A b s t r a c t . 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.
Abstract. Amazon.com paved the way for several large-scale, behavior-based recommendation services as an important value-added expert advice service for online book shops. In this contribution we discuss the effects (and possible reductions of transaction costs) for such services and investigate how such a value-added service can be implemented in the context of scientific libraries. For this purpose we present a new, recently developed recommender system based on a stochastic purchase incidence model, present the underlying stochastic model from repeat-buying theory and analyze whether the underlying assumptions on consumer behavior hold for users of scientific libraries, too. We have analyzed the logfiles with approximately 85 million http-transactions of the web-based online public access catalog (OPAC) of the library of the Universität Karlsruhe (TH) since January 2001 and performed some diagnostic checks. A test prototype is already operational and is currently being evaluated. The recommender service will be fully operational within the library system of the Universität Karlsruhe (TH) by the end of June 2002.
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