Multi-View Clustering for Consumer Behavior Segmentation in Omnichannel Retail Environments
Keywords:
Multi-view clustering, consumer segmentation, omnichannel retail, customer behavior, data fusion, retail analytics, unsupervised learning, customer journey, machine learningAbstract
The evolution of omnichannel retail has redefined how consumers interact with brands, demanding deeper behavioral segmentation. Traditional clustering techniques often fail to capture the multifaceted nature of consumer journeys across digital and physical touchpoints. This paper explores the application of multi-view clustering techniques for consumer behavior segmentation in omnichannel contexts. By integrating data from multiple sources—web interactions, in-store purchases, and mobile engagements—multi-view models provide a holistic understanding of consumer preferences and loyalty drivers. The study applies clustering fusion methods to real-world datasets, showcasing improved segmentation accuracy, interpretability, and personalization potential for retail strategists.
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Copyright (c) 2026 Wojciech Samek, Jason Kim (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
