Click-Based MNL: Algorithmic Frameworks for Modeling Click Data in Assortment Optimization

69 Pages Posted: 17 Mar 2019 Last revised: 3 Jun 2019

See all articles by Ali Aouad

Ali Aouad

London Business School; Massachusetts Institute of Technology (MIT) - Operations Research Center

Jacob Feldman

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

Danny Segev

University of Haifa - Department of Statistics

Dennis Zhang

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

Date Written: February 23, 2019

Abstract

In this paper, we introduce the click-based MNL choice model, a novel framework for capturing customer purchasing decisions in e-commerce settings. Our main modeling idea is to assume that the click behavior within product recommendation or search results pages provides an exact signal regarding the alternatives considered by each customer. We study the resulting assortment optimization problem, where the objective is to select a subset of products, made available for purchase, to maximize the expected revenue. Our main algorithmic contribution comes in the form of a polynomial-time approximation scheme (PTAS) for this problem, showing that the optimal expected revenue can be efficiently approached within any degree of accuracy. In the course of establishing this result, we develop novel technical ideas, including enumeration schemes and stochastic inequalities, which may be of broader interest.

In order to quantify the benefits of incorporating click behavior within choice models, we present a case study based on data acquired in collaboration with the retail giant Alibaba. We fit click-based MNL and standard MNL models to historical sales and click data in a setting where the online platform must present customized six-product displays to users. We demonstrate that utilizing the click-based MNL model leads to substantial improvements over the standard MNL model in terms of prediction accuracy. Furthermore, we generate realistic assortment optimization instances that mirror Alibaba's customization problem, and implement practical variants of our approximation scheme to compute assortment recommendations in these settings. We find that the recommended assortments have the potential to be at least 9% more profitable than those resulting from a standard MNL model. We identify a simple greedy heuristic, which can be implemented at large scale, while also achieving near-optimal revenue performance in our experiments.

Keywords: Choice Models, Retailing Platforms, Assortment Optimization, Approximation Schemes, E-Commerce

Suggested Citation

Aouad, Ali and Feldman, Jacob and Segev, Danny and Zhang, Dennis, Click-Based MNL: Algorithmic Frameworks for Modeling Click Data in Assortment Optimization (February 23, 2019). Available at SSRN: https://ssrn.com/abstract=3340620 or http://dx.doi.org/10.2139/ssrn.3340620

Ali Aouad

London Business School ( email )

Sussex Place
Regent's Park
London, London NW1 4SA
United Kingdom

Massachusetts Institute of Technology (MIT) - Operations Research Center ( email )

77 Massachusetts Avenue
Bldg. E 40-149
Cambridge, MA 02139
United States

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

Danny Segev (Contact Author)

University of Haifa - Department of Statistics ( email )

Haifa 31905
Israel

Dennis Zhang

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

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

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