Meent & Tomaszewski - Fusing Machine Learning and Mixed Integer Linear Programming

Machine learning models combined with mixed integer linear programming help optimize marketing spend allocations and promotional campaigns

Models need regular monitoring and retraining to account for shifts in market conditions, competitor behavior, and customer responsiveness

Key challenges include:

• Limited historical data makes effects hard to measure
• Correlations between variables can cause instability
• Model uncertainty needs to be considered in optimization
• Changes in external factors (weather, competition, etc.) affect predictions

Best practices for implementation:

• Validate models before deploying to optimization
• Use randomized experiments to measure true causal effects
• Consider business constraints beyond mathematical optimality
• Monitor key performance metrics and model drift
• Maintain awareness of model limitations and uncertainty

Response curves help identify “sweet spots” for marketing investments and identify when diminishing returns set in

Regularization and monotonicity constraints can help produce more stable and interpretable models

Combining human knowledge with model outputs leads to better decision-making than pure optimization

Important to design robust solutions that work well across different scenarios rather than solely mathematically optimal ones

Regular model updates and monitoring help adapt to changing market conditions and maintain effectiveness

Critical to validate that model recommendations make business sense before implementation