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Managing Trade-in Programs Based on Product Characteristics and Customer Heterogeneity in Business-to-Business Markets

Kate J. Li (), Duncan K. H. Fong () and Susan H. Xu ()
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Kate J. Li: Department of Information Systems and Operations Management, Sawyer Business School, Suffolk University, Boston, Massachusetts 02108
Duncan K. H. Fong: Department of Marketing, Smeal College of Business, The Pennsylvania State University, University Park, Pennsylvania 16802
Susan H. Xu: Department of Supply Chain and Information Systems, Smeal College of Business, The Pennsylvania State University, University Park, Pennsylvania 16802

Manufacturing & Service Operations Management, 2011, vol. 13, issue 1, 108-123

Abstract: Trade-in programs are offered extensively in business-to-business (B2B) markets. The success of such programs depends on well-designed and executed trade-in policies as well as accurate prediction of return flow to support operational decisions. Motivated by a real problem facing a high-tech company, this paper develops methods to segment customers and forecast product returns based on return merchandise authorization information. Noisy, yet proven to be valuable, returned quantity signals are adjusted by taking product characteristics and customer heterogeneity into account, and the resulting forecast outperforms two benchmark strategies that represent the high-tech company's current practice and a widely adopted method in the literature, respectively. In addition, our methods can serve as tools for companies to uncover the root causes of return merchandise authorization discrepancy, monitor and analyze customer behavior, design segment-specific trade-in policies, and evaluate the effectiveness and efficiency of trade-in programs on a continuous basis.

Keywords: empirical research; trade-in programs; signal-based forecast; count regression models; cluster analysis; customer segmentation (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (15)

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