Predictive Customer Lifetime Value: Transitioning from Segment-Based Classification to Probabilistic Revenue Forecasting

• Author(s): Devansh Mishra; Deepam Singh; Dr. Ishrat Ali; Prof. Sanjay Pachauri
• Paper ID: 1716183
• Page: 1579-1580
• Published Date: 16-04-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 10 April-2026
• DOI: https://doi.org/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.

Citations

IRE Journals:
Devansh Mishra, Deepam Singh, Dr. Ishrat Ali, Prof. Sanjay Pachauri "Predictive Customer Lifetime Value: Transitioning from Segment-Based Classification to Probabilistic Revenue Forecasting" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 1579-1580 https://doi.org/10.64388/IREV9I10-1716183

IEEE:
Devansh Mishra, Deepam Singh, Dr. Ishrat Ali, Prof. Sanjay Pachauri "Predictive Customer Lifetime Value: Transitioning from Segment-Based Classification to Probabilistic Revenue Forecasting" Iconic Research And Engineering Journals, vol. 9, no. 10, Apr. 2026, doi: https://doi.org/10.64388/IREV9I10-1716183

APA:
Devansh Mishra, Deepam Singh, Dr. Ishrat Ali, Prof. Sanjay Pachauri (2026). Predictive Customer Lifetime Value: Transitioning from Segment-Based Classification to Probabilistic Revenue Forecasting. Iconic Research And Engineering Journals, 9(10). doi: https://doi.org/10.64388/IREV9I10-1716183

MLA:
Devansh Mishra, Deepam Singh, Dr. Ishrat Ali, Prof. Sanjay Pachauri "Predictive Customer Lifetime Value: Transitioning from Segment-Based Classification to Probabilistic Revenue Forecasting" Iconic Research And Engineering Journals, vol. 9, no. 10, Apr. 2026. Crossref, https://doi.org/10.64388/IREV9I10-1716183

BibTeX

@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}
}