个性化动态定价:客户流失的潜在后果

Baile Lu, Yuqian Xu, Hongyan Dai, Weihua Zhou
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摘要

利用大规模数据源采用个性化动态定价已经成为零售行业的一个突出实践。直观地说,从营销和运营的角度来看,这种定价策略应该会带来更多的收入和销售;然而,它对客户流失的影响尚未完全揭示。因此,本研究的主要目标是调查个性化动态定价对客户流失的影响。为了回答我们的研究问题,我们从一个领先的按需平台获得了一个独特的大数据集,该平台通过在其许多杂货店的价格折扣实施了个性化的动态定价策略。采用准实验设置,我们利用差异中的差异方法来估计个性化动态定价对客户行为的影响。我们发现,个性化动态定价增加了交易数量和频率,但令人惊讶的是,它也增加了客户流失率。此外,通过分析基于时间的价格波动(即折扣随时间的变化)的影响,我们发现价格波动对客户流失的影响呈u型,即价格波动先减少客户流失,然后增加客户流失。最后,我们探讨了潜在的机制:当价格波动相对较低时,我们观察到折扣探索行为;也就是说,顾客通过更频繁的购物而减少每笔订单的购买量来探索折扣种类。因此,当价格波动较低时,增加波动可以提高客户活动水平,降低客户流失概率。但是,在价格波动已经很大的情况下,客户对价格的不确定性感到负担过重,因此,进一步增加波动可能导致客户最终离开平台。从更广泛的角度来看,我们的论文旨在帮助平台运营经理了解一种重要的变革性营销策略的影响和潜在的潜在后果,并从客户体验和平台运营的角度改进策略。
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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.
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