Personalized Dynamic Pricing: Hidden Consequences for Customer Churn

39 Pages Posted: 13 Nov 2020 Last revised: 2 Jun 2022

See all articles by Baile Lu

Baile Lu

National University of Defense Technology - College of Systems Engineering

Yuqian Xu

University of North Carolina (UNC) at Chapel Hill - Kenan-Flagler Business School

Hongyan Dai

Central University of Finance and Economics

Weihua Zhou

Zhejiang University

Date Written: July 22, 2020

Abstract

Leveraging large-scale data sources to employ personalized dynamic pricing has been a prominent practice in the retail industry. However, the outcome of this pricing strategy could be mixed: On one hand, it may increase revenue and sales by charging a price close to consumers' willingness to pay; on the other hand, it may decrease store revenue due to unintended consequences such as consumer churn. Therefore, this study aims to empirically investigate the impact of personalized dynamic pricing on store performance and individual consumer behavior under different conditions. To achieve our research goal, we obtain unique access to a large data set from a leading on-demand delivery platform that has launched a personalized dynamic pricing strategy through price discounts in some of its grocery stores. Employing a quasi-experimental setting, we utilize the difference-in-differences method to estimate the causal impact of personalized dynamic pricing. First, we conduct store-level analyses and find that personalized dynamic pricing decreases store revenue. Then, to understand the underlying mechanism, we proceed with individual-level analyses and find that personalized dynamic pricing increases the individual transaction amount and frequency while increasing consumer churn. Therefore, the reduced store-level revenue could be primarily driven by increased consumer churn. Finally, we investigate the moderating effects of an essential feature of personalized dynamic pricing: the price fluctuation over time. We find that a bit more fluctuation can mitigate the negative consequences of personalized dynamic pricing by reducing consumer churn and increasing consumer transaction activities. However, too much price fluctuation intensifies the negative consequences of personalized dynamic pricing.

Keywords: personalized, dynamic pricing, revenue, churn, quasi-experiment, difference-in-differences

Suggested Citation

Lu, Baile and Xu, Yuqian and Dai, Hongyan and Zhou, Weihua, Personalized Dynamic Pricing: Hidden Consequences for Customer Churn (July 22, 2020). Available at SSRN: https://ssrn.com/abstract=3658362 or http://dx.doi.org/10.2139/ssrn.3658362

Baile Lu

National University of Defense Technology - College of Systems Engineering ( email )

Hunan, 410073
China

Yuqian Xu (Contact Author)

University of North Carolina (UNC) at Chapel Hill - Kenan-Flagler Business School ( email )

McColl Building
Chapel Hill, NC 27599-3490
United States

Hongyan Dai

Central University of Finance and Economics ( email )

No 39. Xueyuan South Road
Haidai District
Beijing, 100081
China

Weihua Zhou

Zhejiang University ( email )

38 Zheda Road
Hangzhou, Zhejiang 310058
China

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