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Applicability of Customer Churn Forecasts in a Non-Contractual Setting

  • Jörg Hopmann
  • Anke Thede
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

As selling a product to an existing customer is much more cost effective than acquiring new customers companies increasingly focus on retaining profitable customers rather than concentrating all marketing actions on the acquisition of new customers. For retaining customers it is very important to be able to predict whether a customer is still active. Effectless marketing expenses directed towards already inactive customers can be avoided and more intensive marketing actions can be taken in order to support active customers’ purchase intentions. Several methods exist that can be used to predict customer activity. In this paper we apply a stochastic and a data mining method to real-life B2B purchase histories and compare the usability and the quality of churn prediction of each of the methods in a non-contractual B2B environment.

Keywords

Probit Model Error Type Purchase Intention Active Customer Total Error Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin · Heidelberg 2005

Authors and Affiliations

  • Jörg Hopmann
    • 1
  • Anke Thede
    • 1
  1. 1.Institut für Informationswirtschaft und -ManagementUniversität Karlsruhe (TH)KarlsruheGermany

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