Discover how Meridian, Google’s latest innovation in marketing mix models, is redefining the landscape of marketing analytics with a focus on privacy. 

POV By Victoria Stapleton, VP Digital Analytics, Acronym

Google’s Meridian Marketing Mix Model 

Google’s announcement of Meridian, its latest foray into marketing mix models (MMMs), marks another milestone in its preparation for full retirement of third-party cookies on which marketing and web analytics tools have largely depended (learn more about dealing with that change in our prior article). Along with solutions like the Privacy Sandbox, Enhanced Conversions, more statistics-based reporting solutions in Google platform and GA4, Meridian is another important step to understanding marketing performance in a more privacy-focused way.  

Note: at the time of this article’s publishing, Meridian has limited availability and requires an approved application from Google 

What is Meridian? 

Meridian is Google’s open-source MMM designed to answer critical marketing questions. It serves as a compass for marketers, guiding them through the complexities of advertising spend and its impact on sales and conversions. At its core, Meridian is a statistical tool that quantifies the effectiveness of each marketing channel, providing insights into how various elements of the marketing mix contribute to overall business goals. 

What are the key features of Meridian? 

Meridian stands out with its Bayesian hierarchical models, which offer a nuanced understanding of marketing effectiveness across different levels, such as geography or product categories. This approach allows for the incorporation of ROI priors, integrating existing knowledge into new analyses. 

Another key feature is its privacy-centric design. In a world increasingly conscious of data privacy, Meridian provides a framework that respects user privacy while still delivering actionable insights. It also supports scenario planning, enabling marketers to forecast the outcomes of various strategic decisions. 

How does Meridian compare to other self-service MMMs 

When it comes to choosing the right MMM, marketers must weigh the pros and cons of each model against their specific needs:  

 1. Meridian: 

    • Complexity and Customization: While Meridian provides flexibility for customization, its complexity may be a limitation for analysts who are not well-versed in Bayesian modeling or hierarchical geo-level analysis. 
    • Data Requirements: Like any MMM, Meridian requires historical data on marketing spend, sales, and other relevant variables. Insufficient or poor-quality data can impact model performance. 
    • Resource Intensive: Training and maintaining a Meridian model can be resource-intensive due to the need for computational power and expertise. 
    • Privacy Concerns: Although Meridian emphasizes privacy, any data used for modeling should be handled carefully to avoid privacy breaches. 

2. Robyn: 

    • Black Box Nature: Robyn’s AI/ML-powered approach can be seen as a black box, making it challenging for analysts to fully understand the model’s inner workings. 
    • Dependency on Facebook’s Prophet: Robyn relies on Facebook’s Prophet library for time series decomposition. Analysts need to trust the accuracy and reliability of this external component. 
    • Limited Documentation: As an experimental package, Robyn’s documentation may be less comprehensive compared to more established models. 
    • Model Calibration: While Robyn aims to reduce bias, calibration still requires careful validation against ground-truth data. 

3. LightweightMMM: 

    • Simplicity vs. Complexity: While LightweightMMM’s simplicity is an advantage, it may lack some advanced features found in more complex MMMs. 
    • Limited Features: It focuses primarily on channel attribution and budget optimization. Analysts seeking more sophisticated features (e.g., seasonality, external factors) may find it lacking. 
    • Community Support: Being relatively new, LightweightMMM may have limited community support and fewer resources available for troubleshooting. 
    • Bayesian Approach: Bayesian models require prior knowledge or assumptions, which can be a limitation if accurate priors are not available. 


The introduction of Meridian by Google is a testament to the tech giant’s commitment to advancing marketing analytics. It offers a sophisticated, privacy-conscious tool that empowers marketers to make informed decisions.

However, the choice between Meridian, Robyn, LightweightMMM, and other open-source MMM tools depends on the specific needs of the marketing team, their expertise, and the complexity of the marketing challenges they face. Each model has its strengths and weaknesses, and the best choice will align with the organization’s strategic objectives and data capabilities.  

Regardless of which model is chosen, no open-source solution offers expert support, domain expertise, or robust user enablement. For marketing departments inexperienced with MMM or other advanced analyses, Acronym strongly recommends external support.  

Need help deploying your open-source MMM solution? Contact Us today!

The hero image is not attention-grabbing enough. The color and imagery is a bit dull. Could we have something more dynamic? Either something that reads “digital marketing” or a workplace image with people in a creative meeting?