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Uncovering consumer loyalty behavior: A data mining approach in the fast-moving consumer goods sector

Author

Listed:
  • Ahmadi, Sadra
  • Barkhi, Fatemeh
  • Nikhashemi, S.R.

Abstract

Identifying and distinguishing customer purchasing behavior based on payment methods presents a fundamental challenge for businesses today. Both cash and credit (Buy Now, Pay Later or BNPL) significantly influence customer decision-making, but their key differences in fostering loyalty and revenue growth require an appropriate analytical approach. This study employs a data mining method within the retail context to gain deeper insights into consumers’ behavioral differences between cash and credit customers, utilizing the latest RFM models and basket analysis to explore purchasing patterns that identify loyal and non-loyal customers. Additionally, a management dashboard in Power BI is used to evaluate key trends and performance indicators. The findings reveal that credit payments result in a higher average basket value, increased customer acquisition, and improved loyalty rates compared to cash purchases. Furthermore, the results highlight new opportunities for optimizing sales strategies and customer relationship management.

Suggested Citation

  • Ahmadi, Sadra & Barkhi, Fatemeh & Nikhashemi, S.R., 2026. "Uncovering consumer loyalty behavior: A data mining approach in the fast-moving consumer goods sector," Journal of Retailing and Consumer Services, Elsevier, vol. 89(PB).
    • Handle: RePEc:eee:joreco:v:89:y:2026:i:pb:s0969698925004138
    DOI: 10.1016/j.jretconser.2025.104634
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