--- title: "Exploring Meridian: Google's New Open-Source Marketing Mix Model" url: https://adtech.lol/articles/0aa39711-94e9-4e4e-8506-9c7e3f6451f1 source_url: https://searchengineland.com/google-meridian-ppc-guide-494563/ source_site: searchengineland author: "Jason Tabeling" published: 2025-01-01T00:00:00+00:00 tags: ["Google", "Digital Marketing", "Paid Search", "Marketing Mix Modeling", "PPC", "SEO"] publisher: AdTech Radar --- # Exploring Meridian: Google's New Open-Source Marketing Mix Model > Google’s Meridian is revolutionizing paid search measurement by transitioning away from last-click attribution, providing marketers a clearer understanding of their search ads' incremental revenue. ## Introduction [Google’s Meridian](https://searchengineland.com/exploring-meridian-googles-new-open-source-marketing-mix-model-438754) is bringing a new approach to paid search measurement. The open-source marketing mix modeling (MMM) framework helps marketers move beyond last-click attribution to better understand how much incremental revenue their search ads actually drive. Last-click attribution has long made paid search budgets look incredibly efficient. If someone clicked an ad and bought a product, that keyword got 100% of the credit. But with privacy regulations tightening, third-party cookies disappearing, and customer journeys spanning dozens of digital and offline touchpoints, relying solely on user-level digital tracking is no longer a viable long-term strategy. That’s why we’ve seen a massive resurgence of interest in MMM. No longer just an exercise for enterprise brands with massive TV budgets, MMM has modernized, with Google’s Meridian emerging as one of the key offerings. First introduced in 2024 and made available to all marketers and data scientists in early 2025, Meridian is a Python-based, fully Bayesian, open-source MMM framework. The open-source aspect is key. Brands have been right to worry about Google grading its own homework. With Meridian, you can inspect and modify the underlying code, allowing you to understand how the model works and adapt it to fit your business. ## Where other MMM solutions have failed search (and how Meridian fixes it) Historically, PPC marketers hated traditional MMMs, and for good reason. Legacy models were built primarily for offline media like TV and print. They used highly aggregated national-level data that failed to capture the rapid, day-to-day fluctuations of digital performance media. Even worse, traditional models struggle with causality bias in search. When a consumer searches for your brand name on Google and clicks a paid ad, did the ad cause the sale, or did the consumer’s preexisting intent drive both the search and the sale? Traditional models couldn’t tell the difference, often undervaluing or overvaluing search campaigns. Meridian tackles these legacy limitations head-on with several critical features. ### Geo-level hierarchical modeling Instead of analyzing your ad spend at a national level, Meridian runs calculations on regional or local “geo” data. By modeling variations in search spend and performance across regions, Meridian uses geo-level data to estimate incremental impact. ### Google Query Volume (GQV) integration To help address this causality issue, Meridian allows you to incorporate Google search interest data. By using query volume as a control variable, the model can help separate organic search demand from paid search ad performance, giving you a clearer picture of your paid search ROI. ### Time-varying intercepts Consumer demand isn’t static. Meridian accounts for seasonal fluctuations, macroeconomic shifts, and long-term organic growth independently of your active marketing spend, keeping your baseline sales accurate. ## Under the hood: The Bayesian advantage If you’ve looked at other open-source MMM tools — like Meta’s Robyn — you’ll find a major difference in the core math. Robyn uses a frequentist approach with ridge regression. Meridian, on the other hand, is built on a fully Bayesian causal inference framework. Why should you care about Bayesian statistics? Because it allows you to inject real-world industry knowledge (historical data) into the math. In a traditional model, the math starts from scratch. In a Bayesian model, if you’ve already measured the incremental impact of your brand search campaigns through an [incrementality test](https://searchengineland.com/why-incrementality-is-the-only-metric-that-proves-marketings-real-impact-463439) or geo-lift experiment, you can use those results to inform the model’s priors. This “experiment calibration” blends real-world test data with historical aggregate data, generating trustworthy results. ## Feature comparison: Meridian vs. legacy measurement To help visualize how measurement is shifting, here’s a look at how Meridian stacks up against outdated last-click tracking and legacy MMM systems: | | | | | | --- | --- | --- | --- | | **Feature** | **PPC last-click** | **Legacy MMM** | **Google Meridian** | | Data requirements | User-level cookies and pixels | Highly aggregated national data | Aggregated geo-level or national data | | Privacy compliance | Low (relies on user tracking) | High (uses aggregated data) | High (no cookies or PII needed) | | Organic demand bias | Blind to organic intent | Struggles to isolate search demand | Helps account for search demand using Google Query Volume | | Experiment integration | Hard to isolate across channels | Treated as separate data points | Direct calibration using priors | | Planning capabilities | Static historical reporting | Retroactive analysis | Dynamic “what-if” scenario planning | ## 5 steps to getting started with Meridian Operating an open-source model requires some technical data muscle. While Google has democratized the code by hosting it on GitHub, you’ll still need a data scientist or analyst comfortable with Python to set up the environment. Here’s the typical workflow for a marketing team bringing Meridian in-house: ### 1. Data prep and gathering You should collect historical data, ideally at the geo level, with at least two years of weekly observations. This includes your search spend and impressions, offline media metrics, nonmarketing controls (like pricing changes or competitor sales), and your target KPIs (leads, revenue, or store visits). At this stage, you also integrate your Google Query Volume (GQV). ### 2. Model specification Using Python (commonly via a Jupyter Notebook), your technical team installs the google-meridian package and defines the model parameters. This is where you configure how your ad spend is distributed over time using custom adstock (to model delayed ad effects) and saturation curves (to model diminishing returns as budgets scale). ### 3. Model training and diagnostics Because Meridian is Bayesian, it uses Markov chain Monte Carlo (MCMC) sampling to run thousands of iterations. Once complete, you check the convergence metrics and model quality to ensure the math has stabilized and can reliably predict performance. ### 4. Calibration with lift experiments This is the secret sauce for search marketers. You use results from incrementality experiments, such as Google Ads geo-experiments, to inform the model’s priors. This helps calibrate Meridian’s estimates against real-world experimental evidence. ### 5. Running scenario optimizations Once calibrated, Meridian allows you to run “what-if” scenarios. You can ask the tool: “If I increase my YouTube and Search budgets by 15% next quarter while cutting offline spend, what is my projected revenue?” The tool will generate optimized budget allocations designed to maximize your chosen KPI. ## What to watch out for when using Meridian Meridian offers powerful measurement capabilities, but getting reliable results isn’t without its challenges. Before you get started, keep these potential pitfalls in mind: ### The ‘garbage in, garbage out’ rule Meridian is incredibly powerful, but it relies on clean, structured geo-level data. If your regional sales tracking is messy, or if you don’t have clear historical spend records by region, your outputs won’t be reliable. ### High technical barrier This isn’t a “click-and-play” SaaS dashboard. You can’t run Meridian without a strong understanding of Python, package management, and basic statistical analysis. ### Overreliance on Google-only data While Meridian is open source and allows you to input data from Meta, TikTok, and TV, its deep, native integrations are heavily optimized for the Google ecosystem. Ensure you aren’t neglecting accurate data collection for your non-Google channels. ## Ready for Meridian? Start with your data If you want to move away from defending last-click metrics in boardrooms, it’s time to start preparing your data for MMM. Start by auditing your current reporting: Are you tracking marketing spend and revenue at the geo level (DMA or state level) weekly? If not, start organizing your data pipelines today. Once your data foundation is set, pull in your engineering or data science team to download the Meridian package from GitHub and begin running your very first incremental tests. Meridian changes the conversation from “How many clicks did my search ads get?” to “How much incremental revenue did my search ads actually drive?” By leveraging aggregated geo-level data and integrating Google Search volume, Meridian helps address the limitations of last-click attribution and allows search to be measured on a more level playing field with offline and social media channels. Source: [https://searchengineland.com/google-meridian-ppc-guide-494563/](https://searchengineland.com/google-meridian-ppc-guide-494563/)