Factors Affecting Customer Analytics: Evidence from Three Retail Cases
Anastasia Griva (),
Cleopatra Bardaki (),
Katerina Pramatari () and
Georgios Doukidis ()
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Anastasia Griva: National University of Ireland Galway
Cleopatra Bardaki: Harokopio University
Katerina Pramatari: ELTRUN, Athens University of Economics and Business
Georgios Doukidis: ELTRUN, Athens University of Economics and Business
Information Systems Frontiers, 2022, vol. 24, issue 2, No 9, 493-516
Abstract 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.
Keywords: Customer analytics; Retail analytics; Customer segmentation; Visit segmentation; Business analytics; Customer behavior (search for similar items in EconPapers)
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