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Unsupervised Learning Framework for Customer Requisition and Behavioral Pattern Classification

Author

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  • Udoinyang G. Inyang
  • Okure O. Obot
  • Moses E. Ekpenyong
  • Aliu M. Bolanle

Abstract

Maintaining healthy organization-customers relationship has positive influence on customers’ behavioral tendencies as regards preference to products and services, buying behavior, loyalty, satisfaction, and so on. To achieve this, an in-depth analysis of customers’ characteristics and purchasing behavioral trend is required. This paper proposes a hybrid unsupervised learning framework consisting of k-means algorithm and self-organizing maps (SOMs) for customer segmentation and behavior analysis. K-means algorithm was used to partition the entire input space of customers’ transaction dataset into 3 and 4 disjoint segments based on customers’ frequency (F) and monetary value (MV). SOM provided visualization of the underlying clusters and discovered customers’ relationships in the dataset. Interaction of F and MV clusters resulted in 12 sub-clusters. An in-depth analysis of each sub-cluster was also performed and appropriate customer relationship management (CRM) strategies established for each sub-cluster. Discovered knowledge will guide effective allocation of resources to each customer cluster and other organizational decision support functions much required by CRM systems.

Suggested Citation

  • Udoinyang G. Inyang & Okure O. Obot & Moses E. Ekpenyong & Aliu M. Bolanle, 2017. "Unsupervised Learning Framework for Customer Requisition and Behavioral Pattern Classification," Modern Applied Science, Canadian Center of Science and Education, vol. 11(9), pages 151-151, September.
    • Handle: RePEc:ibn:masjnl:v:11:y:2017:i:9:p:151
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    References listed on IDEAS

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    1. Abolfazl Kazemi & Mohammad Esmaeil Babaei & Mahsa Oroojeni Mohammad Javad, 2015. "A data mining approach for turning potential customers into real ones in basket purchase analysis," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 19(2), pages 139-158.
    2. K. Coussement & D. Van Den Poel, 2006. "Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/412, Ghent University, Faculty of Economics and Business Administration.
    3. McCarty, John A. & Hastak, Manoj, 2007. "Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression," Journal of Business Research, Elsevier, vol. 60(6), pages 656-662, June.
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    Cited by:

    1. Jan Niederreiter, 2023. "Broadening Economics in the Era of Artificial Intelligence and Experimental Evidence," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 9(1), pages 265-294, March.

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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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