Customer Interaction Patterns in Electronic Commerce: Maximizing Information Liquidity for Adaptive Decision Making
23 Pages Posted: 23 Oct 2008
Date Written: November 1999
Abstract
Electronic commerce is resulting in unprecedented amounts of transaction and behavior data that are available toorganizations. The emerging bottleneck is that of converting this data into useful information, or that of maximizingthe information liquidity - the rate at which organizations are able to transform the inherent information in a dataset into an economically valuable action. We describe how to overcome this bottleneck, by presenting a model formaximizing information liquidity in electronic commerce. Our model is usable in a variety of situations.Specifically, when a large amount of transaction data already exists, the model is able to exploit this data to generaterules describing preferences that can be used to classify behaviors, and to subsequently map behaviors of non-customersinto known ones. Alternatively, where the predominant data available are about behaviors, the model canbe used to cluster these behaviors and combine the resulting clusters with available transaction data to generate rulesdescribing preferences. In both cases, the central question addressed is "when do I have enough information to makea meaningful offer?� Acting too early can result in inappropriate offers, while acting too late can result in missedopportunities. Good information and timing are therefore critical; the model in this paper is a first step in thisdirection.
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