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Mining Customer Value: From Association Rules to Direct Marketing

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Abstract

Direct marketing is a modern business activity with an aim to maximize the profit generated from marketing to a selected group of customers. A key to direct marketing is to select a subset of customers so as to maximize the profit return while minimizing the cost. Achieving this goal is difficult due to the extremely imbalanced data and the inverse correlation between the probability that a customer responds and the dollar amount generated by a response. We present a solution to this problem based on a creative use of association rules. Association rule mining searches for all rules above an interestingness threshold, as opposed to some rules in a heuristic-based search. Promising association rules are then selected based on the observed value of the customers they summarize. Selected association rules are used to build a model for predicting the value of a future customer. On the challenging KDD-CUP-98 dataset, this approach generates 41% more profit than the KDD-CUP winner and 35% more profit than the best result published thereafter, with 57.7% recall on responders and 78.0% recall on non-responders. The average profit per mail is 3.3 times that of the KDD-CUP winner.

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Authors and Affiliations

  1. School of Computing Science, Simon Fraser University, BC, Canada

    Ke Wang Wong & Senqiang Zhou

  2. Department of Computer Science, The Hong Kong University of Science and Technology, Hong Kong, China

    Qiang Yang

  3. School of Computing Science, Simon Fraser University, BC, Canada

    Jack Man Shun Yeung

Authors
  1. Ke Wang Wong
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  2. Senqiang Zhou
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  3. Qiang Yang
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Correspondence to Ke Wang Wong.

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Wong, K.W., Zhou, S., Yang, Q. et al. Mining Customer Value: From Association Rules to Direct Marketing. Data Min Knowl Disc 11, 57–79 (2005). https://doi.org/10.1007/s10618-005-1355-x

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