Bayesian Marketing Mix Modeling for Global Fashion Retailer
The client faced significant inefficiencies in allocating their annual marketing budget of US$ 3MM distributed across 17 different channels. Their last-click attribution system over-estimated lower-funnel channels by up to 250%, generating approximately US$ 700K annual waste on saturated Display. Additionally, they lacked visibility on adstock effects and channel interactions, resulting in decisions based more on intuition than causal data. They needed to reduce daily prediction error (MAPE) below 15% and create a budget optimizer that could run in less than 90 minutes for weekly planning.
Description
Marketing Mix Modeling with Bayesian Approach
We developed an advanced Marketing Mix Modeling (MMM) system using Bayesian statistics for a global fashion retailer, optimizing the allocation of US$ 3MM annually in marketing. The model integrates adstock effects (temporal carry-over), channel saturation (diminishing returns) and seasonal factors to quantify the real incremental contribution of 17 different channels. Through optimization under business constraints, the system generated a projected 11.4% increase in revenue without increasing budget, simply through intelligent reallocation between channels. A dashboard with automated monthly updates allows continuous strategy adjustment based on causal evidence, not last-click attribution.
Technologies
Objectives
Measure accurately the incremental contribution of 17 marketing channels with ≥ 95% credibility
Reduce daily prediction error (MAPE) below 15% (from initial 18.7%)
Develop a weekly budget optimizer that runs in less than 90 minutes
Identify channel-specific adstock (carry-over) effects
Quantify saturation points in channels to avoid inefficient spending
Implement an auto-updating dashboard with CI/CD for monthly retraining
Achieve positive ROI in less than 3 months with production implementation
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