Customer future profitability assessment: A data-driven segmentation function approach

Chunhua Tian, Wei Ding, Rongzeng Cao, Michelle Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

One of the important tasks in customer relationship management is to find out the future profitability of individual and/or groups of customers. Data mining-based approaches only provide coarse-grained customer segmentation. It is also hard to obtain a high-precision structure model purely by using regression methods. This paper proposes a data-driven segmentation function that provides a precise regression model on top of the segmentation from a data mining approach. For a new customer, a structure model constructed from profit contribution data of current customers is adopted to assess the profitability. For an existing customer, external information such as stock value performance is taken into the regression model as well as historical trend prediction on the profit contribution. In addition, this paper shows how the proposed approach works and how it improves the customer profitability analysis through experiments on the sample data.

Original languageEnglish (US)
Title of host publicationData Engineering Issues in E-Commerce and Services - Second International Workshop, DEECS 2006, Proceedings
PublisherSpringer-Verlag Berlin Heidelberg
Pages28-39
Number of pages12
ISBN (Print)3540354409, 9783540354406
DOIs
StatePublished - Jan 1 2006
Externally publishedYes
Event2nd International Workshop on Data Engineering Issues in E-Commerce and Services, DEECS 2006 - San Francisco, CA, United States
Duration: Jun 26 2006Jun 26 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4055 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Workshop on Data Engineering Issues in E-Commerce and Services, DEECS 2006
CountryUnited States
CitySan Francisco, CA
Period6/26/066/26/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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