Data Mining for Lifetime Value Estimation

Data Mining for Lifetime Value Estimation

Author: 
Figini, Silvia
Place: 
Hershey
Publisher: 
IGI Global
Date published: 
2008
Editor: 
Wang, John
Journal Title: 
Encyclopedia of Data Warehousing and Mining, Second Edition
Source: 
Encyclopedia of Data Warehousing and Mining, Second Edition
Subject: 
Abstract: 

Customer lifetime value (LTV, see e.g. Bauer et al. 2005 and Rosset et al. 2003), which measures the profit generating potential, or value, of a customer, is increasingly being considered a touchstone for administering the CRM (Customer relationship management) process. This in order to provide attractive benefits and retain high-value customers, while maximizing profits from a business standpoint. Robust and accurate techniques for modelling LTV are essential in order to facilitate CRM via LTV. A customer LTV model needs to be explained and understood to a large degree before it can be adopted to facilitate CRM. LTV is usually considered to be composed of two independent components: tenure and value. Though modelling the value (or equivalently, profit) component of LTV, (which takes into account revenue, fixed and variable costs), is a challenge in itself, our experience has revealed that finance departments, to a large degree, well manage this aspect. Therefore, in this paper, our focus will mainly be on modelling tenure rather than value.

CITATION: Figini, Silvia. Data Mining for Lifetime Value Estimation edited by Wang, John . Hershey : IGI Global , 2008. Encyclopedia of Data Warehousing and Mining, Second Edition - Available at: https://library.au.int/frdata-mining-lifetime-value-estimation