Taking Assortment Optimization From Theory to Practice: Evidence From Large Field Experiments on Alibaba

36 Pages Posted: 26 Aug 2018  

Jacob Feldman

Washington University in St. Louis - John M. Olin Business School

Dennis Zhang

Washington University in St. Louis - John M. Olin Business School

Xiaofei Liu

Alibaba Group

Nannan Zhang

Alibaba Group

Date Written: August 15, 2018

Abstract

We compare the performance of two approaches for finding the optimal set of products to display to customers landing on Alibaba's two online marketplaces, Tmall and Taobao. Both approaches were placed online simultaneously and tested on real customers for one week. The first approach we test is Alibaba's current practice. This procedure embeds hundreds of product and customer features within a sophisticated machine learning algorithm that is used to estimate the purchase probabilities of each product for the customer at hand. The products with the largest expected revenue (revenue * predicted purchase probability) are then made available for purchase. The downside of this approach is that it does not incorporate customer substitution patterns; the estimates of the purchase probabilities are independent of the set of products that eventually are displayed. Our second approach uses a featurized multinomial logit (MNL) model to predict purchase probabilities for each arriving customer. In this way we use less sophisticated machinery to estimate purchase probabilities, but we employ a model that was built to capture customer purchasing behavior and, more specifically, substitution patterns. We use historical sales data to fit the MNL model and then, for each arriving customer, we solve the cardinality-constrained assortment optimization problem under the MNL model online to find the optimal set of products to display. Our experiments show that despite the lower prediction power of our MNL-based approach, it generates 28% higher revenue per visit compared to the current machine learning algorithm with the same set of features. We also conduct various heterogeneous-treatment-effect analyses to demonstrate that the current MNL approach performs best for sellers whose customers generally only make a single purchase. In addition to developing the first full-scale, choice-model-based product recommendation system, we also shed light on new directions for improving such systems for future use.

Keywords: Choice Models, Product Assortment, Machine Learning, Field Experiment, Retail Operations

JEL Classification: C25, C61, C93

Suggested Citation

Feldman, Jacob and Zhang, Dennis and Liu, Xiaofei and Zhang, Nannan, Taking Assortment Optimization From Theory to Practice: Evidence From Large Field Experiments on Alibaba (August 15, 2018). Available at SSRN: https://ssrn.com/abstract=3232059 or http://dx.doi.org/10.2139/ssrn.3232059

Jacob Feldman

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

Dennis Zhang (Contact Author)

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

Xiaofei Liu

Alibaba Group ( email )

Nannan Zhang

Alibaba Group ( email )

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