Multi-Source Heterogeneous Data Fusion for Predicting Customer Lifetime Value in the New Energy Vehicle Market
DOI:
https://doi.org/10.15837/ijccc.2026.5.7431Keywords:
Customer Lifetime Value, New Energy Vehicles, Heterogeneous Data Fusion, Electric Vehicle Charging Behavior, Machine LearningAbstract
Accurate prediction of customer lifetime value (CLV) is essential in the new energy vehicle (NEV) market, where long-term profitability increasingly depends on charging-related services and infrastructure interaction rather than one-time vehicle sales. However, NEV customer behavior is influenced by heterogeneous and dynamically evolving factors, including charging usage patterns, spatial infrastructure availability, and regional market conditions, which challenge conventional transaction-based CLV models.This study proposes a multi-source heterogeneous data fusion framework for NEV CLV prediction using exclusively publicly available and verifiable data. The framework integrates user-level charging transaction records with infrastructure interaction features through a modality-aware fusion architecture, in which behavioral and contextual information is encoded and aligned along temporal and spatial dimensions. CLV is estimated under a discounted usage-based revenue formulation, and a fully reproducible experimental protocol is established for evaluation.The results demonstrate that heterogeneous data fusion improves both predictive accuracy and value-based customer ranking, offering practical decision support for NEV charging service providers and ecosystem stakeholders.
References
Fader, P.S.; Hardie, B.G.S.; Lee, K.L. (2005). RFM and CLV: Using iso-value curves for customer base analysis, Journal of Marketing Research, 42(4), 415-430, 2005. https://doi.org/10.1509/jmkr.2005.42.4.415
Schmittlein, D.C.; Morrison, D.G.; Colombo, R. (1987). Counting your customers: Who are they and what will they do next?, Management Science, 33(1), 1-24, 1987. https://doi.org/10.1287/mnsc.33.1.1
Fader, P.S.; Hardie, B.G.S.; Shang, J. (2010). Customer-base analysis in a discrete-time noncontractual setting, Marketing Science, 29(6), 1086-1108, 2010. https://doi.org/10.1287/mksc.1100.0580
Rossi, F.; Basso, L.; Cattaneo, C. (2019). Machine learning approaches for customer lifetime value prediction, Expert Systems with Applications, 129, 156-170, 2019.
Bertsimas, D.; Kallus, N. (2020). From predictive to prescriptive analytics, Management Science, 66(3), 1025-1044, 2020. https://doi.org/10.1287/mnsc.2018.3253
Shmueli, G.; Koppius, O.R. (2011). Predictive analytics in information systems research, MIS Quarterly, 35(3), 553-572, 2011. https://doi.org/10.2307/23042796
Neslin, S.A.; Gupta, S.; Kamakura, W.; Lu, J.; Mason, C.H. (2006). Defection detection: Measuring and understanding the predictive accuracy of customer churn models, Journal of Marketing Research, 43(2), 204-211, 2006. https://doi.org/10.1509/jmkr.43.2.204
Kumar, V.; Reinartz, W. (2018). Customer Relationship Management: Concept, Strategy, and Tools, Springer, Berlin, Germany, 2018.
Sun, M.; Li, S. (2021). Context-aware consumer behavior modeling with heterogeneous data, Decision Support Systems, 142, Article 113473, 2021. https://doi.org/10.1016/j.dss.2020.113473
Hall, D.L.; Llinas, J. (1997). An introduction to multisensor data fusion, Proceedings of the IEEE, 85(1), 6-23, 1997. https://doi.org/10.1109/5.554205
Chen, J.I.-Z.; Chung, Y.-N. (2012). A data fusion methodology for wireless sensor systems, International Journal of Computers Communications & Control, 7(1), 39-52, 2012. DOI: 10.15837/ijccc.2012.1.208 https://doi.org/10.15837/ijccc.2012.1.1421
Zhang, M.; Lu, J.; Ma, N.; Cheng, T.C.E.; Hua, G. (2022). A feature engineering and ensemble learning based approach for repeated buyers prediction, International Journal of Computers Communications & Control, 17(6), Article 4988, 2022. DOI: 10.15837/ijccc.2022.6.4988 https://doi.org/10.15837/ijccc.2022.6.4988
Hu, F. (2025). An approach of sentiment-topic mining based on user online comments on new energy vehicles, International Journal of Computers Communications & Control, 20(6), Article 6934, 2025. DOI: 10.15837/ijccc.2025.6.6934 https://doi.org/10.15837/ijccc.2025.6.6934
Shmueli, G.; Bruce, P.; Yahav, I.; Patel, N.R.; Lichtendahl, K.C. (2020). Data Mining for Business Analytics, Wiley, Hoboken, NJ, USA, 2020.
Burez, J.; Van den Poel, D. (2009). Handling class imbalance in customer churn prediction, Expert Systems with Applications, 36(3), 4626-4636, 2009. DOI: 10.1016/j.eswa.2008.05.027 https://doi.org/10.1016/j.eswa.2008.05.027
Verbeke, W.; Dejaeger, K.; Martens, D.; Hur, J.; Baesens, B. (2011). Predictive modeling for customer churn, Decision Support Systems, 52(1), 164-173, 2011. DOI: 10.1016/j.dss.2011.05.011
Sadeghianpourhamami, N.; Refa, N.; Develder, C.; Strobbe, M. (2019). Electric vehicle charging demand forecasting using real-world data, Applied Energy, 238, 1507-1518, 2019. DOI: 10.1016/j.apenergy.2019.01.083 https://doi.org/10.1016/j.apenergy.2019.01.083
Liu, Z.; Wen, F.; Ledwich, G. (2013). Optimal planning of electric-vehicle charging stations, IEEE Transactions on Power Delivery, 28(1), 354-362, 2013. https://doi.org/10.1109/TPWRD.2012.2223489
Shao, C.; Yang, H.; Zhang, Y.; Ke, J. (2013). A simple approximation for the charging demand of electric vehicles, Transportation Research Part D: Transport and Environment, 21, 7-13, 2013. DOI: 10.1016/j.trd.2013.01.004 https://doi.org/10.1016/j.trd.2013.01.004
Li, X.; Zhang, Y. (2020). Mining user perceptions of electric vehicles from online reviews, Energy Policy, 138, Article 111245, 2020.
Lee, Z.J.; Li, T.; Low, S.H. (2019). ACN-Data: Analysis and applications of an open electric vehicle charging dataset, Energy Informatics, 2(1), Article 16, 2019. DOI: 10.1186/s42162-019- 0079-9
National Renewable Energy Laboratory (NREL). (2023). Alternative Fueling Station Locations, U.S. Department of Energy, Open Dataset, 2023.
Gupta, S.; Lehmann, D.R.; Stuart, J.A. (2004). Valuing customers, Journal of Marketing Research, 41(1), 7-18, 2004. DOI: 10.1509/jmkr.41.1.7.25084 https://doi.org/10.1509/jmkr.41.1.7.25084
Batislam, E.P.; Denizel, M.; Filiztekin, A. (2007). Empirical validation and comparison of models for customer base analysis, International Journal of Research in Marketing, 24(3), 201-209, 2007. DOI: 10.1016/j.ijresmar.2006.12.005 https://doi.org/10.1016/j.ijresmar.2006.12.005
Hardman, S.; et al. (2018). A review of consumer preferences of and interactions with electric vehicle charging infrastructure, Transportation Research Part D: Transport and Environment, 62, 508-523, 2018. DOI: 10.1016/j.trd.2018.04.002 https://doi.org/10.1016/j.trd.2018.04.002
Nicholas, M.A.; Tal, G.; Turrentine, T. (2017). Advanced plug-in electric vehicle travel and charging behavior interim report, Transportation Research Record, 2654(1), 1-10, 2017. DOI: 10.3141/2654-01 https://doi.org/10.3141/2654-01
Hyndman, R.J.; Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.), OTexts, 2021.
Athey, S. (2018). The impact of machine learning on economics, In: The Economics of Artificial Intelligence, University of Chicago Press, 2018. https://doi.org/10.7208/chicago/9780226613475.003.0021
Additional Files
Published
Issue
Section
License
Copyright (c) 2026 Lei Mei
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
