A Comprehensive RFM-A Framework: Integrating Age for Enhanced Customer Segmentation and Marketing Strategies

Authors

  • Mansoureh Naderipour * Department of Industrial Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
  • Hamed Hosseini Nasabian Department of Industrial Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.

https://doi.org/10.48314/tsc.v1i1.34

Abstract

Customer segmentation is seen as one of the pillars of a successful advertising campaign. Marketers give great importance to this flagship phase in the process of new products marketing. Successful segmentation will involve successful ‘‘Customer Targeting” and therefore a profitable customer marketing campaign. Many works have dealt with customer segmentation by applying the famous Recency, Frequency and Monetary model. This model suffers from insufficiency by ignoring other important parameters according to the field of application. In this article, a new classification model is presented by adding the age ("A") as the fourth parameter, referring to the age of customers. The segmentation based on RFM-A is applied in a retail market in order to detect behavior patterns for a customer. The proposed model increases the quality of the prediction of customer behavior and Companies could predict, customers who will respond positively.

Keywords:

RFM, Segmentation, Customer, Age, Marketing, Prediction

References

  1. [1] Dubgorn, A., Zaychenko, I., Alekseev, A., Paardenkooper, K., & Esser, M. (2021). Analysis of technological innovations in supply chain management and their application in modern companies. In Technological transformation: A new role for human, machines and management: TT-2020 (pp. 168–178). Springer. http://dx.doi.org/10.1007/978-3-030-64430-7_14
  2. [2] Saura, J. R., Palacios-Marqués, D., & Ribeiro-Soriano, D. (2023). Digital marketing in SMEs via data-driven strategies: Reviewing the current state of research. Journal of small business management, 61(4), 1-36. http://dx.doi.org/10.1080/00472778.2021.1955127
  3. [3] Saleem, H., Muhammad, K. Bin, Nizamani, A. H., Saleem, S., & Aslam, A. M. (2019). Data science and machine learning approach to improve E-commerce sales performance on the social web. International Journal of computer science and network security (IJCSNS), 12(4). 401-424. http://dx.doi.org/10.34218/IJARET.12.4.2021.040
  4. [4] Pantano, E., Viassone, M., Boardman, R., & Dennis, C. (2022). Inclusive or exclusive? Investigating how retail technology can reduce old consumers’ barriers to shopping. Journal of retailing and consumer services, 68, 103074. https://doi.org/10.1016/j.jretconser.2022.103074
  5. [5] Hughes, A. M. (1994). Strategic database marketing: The master plan for starting and managing a profitable, customer-based marketing program. Probus Pub Co. https://B2n.ir/du5617
  6. [6] Shirole, R., Salokhe, L., & Jadhav, S. (2021). Customer segmentation using the RFM model and k-means clustering. International journal of scientific research in science and technology, 8(3), 591–597. https://doi.org/10.32628/IJSRST2183118
  7. [7] Siahaan, V., & Sianipar, R. H. (2022). RFM analysis and K-means clustering: A case study analysis, clustering, and prediction on retail store transactions with Python GUI. Independently published. https://B2n.ir/rq1781
  8. [8] Chugh, J. (2021). Using K-Means to segment customers based on RFM variables. https://medium.com/web-mining-is688-spring-2021
  9. [9] Chandana, D. (2021). Exploring customer segmentation with RFM analysis and k-means clustering. https://B2n.ir/ub4286
  10. [10] Ong, D. (2023). RFM customer segmentation using K-means clustering on Snowpark. https://B2n.ir/jh3806
  11. [11] Azad, M. S., Khan, S., Hossain, R., Rahman, R., & Momen, S. (2023). Predictive modeling of consumer purchase behavior on social media: Integrating theory of planned behavior and machine learning for actionable insights. PLOS ONE, 18, e0296336. http://dx.doi.org/10.1371/journal.pone.0296336
  12. [12] Rane, N., Choudhary, S., & Rane, J. (2023). Metaverse for enhancing customer loyalty: Effective strategies to improve customer relationship, service, engagement, satisfaction, and experience. 1-26. http://dx.doi.org/10.2139/ssrn.4624197

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Published

2025-01-27

Issue

Vol. 1 No. 1 (2025): Transactions on Soft Computing (TSC)

Section

Articles

How to Cite

A Comprehensive RFM-A Framework: Integrating Age for Enhanced Customer Segmentation and Marketing Strategies. (2025). Transactions on Soft Computing , 1(1), 14-26. https://doi.org/10.48314/tsc.v1i1.34