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Machine Learning-Based Vehicle Renewal Prediction: A Hybrid Approach for Customer Retention in Premium Automotive Markets

Selçuk Bayracı1✉,
Garen Bozoğlanoğlu2,
Turgay Tugay Bilgin3
1Borusan Otomotiv R&D Center
2Borusan Otomotiv R&D Center
3Bursa Technical University
Published:December 7, 2025
DOI: 10.56038/ejrnd.v5i1.692
Vol. 5, No. 1

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.

Keywords
Customer retention predictionVehicle renewal forecastingAutomotive analyticsMachine learningLogistic regressionPredictive modelingRandom forest

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Cite This Article
Bayracı, S., Bozoğlanoğlu, G., Bilgin, T. T. (2025). Machine Learning-Based Vehicle Renewal Prediction: A Hybrid Approach for Customer Retention in Premium Automotive Markets. *The European Journal of Research and Development*, 5(1), 355–377. https://doi.org/10.56038/ejrnd.v5i1.692

Bibliographic Info

JournalThe European Journal of Research and Development
Volume5
Issue1
Pages355–377
PublishedDecember 7, 2025
eISSN2822-2296