Factors Affecting Customer Analytics: Evidence from Three Retail Cases

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.

Automated summary

The abundance of customer behavioral data has changed the design and application of customer analytics systems and methods. The term "visit segmentation" and a new approach have been introduced to improve segmentation results. Attention to factors such as input data, application context, and relevant requirements is crucial for reliable segmentation and improved retail decisions.