Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales

  • Edward Apeh
  • Indrė Žliobaitė
  • Mykola Pechenizkiy
  • Bogdan Gabrys
Conference paper

Abstract

Predicting the class of customer profiles is a key task in marketing, which enables businesses to approach the customers in a right way to satisfy the customer’s evolving needs. However, due to costs, privacy and/or data protection, only the business’ owned transactional data is typically available for constructing customer profiles. We present a new approach that is designed to efficiently and accurately handle the multi-class classification of customer profiles built using sparse and skewed transactional data. Our approach first bins the customer profiles on the basis of the number of items transacted. The discovered bins are then partitioned and prototypes within each of the discovered bins selected to build the multi-class classifier models. The results obtained from using four multi-class classifiers on real-world transactional data consistently show the critical numbers of items at which the predictive performance of customer profiles can be substantially improved.

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References

  1. Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: F. Ricci, L. Rokach,B. Shapira, P.B. Kantor (eds.) Context-Aware Recommender Systems, pp. 217–253. Springer (2011)Google Scholar
  2. Apeh, E., Gabrys, B., Schierz, A.: Customer profile classification using transactional data. In: Proceedings of the Third World Congress on Nature and Biologically Inspired Computing (NaBIC2011) (2011)Google Scholar
  3. ˇ Zliobait˙e, I., Bakker, J., Pechenizkiy, M.: Beating the baseline prediction in food sales: How intelligent an intelligent predictor is? Expert Syst. Appl. 39(1), 806–815 (2012)Google Scholar

Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  • Edward Apeh
    • 1
  • Indrė Žliobaitė
    • 1
  • Mykola Pechenizkiy
    • 2
  • Bogdan Gabrys
    • 1
  1. 1.Smart Technology Research CentreBournemouth UniversityDorsetUK
  2. 2.Eindhoven University of TechnologyEindhoventhe Netherlands

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