Managing Customer Acquisition Risk Using Co-operative Databases |
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| Institution: | 1. Department of Marketing, University of Connecticut, 2100 Hillside Rd., U-1041, Storrs, CT 06269-1041, USA;2. Alliant Inc., 301 Fields Lane, Brewster, NY 10509-2621, USA;1. University of Illinois, Urbana-Champaign, 515 E. Gregory Drive, Champaign, IL 61820, USA;2. George Washington University School of Business, 2201 G Street NW, Washington, DC 20052, USA;1. Paul Merage School of Business, University of California, Irvine, CA 92697, USA;2. E. J. Ourso College of Business, Louisiana State University, Baton Rouge, LA 70803, USA;3. Marshall School of Business, University of Southern California, Los Angeles, CA 90089, USA;1. Business Administration Department Facultad de Economía y Empresa, University of Oviedo, Avda del Cristo, s/n, 33071 Oviedo, Spain;2. Center in Franchising, Retail & Service Chains, Graduate School of Management (IGR-IAE Rennes), CREM UMR CNRS 6211, University of Rennes 1, 11 rue Jean Macé – CS 70803, 35708 Rennes Cedex 7, France;1. Department of Marketing, School of Business, Hong Kong Baptist University, Hong Kong;2. Department of Marketing, City University of Hong Kong, Hong Kong;3. College of Management, Lawrence Technological University, United States;1. School of Business, Montclair State University, Montclair, NJ 07043, USA;2. Kent Business School, University of Kent, Canterbury, Kent CT2 7PE, UK |
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| Abstract: | Acquisition of new customers involves both opportunity and risk, and it is important for firms to predict and manage the risks involved in customer acquisition. Despite its importance, the management of customer acquisition risk has not been the subject of much academic research. This paper develops a framework for firms to manage customer acquisition risk using co-operative databases. We illustrate this framework in the context of the optimal selection of customers for direct mail with a ‘buy now, pay later’ payment option when the acquisition risk manifests as bad debt risk. Using data from a large scale direct marketing campaign, we show that our empirical model that incorporates bad debt risk substantially outperforms suboptimal targeting schemes that overlook bad debt risk. We also demonstrate how alleviating bad debt risk is one beneficial outcome of a fairly recent trend in database marketing, namely the emergence of co-operative databases. |
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