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Analysis of Customer Behavior

In: Multi-variant User Interfaces in E-commerce

Author

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  • Adam Wasilewski

    (Wrocław University of Science and Technology)

Abstract

To provide a customized user interface in e-commerce, it is essential to obtain knowledge about customers, including their behaviors, preferences, and decision-making processes. To achieve this, it is imperative to collect data on all users’ actions within the e-shop. This data collection should be conducted with utmost respect for customer privacy and adherence to legal requirements. One viable method for gathering this data involves using cookies with the prior consent of clients, in conjunction with tag management tools and web analytics. The information thus acquired can then be examined to identify patterns and dependencies, allowing for the classification of customers based on their behavior. Various grouping techniques can be used for this purpose, with the choice of the appropriate algorithm depending on the dataset—a crucial factor in advancing interface variant implementation. Additionally, the UX specialist should utilize the gathered data on user behavior to suggest modifications that will mold the interface variants offered to specific consumer segments. This chapter outlines methods for collecting customer behavioral data in online retail environments, delves into clustering as a technique for grouping users based on their behavior, and highlights current and potential applications of this approach in e-commerce. The chapter also explores ways to analyze the resulting clusters in terms of their utility for designing specialized layout variants.

Suggested Citation

  • Adam Wasilewski, 2024. "Analysis of Customer Behavior," Progress in IS, in: Multi-variant User Interfaces in E-commerce, chapter 0, pages 45-65, Springer.
    • Handle: RePEc:spr:prochp:978-3-031-67758-8_3
    DOI: 10.1007/978-3-031-67758-8_3
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