Probability-Calibrated Ensemble Methods for Automotive CRM Lead Scoring

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Abstract
Accurately predicting sales conversion in automotive CRM systems is critical for optimizing marketing spend and sales team efficiency. This study presents a calibrated ensemble framework combining XGBoost, Gradient Boosting, and Random Forest classifiers to predict lead conversion probability in automotive dealership operations. Using 62,859 real-world leads collected between July 2024 and July 2025, we developed a systematic pipeline encompassing behavioral feature engineering, statistical feature selection, ensemble modeling, and probability calibration via Platt scaling. The calibrated ensemble achieved an AUC of 0.841, Brier score of 0.146, and 19% improvement in top-decile precision over baseline logistic regression. The framework provides actionable lead segmentation into four priority tiers, directly supporting sales resource allocation and marketing campaign optimization. Results confirm that probability calibration is essential for automotive CRM applications where predicted scores inform operational decisions.

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