI than classic dashboards suggest. MMM gives you the confidence needed to accelerate on these channels."}}]} ```