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Customer segmentation by web content mining

Author

Listed:
  • Zhou, Jinfeng
  • Wei, Jinliang
  • Xu, Bugao

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.

Suggested Citation

  • Zhou, Jinfeng & Wei, Jinliang & Xu, Bugao, 2021. "Customer segmentation by web content mining," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    • Handle: RePEc:eee:joreco:v:61:y:2021:i:c:s0969698921001545
    DOI: 10.1016/j.jretconser.2021.102588
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    References listed on IDEAS

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    1. Farid Huseynov & Sevgi Özkan Yıldırım, 2017. "Behavioural segmentation analysis of online consumer audience in Turkey by using real e-commerce transaction data," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 14(1), pages 12-28.
    2. Cassandra Elrod & Sarah Stanley & Elizabeth Cudney & Caroline Fisher, 2015. "Empirical Study Utilizing QFD to Develop an International Marketing Strategy," Sustainability, MDPI, vol. 7(8), pages 1-14, August.
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    Cited by:

    1. Chen, Yanhong & Liu, Luning & Zheng, Dequan & Li, Bin, 2023. "Estimating travellers’ value when purchasing auxiliary services in the airline industry based on the RFM model," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    2. Kessara Kanchanapoom & Jongsawas Chongwatpol, 2023. "Integrated customer lifetime value (CLV) and customer migration model to improve customer segmentation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 172-185, June.
    3. Joni Salminen & Mekhail Mustak & Muhammad Sufyan & Bernard J. Jansen, 2023. "How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 677-692, December.

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