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

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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)

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.

Suggested Citation

  • Kate J. Li & Duncan K. H. Fong & Susan H. Xu, 2011. "Managing Trade-in Programs Based on Product Characteristics and Customer Heterogeneity in Business-to-Business Markets," Manufacturing & Service Operations Management, INFORMS, vol. 13(1), pages 108-123, October.
    • Handle: RePEc:inm:ormsom:v:13:y:2011:i:1:p:108-123
    DOI: 10.1287/msom.1100.0307
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    2. Long Gao & Susan H. Xu & Michael O. Ball, 2012. "Managing an Available-to-Promise Assembly System with Dynamic Short-Term Pseudo-Order Forecast," Management Science, INFORMS, vol. 58(4), pages 770-790, April.
    3. Choi, Tsan-Ming & Chow, Pui-Sze & Lee, Chang Hwan & Shen, Bin, 2018. "Used intimate apparel collection programs: A game-theoretic analytical study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 44-62.
    4. Quan, Yuting & Hong, Jiangtao & Song, Jingpu & Leng, Mingming, 2021. "Game-theoretic analysis of trade-in services in closed-loop supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    5. Xiao, Lu & Wang, Xian-Jia & Chin, Kwai-Sang, 2020. "Trade-in strategies in retail channel and dual-channel closed-loop supply chain with remanufacturing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    6. Genc, Talat S. & De Giovanni, Pietro, 2018. "Optimal return and rebate mechanism in a closed-loop supply chain game," European Journal of Operational Research, Elsevier, vol. 269(2), pages 661-681.
    7. Jayashree Mahajan & Asoo J Vakharia, 2016. "Waste Management: A Reverse Supply Chain Perspective," Vikalpa: The Journal for Decision Makers, , vol. 41(3), pages 197-208, September.
    8. Chen, Jen-Ming & Hsu, Yu-Ting, 2017. "Revenue management for durable goods using trade-ins with certified pre-owned options," International Journal of Production Economics, Elsevier, vol. 186(C), pages 55-70.
    9. Cao, Kaiying & Han, Guoxin & Xu, Bing & Wang, Jia, 2020. "Gift card payment or cash payment: Which payment is suitable for trade-in rebate?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 134(C).
    10. Cao, Kaiying & Xu, Xiaoyan & Bian, Yiwen & Sun, Yanhong, 2019. "Optimal trade-in strategy of business-to-consumer platform with dual-format retailing model," Omega, Elsevier, vol. 82(C), pages 181-192.
    11. Shin, Youngchul & Lee, Sangyoon & Moon, Ilkyeong, 2020. "Robust multiperiod inventory model considering trade-in program and refurbishment service: Implications to emerging markets," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    12. Miao, Zhaowei & Fu, Ke & Xia, Zhiqiang & Wang, Yu, 2017. "Models for closed-loop supply chain with trade-ins," Omega, Elsevier, vol. 66(PB), pages 308-326.
    13. Ma, Peng & Gong, Yeming & Mirchandani, Prakash, 2020. "Trade-in for remanufactured products: Pricing with double reference effects," International Journal of Production Economics, Elsevier, vol. 230(C).
    14. Joni Salminen & Mekhail Mustak & Muhammad Sufyan & Bernard J. Jansen, 2023. "How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 677-692, December.

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