Understanding multi-channel consumer behavior: A comparison between segmentations of multi-channel purchases by product category and overall products |
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| Affiliation: | 1. IQS School of Management, Universitat Ramon Llull, Via Augusta, 390, 08017, Barcelona, Spain;2. Tecnológico de Monterrey, Avenida General Ramón Corona 2514, Nuevo México, 45138, Zapopan, JAL, Mexico;1. Ehrenberg-Bass Institute, UniSA Business, University of South Australia, City West Campus Level 4, Yungondi Building, North Terrace Adelaide, SA 5000, Australia;2. Massey University, Auckland, New Zealand;1. School of Industrial Engineering, College of Engineering, University of Tehran, Iran;2. School of Business and Economics, RWTH Aachen University, Aachen, Germany;3. Centre for Research in Sustainable Supply Chain Analytics, Rowe School of Business, Dalhousie University, Halifax, NS, Canada;1. Instituto Multidisciplinar de Empresa, University of Salamanca, Campus Unamuno. Edificio FES, 37007, Salamanca, Spain;2. Instituto Multidisciplinar de Empresa, Universidad Laica Eloy Alfaro de Manabí, Av. Circunvalacion, Manta, Ecuador;3. Instituto Multidisciplinar de Empres, University of Sao Paulo, Brazil;4. Instituto Multidisciplinar de Empresa University of Salamanca, Spain |
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| Abstract: | This study explores how customers utilize multiple channels in a recent retail environment. It provides a compatible framework for customer segmentation for overall products and major online-focused product categories, which reflect dynamic purchasing needs in multiple channels, using latent class cluster analysis and focusing on demographic characteristics. It extends prior studies’ frameworks by analyzing 15,938 Japanese single-source panelists’ data on low-involvement frequently purchased categories with customers’ demographics. The analyses revealed eight segments, including the specific properties of multi-channel enthusiasts. The findings imply that FMCG product classes and the corresponding consumer segments that are appropriate for cross-selling can be specified in our system, which incorporates possible future dynamic customer needs and can reduce the possibility of significant profit losses. |
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| Keywords: | Multi-channel Shopping behavior Latent-class cluster analysis Dynamic segmentation Cross-selling in channel |
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