The abundance of customer behavioral data alters the design and application of customer analytics systems and approaches. Segmentation is a common customer analytics practice, but researchers highlight that traditional segmentation approaches are not enough. We coin the term “visit segmentation” and devise a visit segmentation approach. When designing or applying a new information system or approach, it is important to consider factors related to the input data, the application context, the users, and all the relevant requirements. Considering the literature, this paper identifies such factors that affect customer analytics approaches and systems. We explore how these factors affect segmentation through applying our segmentation approach to three heterogeneous retailers, e.g., the products’ variety a shopper purchases in each visit seems to be crucial to the segmentation. The more attention data analysts and designers pay to these factors, the more reliable segmentation results they will get and, thus, improved retail decisions are expected.

大量的客户行为数据改变了客户分析系统和方法的设计和应用。细分是一种常见的客户分析方法，但研究人员强调，传统的细分方法还不够。我们创造了“访问细分”一词，并设计了访问细分方法。在设计或应用新的信息系统或方法时，重要的是要考虑与输入数据，应用程序上下文，用户以及所有相关需求有关的因素。考虑到文献，本文确定了影响客户分析方法和系统的因素。通过将细分方法应用于三个异构零售商，我们探索了这些因素如何影响细分，例如，购物者在每次访问中购买的产品种类对于细分至关重要。数据分析师和设计人员对这些因素的关注越多，他们得到的细分结果就越可靠，因此可以预期改善零售决策。