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MeridianGoogle’s new open-source Marketing Mix Model (MMM), has entered the rapidly evolving market for advanced marketing analytics and forecasting tools.
This article examines Meridian’s key features, capabilities, and limitations and compares them to Meta’s MMM named Robyn.
It explores how Meridian uses advanced techniques such as geo-level hierarchical modeling, Bayesian methods and scenario analysis to provide actionable insights for cross-channel budget optimization and marketing strategy development.
Understanding marketing mix models
The marketing mix model allows marketers to analyze how different marketing strategies affect sales and predict future results.
Essentially, MMMs break down the drivers of sales into factors (e.g., price, product features, distribution, promotions) and external issues (e.g., economic conditions or competitive moves).
By analyzing historical data, these models assign numerical values to each element of the marketing mix in relation to total sales, requiring statistical methods to assess 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 causality relationships, among other things.
This enables companies to make data-driven decisions, optimize resource allocation to key activities such as product pricing, and improve brand loyalty through better consumer understanding.
When navigating a complex marketplace, the precision and insights that marketing mix models provide are essential for 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 results and decision making. It places a strong emphasis on privacy, advanced measurement, and accessibility for marketers.
According to Google, Meridian produces innovations that provide more accurate and actionable insights. It includes features such as calibration with incrementality experiments, reach and frequency integration, and specialized guidance on measuring searches across all media channels.
What sets Meridian apart is its transparency, allowing users to tailor the code and parameters to their specific requirements. This makes it a very effective tool for improving measurement strategies.
Additionally, it provides actionable data input and modeling guidance for optimizing multi-channel budgets. It also provides extensive educational tools and implementation support.
As companies increasingly recognize the value of MMMs in achieving revenue goals, Meridian offers a solution that combines innovation, transparency and usability.
Based on the press release, it appears that Meridian is 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 shows that Google’s Meridian takes a more advanced approach than other solutions.
Although Google’s documentation is extensive, it is essential not to underestimate the complexity of implementing and processing data. Technical and analytical support for modeling work is strongly recommended.
Implementing MMMs can be challenging even without prior experience, as it requires selecting the right data, training the model, and adjusting various parameters.
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The possibilities and limitations of Meridian
Modeling at the local versus national level
Meridian is a powerful tool that takes your marketing data to the next level.
Unlike traditional national-level models, Meridian allows you to drill down into your marketing efforts at a local or regional scale using hierarchical geo-level models.
This approach gives you more detailed insights and often results in more reliable numbers on how effective your marketing strategies are, especially in terms of ROI.
With Meridian you are 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 at crunching numbers.
Thanks to the use of advanced technology such as Tensorflow Probability and the XLA Compiler and the ability to use GPU hardware through tools like Google Colab Pro+, Meridian runs quickly and keeps pace with your needs.
For those times when you don’t have local data, Meridian still supports the traditional national approach. However, one of the standout features is that you can take what you already know with you.
Integrating past knowledge for Bayesian modeling
Using Bayesian models, you can bring your past knowledge about the performance of your media to Meridian. This includes insights from previous experiments, other marketing mix models, industry knowledge or benchmarks. This way you don’t start from scratch, but you build on what you already know.
Meridian intelligently models the declining effectiveness of marketing strategies over time and the dispersion of their impact, increasing the accuracy of its predictions. Additionally, it delves into the influence of unique viewers and ad frequency on marketing, providing deeper insights into strategy effectiveness.
It doesn’t stop there.
Meridian is also about making wise 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 across channels or by suggesting the best overall budget to achieve your goals.
Meridian also lets you play with “what-if” scenarios to see how different strategies could have turned out. Finally, it gives you a clear report on how well it fits your data, so you can decide which strategies work best.
Limitations in analyzing marketing performance
Meridian has significant limitations, most notably the lack of upper versus lower funnel support, a common problem with most MMMs.
This makes it challenging to separate and analyze these components independently. However, if Meridian had this feature, it would be able to differentiate itself more from the competition.
Another limitation is that Meridian does not take into account fluctuations in performance within the analyzed time frame.
In real-world marketing, events can significantly impact the performance of individual channels. As a result, Meridian’s inability to take this into account could lead to inaccurate forecasts and analysis, especially when dealing with longer time frames.
Meta’s MMM Robyn appears more advanced, putting pressure on Google to deliver a competitive tool as the leading global 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 building theirs, with limited access through applications. Robyn is accessible to everyone via GitHub and promotes community support.
The effectiveness of Meridian and Robyn will be determined as more advertisers use them, making their strengths visible. These MMM tools also serve as crucial marketing capabilities for advertising platforms. Meridian could drive paid search traffic, while Robyn could favor high-impression ads on Meta’s platforms, although this will become more apparent with continued use.
As of now, Meridian is a fun early access project to play with. It will have to show whether implementation and analysis with real data can benefit advertisers.
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The opinions expressed in this article are those of the guest author and not necessarily those of Search Engine Land. Staff authors are credited here.
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