Customer Lifetime Value Prediction

• Author(s): Devansh Mishra; Deepam Singh; Dr. Ishrat Ali; Prof. Sanjay Pachauri
• Paper ID: 1712215
• Page: 1833-1836
• Published Date: 25-11-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 5 November-2025
• DOI: https://doi.org/10.64388/IREV9I5-1712215

Abstract

In the contemporary business landscape, organizations are shifting from transactional metrics to a more relationship-oriented view of their customers. Central to this evolution is the concept of Customer Lifetime Value (CLV), a forward-looking prediction of the total net profit a business can expect from a customer over the entire duration of their relationship.4 Unlike metrics that measure past performance, predictive CLV uses data to forecast future value, empowering businesses to make proactive, data-informed decisions regarding marketing spend, customer acquisition, and retention strategies.6 The ability to accurately predict CLV allows a company to identify its most valuable customers, tailor its engagement strategies, and ultimately foster long-term, sustainable growth.

Citations

IRE Journals:
Devansh Mishra, Deepam Singh, Dr. Ishrat Ali, Prof. Sanjay Pachauri "Customer Lifetime Value Prediction" Iconic Research And Engineering Journals Volume 9 Issue 5 2025 Page 1833-1836 https://doi.org/10.64388/IREV9I5-1712215

IEEE:
Devansh Mishra, Deepam Singh, Dr. Ishrat Ali, Prof. Sanjay Pachauri "Customer Lifetime Value Prediction" Iconic Research And Engineering Journals, vol. 9, no. 5, Nov. 2025, doi: https://doi.org/10.64388/IREV9I5-1712215

APA:
Devansh Mishra, Deepam Singh, Dr. Ishrat Ali, Prof. Sanjay Pachauri (2025). Customer Lifetime Value Prediction. Iconic Research And Engineering Journals, 9(5). doi: https://doi.org/10.64388/IREV9I5-1712215

MLA:
Devansh Mishra, Deepam Singh, Dr. Ishrat Ali, Prof. Sanjay Pachauri "Customer Lifetime Value Prediction" Iconic Research And Engineering Journals, vol. 9, no. 5, Nov. 2025. Crossref, https://doi.org/10.64388/IREV9I5-1712215

BibTeX

@article{1712215,
author = {Devansh Mishra, Deepam Singh, Dr. Ishrat Ali, Prof. Sanjay Pachauri},
title = {Customer Lifetime Value Prediction},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {5},
pages = {1833-1836},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712215.pdf},
abstract = {In the contemporary business landscape, organizations are shifting from transactional metrics to a more relationship-oriented view of their customers. Central to this evolution is the concept of Customer Lifetime Value (CLV), a forward-looking prediction of the total net profit a business can expect from a customer over the entire duration of their relationship.4 Unlike metrics that measure past performance, predictive CLV uses data to forecast future value, empowering businesses to make proactive, data-informed decisions regarding marketing spend, customer acquisition, and retention strategies.6 The ability to accurately predict CLV allows a company to identify its most valuable customers, tailor its engagement strategies, and ultimately foster long-term, sustainable growth.},
month = {November}
}