Recommending What to Search: Sales Volume and Consumption Diversity Effects of a Query Recommender System

46 Pages Posted: 20 Dec 2023

See all articles by Shuang Zheng

Shuang Zheng

Dalian University of Technology

Siliang Tong

Nanyang Business School, Nanyang Technological University

Hyeokkoo Eric Kwon

Nanyang Business School, Nanyang Technological University, Singapore

Gordon Burtch

Boston University - Questrom School of Business

Xianneng Li

Dalian University of Technology

Date Written: December 18, 2023

Abstract

We consider the sales volume and consumption diversity effects of a query recommender system (QRS). Collaborating with the leading mobile, on-demand food take-out app in Asia, we design and implement a field experiment wherein we randomly assign some users' access to a QRS within their text-based search interface. We show that the QRS drives an approximate 1-2% increase in user order volumes over a 30-day period. Further, we show that the QRS also increases diversity of consumption, not only at the individual level, but also at the level of the market. We provide direct evidence of the QRS's diversity-enhancing impact, showing that treated users begin to employ systematically more generic and shorter search queries, exposing them to a broader array of merchants and products. Finally, we demonstrate that the the value of the QRS depends directly upon the complementary auto-complete feature. We show that the users who respond most strongly to the treatment are those who relied on the auto-complete to a greater degree prior to the introduction of the QRS. Further, we show that treated users increase their reliance on the auto-complete feature relative to users in control, such that treated users reduce their query volumes when not using auto-complete, and increase their query volumes when using auto-complete. Lastly, our findings help to inform platform managers about the impacts of incorporating a QRS both in terms of changes in user search behavior and, ultimately, the economic value that it generates within a platform.

Keywords: query recommendation, m-commerce, mobile search, field experiment, algorithm design, auto-complete, market concentration

JEL Classification: L86,D12,L11

Suggested Citation

Zheng, Shuang and Tong, Siliang and Kwon, Hyeokkoo Eric and Burtch, Gordon and Li, Xianneng, Recommending What to Search: Sales Volume and Consumption Diversity Effects of a Query Recommender System (December 18, 2023). Available at SSRN: https://ssrn.com/abstract=4667778 or http://dx.doi.org/10.2139/ssrn.4667778

Shuang Zheng

Dalian University of Technology ( email )

Huiying Rd
DaLian, LiaoNing, Liaoning 116024
China

Siliang Tong

Nanyang Business School, Nanyang Technological University ( email )

Singapore, 639798
Singapore

Hyeokkoo Eric Kwon

Nanyang Business School, Nanyang Technological University, Singapore ( email )

S3 B2B-71, 50 Nanyang Avenue
Singapore, 639798
Singapore

HOME PAGE: http://sites.google.com/view/erickwon/

Gordon Burtch (Contact Author)

Boston University - Questrom School of Business ( email )

595 Commonwealth Avenue
Boston, MA 02215
United States

Xianneng Li

Dalian University of Technology ( email )

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