ABSTRACT

Online retailers are increasingly focused on maintaining a long-term relationship with customers, encouraging repeat visits rather than single-time purchases to increase customer lifetime value. To help retailers maximize the probabilities of customers’ revisiting and purchasing, we develop a two-stage model to better characterize and predict these two fundamental customer activities. In the first stage, we characterize the propensity of a customer revisiting the retailer’s website. In the second stage, we develop a stochastic model that predicts revisits while also incorporating individual customer heterogeneity in exerted search effort during repeated visits. This heterogeneity is based on individual customer preferences in the choice of consideration sets, product information, pricing, and the search environment. Using customer level clickstream data, we show that our approach is not only better at predicting repeat customer visits, compared to existing methods, but also explainable and managerially interpretable. Most importantly, using computationally efficient simulation-based prescriptive analytics, we leverage our modeling approach to propose practical intervention strategies that maximize the joint likelihoods of customers revisiting and purchasing at the individual customer level.

摘要

在线零售商越来越注重与客户保持长期关系，鼓励重复访问而不是一次性购买，以增加客户的终身价值。为了帮助零售商最大限度地提高客户再次访问和购买的可能性，我们开发了一个两阶段模型，以更好地描述和预测这两种基本的客户活动。在第一阶段，我们描述客户重新访问零售商网站的倾向。在第二阶段，我们开发了一个随机模型，可以预测重访，同时在重复访问期间将个体客户的异质性纳入搜索工作中。这种异质性是基于个人客户在选择考虑因素、产品信息、定价和搜索环境。使用客户级别的点击流数据，我们表明，与现有方法相比，我们的方法不仅可以更好地预测重复客户访问，而且可以解释和管理上可解释。最重要的是，通过使用计算高效的基于模拟的规范性分析，我们利用我们的建模方法提出实用的干预策略，最大限度地提高客户在个人客户层面重新访问和购买的联合可能性。