Machine Learning Framework for Electric Vehicle Customer Acquisition in the Automotive Industry
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I3P101Keywords:
Machine Learning, Data-Driven Marketing, Luxury Automotive, Customer Acquisition, Electric Vehicles (EV)Abstract
The rapid growth of the EV market presents a major opportunity for automotive companies to expand their customer base and drive sustainable revenue. This paper outlines the development of a data-driven customer acquisition strategy for an automotive client—their first such initiative. The strategy leveraged historical EV sales trends to identify two key segments: existing brand customers and conquest customers. Initially focusing on existing customers, we built a scalable framework for future expansion. A machine-learning-based propensity model was developed using demographic and lifestyle attributes, sales and service history, charging infrastructure, state incentives, and fuel price trends. Key predictors included purchase recency, financial health, infrastructure availability, and technological affinity. Implemented end-to-end, from data preparation to model deployment. This paper explores the methodology, insights, challenges, and the broader impact of data science in advancing EV adoption
References
1. S. Afandizadeh, D. Sharifi, N. Kalantari, and H. Mirzahossein, "Using machine learning methods to predict electric vehicle penetration in the automotive market," Scientific Reports, vol. 13, p. 8345, 2023.
[Online]. Available: https://www.nature.com/articles/s41598-023-35366-3
2. J.-Y. Yeh and Y.-T. Wang, "A Prediction Model for Electric Vehicle Sales Using Machine Learning Approaches," Journal of Global Information Management, vol. 31, no. 1, 2023.
[Online]. Available: https://www.igi-global.com/article/a-prediction-model-for-electric-vehicle-sales-using-machine-learning-approaches/327277
3. Z. Li, H. Fan, and S. Dong, "Electric Vehicle Sales Forecasting Model Considering Green Premium: A Chinese Market-based Perspective," arXiv preprint, arXiv:2302.13893, 2023.
[Online]. Available: https://arxiv.org/abs/2302.13893
4. Yeğin, Tuğba & Ikram, Muhammad. (2022). Analysis of Consumers' Electric Vehicle Purchase Intentions: An Expansion of the Theory of Planned Behavior. Sustainability. 14. 10.3390/su141912091.
5. Lilhore, Aakash & Prasad, Kavita & Agarwal, Vivek. (2023). Machine Learning-based Electric Vehicle User Behavior Prediction. 1-6. 10.1109/GlobConHT56829.2023.10087780.
6. N. Arechiga, F. Chen, R. Iliev, and A. Molnar, "Understanding and Shifting Preferences for Battery Electric Vehicles," arXiv preprint, arXiv:2202.08963, 2022.
[Online]. Available: https://arxiv.org/abs/2202.08963
7. Tummalapalli Vaibhav. (2025). Stratified sampling in Cohort-based data for Machine learning Model development. International Scientific Journal of Engineering and Management. 04. 1-8. 10.55041/ISJEM03377
8. V. Tummalapalli, “Feature Engineering for Building Machine Learning Models in Automotive Industry,” International Scientific Journal of Engineering and Management, vol. 4, no. 8, pp. 1–9, 2025. doi: 10.55041/ISJEM04985.
9. V. Tummalapalli, “Comprehensive study of data imputation techniques for machine learning models,” International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, vol. 13, no. 4, 2025, doi: 10.37082/IJIRMPS.v13.i4.232674
10. V. Tummalapalli, “Understanding distance metrics in KNN imputation: Theoretical insights and applications,” Journal of Mathematical & Computer Applications, vol. 4, no. 4, pp. 1–4, 2025. doi: 10.47363/JMCA/2025(4)208.
11. Vaibhav Tummalapalli. (2025). Outlier Detection & Treatment for Machine Learning Models. International Journal of Innovative Research and Creative Technology, 11(3), 1–8. https://doi.org/10.5281/zenodo.16500050
12. V.Tummalapalli and K. Konakalla, "Statistical Techniques for Feature Selection in Machine Learning Models," International Journal for Innovative Research in Multidisciplinary Pursuit and Studies (IJIRMPS), vol. 13, no. 3, pp. 1-8, 2025, doi: 10.37082/IJIRMPS.v13.i3.232566
13. V. Tummalapalli, “Machine learning pipeline for automotive propensity models,” International Journal of Core Engineering & Management, vol. 8, no. 3, 2025, ISSN 2348-9510
14. https://ijcem.in/wp-content/uploads/MACHINE-LEARNING-PIPELINE-FOR-AUTOMOTIVE-PROPENSITY-MODELS.pdf
15. Tummalapalli, V. (2026). Cohort-Based Segmentation Framework for Machine Learning: Structuring Temporal Data for Enhanced Feature Engineering. International Journal of Intelligent Data and Machine Learning, 3(03), 05-17. https://doi.org/10.55640/ijidml-v03i03-02
16. Veershetty, G. (2026). Automated Root Cause Analysis in SAP Landscapes Using Large Language Models and Operational Telemetry. International Journal of Emerging Trends in Computer Science and Information Technology, 7(1), 186-191. https://doi.org/10.63282/3050-9246.IJETCSIT-V7I1P127
17. Shashank, A. (2025). AI-Enhanced ETL Processes: Leveraging Artificial Intelligence for Optimized Data Integration Systems. Journal Of Multidisciplinary, 5(8), 219-225.
18. Kaur, M., Bonkra, A., Verma, R., Khanna, N., Maken, P., & Sunkara, S. K. (2025). Comparative study of traditional and hybrid models in short-term financial forecasting using machine learning. In Innovations in Computing (pp. 13-18). CRC Press.
