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Our response to Google's Meridian launch

About the Author

Philippa is Head of Content at ScanmarQED, where she defines the company’s content strategy at the intersection of marketing, data, and thought leadership. She leads the development of content across all product lines—from white papers and blogs to website content—ensuring clarity, consistency, and relevance. Her work focuses on translating complex data and analytics concepts into clear, insight-led narratives that support better decision-making. Previously, she served as Head of Learning, where she created ScanmarQED’s Marketing Mix Modeling certification program.

Google's announcement of Meridian, its new open-source marketing mix model (MMM), marks another significant step in the evolution of marketing measurement. As a long-time advocate for innovation in marketing analytics, we at ScanmarQED welcome this addition to the ecosystem and recognize the potential value it brings to data-driven decision-making. Open-source solutions like Meridian promise transparency, customization, and modern methodologies—critical elements for brands looking to improve their media measurement strategies. This blog explores the innovations within Meridian, the broader challenges of open-source MMM compared to strataQED (part of our PulseQED solution), and how PulseQED provides a flexible framework for brands looking to scale self-built models within their existing setup. 

In-blog A taxonomy of biases in Marketing Mix Model effect estimation – Part 1

One of the most exciting aspects of Meridian from a technical standpoint is the ability to use reach and frequency data directly in the saturation behavior of the model. This enhances the accuracy of media impact measurement and can improve budget allocation decisions. Additionally, the inclusion of own-brand Google search volume as a control variable—helping to account for brand interest or saliency—is noteworthy. However, both innovations depend more on access to the relevant data than on the specifics of the Meridian package itself. Essentially, reach and frequency data could be used in planning once a saturation curve has been selected, even if we do not use that data to inform the model directly. 

As we discussed in our blog, Open- source MMM vs strataQED, open-source solutions, while powerful, are not without challenges. While they provide flexibility and access to advanced modeling techniques, they also demand significant internal resources, specialized expertise, and a deep understanding of MMM best practices. Many businesses lack the in-house capabilities to fully leverage open-source models without encountering roadblocks. As we've seen with brands testing Facebook’s Robyn and Google's lightweight MMM, the challenges often lie in implementation: setting up and maintaining the model, ensuring the accuracy of inputs, and correctly interpreting the results. Without the right team—including experienced data scientists and MMM specialists—open-source solutions can introduce complexity rather than clarity. Additionally, the total cost of ownership can be higher than expected, with expenses for hiring skilled talent, ongoing maintenance, and ensuring cross-functional usability adding up over time. 

For brands with existing MMM approaches choosing to leverage open-source MMM like Meridian, our PulseQED solution offers a flexible framework to enhance and scale self-built models within their own setup. PulseQED allows teams to integrate their custom methodologies while benefiting from AI automation, data harmonization, automated model updates, scenario planning, and optimization—all within their current environment. This hybrid approach ensures that businesses retain control over their proprietary modeling techniques while gaining efficiency and usability advantages that are often missing from purely open-source solutions.  

Ultimately, transparency in marketing measurement is not just about open code—it’s about empowering teams to make better decisions with clear, actionable insights. Meridian is a great addition to the industry, and we look forward to seeing how it evolves. Whether brands want to build their own models with the right level of support or leverage PulseQED’s flexible framework to integrate and scale their MMM approach, our solution ensures efficiency, accuracy, and ease of use—all within their existing setup.