Getting Started with MMM Using Google Meridian

Introduction

Google recently launched Meridian, an open-source Marketing Mix Modeling (MMM) tool designed to help businesses measure the impact of their marketing efforts. With advanced features like integrating reach and frequency data, search query volume, and incrementality experiment calibration, Meridian aims to enhance the accuracy of marketing measurement and budget allocation.

This blog complements our video walkthrough on Google Meridian. Watch the video below for a step-by-step demonstration:
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What is Marketing Mix Modeling (MMM)?

Marketing Mix Modeling (MMM) is a statistical technique that evaluates the impact of various marketing activities on revenue or other business outcomes. Traditionally, MMM relied on linear regression models, but modern tools like Meridian use Bayesian methods and Markov Chain Monte Carlo (MCMC) simulations to estimate marketing performance more robustly.

Why Use Google Meridian for MMM?

Meridian is not the first open-source MMM tool—Facebook’s Robyn and PMC's marketing package are also widely used. However, Google’s offering introduces several compelling features:

• Integration of reach and frequency data, previously challenging to incorporate in MMM models.
• Support for search query volume, allowing better modeling of organic brand demand.
• Calibration with past incrementality experiments, reducing biases from observational data.
• Built-in budget optimization, helping marketers allocate spend more effectively.

Key Features of Google Meridian

1. Bayesian Approach to MMM

Unlike traditional MMM models that provide a single point estimate, Meridian uses a Bayesian inference approach, generating a probability distribution for key marketing metrics such as incremental ROAS (iROAS). This provides more nuanced insights and confidence intervals around estimates.

2. Calibration with Incrementality Experiments

One of the biggest limitations of standard MMM models is their reliance on observational data, which may introduce biases due to seasonality, promotions, or external factors. Meridian allows users to incorporate results from Geo Lift Tests, A/B Tests, or Randomized Control Trials (RCTs) to improve accuracy.

3. Google Colab Implementation

Meridian provides an easy Google Colab notebook, allowing users to quickly run MMM analyses without complex setup. The process involves:

1. Installing the Meridian library
2. Loading marketing and conversion data
3. Configuring model parameters
4. Running simulations using MCMC methods
5. Interpreting outputs like channel contribution and ROAS

4. Advanced Budget Optimization

Meridian includes an Optimization Module that recommends budget allocations based on historical performance and saturation curves. This helps marketers avoid overspending on channels with diminishing returns while reallocating budgets to high-efficiency channels.

How to Get Started with Meridian

Step 1: Install Meridian

Open Google Colab and install Meridian using:

!pip install meridian

Step 2: Load Sample Data

Google provides a sample dataset with marketing spend and conversion metrics. The dataset includes:

• Media spend (e.g., TV, digital, search, social)
• Impressions and clicks
• Conversion events
• Control variables (e.g., seasonality, promotions)

Step 3: Define Model Parameters

Configure ROI priors to incorporate historical marketing knowledge:

roi_mu = 0.2  # Mean prior ROI estimate
roi_sigma = 0.9  # Standard deviation of ROI prior

Step 4: Run the Model

Meridian uses MCMC simulation to estimate marketing contributions:

meridian.run_model()

Step 5: Analyze Results

Once the model runs (takes ~10-20 minutes), Meridian provides:

• Incremental revenue breakdown by channel
• Expected vs. actual revenue trends
• Saturation and diminishing return curves

Step 6: Optimize Budget Allocation

The budget optimizer suggests reallocating marketing spend for higher efficiency:

meridian.optimize_budget()

The Need for a More User-Friendly MMM Solution: Introducing Stella

While Google Meridian is a powerful open-source MMM tool, it comes with significant complexity. To execute it properly, businesses need a data scientist to handle the model setup and a senior marketer to interpret the results correctly. Without these skills, it's easy to misinterpret the findings, leading to incorrect marketing decisions.

That's where Stella comes in.

Why Choose Stella Over Meridian?

Stella is designed to be the most user-friendly MMM solution while still maintaining statistical rigor. Unlike Meridian, Stella:

• Eliminates the need for coding or technical expertise, making MMM accessible to all marketers.
• Provides clear, AI-generated insights that explain results in plain English.
• Includes built-in incrementality testing, allowing for a more accurate and actionable measurement of marketing impact.
• Offers budget allocation recommendations based on real-world marketing constraints.
• Is the most affordable paid MMM tool, delivering enterprise-level analytics at a fraction of the cost of alternatives.

If you're looking for an MMM tool that balances power, ease of use, and affordability, Stella is the best solution for your business.
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Final Thoughts

Google Meridian is a powerful tool for marketers looking to refine their MMM strategy. By integrating Bayesian statistics, reach/frequency modeling, and past experiment calibration, it offers a more accurate and actionable approach to marketing measurement.

If you’re interested in learning more, watch our full video walkthrough above and try running Meridian for your own marketing data!

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