How to Properly Choose Your Marketing Mix Modeling Partner

Marketing Mix Modeling (MMM) has become an essential tool for businesses looking to optimize their marketing investments. However, selecting the right MMM partner is not as straightforward as it may seem. Many vendors claim to provide the best solutions, but the reality is that MMM is a complex process requiring careful consideration. Here’s how to properly choose your MMM partner.

Understanding Data Requirements: Comprehensive but Not Perfect

MMM relies on multiple data sources, including key performance indicators (KPIs), media spend, marketing activities, and external factors such as seasonality and economic indicators. However, ensuring that data is 100% comprehensive and complete is virtually impossible. No vendor can guarantee absolute completeness.

A more reasonable approach is to collect as much logically and conceptually relevant data as possible. The goal is not to achieve perfect data coverage but to use the available data to explain the variations in the KPI effectively. MMM is an approximation process, where insights are drawn from a limited but meaningful dataset. An MMM partner who understands this and works to maximize the utility of known data is far more valuable than one making unrealistic claims of data perfection.

Different Modeling Approaches: There’s No One-Size-Fits-All

Different MMM vendors employ different modeling techniques. Some may use traditional linear regression, while others might leverage Bayesian models. The key question isn’t which method is superior; rather, it is whether the model logically and reasonably explains the KPI variations.

Unlike some fields with definitive ground truth and governing bodies, MMM lacks a universally accepted methodology or regulatory authority. This means that as long as the modeling approach provides a reasonable and transparent way to approximate KPIs, it is a valid option. A good MMM partner will be open about their approach and explain their reasoning in a clear and understandable way.

Model Assessment: No Standardized Evaluation

Since there is no universally accepted gold standard for assessing MMM models, different vendors use different validation techniques. Some emphasize traditional statistical tests to ensure model validity and reliability, while others prioritize metrics such as R-squared, p-values, mean squared error, or hold-out testing.

The right MMM partner should not just focus on one or two metrics but rather take a holistic approach to model evaluation. They should be able to justify their choice of validation methods and demonstrate how these ensure the accuracy and reliability of the model.

Balancing Statistical Rigor with Business Relevance

MMM is not just a statistical exercise; it must also align with business and commercial considerations. The ultimate users of MMM insights are media strategists and planners, who need practical and actionable insights to inform their strategies.

A model that meets all statistical criteria but fails to provide actionable recommendations for marketing decision-makers is of little value. The best MMM partners balance statistical rigor with commercial applicability, ensuring that the model’s outputs are both reliable and useful for business strategy.

Beware of AI-Driven Hype

Artificial Intelligence (AI) has become a buzzword in marketing analytics, but it’s important to remain skeptical. Some vendors overstate AI’s role, using it as a catch-all term to obscure a lack of transparency in their models. If an MMM vendor cannot clearly explain their model and instead attributes its performance to AI, this is a red flag.

MMM has been around for decades, long before AI became a mainstream technology. While AI may help automate certain aspects of modeling, it does not replace the fundamental principles of MMM. A trustworthy MMM partner should be able to explain their approach without relying on vague AI jargon.

The Ultimate Test: Reproducibility

One of the most important aspects of any MMM model is its reproducibility. A well-documented model should allow users to reproduce results using the same input data and transformation formulas. Transparency in modeling parameters and mathematical transformations is crucial to ensure credibility.

If an MMM vendor provides a "black-box" solution where the methodology is unclear or non-reproducible, this is a major warning sign. The best way to verify an MMM model is to rerun it with the same inputs and obtain the same or highly similar results. A reputable MMM partner will provide all necessary details to facilitate this process.

Conclusion: Finding the Right MMM Partner

Choosing the right MMM partner requires careful consideration of their approach to data, modeling methodologies, validation techniques, business relevance, and transparency. Beware of vendors who overpromise on data completeness, rely on AI buzzwords, or offer black-box solutions with no reproducibility.

The best MMM partners understand that modeling is an approximation process, that different methodologies can be valid, and that statistical rigor must be balanced with business needs. They provide transparent, reproducible models that empower businesses to make informed marketing decisions.

By focusing on these criteria, you can select an MMM partner who truly adds value to your marketing strategy rather than one who simply follows industry hype.

If you are looking for an MMM partner but are unsure about where to start, why not just talk to us? We are here to help!