{"title":"个性化动态定价:客户流失的潜在后果","authors":"Baile Lu, Yuqian Xu, Hongyan Dai, Weihua Zhou","doi":"10.2139/ssrn.3658362","DOIUrl":null,"url":null,"abstract":"Leveraging large-scale data sources to employ personalized dynamic pricing has been a prominent practice in the retail industry. Intuitively, from a marketing and operations perspective, this pricing strategy should lead to more revenue and sales; however, its impact on customer churn has not been fully uncovered. Therefore, the primary goal of this study is to investigate the impact of personalized dynamic pricing on customer churn. To answer our research question, we obtain unique access to a large data set from one leading on-demand platform that has implemented a personalized dynamic pricing strategy through price discounts in a number of its grocery stores. Employing a quasi-experimental setting, we utilize the difference-in-differences method to estimate the impact of personalized dynamic pricing on customer behaviors. We discover that personalized dynamic pricing increases transaction amount and frequency, while surprisingly, it also increases customer churn. Moreover, based on an analysis of the impact of time-based price fluctuation (i.e., discount variation across time), we find that price fluctuation has a U-shaped impact on customer churn, i.e., price fluctuation first decreases customer churn, and then increases it. Finally, we explore the underlying mechanisms: when the price fluctuation is relatively low, we observe a discount exploration behavior; that is, customers explore the discount variety via more frequent shopping but less purchase per order. Thus, when the price fluctuation is low, increasing the fluctuation can enhance customer activity level and decrease churn probability. However, when the price fluctuation is already high, customers feel too burdened by the price uncertainty, and hence, further increasing the fluctuation may lead customers to eventually leave the platform. On a broader note, our paper aims to help platform operations managers understand the impact and potential hidden consequences of one important type of transformative marketing strategies and to improve the strategy from customer experience and platform operations perspectives.","PeriodicalId":23435,"journal":{"name":"UNSW Business School Research Paper Series","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Personalized Dynamic Pricing: Hidden Consequences for Customer Churn\",\"authors\":\"Baile Lu, Yuqian Xu, Hongyan Dai, Weihua Zhou\",\"doi\":\"10.2139/ssrn.3658362\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Leveraging large-scale data sources to employ personalized dynamic pricing has been a prominent practice in the retail industry. Intuitively, from a marketing and operations perspective, this pricing strategy should lead to more revenue and sales; however, its impact on customer churn has not been fully uncovered. Therefore, the primary goal of this study is to investigate the impact of personalized dynamic pricing on customer churn. To answer our research question, we obtain unique access to a large data set from one leading on-demand platform that has implemented a personalized dynamic pricing strategy through price discounts in a number of its grocery stores. Employing a quasi-experimental setting, we utilize the difference-in-differences method to estimate the impact of personalized dynamic pricing on customer behaviors. We discover that personalized dynamic pricing increases transaction amount and frequency, while surprisingly, it also increases customer churn. Moreover, based on an analysis of the impact of time-based price fluctuation (i.e., discount variation across time), we find that price fluctuation has a U-shaped impact on customer churn, i.e., price fluctuation first decreases customer churn, and then increases it. Finally, we explore the underlying mechanisms: when the price fluctuation is relatively low, we observe a discount exploration behavior; that is, customers explore the discount variety via more frequent shopping but less purchase per order. Thus, when the price fluctuation is low, increasing the fluctuation can enhance customer activity level and decrease churn probability. However, when the price fluctuation is already high, customers feel too burdened by the price uncertainty, and hence, further increasing the fluctuation may lead customers to eventually leave the platform. On a broader note, our paper aims to help platform operations managers understand the impact and potential hidden consequences of one important type of transformative marketing strategies and to improve the strategy from customer experience and platform operations perspectives.\",\"PeriodicalId\":23435,\"journal\":{\"name\":\"UNSW Business School Research Paper Series\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"UNSW Business School Research Paper Series\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3658362\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"UNSW Business School Research Paper Series","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3658362","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Personalized Dynamic Pricing: Hidden Consequences for Customer Churn
Leveraging large-scale data sources to employ personalized dynamic pricing has been a prominent practice in the retail industry. Intuitively, from a marketing and operations perspective, this pricing strategy should lead to more revenue and sales; however, its impact on customer churn has not been fully uncovered. Therefore, the primary goal of this study is to investigate the impact of personalized dynamic pricing on customer churn. To answer our research question, we obtain unique access to a large data set from one leading on-demand platform that has implemented a personalized dynamic pricing strategy through price discounts in a number of its grocery stores. Employing a quasi-experimental setting, we utilize the difference-in-differences method to estimate the impact of personalized dynamic pricing on customer behaviors. We discover that personalized dynamic pricing increases transaction amount and frequency, while surprisingly, it also increases customer churn. Moreover, based on an analysis of the impact of time-based price fluctuation (i.e., discount variation across time), we find that price fluctuation has a U-shaped impact on customer churn, i.e., price fluctuation first decreases customer churn, and then increases it. Finally, we explore the underlying mechanisms: when the price fluctuation is relatively low, we observe a discount exploration behavior; that is, customers explore the discount variety via more frequent shopping but less purchase per order. Thus, when the price fluctuation is low, increasing the fluctuation can enhance customer activity level and decrease churn probability. However, when the price fluctuation is already high, customers feel too burdened by the price uncertainty, and hence, further increasing the fluctuation may lead customers to eventually leave the platform. On a broader note, our paper aims to help platform operations managers understand the impact and potential hidden consequences of one important type of transformative marketing strategies and to improve the strategy from customer experience and platform operations perspectives.