Why traditional Marketing Mix Modelling still matters

For decades, Marketing Mix Modelling (MMM) has helped marketers understand the performance of media and the influence of factors such as competitor spend and seasonality on business KPIs. MMM’s strength lies in cutting through the noise to isolate media’s effect and determine Return on Investment (ROI), making it a trusted measurement tool.

Today, MMM’s relevance has only grown. Privacy restrictions, cookie deprecation and tracking limitations have undermined user-level attribution, making it harder to capture full customer journeys. Unlike those methods, MMM works on aggregated spend and outcome data, making it resilient to these shifts. It’s therefore re-established itself as a core framework for marketing measurement.

The limitation of traditional MMM in measuring ROI

Traditionally, MMM has produced single ROI estimates that downplay the uncertainty behind them. This leaves decision-makers without a clear sense of investment risk. Bayesian MMM addresses this by generating an ROI range for different variables such as media channels. This shifts the focus from single estimates to a range of ROIs with their associated likelihood of being reached. This allows stakeholders to consider both expected returns and the likelihood of achieving them, making Bayesian approaches increasingly attractive in today’s market.

From single ROI estimates to risk-aware decisions

Marketing measurement is moving from an era of single-number estimates to outputting a range of estimates with their associated probabilities that allow for risk-aware decision-making. Traditional MMM has long been effective in quantifying how different media channels have affected business metrics, but its treatment of uncertainty leaves investment risk hidden. Bayesian MMM fills this gap by reframing outputs into probabilities, enabling scenario simulations and supporting more robust budget optimisation.

Bayesian MMM generates a range of values that show how different media channels have affected business metrics. The outputs of this probabilistic method are a range of media contributions, ROIs and response curves with uncertainty bands. These equip marketers with a richer and more realistic understanding of their investments. In a world of shrinking user-level data and growing measurement challenges, it is not just a technical upgrade but a practical evolution, one that is increasingly shaping how marketing decisions are made today.