Google Meridian MMM: The 2025 Guide For Marketers

Introduction

Google Meridian MMM is an open-source marketing mix modelling (MMM) framework developed by Google to help marketers measure and optimise media impact. It uses Bayesian inference and geo-level data to forecast ROI, calibrate campaigns with experiments, and analyse reach and frequency.

Meridian operates on aggregated, privacy-safe data and supports customisable modelling, making it ideal for budget optimisation across channels. The tool integrates with existing marketing platforms and offers detailed documentation via GitHub. Meridian enables marketers to make data-driven decisions and improve performance measurement without relying on third-party cookies.

Enterprise marketing teams are under pressure to prove ROI in a world where privacy rules, fragmented channels, and shrinking budgets complicate attribution. Without a clear picture of which channels drive true business impact, decision-making stalls and media waste increases.

Unlike multi-touch attribution, Meridian MMM is cookie-less, scalable, and built for modern marketing complexity. Marketers, analysts, and finance leaders use it to align spend with performance and simulate future growth.

This blog unpacks everything you need to know about Google Meridian MMM from setup to modelling to interpreting results so that you can drive smarter strategy, faster decisions, and better ROI.

What is Google Meridian MMM?

Google Meridian MMM is an open-source framework designed to help marketers understand the true, incremental impact of their marketing efforts. Unlike traditional attribution models that rely heavily on user-level tracking, Meridian uses aggregate data and Bayesian statistical methods to measure effectiveness, without the need for cookies or individual identifiers.

Launched by Google in 2024, Meridian represents a major shift in how media mix modelling (MMM) can be approached: with transparency, scalability, and full flexibility.

It’s built for marketers and analysts who need accurate ROI measurement across digital and offline channels - search, TV, social, out-of-home, and more.

The name “Meridian” isn’t just a nod to Google's love for mapping systems. It represents a central point of alignment, bringing together disparate signals into one unified view of marketing performance.

Here’s what makes it stand out:

• It’s completely open-source.
• It’s based on privacy-safe principles, addressing growing concerns in the cookieless era.
• It allows for calibration with real-world experiments, improving accuracy beyond what most black-box MMM tools offer.

Google built Meridian to democratise MMM—giving brands, agencies, and even smaller businesses access to the kind of measurement infrastructure once reserved for Fortune 500s.

Next, let’s break down how it works.

How Does Google Meridian MMM Work?

At its core, Google Meridian MMM uses Bayesian regression models to estimate the incremental effect of different marketing channels on business outcomes. Unlike multi-touch attribution (MTA), which depends on user-level tracking and cookies, Meridian uses aggregate data, making it privacy-safe and scalable.

The framework accounts for time-based lags (adstock), diminishing returns (saturation), and external factors like seasonality or macroeconomic trends (control variables). It operates on geo-level data, often at the DMA (Designated Market Area) or region level, enabling marketers to assess effectiveness across territories.

Let’s break it down:

• Data Ingestion: It starts by ingesting historical marketing and sales data across channels and regions.
• Model Specification: Analysts define model priors and select relevant control variables.
• Model Fitting: It runs Bayesian sampling (using PyMC or similar libraries) to infer the posterior distribution of media effects.
• Experiment Calibration: Teams can calibrate models using A/B test data or incrementality experiments.
• Result Interpretation: Outputs include ROI per channel, incremental KPIs, and budget allocation recommendations.

A Reddit user shared insights on the announcement, noting how Meridian could address common challenges smaller businesses face, where data is often noisier. Using organic query data helps account for external confounding factors and reduce noise.

This approach enables you to simulate different budget scenarios, forecast future performance, and make confident, data-backed marketing decisions.

The Goal of Google with MMM

Google launched Meridian with a clear mission: to make marketing measurement more accessible, accurate, and privacy-safe in a post-cookie world.

Traditional MMM solutions were costly, black-box, and difficult to scale. Meanwhile, MTA tools became less effective as third-party cookies and user-level identifiers disappeared from digital ecosystems.

With Meridian, Google aimed to:

• Democratise MMM by making it open-source and flexible.
• Support holistic measurement across online and offline media.
• Promote transparency by allowing teams to see exactly how the models work.
• Encourage experimentation by integrating calibration with real-world lift tests.

By focusing on geo-level data and privacy-safe modelling, Meridian helps brands stay compliant while still getting meaningful marketing insights.

The 4 Pillars of Google Meridian

Google describes Meridian as being built around four core pillars that distinguish it from legacy MMM tools:

1. Accuracy: Uses Bayesian statistics and calibration with real experiments to improve reliability.
2. Actionability: Provides channel-level ROI and budget optimisation scenarios directly from model outputs.
3. Adaptability: Open-source framework allows teams to modify model structures and inputs based on their unique needs.
4. Privacy-Durability: Relies on aggregated data rather than individual identifiers, making it future-proof.

Each pillar addresses a critical need in today’s marketing measurement ecosystem. Together, they form the foundation for modern, enterprise-ready MMM.

The Evolution from Lightweight MMM to Meridian

Before Meridian, Google introduced the Lightweight MMM project - a simplified model designed to help teams get started with MMM without deep statistical expertise. It was popular among smaller teams but lacked the flexibility and robustness needed for larger enterprises or more complex media mixes.

Google's introduction of the Lightweight MMM model in 2017 marked a significant advancement in Hierarchical Bayesian Modelling, addressing the complexities of measuring and optimizing media mix.

This open-source initiative was designed to provide marketers and analysts with a robust framework for dissecting the multifaceted relationships between media expenditures and significant business KPIs, such as weekly sales performance.

Meridian, now emerging in 2024 with limited availability, builds upon this foundation, offering enhanced capabilities for navigating the intricate dynamics of today’s marketing ecosystems. Its goal remains firmly rooted in maximising the effectiveness of the Media Mix Modelling process.

Lightweight MMM served as a testing ground for what would become Meridian. The Google team took community feedback from GitHub, input from partners, and lessons from internal use cases to build something more powerful and scalable.

The shift from Lightweight to Meridian reflects a broader trend: moving from static, simplified models to flexible, experiment-calibrated frameworks that scale across industries and marketing budgets.

Unpacking the Lightweight Model

The core equation of the Lightweight MMM,

kpi=α+trend+seasonality+M+C

Outlines a (non)linear framework where media-related KPIs (M) and control factors (C) combine to forecast outcomes like weekly sales.

However, the real game-changer is understanding the multiplicative nature of these models and the profound influence of incorporating prior knowledge to refine predictions and strategies.

Trend Analysis: Distinguishing between background sales levels sans media spending and regional minimum sales levels underscores the nuanced effects of market dynamics and consumer behaviour over time.

Seasonal Adjustments: Tailoring the model to capture seasonal variances accurately is critical, especially in industries with pronounced cyclical trends.

The Lightweight MMM focused on:

• Simplicity over customisation
• Running fast with minimal data
• Using priors and assumptions baked into the model
• Limited support for calibration or hierarchical modelling

While useful for early-stage teams, it often fell short in handling real-world complexity. Teams struggled to apply it across both digital and offline channels or to integrate it with ongoing experimentation frameworks.

Meridian fixes these issues with a modular architecture, Bayesian inference engine, and integrations for reach, frequency, and geo-level calibrations.

Meridian’s Modelling Features

Some of the standout modelling features in Meridian include:

• Bayesian Causal Inference: Helps estimate true incremental lift by accounting for uncertainty and prior knowledge.
• Adstock & Saturation Functions: Models the delayed and diminishing effects of media spend.
• Geo-Level Hierarchical Modelling: Allows marketers to analyse performance across different regions or test markets.
• Control Variables: Adjusts for external influences like economic shifts or seasonality.
• Experiment Calibration: Enables teams to blend lift test data with model outputs for greater accuracy.
• Search Data Integration: Uses Google Trends or search volume data as a control for organic demand.

These features collectively provide a more realistic and adaptable picture of marketing effectiveness.

Also Read - Your Ultimate Comparison Guide For Open-sourced MMM

Applications of Meridian’s MMM

Marketers and agencies have begun using Meridian in a variety of contexts, from large-scale TV campaigns to regional performance comparisons.

For example, a retail brand running seasonal campaigns across both online and offline media used Meridian to:

• Separate true lift from seasonal fluctuations
• Adjust spend toward higher-ROI market
• Improve collaboration between marketing and finance

60% of US advertisers are already using MMMs, with another 58% of non-users actively considering adoption as a data-driven strategy. This highlights how advanced techniques like machine learning and scenario planning help marketers optimise budgets and improve ROI across channels.

In the B2B space, marketers are exploring how Meridian can complement account-based marketing strategies by modelling the effects of brand awareness campaigns on deal velocity and pipeline health.

Fundamental Concepts in Measuring Media’s Impact

Understanding a few core MMM concepts is crucial to using Meridian effectively:

Diminishing Returns

Not all spending drives equal impact. Meridian’s saturation curves help teams visualise when increasing spend yields less incremental return.

The Diminishing Returns (DR) in Media Spend law explains that as media spending increases, the incremental value received from it declines. In other words, media reaches a saturation point, and as a consequence of DR, reaching a higher sales target by solely increasing media spend becomes increasingly harder (learn more about DR here)

The DR law applies not only to the channels but also to the total marketing spend.

Let's consider a firm that allocates $10 million in marketing & advertising. The company's total revenue is approximately $325 million, resulting in an ROI of $32 for every dollar spent.

At this level, they're delivering +16 ppt incremental value over the baseline—$275 m revenue, in this case.

If nothing else is changed (launch a new product, increased competitor pressure), how much additional marketing spend is needed to deliver the same incremental value? The answer is $100M as the result of DR. This is exactly marketing spend optimization in Performance Marketing.

Control Variables: The Catchall for Remaining Anomalies

After accounting for baseline, trend, seasonality, and media spend impacts, control variables serve as the analytical Swiss Army knife, capturing all other factors influencing the outcome.

Reach and Frequency KPIs

The use of reach and frequency is one of the innovative aspects of Meridian. Note that this feature is only applicable to modern media like Google Ads and is rarely useful for traditional media.

MMM models typically rely on impressions as input (like in Google Lightweight MMM), but ignore the fact that a person can be exposed to ads multiple times, and the impact can vary depending on exposure frequency.

Meridian offers the option to model the effect of any media channel based on its reach and frequency data. This method potentially produces a more precise measurement of marketing impact.

Also, it's important to note that the addition of reach and frequency leads to a more complex MMM model and, hence, the usual issue with the data size in MMMs.

Incremental KPI - A Causal Definition

One of Meridian's great aspects is its intersection with causal modelling. In Meridian, the MMM model generates Counterfactual predictions.

So, instead of performing experiments like holdout or A/B testing, Meridian offers a creative way to measure the causal impact of Media spend on incremental sales (target KPI).

However, this doesn't eliminate the need for experimentation. We should still perform experiments at regular intervals to ensure that we have sufficient evidence to calibrate the MMM model.

Incremental sales are defined as the difference in sales with and without channel spend:

Yₘ=1 − Yₘ=0​

This is the value of sales when we spend on an arbitrary channel m.

The Yₘ=1 is the predicted sales when no spending was made on the channel. We use the MMM model to predict the value of ​Yₘ=0.

In Causal modelling lingo, Yₘ=0 is known as potential outcomes

Choosing Control Variables

Choosing the right control variables is both an art and a science. They should reflect non-marketing factors that might influence performance.

Examples:

• Weather data for seasonal products
• Inflation or job market trends for financial services
• Competitor pricing or product launches for retail

These variables help isolate marketing’s true effect and prevent over-attribution.

The Meridian uses a Causal Graph to formalise how we should choose the control variable, where the control variable Z must satisfy the backdoor criterion.

In the simplest terms, the variable Z's interaction with Spend and Sales must have an upward-pointing V-shape.

Choosing control variables with no causal loop satisfies the backdoor criterion for causal graphs.

Let's look at examples of valid and invalid control variables:

Valid: Black Friday sales. Because the increase or decrease in sales doesn't define when Black Friday happens.

Invalid: PR and sales are correlated, but the causal direction is bidirectional (reciprocal causation in Causal language). A PR can lead to higher sales, and higher sales can lead to a PR.

MMM Data Platform

Google's MMM Data Platform provides access to various data, including Google Query Volume (GQV), reach and frequency, and paid search data. The GQV is added to the MMM as a Control variable (See Control Variables section above).

Although Meridian does not require GQV data, including GQV helps to get an unbiased measurement of the ROI of the Paid Search channel.

Source

While Meridian provides the modelling framework, it still requires a robust data platform to prepare and manage inputs.

Best practices include:

• Using clean, structured geo-level data (weekly or daily granularity)
• Centralising marketing and sales data with consistent naming conventions
• Creating ETL pipelines to feed new data into the model regularly

Tools like BigQuery, dbt, and Airflow can be used in tandem with Meridian to manage this workflow.

Engaging with Google Meridian MMM

Getting started with Meridian isn’t just plug-and-play, but it’s accessible to teams with Python experience and a good data foundation.

The Meridian repository includes:

• Sample data sets
• Model templates
• Jupyter notebooks for experimentation
• Detailed setup and configuration guides

Google’s team also encourages community contributions, with partners and agencies already adding custom features and automation scripts.

Now, as we stand on the cusp of broader availability, the opportunity to leverage Meridian’s advanced modelling capabilities beckons.

What Marketers Think About Meridian MMM So Far?

Early adopters are praising Meridian’s flexibility and transparency. Agencies value the ability to customise models. Brands appreciate the privacy-friendly design and the connection to experimentation.

Many enterprise advertisers are testing Meridian to transition away from cookie-based measurement and streamline budget decisions across departments.

Feedback points to a strong desire for more documentation, user-friendly UI layers, and calibration best practices.

How can Marketers Use Google Meridian MMM?

Marketers can leverage Google Meridian MMM in several ways:

• Conduct weekly budget allocation reviews, supported by detailed media ROI reports.
• Compare the effectiveness of offline media channels like TV and print against digital platforms.
• Optimise regional strategies using geo-level performance insights.
• Test and measure the impact of brand versus performance media over time.
• Utilise tools such as Google Cloud, Looker, and BigQuery to visualise results and run “what-if” scenarios directly in dashboards.

According to Forrester’s Marketing Survey, 30% of B2C marketers report using MMM tools to understand marketing value.

Some teams pair it with tools like Google Cloud, Looker, and BigQuery to visualise results and run “what-if” scenarios directly in dashboards.

Let’s assume that there’s a B2B SaaS company in software development. It could use Google Meridian MMM to measure the ROI of campaigns across search ads, LinkedIn, and webinars.

By identifying which channels drive the highest-value leads and where diminishing returns occur, they can reallocate budgets and optimise messaging for better customer acquisition efficiency and predictable growth.

The Future of Google Meridian MMM

As adoption grows, Meridian is likely to evolve with:

• Deeper integrations into the Google Ads ecosystem
• Auto-calibration tools for faster experiment alignment
• Community-driven modules for specific industries (e.g., CPG, retail)
• Templates for “lift test + MMM” hybrid frameworks

Its open-source nature ensures rapid iteration—marketers and developers alike can shape its future. In a world without third-party cookies, privacy-safe MMM tools like Meridian may become the new norm.

Conclusion

Attribution has always been messy. But with the rise of privacy regulations and fading cookies, messy became unmanageable.

Google Meridian MMM offers a way forward, grounded in science, open to adaptation, and focused on helping marketers measure what matters. If you’ve been stuck in attribution debates or are unsure where to shift spend, this framework can bring clarity.

• Start with the basics: geo-level data, channel spend, and sales outcomes.
• Apply Bayesian thinking and experiment calibration.
• Focus on incrementality, not just correlation.

So ask yourself: If you could measure true ROI across every marketing channel - accurately and transparently - what decisions would you make tomorrow?

With Google Meridian MMM, that decision starts today, and ELIYA is here to guide you!

FAQs

1. What types of businesses are best suited for using Google Meridian MMM?

Retail, consumer packaged goods (CPG), and e-commerce brands with campaigns spanning both digital and offline media can benefit most from Google Meridian MMM’s geo-level modelling and incrementality insights.

1. How does Google Meridian MMM address data privacy concerns?

Google Meridian MMM uses aggregate data rather than cookies, ensuring compliance with strict privacy regulations and protecting consumer data.

1. What are the recommended team skills for implementing Google Meridian MMM?

A strong data analytics team with Python expertise, experience in Bayesian modelling, and familiarity with Google Cloud tools can effectively implement Meridian MMM.

1. How steep is the learning curve for adopting Google Meridian MMM?

Teams familiar with Python and statistical modelling find the learning curve manageable, but those new to MMM may need additional training in Bayesian inference and geo-level modelling.

1. Are there best practices for integrating Google Meridian MMM into existing marketing strategies?

Start with structured geo-level data, run controlled experiments to calibrate the model, and regularly review ROI metrics and saturation curves for ongoing optimization.