Meridian, Google’s new open source Marketing Mix Model (MMM), has entered the rapidly evolving market of advanced marketing analytics and forecasting tools.
This article explores Meridian’s key features, capabilities, and limitations, comparing it to Meta’s MMM called Robyn.
It explores how Meridian leverages advanced techniques such as geographic hierarchical modeling, Bayesian methods, and scenario analysis to provide actionable insights for cross-channel budget optimization and marketing strategy development.
Understand marketing mix models
The marketing mix model allows marketers to analyze how various marketing strategies influence sales and forecast future results.
In essence, MMMs divide sales drivers into factors (eg, price, product attributes, distribution, promotional actions) and external issues (eg, economic status or competitive moves).
By analyzing historical data, these models assign numerical values to each component of the marketing mix relative to total sales, and require statistical methods to evaluate individual marketing activities and external factors.
Consequently, this knowledge allows marketers to optimize strategies, allocate budgets more wisely, and predict how a change in one element will affect future sales.
MMMs use regression analysis or similar statistical techniques on large amounts of data related to sales and marketing to identify patterns and causal relationships, among others.
This enables companies to make data-driven decisions, optimizing resource allocation in key activities such as product pricing and improving brand loyalty through better consumer understanding.
When navigating a complex market, the precision and insights provided by marketing mix models are essential to strategic planning.
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How does Meridian fit into the MMM landscape and what does it offer?
Meridian is an open source MMM that aims to support teams in developing models that provide deeper insights into marketing outcomes and decision making. It strongly emphasizes privacy, advanced measurement, and accessibility for marketers.
According to Google, Meridian offers innovations that deliver more accurate and actionable information. It includes features such as calibration with incrementality experiments, the incorporation of reach and frequency, and specialized targeting to measure search across all media channels.
What makes Meridian stand out is its transparency, which allows users to customize the code and parameters to meet their specific requirements. This makes it a very effective tool for improving measurement strategies.
It also provides actionable data inputs and modeling guidance to optimize multichannel budgets. It also offers comprehensive educational resources and implementation support.
As companies increasingly recognize the value of MMMs in achieving revenue goals, Meridian offers a solution that combines innovation, transparency and practicality.
According to the press release, Meridian appears to be no different from other MMM tools. Reputable MMM tools prioritize privacy, use Bayesian methods, and offer a wide selection of control variables and customizable settings.
The documentation reveals that Google’s Meridian uses a more advanced approach than other solutions.
Although Google’s documentation is extensive, it is essential not to underestimate the complexity of implementing and managing data. Technical and analytical support for modeling work is highly recommended.
Implementing MMM can be challenging even without prior experience, as it requires selecting the right data, training the model, and tuning various parameters.
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Meridian Capabilities and Limitations
Modeling at the local level vs. national
Meridian is a powerful tool that takes your marketing data to the next level.
Unlike traditional national models, Meridian allows you to expand your marketing efforts on a local or regional scale through hierarchical geographic modeling.
This approach gives you more detailed information and often results in more reliable numbers about the effectiveness of your marketing strategies, especially in terms of ROI.
With Meridian, you’re not limited to just a few data points. It can handle over 50 geographic locations and 2-3 years of weekly data, making it a beast for the number cruncher.
Thanks to its use of advanced technology com Tensorflow Probability and the XLA Compiler and the option to use GPU hardware through tools like Google Colab Pro+, Meridian works fast, keeping pace with your needs.
For those times when you don’t have local data, Meridian still supports the traditional nationwide approach. However, one of its best features is that it allows you to bring what you already know into the equation.
Incorporating past knowledge for Bayesian modeling
Using Bayesian models, you can add your past knowledge of how your media is performing to Meridian. This includes information from past experiments, other marketing mix models, industry knowledge or benchmarks. That way, you won’t be starting from scratch, but building on what you already know.
Meridian intelligently models the decline in effectiveness of marketing strategies over time and the spread of their impact, improving the accuracy of predictions. It also delves into the influence of unique viewers and ad frequency on marketing, providing deeper insight into strategy effectiveness.
It doesn’t stop there.
Meridian is also about making smart decisions, especially with online channels like paid search, using data like Google query volume. This helps you see the real impact of your strategies.
When you spend your marketing budget wisely, Meridian shines by helping you find the best way to spread your budget between different channels or by suggesting the best total budget to achieve your goals.
With Meridian, you can also play with “what if” scenarios to see what different strategies might have looked like. Finally, it gives you a clear report on how they fit your data, helping you decide which strategies work best.
Limitations in Marketing Performance Analysis
Meridian has significant limitations, most notably its lack of upper and lower funnel support, a common problem with most MMMs.
This makes it difficult to separate and analyze these components independently. However, if Meridian had this feature, it could stand out more compared to the competition.
Another limitation is that Meridian does not account for performance fluctuations within the analyzed time period.
In real-world marketing, events can significantly affect the performance of individual channels. As a result, Meridian’s failure to take this into account could lead to inaccurate forecasts and analysis, especially when dealing with longer time frames.
Meta’s MMM Robyn seems more forward, pushing Google to offer a competitive tool as the world’s leading advertising platform.
Despite Robyn’s compact presentation, it shares many features with Google’s Meridian.
Meta has published case studies for Robyn, while Google is still in the process of building its own, with limited access through the app. Robyn is accessible to everyone via GitHub, encouraging community support.
Meridian and Robyn’s effectiveness will be determined as more advertisers use them, revealing their strengths. These MMM tools also serve as crucial marketing opportunities for advertising platforms. Meridian may increase paid search traffic, while Robyn may favor impression-heavy ads on Meta platforms, although this will become clearer with continued use.
As of now, Meridian is a good early access project to play. It will need to demonstrate whether implementation and analysis with real data can benefit advertisers.
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The views expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.
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