Meridian: Google’s New Open-Source Marketing Mix Model

Meridian: Google's New Open-Source Marketing Mix Model

Explore Meridian’s abilities and limitations, and how it compares to Meta’s Robyn in the developing MMM scene.

Meridian, Google’s new open-source Marketing Mix Model (MMM), has joined the fast-growing market for advanced marketing analytics and forecasting software.

This article examines Meridian’s primary features, capabilities, and limitations, and compares it to Meta’s MMM, Robyn.

It discusses how Meridian uses complicated methods such as hierarchical geo-level modeling, Bayesian methodologies, and scenario analysis to provide actionable insights for cross-channel budget optimization and marketing plan formulation.

Understanding the marketing mix models

The marketing mix model enables marketers to analyze how different marketing methods affect sales and estimate future results.

MMMs divide sales drivers into elements (e.g., pricing, product features, distribution, promotional actions) and external concerns (e.g., economic situation or competition moves).

These models provide numerical values to each marketing mix component about total sales based on historical data, enabling statistical tools to evaluate individual marketing actions and external variables.

As a result, this knowledge enables marketers to optimize plans, allocate money more intelligently, and predict how a change in one factor will influence future sales.

MMMs use regression analysis or similar statistical approaches on vast amounts of sales and marketing data to uncover patterns and causal linkages, among other things.

This allows businesses to make data-driven decisions, optimizing resource allocation across important operations such as product pricing and increasing brand loyalty via better customer awareness.

In negotiating a complicated market, the accuracy and insights of marketing mix models are critical for strategic planning.

How does Meridian fit into the MMM environment, and what does it provide?

Meridian is an open-source MMM that intends to help teams create models that give more detailed insights into marketing results and decision-making. It highly emphasizes privacy, sophisticated measurement, and accessibility for advertisers.

According to Google, Meridian introduces advances that provide more precise and actionable information. It contains features such as incrementality experiment calibration, reach and frequency inclusion, and specialized instruction on measuring search across all media platforms. 

Meridian stands out because it is transparent, allowing customers to customize the code and settings to match their individual needs. This makes it a very useful tool for improving measuring procedures.

It also offers practical data inputs and modeling help for optimizing cross-channel budgets. It also provides extensive teaching resources and implementation help.

Meridian offers a system that blends innovation, openness, and pragmatism as businesses see the significance of MMMs in meeting revenue targets.

According to the press announcement, Meridian appears to be similar to previous MMM products. Reputable MMM products prioritize privacy, use Bayesian methodologies, and provide a large number of control variables and customizable options.

The documentation suggests that Google Meridian takes a more complex approach than other options.

While Google’s documentation is comprehensive, the complexity of deploying and managing data should not be underestimated. Technical and analytical help for modeling work is strongly advised.

Even if you have no prior expertise, implementing MMMs might be difficult since it needs picking the proper data, training the model, and modifying several parameters.

Meridian’s abilities and limitations

Local vs national-level modelling

Meridian is a fantastic tool for taking your marketing data to the next level. Meridian, unlike typical national-level models, allows you to focus your marketing efforts on a local or regional scale using hierarchical geo-level modeling.

This strategy provides deeper insights and frequently yields more dependable numbers on how effective your marketing initiatives are, particularly in terms of ROI. 

Meridian allows you to work with more than simply a few data points. It can handle over 50 geographical areas and 2-3 years of weekly data, making it a powerhouse at number crunching.

Meridian works quickly and keeps up with your demands because of its usage of sophisticated technology such as Tensorflow Probability and the XLA compiler, as well as the possibility to access GPU hardware via tools such as Google Colab Pro+.

Meridian continues to support the old national-level strategy when no local data is available. One of its most notable aspects is the ability to incorporate prior knowledge into the calculation. 

Using previous information for Bayesian modeling

You may use Bayesian models to integrate your previous knowledge of how your media performs with Meridian. This contains information from prior experiments, other marketing mix models, industry knowledge, and benchmarks. This way, you don’t have to start from zero, but rather build on your existing knowledge.

Meridian intelligently evaluates the declining efficacy of marketing initiatives over time, as well as their influence dispersion, to improve forecast accuracy. It also investigates the impact of unique viewers and ad frequency on marketing, providing additional insights into strategy performance.

It does not stop there.

Meridian is also about making sound decisions, particularly through internet channels like sponsored search, based on data such as Google Query Volume. This allows you to examine the actual impact of your strategies.

Meridian shines when it comes to spending your marketing budget intelligently, advising you on the best approach to split your cash across several channels or recommending the ideal overall budget to reach your goals.

Meridian also allows you to experiment with “what-if” scenarios to explore how other techniques could have worked. Finally, it provides a clear report on how well it matches your data, allowing you to determine which tactics are most effective.

Limitations in evaluating marketing performance

Meridian has many drawbacks, most notably a lack of upper vs. lower funnel support, which is prevalent with MMMs.

This makes it difficult to isolate and analyze these components individually. However, if Meridian had this function, it would be able to differentiate itself from competitors.

Google’s Meridian against Meta’s Robyn

Meta’s MMM Robyn seems more advanced, placing pressure on Google to provide a competing tool as the world’s biggest advertising platform.

Despite Robyn’s tiny design, it shares several functions with Google Meridian.

Meta has released case studies for Robyn, but Google is presently developing theirs, with restricted access through the application. Robyn is open to everyone via GitHub, which encourages community support. 

Meridian also does not account for performance changes within the period under consideration.

Real-world marketing events can have a substantial influence on the success of specific channels. Meridian’s inability to understand this might result in erroneous projections and analysis, especially when dealing with longer durations.

Meridian and Robyn’s efficacy will be assessed when more advertisers employ them, exposing their respective strengths. These MMM technologies also provide significant marketing prospects for advertising networks. Meridian may increase paid search traffic, whereas Robyn may prefer impression-heavy advertisements on Meta’s platforms, but this may become evident with further use.

Meridian is currently a good early-access project to play around with. It has to show that the use and analysis of actual data can help advertising.

Source- searchengineland

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