Customer segmentation by web content mining

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...

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Bibliographic Details
Published in:Journal of retailing and consumer services Vol. 61; p. 102588
Main Authors: Zhou, Jinfeng, Wei, Jinliang, Xu, Bugao
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.07.2021
Subjects:
Customer segmentation
Hierarchical clustering
Interpurchase time
Web content mining
Customer segmentation
Interpurchase time
Hierarchical clustering
Web content mining
ISSN:0969-6989, 1873-1384
Online Access:Get full text
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Summary: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. •Expanding the traditional RFM model to the RFMT model by introducing a new dimension, interpurchase time, to include information on shopping regularity in a long-term period.•Deriving seven characteristic customer clusters from a large dataset retrieved on a global retailer's website based on the RFMT model.•Profiling customer purchasing behaviors by the cluster analysis.
ISSN:0969-6989
1873-1384
DOI:10.1016/j.jretconser.2021.102588