 Customer segmentation by web content miningJinfeng Zhou, Jinliang Wei and Bugao Xu Journal of Retailing and Consumer Services, 2021, vol. 61, issue C Abstract: This article introduces a new dimension, Interpurchase Time (T), into the existing RFM (Recency, Frequency, and Monetary) model to form an expanded RFMT model for parsing consumers' online purchase sequences in a long period to implement customer segmentation. The proposed RFMT model can track and discern changes in customer purchasing behaviors during their whole shopping cycle. Firstly, a web content retrieving system was developed to fetch publicly available customer data on a retailer's website, including demographic information (gender, age, location, etc.) and product information (name, price, date, etc.) of each purchase in a period from 2008 to 2019. The RFMT values of a customer were then computed from the retrieved data and subsequently analyzed by the hierarchical clustering to derive seven homogeneous clusters with specific customer profiles. Subsequently, demographic features and product preferences were identified for each cluster with business insights that can help the retailer to improve customer relationships and to implement targeted recommendation strategies. Keywords: Customer segmentation; Web content mining; Interpurchase time; Hierarchical clustering (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:joreco:v:61:y:2021:i:c:s0969698921001545 DOI: 10.1016/j.jretconser.2021.102588 Access Statistics for this article Journal of Retailing and Consumer Services is currently edited by Harry Timmermans More articles in Journal of Retailing and Consumer Services from Elsevier |
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