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Determining profit-optimizing return policies – a two-step approach on data from taobao.com

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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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Acknowledgments

The authors gratelfully acknowledge the financial support from the China Scholarship Council (No. 201307080002).

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Authors and Affiliations

  1. Institute of information management, TU Darmstadt/Electronic Markets, Hochschulstraße 1, 64289, Darmstadt, Germany

    Wenyan Zhou & Oliver Hinz

Authors
  1. Wenyan Zhou
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  2. Oliver Hinz
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Correspondence to Wenyan Zhou.

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Responsible Editor: Andreja Pucihar

Appendix

Appendix

Change for sales (∆S): Change due to return policy [α2 or α3] /Average number of sales [116.726]

Change for returns (∆R): Change due to return policy [ß2 or ß3]* Average return rate (with return record) [0.134]

Change for return probability (RP): Change due to return policy [γ2 or γ3]* Non-return rate [45.5 %]

Profit (P) = (1 + ∆S)*[1-∆R*(1- RP)]* Margin - ∆R*(1- RP)* Return cost

where Margin is 10 % (Dobbs et al. 2013) of revenue, while Return cost is 3.8 % (Blanchard 2007) of revenue.

Profit of normal sellers (P normal ) = 1*(1–7.4 %)*10 %–7.4 %*3.8 % = 8.9788 %

Profit of sellers using Seven-Day Return (P 7-day ) = (1 + 0.0768) * [1–0.1895 * (1–45.7 %)] * 10 % - 0.1895 * (1–45.7 %)*3.8 % = 9.269 %

Increase in Profit from by Seven-Day Return Policy (∆P 7-day ) = P 7-day - P normal  = 0.29 %

Profit of sellers using Guarantee Money (P money ) = (1 + 0.00016) * [1–0.13397 * (1–45.58 %)] *10 % - 0.13397 * (1–45.58 %) *3.8 % = 8.995 %

Increase in Profit from by Guarantee Money Policy (∆P money ) = P money - P normal  = 0.016 %

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Zhou, W., Hinz, O. Determining profit-optimizing return policies – a two-step approach on data from taobao.com. Electron Markets 26, 103–114 (2016). https://doi.org/10.1007/s12525-015-0198-6

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