Why Machine Learning is a Helpful Addition to Marketing Mix Modeling

Marketing Mix Modeling (MMM) is a proven way to measure how marketing activities drive results like sales or leads. Traditionally, MMM uses statistical or Bayesian methods, which are great for bringing in business knowledge, keeping things interpretable, and measuring uncertainty.

However, marketing today is much more complex. People see ads across many channels, and campaigns interact in ways that are hard to predict. Machine learning is a valuable extra tool alongside Bayesian MMM, helping to handle this new complexity.

Here’s why machine learning can help:

1. It captures complex interactions

Marketing channels do not work in isolation. Search can support TV, and promotions can amplify influencer campaigns. Machine learning can automatically detect and measure these interactions, even if you do not define them ahead of time.

2. It handles seasonality with more flexibility

Seasonal patterns are not always regular. Machine learning can learn seasonal effects directly from the data, without needing a strict seasonal formula. This makes it easier to adapt when consumer behavior changes.

3. It captures diminishing returns and adstock automatically

In traditional MMM, you usually have to set up formulas to show saturation effects or adstock decay. Machine learning can pick up these patterns on its own, which can save time and reduce the chance of using the wrong assumptions.

4. It avoids a false sense of control

Bayesian models sometimes give a feeling of confidence based on priors you chose yourself, which can create bias. Machine learning relies on data to find relationships, which can reduce this false sense of control.

5. It is harder to control and interpret

The downside of machine learning is that it can be harder to explain. Bayesian MMM is easier to align with business logic, while machine learning models are often more of a black box. That means you may need to work harder to translate results for decision-makers.

In summary, machine learning is a great complement to Bayesian MMM. It can handle complex patterns, adapt to changing data, and avoid overconfident assumptions. Used together, these tools can give a more complete picture of what drives marketing success.