Bayesian MMM

What is Bayesian MMM?

Bayesian Marketing Mix Modeling (MMM) is a statistical approach to MMM that incorporates probabilistic models and prior distributions, allowing marketers to blend historical data and expert insights into performance measurement.

Why Bayesian MMM Matters

Traditional MMM relies heavily on regression models and can miss nuance when data is limited. Bayesian MMM enhances the process by:

• Incorporating prior knowledge from experts or past campaigns.
• Delivering more stable results in small or noisy datasets.
• Allowing for probabilistic ranges (credible intervals) instead of a single estimate.

Benefits

• Flexibility: Adjusts to different levels of data availability.
• Transparency: Provides a probabilistic view of channel performance.
• Scalability: Useful for both large enterprises and mid-market brands with fewer data points.

Common Applications

• Forecasting channel performance under budget changes.
• Testing scenarios for new campaigns.
• Validating short-term Attribution results with long-term insights.
  
  

Related Terms

• Marketing Mix Modeling (MMM)
• Priors
• Calibration
• Unified Measurement