Leveraging the Power of Images in Managing Product Return Rates

54 Pages Posted: 27 Jul 2018 Last revised: 30 Sep 2022

See all articles by Daria Dzyabura

Daria Dzyabura

New economic school

Siham El Kihal

Frankfurt School of Finance & Management

John R. Hauser

MIT Sloan School of Management

Marat Ibragimov

Massachusetts Institute of Technology (MIT); New Economic School (NES)

Date Written: September 4, 2019

Abstract

In online channels products are returned at high rates. Shipping, processing, and refurbishing are so costly that a retailer’s profit is extremely sensitive to return rates. Using a large dataset from a European apparel retailer, we observe that return rates for fashion items bought online range from 13% to 96%, with an average of 53% – many items are not profitable. Because fashion seasons are over before sufficient data on return rates are observed, retailers need to anticipate each item’s return rate prior to launch. We use product images and traditional measures available prelaunch to predict individual item return rates and decide whether to include the item in the retailer’s assortment. We complement machine-based prediction with tools to interpret automatically extracted image-based interpretable features. Insights suggest how to select and design fashion items that are less likely to be returned. Our illustrative machine-learning models predict well and provide face-valid interpretations – the focal retailer can improve profit by 8.3% and identify items with features less likely to be returned. We demonstrate that other machine-learning models do almost as well, reinforcing the value of using prelaunch images to manage returns.

Keywords: machine learning, image processing, deep learning, product returns

Suggested Citation

Dzyabura, Daria and El Kihal, Siham and Hauser, John R. and Ibragimov, Marat, Leveraging the Power of Images in Managing Product Return Rates (September 4, 2019). Available at SSRN: https://ssrn.com/abstract=3209307 or http://dx.doi.org/10.2139/ssrn.3209307

Daria Dzyabura (Contact Author)

New economic school ( email )

100A Novaya Street
Moscow, Skolkovo 143026
Russia

Siham El Kihal

Frankfurt School of Finance & Management ( email )

Adickesallee 32-34
Frankfurt am Main, 60322
Germany

John R. Hauser

MIT Sloan School of Management ( email )

International Center for Research on the Mngmt Tech.
Cambridge, MA 02142
United States
617-253-2929 (Phone)
617-258-7597 (Fax)

Marat Ibragimov

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

New Economic School (NES) ( email )

100A Novaya Street
Moscow, Skolkovo 143026
Russia

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