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Determining Profit-Optimizing Return Policies - A Two-Step Approach on Data from Taobao.com

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  • Zhou, Wenyan
  • Hinz, Oliver

Abstract

Selecting an optimal return policy requires taking into account two effects: the potential positive effect on sales and the potential negative effect of higher costs. We propose a two-step model, in which we first utilize a robust regression to explain purchase behavior, and then apply a zero-inflated negative binominal regression to model the return behavior. We apply this model to data from the most important online platform in China and obtain three main findings. First, the adoption of return policies results in increased sales, while reputation works as a moderator in this process. Second, good reputation and traditional customer friendly return policies (like the Seven-Day Return policy) can significantly increase the number of returns, while more guarantee credibility (enhanced by Guarantee Money) is related to fewer returns. Taken together, both the Seven-Day Return policy (profit increase of +0.29 %) and Guarantee Money (profit increase of +0.016 % per Yuan guarantee) ultimately increase firms’ profit.
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Suggested Citation

  • Zhou, Wenyan & Hinz, Oliver, 2016. "Determining Profit-Optimizing Return Policies - A Two-Step Approach on Data from Taobao.com," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 84851, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    • Handle: RePEc:dar:wpaper:84851
    Note: for complete metadata visit http://tubiblio.ulb.tu-darmstadt.de/84851/
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    Cited by:

    1. Assarzadegan, Parisa & Rasti-Barzoki, Morteza, 2020. "A game theoretic approach for pricing under a return policy and a money back guarantee in a closed loop supply chain," International Journal of Production Economics, Elsevier, vol. 222(C).
    2. Xiaoyu Yu & Yajie Li & Daniel Q. Chen & Xiaotong Meng & Xiangming Tao, 2019. "Entrepreneurial bricolage and online store performance in emerging economies," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 167-185, June.
    3. Rainer Alt & Carsta Militzer-Horstmann & Hans-Dieter Zimmermann, 2016. "Electronic Markets on the impact factor," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(2), pages 95-101, May.
    4. Wenyan Zhou & Oliver Hinz & Alexander Benlian, 2018. "The impact of the package opening process on product returns," Business Research, Springer;German Academic Association for Business Research, vol. 11(2), pages 279-308, September.
    5. Xiaoyu Yu & Yida Tao & Yi Chen & Weiyong Zhang & Pinglei Xu, 2019. "Social networks and online store performance in emerging economies: the mediating effect of legitimacy," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 201-218, June.
    6. Gianfranco Walsh & Michael Möhring, 2017. "Effectiveness of product return-prevention instruments: Empirical evidence," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(4), pages 341-350, November.
    7. Sharma, Dheeraj & Pandey, Shivendra, 2020. "The role payment depreciation in short temporal separations: Should online retailer make customers wait?," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    8. Martínez-López, Francisco J. & Feng, Changyuan & Li, Yangchun & López-López, David, 2022. "Using instant refunds to improve online return experiences," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).

    More about this item

    JEL classification:

    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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