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Predictive Customer Lifetime Value: Transitioning from Segment-Based Classification to Probabilistic Revenue Forecasting
Subject area: Science,Engineering and Technology · Area of research: Data Science
DOI: 10.64388/IREV9I10-1716183
Abstract
Earlier approaches to Customer Lifetime Value (CLV) have largely focused on segmentation techniques such as RFM analysis combined with clustering methods like K-Means [1]. While these methods are useful for identifying customer groups, they do not directly estimate how much value a customer will generate in the future. In this paper, we propose a predictive framework that moves beyond descriptive segmentation toward revenue forecasting. The approach combines the BG/NBD probabilistic model to estimate whether a customer is still active [2] with an XGBoost regression model to predict future spending [3]. This hybrid setup allows businesses to move from static customer grouping to a more dynamic and practical forecasting system.
References
[1] Bult, J. R., & Wansbeek, T. (1995). “Optimal Selection for Direct Mail.” Marketing Science, 14(4), 378–394.
[2] Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005). “Counting Your Customers the Easy Way: An Alternative to the Pareto/NBD Model.” Marketing Science, 24(2), 275–284.
[3] Chen, T., & Guestrin, C. (2016). “XGBoost: A Scalable Tree Boosting System.” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
[4] Lundberg, S. M., & Lee, S.-I. (2017). “A Unified Approach to Interpreting Model Predictions.” Advances in Neural Information Processing Systems (NeurIPS).
How to cite this paper
@article{1716183,
author = {Devansh Mishra, Deepam Singh, Dr. Ishrat Ali, Prof. Sanjay Pachauri},
title = {Predictive Customer Lifetime Value: Transitioning from Segment-Based Classification to Probabilistic Revenue Forecasting},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {1579-1580},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716183.pdf},
abstract = {Earlier approaches to Customer Lifetime Value (CLV) have largely focused on segmentation techniques such as RFM analysis combined with clustering methods like K-Means [1]. While these methods are useful for identifying customer groups, they do not directly estimate how much value a customer will generate in the future. In this paper, we propose a predictive framework that moves beyond descriptive segmentation toward revenue forecasting. The approach combines the BG/NBD probabilistic model to estimate whether a customer is still active [2] with an XGBoost regression model to predict future spending [3]. This hybrid setup allows businesses to move from static customer grouping to a more dynamic and practical forecasting system.},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1716183}
}