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A Classification Approach For Naïve Bayes Of Online Retailers

Author

Listed:
  • Aida Mustapha

    (Faculty of Computer Faculty Computer Science & Information Technology, University Tun Hussein Onn Malaysia, Johor, Malaysia)

  • Shazwani Mustapa

    (Faculty of Computer Faculty Computer Science & Information Technology, University Tun Hussein Onn Malaysia, Johor, Malaysia)

  • Nurfarahim Md.Azlan

    (Faculty of Computer Faculty Computer Science & Information Technology, University Tun Hussein Onn Malaysia, Johor, Malaysia)

  • Noor Fatin Ishmah Saifarrudin

    (Faculty of Computer Faculty Computer Science & Information Technology, University Tun Hussein Onn Malaysia, Johor, Malaysia)

  • Shahreen Kasim

    (Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja, 86400 Batu Pahat,Johor, Malaysia)

  • Mohd. Farhan Md. Fuzzee

    (Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja, 86400 Batu Pahat,Johor, Malaysia)

  • Azizul Azhar Ramli

    (Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja, 86400 Batu Pahat,Johor, Malaysia)

  • Hairulnizam Mahdin

    (Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja, 86400 Batu Pahat,Johor, Malaysia)

  • Seah Choon Sen

    (Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja, 86400 Batu Pahat,Johor, Malaysia)

Abstract

Many small online retailers and new entrants to the online retail sector are keen to practice data mining and consumer-centric marketing in their businesses yet technically lack the necessary knowledge and expertise to do so. In this article a case study of using data mining techniques in customer-centric business intelligence for an online retailer is presented. The main purpose of this analysis is to help the business better understand its customers and therefore conduct customer-centric marketing more effectively. On the basis of the Recency, Frequency, and Monetary model, customers of the business have been segmented into various meaningful groups using the classification and naïve bayes algorithm, and the main characteristics of the consumers in each segment have been clearly identify ed. Accordingly a set of recommendations is further provided to the business on consumer-centric marketing.

Suggested Citation

  • Aida Mustapha & Shazwani Mustapa & Nurfarahim Md.Azlan & Noor Fatin Ishmah Saifarrudin & Shahreen Kasim & Mohd. Farhan Md. Fuzzee & Azizul Azhar Ramli & Hairulnizam Mahdin & Seah Choon Sen, 2017. "A Classification Approach For Naïve Bayes Of Online Retailers," Acta Informatica Malaysia (AIM), Zibeline International Publishing, vol. 1(1), pages 26-28, February.
    • Handle: RePEc:zib:zbnaim:v:1:y:2017:i:1:p:26-28
    DOI: 10.26480/aim.01.2017.26.28
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