IDEAS home Printed from https://ideas.repec.org/a/eee/jouret/v97y2021i4p676-696.html
   My bibliography  Save this article

A Comparison of Return Rate Calculation Methods: Evidence from 16 Retailers

Author

Listed:
  • El Kihal, Siham
  • Nurullayev, Namig
  • Schulze, Christian
  • Skiera, Bernd

Abstract

The product return rate (RR) is an important metric for retailers; even small RR changes can significantly impact retailers’ profit. Companies and researchers typically favor and employ one of three methods to calculate the RR: based on the number of returned items, these items’ revenue, or their profit contribution. Interviews with 24 managers and industry experts reveal that two methods, item-based and revenue-based, are often used. However, little is known about how much the interpretation of RRs depends on the calculation method. In this article, the authors rely on extensive datasets to investigate these potential differences empirically. Analyzing more than 8 million transactions at sixteen different retailers, the authors find that RRs calculated via the three methods differ on average by 24.3%. The size of these differences makes cross-sectional comparisons of RRs calculated via different methods difficult. In contrast, the authors find that the developments of the RRs over time are similar, which allows for a meaningful time-series comparison of RR developments, regardless of the method. Finally, this research shows that all three calculation methods result in RRs that are equally insightful (leading) indicators of relevant retailer performance metrics.

Suggested Citation

  • El Kihal, Siham & Nurullayev, Namig & Schulze, Christian & Skiera, Bernd, 2021. "A Comparison of Return Rate Calculation Methods: Evidence from 16 Retailers," Journal of Retailing, Elsevier, vol. 97(4), pages 676-696.
    • Handle: RePEc:eee:jouret:v:97:y:2021:i:4:p:676-696
    DOI: 10.1016/j.jretai.2021.04.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0022435921000312
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jretai.2021.04.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jian Ni & Scott A. Neslin & Baohong Sun, 2012. "Database Submission--The ISMS Durable Goods Data Sets," Marketing Science, INFORMS, vol. 31(6), pages 1008-1013, November.
    2. Banker, Rd & Datar, Sm, 1989. "Sensitivity, Precision, And Linear Aggregation Of Signals For Performance Evaluation," Journal of Accounting Research, Wiley Blackwell, vol. 27(1), pages 21-39.
    3. Eric T. Anderson & Karsten Hansen & Duncan Simester, 2009. "The Option Value of Returns: Theory and Empirical Evidence," Marketing Science, INFORMS, vol. 28(3), pages 405-423, 05-06.
    4. Mostard, Julien & Teunter, Ruud, 2006. "The newsboy problem with resalable returns: A single period model and case study," European Journal of Operational Research, Elsevier, vol. 169(1), pages 81-96, February.
    5. Srikant Datar & Susan Cohen Kulp & Richard A. Lambert, 2001. "Balancing Performance Measures," Journal of Accounting Research, Wiley Blackwell, vol. 39(1), pages 75-92, June.
    6. Nachiketa Sahoo & Chrysanthos Dellarocas & Shuba Srinivasan, 2018. "The Impact of Online Product Reviews on Product Returns," Information Systems Research, INFORMS, vol. 29(3), pages 723-738, September.
    7. Toktay, B. & van der Laan, E.A. & de Brito, M.P., 2003. "Managing Product Returns: The Role of Forecasting," ERIM Report Series Research in Management ERS-2003-023-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    8. Cui, Hailong & Rajagopalan, Sampath & Ward, Amy R., 2020. "Predicting product return volume using machine learning methods," European Journal of Operational Research, Elsevier, vol. 281(3), pages 612-627.
    9. Petersen, J. Andrew & McAlister, Leigh & Reibstein, David J. & Winer, Russell S. & Kumar, V. & Atkinson, Geoff, 2009. "Choosing the Right Metrics to Maximize Profitability and Shareholder Value," Journal of Retailing, Elsevier, vol. 85(1), pages 95-111.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. El Kihal, Siham & Shehu, Edlira, 2022. "It's not only what they buy, it's also what they keep: Linking marketing instruments to product returns," Journal of Retailing, Elsevier, vol. 98(3), pages 558-571.
    2. Maier, Erik & Bornschein, Rico & Manss, Rico & Hesse, Damian, 2023. "Financial consequences of adding bricks to clicks," International Journal of Research in Marketing, Elsevier, vol. 40(3), pages 609-628.
    3. Duong, Quang Huy & Zhou, Li & Meng, Meng & Nguyen, Truong Van & Ieromonachou, Petros & Nguyen, Duy Tiep, 2022. "Understanding product returns: A systematic literature review using machine learning and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 243(C).
    4. Krallman, Alexandra & Barnes, Donald C. & Lastner, Matthew M. & Collier, Joel E., 2023. "You can’t touch this: Driving purchase justification for hedonic online purchases," Journal of Business Research, Elsevier, vol. 155(PB).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Duong, Quang Huy & Zhou, Li & Meng, Meng & Nguyen, Truong Van & Ieromonachou, Petros & Nguyen, Duy Tiep, 2022. "Understanding product returns: A systematic literature review using machine learning and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 243(C).
    2. Yang, Hui & Chen, Jing & Chen, Xu & Chen, Bintong, 2017. "The impact of customer returns in a supply chain with a common retailer," European Journal of Operational Research, Elsevier, vol. 256(1), pages 139-150.
    3. Mostafa Rezaei & Ivor Cribben & Michele Samorani, 2021. "A clustering-based feature selection method for automatically generated relational attributes," Annals of Operations Research, Springer, vol. 303(1), pages 233-263, August.
    4. Michael Raith, 2008. "Specific knowledge and performance measurement," RAND Journal of Economics, RAND Corporation, vol. 39(4), pages 1059-1079, December.
    5. Ülkü, M. Ali & Gürler, Ülkü, 2018. "The impact of abusing return policies: A newsvendor model with opportunistic consumers," International Journal of Production Economics, Elsevier, vol. 203(C), pages 124-133.
    6. Sagnika Sen & T. S. Raghu, 2013. "Interdependencies in IT Infrastructure Services: Analyzing Service Processes for Optimal Incentive Design," Information Systems Research, INFORMS, vol. 24(3), pages 822-841, September.
    7. Thiele, Veikko, 2007. "Task-Specific Abilities in Multi-Task Agency Relations," MPRA Paper 2470, University Library of Munich, Germany.
    8. Chen, Jing & Chen, Bintong & Li, Wei, 2018. "Who should be pricing leader in the presence of customer returns?," European Journal of Operational Research, Elsevier, vol. 265(2), pages 735-747.
    9. Schulz, Petra & Shehu, Edlira & Clement, Michel, 2019. "When consumers can return digital products: Influence of firm- and consumer-induced communication on the returns and profitability of news articles," International Journal of Research in Marketing, Elsevier, vol. 36(3), pages 454-470.
    10. Park, Sohee, 2023. "Customer prospects and pay-performance sensitivity: Evidence from Korea," Research in International Business and Finance, Elsevier, vol. 64(C).
    11. Dirk Sliwka, 2002. "On the Use of Nonfinancial Performance Measures in Management Compensation," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 11(3), pages 487-511, September.
    12. Hu, Wei & Li, Yongjian & Govindan, Kannan, 2014. "The impact of consumer returns policies on consignment contracts with inventory control," European Journal of Operational Research, Elsevier, vol. 233(2), pages 398-407.
    13. Budde, Jörg, 2006. "Distorted performance measurement and relational contracts," Discussion Paper Series of SFB/TR 15 Governance and the Efficiency of Economic Systems 203, Free University of Berlin, Humboldt University of Berlin, University of Bonn, University of Mannheim, University of Munich.
    14. Ola Kvaløy & Trond E. Olsen, 2023. "Balanced Scorecards: A Relational Contract Approach," Journal of Accounting Research, Wiley Blackwell, vol. 61(2), pages 619-652, May.
    15. Daria Dzyabura & Siham El Kihal & John R. Hauser & Marat Ibragimov, 2023. "Leveraging the Power of Images in Managing Product Return Rates," Marketing Science, INFORMS, vol. 42(6), pages 1125-1142, November.
    16. Michal Matv{e}jka & Kenneth A. Merchant & Wim A. Van der Stede, 2009. "Employment Horizon and the Choice of Performance Measures: Empirical Evidence from Annual Bonus Plans of Loss-Making Entities," Management Science, INFORMS, vol. 55(6), pages 890-905, June.
    17. Xu, Senyu & Tang, Huajun & Lin, Zhijun & Lu, Jing, 2022. "Pricing and sales-effort analysis of dual-channel supply chain with channel preference, cross-channel return and free riding behavior based on revenue-sharing contract," International Journal of Production Economics, Elsevier, vol. 249(C).
    18. Margaret A. Abernethy & Jan Bouwens & Laurence Van Lent, 2013. "The Role of Performance Measures in the Intertemporal Decisions of Business Unit Managers," Contemporary Accounting Research, John Wiley & Sons, vol. 30(3), pages 925-961, September.
    19. Sunil Dutta & Stefan Reichelstein, 2003. "Leading Indicator Variables, Performance Measurement, and Long‐Term Versus Short‐Term Contracts," Journal of Accounting Research, Wiley Blackwell, vol. 41(5), pages 837-866, December.
    20. Wendelin Schnedler, 2008. "When Is It Foolish to Reward for A While Benefiting from B?," Journal of Labor Economics, University of Chicago Press, vol. 26(4), pages 595-619, October.

    More about this item

    Keywords

    Retailing; Product return rate; Product returns; Marketing metrics; Electronic commerce;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jouret:v:97:y:2021:i:4:p:676-696. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-retailing .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.