Optimizing Automotive Sales Incentives: A Machine Learning Approach with XGBoost and SHAP Analysis

The automotive industry is facing increasing complexity and evolving market conditions, making a clear, data-driven evaluation of sales incentives more important than ever. This white paper introduces a robust analytics framework built by MathCo that uses XGBoost machine learning models and SHAP (SHapley Additive exPlanations) to accurately quantify the contribution of individual and bundled sales incentives. Unlike traditional evaluation techniques, our advanced approach captures both high-level and granular impacts across program groups, individual codes, and specific dollar amounts. For scenarios with limited data, a complementary RFV (Relevance, Frequency, Value) scoring model ensures even low-volume programs are assessed with statistical rigor.   Key highlights include:

• Granular analysis of incentive performance across three levels
• Integration of external variables like inventory, seasonality, and program bundling
• Scalable machine learning pipeline with fallback logic for low-data scenarios
• Interactive dashboards for data visualization and business decision-making

  By combining SHAP values with real-world sales outcomes, this methodology empowers automotive manufacturers and dealers to optimize resource allocation, refine promotional strategies, and improve return on investment.