People Do Not Know What They Want Until You Show it to Them. But When?

Posted: 6 Mar 2021

See all articles by Mei Li

Mei Li

University of Oklahoma - Michael F. Price College of Business

Xi Xiong

JD.com American Technologies Corporation

Xiangyu Chang

Xi'an Jiaotong University (XJTU) - School of Management

Anjana Susarla

Michigan State University - The Eli Broad College of Business and The Eli Broad Graduate School of Management

Subodha Kumar

Temple University - Department of Marketing and Supply Chain Management

Date Written: January 8, 2021

Abstract

Recommendation systems are critical tools for online retailers in their pursuit of enhanced operational performance and improved shopper experience. As such, firms invest heavily to improve their algorithms. Notwithstanding these efforts, there is usually a serious omission of demand-timing element in prevalent online recommendation systems. As a result, recommendations are often presented out of synchronization with the next demand cycle, leading to squandered marketing opportunities and customer dissatisfaction. In this research, we propose a novel demand-driven recommendation system that factors in predicted demand timing. The core of our novel design consists of a predictive model that forecasts product-level repurchase cycles for the online retail environment. We propose a new approach to incorporate the predicted repurchase cycles into three key recommendation generating stages: (i) \textit{retrieval}, (ii) \textit{ranking}, and (iii) \textit{re-ranking}. Using large-scale online experiments, we demonstrate that this novel demand-driven approach outperforms the prevalent recency-based recommendation system in two of the three stages, resulting in higher recommendation click value rate (CVR), higher revenue per mille (RPM), and improved customer satisfaction. Our study contributes to the growing literature on recommendation system design in general and recommendation timing research in particular. Further, our predictive model of the product-level repurchase cycle is novel for the online retail environment and can serve as the basis for improvements and planning of many business decisions. Our research findings are actionable and impactful to online retailers in their pursuits of revenue growth via efficient recommendation-system design. Due to its proven effectiveness, our recommendation system has been implemented by a large online retailer in its commercial platform.

Keywords: Online recommendation, Recommendation system design, Repurchase cycle, Recommendation timing, Demand-driven recommendation system, Large-scale online experiment

Suggested Citation

Li, Mei and Xiong, Xi and Chang, Xiangyu and Susarla, Anjana and Kumar, Subodha, People Do Not Know What They Want Until You Show it to Them. But When? (January 8, 2021). Available at SSRN: https://ssrn.com/abstract=3762339

Mei Li

University of Oklahoma - Michael F. Price College of Business ( email )

307 West Brooks
Norman, OK 73019-4004
United States

Xi Xiong

JD.com American Technologies Corporation ( email )

United States

Xiangyu Chang (Contact Author)

Xi'an Jiaotong University (XJTU) - School of Management ( email )

28,Xianning West Road
Xi'an, Shaanxi 710049
China

Anjana Susarla

Michigan State University - The Eli Broad College of Business and The Eli Broad Graduate School of Management ( email )

East Lansing, MI 48824-1121
United States

Subodha Kumar

Temple University - Department of Marketing and Supply Chain Management ( email )

Philadelphia, PA 19122
United States

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