In today’s online environment, consumers and sellers interact through multiple channels such as email, search engines, banner ads, affiliate websites and comparison-shopping websites. In this paper, we investigate whether knowing the history of channels the consumer has used until a point of time is predictive of their future visit patterns and purchase conversions. We propose a model in which future visits and conversions are stochastically dependent on the channels a consumer used on their path up to a point. Salient features of our model are: (1) visits by consumers are allowed to be clustered, which enables separation of their visits into intra- and inter-session components, (2) interaction effects between channels where prior visits and conversions from channels impact future inter-session visits, intra-session visits and conversions through a latent variable reflecting the cumulative weighted inventory of prior visits, (3) each channel attracts inter-session and intra-session visits differently, (4) each channel has different association with conversion conditional on a customer’s arrival to the website through that channel, (5) each channel engages customers differently (i.e., keeps the customer alive for a next session or for a next visit within a session), (6) the channel from which there was an arrival in the previous session can have an enhanced ability to generate an arrival for the same channel in the current session (channel persistence), and (7) parsimonious specification for high dimensionality in a low-velocity, sparse-data environment. We estimate the model on easy-to-collect first-party data obtained from an online retailer selling a durable good and find that information on the identities of channels and incorporation of inter- and intra-session visits have significant predictive power for future visitation and conversion behavior. We find that some channels act as “closers” and others as “engagers”—consumers arriving through the former are more likely to make a purchase, while consumers arriving through the latter, even if they do not make a purchase, are more likely to visit again in the future or extend the current session. We also find that some channels engage customers more than others, and that there are interaction effects between the channels visited. Our estimates show that the effect of prior inventory of visits is different from the immediate prior visit, and that visit and purchase probabilities can increase or decrease based on the history of channels used. We discuss several managerial implications of the model including using the predictions of the model to aid in selecting customers for marketing actions and using the model to evaluate a policy change regarding the obscuring of channel information.

在当今的在线环境中，消费者和卖家通过电子邮件、搜索引擎、横幅广告、附属网站和比较购物网站等多种渠道进行互动。在本文中，我们调查了解消费者在某个时间点之前使用过的渠道历史是否可以预测他们未来的访问模式和购买转化。我们提出了一个模型，在该模型中，未来的访问和转化随机地取决于消费者在其路径上使用的渠道。我们模型的显着特点是：（1）允许消费者的访问被聚类，这使得他们的访问可以分为会话内和会话间组件，（2）渠道之间的交互效应，其中先前的访问和来自渠道的转换会影响未来会间访问，我们估计了从销售耐用品的在线零售商处获得的易于收集的第一方数据的模型，并发现有关渠道身份的信息以及会话间和会话内访问的结合对未来访问和转换行为。我们发现，一些渠道充当“关闭者”，而另一些则充当“参与者”——通过前者到达的消费者更有可能进行购买，而通过后者到达的消费者，即使他们没有购买，也更有可能购买将来再次访问或延长当前会话。我们还发现，某些渠道比其他渠道更能吸引客户，并且访问的渠道之间存在互动效应。我们的估计表明，先前访问清单的效果与前一次访问不同，并且访问和购买概率可以根据使用的渠道历史增加或减少。我们讨论了该模型的几个管理含义，包括使用模型的预测来帮助选择客户进行营销活动，以及使用该模型来评估有关隐藏渠道信息的政策变化。