Accessibility ≠ Accuracy: Rethinking MMM in the Age of Robyn & Meridian
The real question is no longer “Can we build a model?” – it is “Can we trust the results?”
Marketing Mix Modeling has never been more accessible. Just plug your data into Robyn or Meridian, hit run, and voilà: insights galore. Or so the story goes. But here is the uncomfortable truth: accessibility does not equal accuracy.
The rise of open-source MMM tools from Meta and Google has democratized the space – but it is also lured many into a false sense of security. Just because you can run the code does not mean you understand the model. And worse: it does not mean the model is right.
The real question is no longer “Can we build a model?” – it is “Can we trust the results?”
Because without deep expertise in data, marketing logic, and statistical nuance, you are not building models. You are building myths.
This article unpacks why true MMM success does not come from the algorithm – but from the analyst. It is a call to critically evaluate the human role in a world increasingly obsessed with automation and AI.
The hidden driver of MMM Success
Creating meaningful MMM results requires more than inserting data and running a script. It is an art that demands deep expertise in data, statistics, and marketing, as well as an understanding of business dynamics and marketing channels. The temptation to blindly trust impressive statistical outputs is high, but every “black box” relies on assumptions, decisions, and interpretations that only an expert can critically evaluate.
An experienced analyst is able to open the “black box” and understands the underlying mechanisms. He knows how to select the right variables, how to test assumptions, and how to interpret results. By putting the analyst’s expertise in the foreground, you can ensure that MMM becomes a valuable tool for data-driven marketing decisions. An analyst-driven MMM differs significantly from an algorithmically automated MMM, as validation takes place throughout the entire process. It is not just a final test, but a continuous, iterative review and adjustment enabled by the expertise and critical evaluation of the analyst.
At Exactag, the path to creating a successful MMM Model follows a structured, established process.
PART 1: Foundation
Before building the model, an experienced analyst ensures that the core question is precisely formulated, enabling meaningful validation during development:
Variable Selection: Which variables are relevant? Which can be neglected? Because selecting the right variables is crucial for the accuracy of the model.
Assumptions: What assumptions are made? Are these assumptions realistic and justified? Hypothesis-driven modeling is the key to meaningful interpretable model results
Data selection: A crucial next step in the process is data selection. An experienced analyst carefully evaluates and refines the dataset to ensure it accurately represents the underlying business dynamics. Which data points are truly relevant? Which sources are reliable?
High-quality data is the foundation of any meaningful model – if the input is flawed, even the most sophisticated MMM will produce misleading results.
PART 2: Modeling & Validating
At Exactag, Modeling itself is an ongoing, iterative process – not a one-time setup. Our analysts test various models, evaluate their performance, and continuously improve them – ensuring not only statistical validity but also real-world relevance.
- Model Development: It begins with the development of an MMM model based on data and assumptions. It is crucial to select the right variables and consider the relevant relationships.
- Preliminary Validation: The initial assessment of model accuracy is done through preliminary validations, where model predictions are compared with historical data.
- Ready to use: If an acceptable agreement is achieved at stage 2, the model is considered ready for use. Based on this, further analyses such as optimizations or scenario planning can be initiated.
- Model Refinement: Models are refined based on available results. This may mean adjusting variables, reconsidering assumptions, or integrating new data sources.
- Renewed Validation: Every model adjustment and change requires a renewed validation of the model. The importance lies in ensuring that the changes made do not impair model accuracy and continue to provide valid results. Especially when integrating new data sources or adjusting assumptions, renewed validation is essential to ensure the integrity of the model.
- Iterative Process: This process is continuously repeated to improve the accuracy and reliability of the model. The model grows and learns with each iteration.
PART 3: Interpretation & Actionability
After modeling and validation, the final – and most critical – step is interpretation: making sense of the results and translating them into actionable insights. This requires a deep understanding of the business and marketing strategy to accurately evaluate the model’s findings. How should the results be interpreted? What conclusions can be drawn?
An analyst must grasp the dynamics of various marketing channels to assess the impact of changes, critically question model outputs, and explore alternative explanations to ensure that insights are both accurate and actionable.
Ultimately, the responsibility for the validation and interpretation of MMM results rests with the analyst. Open-source tools can support us, but they cannot replace thinking and acting. They provide a solid foundation and guidance, but it’s human intelligence contextualizing data, understanding nuances, and making informed decisions that turns numbers into real business impact.
Organizational resistance
However, even the most well-constructed model can fail due to organizational resistance, not methodological flaws. Insights are only valuable when implemented, yet many brands struggle with changes that challenge existing processes. This underscores the importance of expert guidance to prepare companies for action. An analyst must ask: Are you truly ready to work with the results and embrace the consequences? Without the willingness to adapt, even the most precise MMM remains just another report.
Balancing tools and expertise
It is essential to recognize the complexity of MMM and the challenges of validation. Models do not have to be fully validated before they are used. Rather, a meaningful structure in the modeling process from the outset and continuous review and adjustment are crucial.
Open-source tools from Google and Meta provide a valuable starting point, but a critical examination of their underlying assumptions and methods is essential. They are to be understood as a basis that should be supplemented by the development of company-specific validation methods. The use of these tools should not be considered a substitute for a sound, analytical approach, but as support to be refined by own validation strategies.
Setting up MMM has become easy. Ensuring its accuracy, reliability, and impact – that is the real challenge.
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