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Using association rules to assess purchase probability in online stores

Author

Listed:
  • Grażyna Suchacka

    (Opole University)

  • Grzegorz Chodak

    (Wrocław University of Technology)

Abstract

The paper addresses the problem of e-customer behavior characterization based on Web server log data. We describe user sessions with the number of session features and aim to identify the features indicating a high probability of making a purchase for two customer groups: traditional customers and innovative customers. We discuss our approach aimed at assessing a purchase probability in a user session depending on categories of viewed products and session features. We apply association rule mining to real online bookstore data. The results show differences in factors indicating a high purchase probability in session for both customer types. The discovered association rules allow us to formulate some predictions for the online store, e.g. that a logged user who has viewed only traditional, printed books, has been staying in the store from 10 to 25 min, and has opened between 30 and 75 pages, will decide to confirm a purchase with the probability of more than 92 %.

Suggested Citation

  • Grażyna Suchacka & Grzegorz Chodak, 2017. "Using association rules to assess purchase probability in online stores," Information Systems and e-Business Management, Springer, vol. 15(3), pages 751-780, August.
    • Handle: RePEc:spr:infsem:v:15:y:2017:i:3:d:10.1007_s10257-016-0329-4
    DOI: 10.1007/s10257-016-0329-4
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    References listed on IDEAS

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    1. Chodak, Grzegorz & Suchacka, Grażyna, 2013. "Practical Aspects of Log File Analysis for E-Commerce," MPRA Paper 48131, University Library of Munich, Germany.
    2. Van den Poel, Dirk & Buckinx, Wouter, 2005. "Predicting online-purchasing behaviour," European Journal of Operational Research, Elsevier, vol. 166(2), pages 557-575, October.
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    Cited by:

    1. Barney, Christian & Hancock, Tyler & Esmark Jones, Carol L. & Kazandjian, Brett & Collier, Joel E., 2022. "Ideally human-ish: How anthropomorphized do you have to be in shopper-facing retail technology?," Journal of Retailing, Elsevier, vol. 98(4), pages 685-705.
    2. Catalina Costache & Danut-Dumitru Dumitrascu & Ionela Maniu, 2021. "Facilitators of and Barriers to Sustainable Development in Small and Medium-Sized Enterprises: A Descriptive Exploratory Study in Romania," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    3. Nanik Istianingsih & Sarjon Defit, 2021. "Rough Set Method for Determining Knowledge Attribute on Customer Satisfaction," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 66-78.
    4. Guo, Xin & Wang, David Z.W. & Wu, Jianjun & Sun, Huijun & Zhou, Li, 2020. "Mining commuting behavior of urban rail transit network by using association rules," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    5. Yongyoon Suh & Yongtae Park, 2018. "Identifying and structuring service functions of mobile applications in Google’s Android Market," Information Systems and e-Business Management, Springer, vol. 16(2), pages 383-406, May.

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