Managing Trade-in programs based on product characteristics and customer heterogeneity in business-to-business markets

Kate J. Li, Duncan K.H. Fong, Susan H. Xu

Research output: Contribution to journalArticle

20 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)108-123
Number of pages16
JournalManufacturing and Service Operations Management
Volume13
Issue number1
DOIs
StatePublished - Dec 1 2011

Fingerprint

Business-to-business market
Customer heterogeneity
Product characteristics
Authorization
High-tech companies
Prediction
Product returns
Business-to-business (B2B)
Discrepancy
Customer behavior
Benchmark

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research

Cite this

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Managing Trade-in programs based on product characteristics and customer heterogeneity in business-to-business markets. / Li, Kate J.; Fong, Duncan K.H.; Xu, Susan H.

In: Manufacturing and Service Operations Management, Vol. 13, No. 1, 01.12.2011, p. 108-123.

Research output: Contribution to journalArticle

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