Machine Learning-Based Vehicle Renewal Prediction: A Hybrid Approach for Customer Retention in Premium Automotive Markets
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Abstract
Customer retention and vehicle renewal prediction remain critical challenges in premium automotive markets. This study presents a comprehensive data-driven framework for predicting BMW customer renewal probability using historical transactional and behavioral data from Borusan Otomotiv's enterprise systems. We developed a hybrid machine learning model that integrates Random Forest feature selection with Binary Logistic Regression to achieve interpretability while maintaining predictive accuracy. The model leverages customer demographics, service engagement metrics, and ownership patterns to generate individual-level renewal probability scores.
Evaluated on 1,211 holdout observations through temporal validation, the model achieved 77% overall accuracy and an AUC-ROC of 0.80, demonstrating strong discriminatory power in distinguishing between renewal and non-renewal customers. Model outputs are transformed into five operational risk grades (G1-G5) and seamlessly integrated into Salesforce CRM, enabling proactive customer relationship management and targeted retention strategies.
Key empirical findings indicate that service expenditure patterns, time since last purchase, and multi-vehicle ownership significantly influence renewal likelihood. The framework bridges predictive analytics with operational deployment through automated data pipelines and continuous model monitoring, representing a practical approach to data-driven customer retention in the automotive sector.
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