A Modern Guide to Marketing Mix Modeling (MMM) with Google’s Meridian

Understanding MMM

Marketing Mix Modeling (MMM) is a powerful tool that helps marketers understand how different channels like TV, digital, or print contribute to business performance. At its core, MMM is about measuring the return on investment (ROI) from marketing activities and using those insights to make smarter budget decisions.

Think of MMM as a compass that shows where to reduce spend and where to invest more based on how each channel is actually performing. It’s not just about reporting results, but also uncovering causal relationships between marketing actions and business outcomes.

Before diving into MMM, it’s important to understand a few key concepts. One of them is saturation, or diminishing returns. After a point, spending more on a channel brings smaller gains. For example, spending ₹50,000 on online ads might get you 500 customers, but doubling it to ₹1,00,000 might only result in 800 not 1,000. That’s because the most responsive audience has already been reached, and reaching the rest becomes harder and costlier.

Another important idea is the lagged effect of advertising. People don’t always act immediately after seeing an ad, they might take a few days or weeks to respond. This delayed impact is called the carryover effect, and MMM accounts for it by modeling how campaign influence fades over time.

A big challenge in MMM is multicollinearity when two or more input variables are highly correlated. For example, if a brand increases both TV and YouTube ads during major campaigns, it’s hard for the model to separate their individual effects. This is common in marketing, where channels are often activated together. While it doesn’t hurt predictions, it can make insights harder to interpret especially when you're trying to figure out which channel drove results.

To deal with this, Ridge Regression is often used. It handles multicollinearity well but sacrifices interpretability. That’s why many researchers turn to Bayesian modeling, which offers better transparency and more accurate estimates in such cases.

There are several tools available for MMM. Robyn, built by Meta, is an open-source R package that uses Ridge Regression and machine learning. It works well for analyzing both online and offline media, while also factoring in elements like price and seasonality.

Google previously offered Lightweight MMM, a Python-based tool using Bayesian methods. While powerful, it required significant technical skills. Google now recommends moving to Meridian, its newer and more advanced MMM framework designed for better accuracy, customization, and usability.

Google’s Meridian: A Modern Take on Marketing Mix Modeling

Meridian is Google’s next-gen, open-source solution for Marketing Mix Modeling (MMM), built on a Bayesian framework. It’s designed to provide more accurate, customizable, and privacy-safe insights into marketing performance without relying on cookies or user-level data. With native support for tools like Google Ads, YouTube, and GA4 through the MMM Data Platform, it’s built for real-world scale and actionability.

What Makes Meridian Stand Out

1. Flexible Use of Prior Knowledge
Meridian allows you to integrate existing knowledge like past MMM results, benchmarks, or experiments through ROI priors. This is especially useful when your data is limited or noisy, helping the model stay grounded in reality.

2. Geo-Level Hierarchical Modeling
Marketing doesn’t perform the same everywhere. A campaign might work in urban areas but not in smaller towns. Meridian supports geo-level modeling, so you can analyze effectiveness by region. It scales to 50+ geographic areas and several years of weekly data, using TensorFlow Probability and XLA with optional GPU acceleration for speed.

3. Built-In Uncertainty Estimation
Instead of giving one fixed prediction, Meridian outputs a range of possible outcomes with confidence intervals. This helps you plan better by understanding not just what might happen, but how likely different outcomes are.

4. Full Customizability
Since it’s open-source, Meridian is highly customizable. You can tweak model components to fit your business unlike rigid black-box tools. However, you’ll need some technical expertise to fully take advantage of this flexibility.

5. Smarter Budgeting & What-If Analysis
Meridian helps optimize your marketing spend both across channels and overall. It can simulate various budget scenarios to estimate the impact on ROI, helping guide data-driven planning.

6. Real-World Marketing Dynamics Modeled Accurately

• Saturation: Captures diminishing returns through Hill functions (e.g., doubling spend doesn’t always double impact).
  
• Lagged Impact: Uses adstock functions to model delayed effects of media.
  
• Reach & Frequency (optional): You can include data on how many unique users saw your ads (reach) and how many times they saw them (frequency). This improves the model's ability to estimate impact especially for platforms like YouTube or TV, (part of DV360) where repeated exposure to video ads can influence results.

Challenges to Keep in Mind

• Computational Demand: Meridian uses Bayesian posterior sampling, which can be resource-intensive. GPUs are often recommended, especially with large or geo-level datasets.
  
• Setting priors isn’t easy: Choosing the right prior distributions can be subjective and difficult even for experts. The results can vary depending on your assumptions.
  

Common Issues and Fixes

• Negative Baseline Problem: If the model attributes all impact to media, it might show a negative baseline.
  Fix:

• Narrow down ROI sigma ( standard deviation ) for paid channels.
  
• Add control variables like Holiday flag, economic trends, Google trend score  or organic data.
  

• Convergence Errors: These can happen when the model struggles to stabilize.
  Fix:

• Watch out for multicollinearity (e.g., clicks and impressions rising together). Remove one of the variables.
  
• Adjust MCMC sampling settings like increasing the number of chains or iterations to improve stability.

In today’s fast-changing marketing landscape, where budgets are tight and privacy rules are stricter than ever, tools like MMM and especially advanced frameworks like Google’s Meridian are becoming essential for data-driven decision-making. Meridian brings together data science and practical flexibility, helping marketers move beyond guesswork to make smarter choices for future business decisions.