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

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, 9(10) https://doi.org/10.64388/IREV9I10-1716183