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Optimizing E-tailer Profits and Customer Savings: Pricing Multistage Customized Online Bundles

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  • Yuanchun Jiang

    (School of Management, Hefei University of Technology, Hefei, Anhui 230009, China)

  • Jennifer Shang

    (Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, Pennsylvania 15260)

  • Chris F. Kemerer

    (Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, Pennsylvania 15260)

  • Yezheng Liu

    (School of Management, Hefei University of Technology, Hefei, Anhui 230009, China)

Abstract

Online retailing provides an opportunity for new pricing options that are not feasible in traditional retail settings. This paper proposes an interactive, dynamic pricing strategy from the perspective of customized bundling to derive savings for customers while maximizing profits for electronic retailers ("e-tailers"). Given product costs, posted prices, shipping fees, and customers' reservation prices, we propose a nonlinear mixed-integer programming model to increase e-tailers' profits by sequentially pricing customized bundles. The model is flexible in terms of the number and variety of products customers may choose to incorporate during the various stages of their online shopping. Our computational study suggests that the proposed model not only attracts more customers to purchase the discounted bundle but also noticeably increases profits for e-tailers. This online dynamic bundle pricing model is robust under various bundle sizes and scenarios. It improves e-tailer profit and customer savings the most when facing divergent views about product values, lower budgets, and higher cost ratios.

Suggested Citation

  • Yuanchun Jiang & Jennifer Shang & Chris F. Kemerer & Yezheng Liu, 2011. "Optimizing E-tailer Profits and Customer Savings: Pricing Multistage Customized Online Bundles," Marketing Science, INFORMS, vol. 30(4), pages 737-752, July.
    • Handle: RePEc:inm:ormksc:v:30:y:2011:i:4:p:737-752
    DOI: 10.1287/mksc.1100.0631
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    Cited by:

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    4. Ritwik Raj & Mark H. Karwan & Chase Murray & Lei Sun, 2022. "Itemized pricing in B2B bundles with diminishing reservation prices and loss averse customers," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(4), pages 375-392, August.
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    6. Huang, Yeu-Shiang & Ho, Jyh-Wen & Wu, Guan-Jin, 2022. "A study on promotion with strategic two-stage customized bundling," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    7. Kanishka Misra & Eric M. Schwartz & Jacob Abernethy, 2019. "Dynamic Online Pricing with Incomplete Information Using Multiarmed Bandit Experiments," Marketing Science, INFORMS, vol. 38(2), pages 226-252, March.
    8. Rong Zhang & Jiatong Li & Zongsheng Huang & Bin Liu, 2019. "Return Strategies and Online Product Customization in a Dual-Channel Supply Chain," Sustainability, MDPI, vol. 11(12), pages 1-17, June.
    9. Jiang, Guoyin & Tadikamalla, Pandu R. & Shang, Jennifer & Zhao, Ling, 2016. "Impacts of knowledge on online brand success: an agent-based model for online market share enhancement," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1093-1103.
    10. Cao, Qingning & Tang, Yuanzhao & Perera, Sandun & Zhang, Jianqiang, 2022. "Manufacturer- versus retailer-initiated bundling: Implications for the supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
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