Meridian, Google’s new open-source Marketing Mix Model (MMM), has entered the rapidly evolving market for advanced marketing analytics and forecasting tools.
This article explores Meridian’s key features, capabilities and limitations, comparing it with Meta’s MMM called Robyn.
It delves into how Meridian leverages advanced techniques like hierarchical geo-level modeling, Bayesian methods and scenario analysis to offer actionable insights for cross-channel budget optimization and marketing strategy development.
Understanding marketing mix models
The marketing mix model empowers marketers to analyze how various marketing strategies influence sales and forecast future results.
In essence, MMMs split the drivers of sales into factors (e.g., price, product attributes, distribution, promotional actions) and external issues (e.g., economic state or competitive moves).
By analyzing historical data, these models assign numerical values to each component 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 forecast how a change in one element will affect future sales.
MMMs employ regression analysis or similar statistical techniques on large quantities of data related to sales and marketing to identify patterns and causality relationships, among others.
This enables companies to make data-driven decisions, optimizing resource allocation across key activities like product pricing and improving brand loyalty through enhanced consumer understanding.
In navigating a complex market, the precision and insights 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 outcomes and decision-making. It strongly emphasizes privacy, advanced measurement and accessibility for marketers.
Meridian brings forth innovations that offer more precise and actionable insights, according to Google. It includes features like calibration with incrementality experiments, incorporation of reach and frequency and specialized guidance on measuring search across all media channels.
What makes Meridian stand out is its transparency, allowing users to customize the code and parameters to meet their specific requirements. This makes it a highly effective tool for enhancing measurement strategies.
Additionally, it provides actionable data inputs and modeling guidance for optimizing cross-channel budgets. It also offers comprehensive educational resources and support for implementation.
As companies increasingly recognize the value of MMMs in achieving revenue goals, Meridian provides a solution that combines innovation, transparency and practicality.
Based on the press release, it seems Meridian does not differ from other MMM tools. Reputable MMM tools prioritize privacy, employ Bayesian methods and offer a wide selection of control variables and customizable settings.
The documentation reveals that Google’s Meridian employs a more advanced approach than other solutions.
While Google’s documentation is extensive, it’s essential not to underestimate the complexity of implementing and handling data. Technical and analytical support for modeling work is highly 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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Meridian’s capabilities and limitations
Local vs. national-level modeling
Meridian is a powerful tool that takes your marketing data to the next level.
Unlike the traditional, national-level models, Meridian lets you zoom into your marketing efforts on a local or regional scale using hierarchical geo-level modeling.
This approach gives you more detailed insights and often results in more reliable figures on how effective your marketing strategies are, particularly in terms of ROI.
With Meridian, you’re not limited to just a few data points. It can handle over 50 geographical locations and 2-3 years of weekly data, making it a beast at crunching numbers.
Thanks to its use of advanced tech like 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 national-level approach. However, one of its standout features is that it lets you bring what you already know into the equation.
Incorporating past knowledge for Bayesian modeling
Using Bayesian models, you can add your past knowledge about how your media performs into Meridian. This includes insights from previous experiments, other marketing mix models, industry know-how or benchmarks. This way, you’re not starting from scratch but building on what you already know.
Meridian intelligently models the waning effectiveness of marketing strategies over time and their spread of impact, enhancing prediction accuracy. Additionally, it delves into the influence of unique viewers and ad frequency on marketing, offering 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 spending your marketing budget wisely, Meridian shines by helping you figure out the best way to spread your budget across different channels or suggesting the best total budget to meet your goals.
With Meridian, you can also play around with “what-if” scenarios to see how different strategies could have played out. Finally, it gives you a clear report on how well it fits your data, helping you decide which strategies work best.
Limitations in analyzing marketing performance
Meridian has significant limitations, notably its lack of upper vs. lower funnel support, a common issue with most MMMs.
This makes it challenging to separate and analyze these components independently. However, if Meridian had this feature, it could stand out more compared to competitors.
Another limitation is that Meridian doesn’t account for 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 failure to consider this could lead to inaccurate forecasts and analysis, particularly when dealing with longer timeframes.
Google’s Meridian vs. Meta’s Robyn
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, whereas Google is still in the process of building theirs, with limited access via application. Robyn is accessible to all via GitHub, fostering community support.
The effectiveness of Meridian and Robyn 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 boost paid search traffic, while Robyn might favor impression-heavy ads on Meta’s platforms, though this will become clearer with continued usage.
As of now, Meridian is a nice early-access project to play around with. It will have to show if implementation and analysis with real data can benefit advertisers.
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