Vol. 5 No. 1 (2020): Turkish Journal of Marketing
Articles

CUSTOMER SEGMENTATION AND PROFILING WITH RFM ANALYSIS

PDF
  • İbrahim SABUNCU,
  • Edanur TÜRKAN,
  • Hilal POLAT
İbrahim SABUNCU
Asisst. Prof. Dr.
DOI: https://doi.org/10.30685/tujom.v5i1.84

Published 04/25/2020

Keywords

  • Müşteri Yaşam Boyu Değeri,
  • RFM Modeli,
  • Bölümlendirme
  • Customer Lifetime Value,
  • Segmentation,
  • RFM Model

How to Cite

SABUNCU, İbrahim, TÜRKAN, E., & POLAT, H. (2020). CUSTOMER SEGMENTATION AND PROFILING WITH RFM ANALYSIS. Turkish Journal of Marketing, 5(1), 22–36. https://doi.org/10.30685/tujom.v5i1.84

Abstract

This paper is a case study on segmentation and profiling of customers according to their lifetime value by using the RFM (Recency, Frequency and Monetary Value) model which is an analytical method for behavioral customer segmentation. Real customer data that is gathered from a fuel station in Istanbul, Turkey is used for the case study. The data contain 1015 customers? arrival frequency, last arrival date and total spend amount in the first half of 2016, and 10 descriptor variables of customers. First, demographic characteristics of fuel station customers were analyzed by descriptive statistics. Then customers' RFM score was calculated through SPSS program, and customers were divided into 5 segments according to their RFM scores by cluster analysis. Finally, the customer profile of segments has been created by using Correspond analysis and Discriminant analysis. Although fuel station managers think that the most valuable customer for their company are automobile drivers, result of the analysis suggests that the most valuable customers are Truck drivers. At the end of the paper, recommendations are made based on customer profiles of two most valuables segments that are named VIP and GOLD.

PDF

Downloads

Download data is not yet available.

References

  1. Altan, D. (2019). Güncellik/Sıklık/Parasallık (RFM) Analizi İle Hedef Kitle Seçimi: Hava Yolu Sektöründe Bir Uygulama. Hacettepe Üniversitesi Sosyal Bilimler Enstitüsü.
  2. Armstrong, G., Kotler, P., & Opresnik, M. O. (2016). Marketing: An Introduction (Global Edi). Pearson Education.
  3. Berman, B. (2006). Developing an effective customer loyalty program. California Management Review, 49(1), 123?148.
  4. Birant, D. (2011). Data mining using RFM analysis. In Knowledge-oriented applications in data mining. IntechOpen.
  5. Buckinx, W., & Van den Poel, D. (2005). Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. European Journal of Operational Research, 164(1), 252?268.
  6. Charan, A. (2015). Marketing analytics: A Practitioner?s Guide to Marketing Analytics and Research Methods. World Scientific Publishing Company.
  7. Cheng, C.-H., & Chen, Y.-S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert Systems with Applications, 36(3), 4176?4184.
  8. Dursun, A., & Caber, M. (2016). Using data mining techniques for profiling profitable hotel customers: An application of RFM analysis. Tourism Management Perspectives, 18, 153?160.
  9. Ekergil, V., & Ersoy, N. F. (2016). B2B/Endüstriyel Pazarlar Için Anahtar Müsteri Yönetimine Iliskin Müsteri Yasam Boyu Degerinin Hesaplanmasinda Muhasebe ve Pazarlamanin Rolü. Business and Economics Research Journal, 7(4), 159?180. http://search.proquest.com/docview/1833938122/
  10. Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005). ?Counting your customers? the easy way: An alternative to the Pareto/NBD model. Marketing Science, 24(2), 275?284.
  11. Hiziroglu, A., & Sengul, S. (2012). Investigating Two Customer Lifetime Value Models from Segmentation Perspective. Procedia - Social and Behavioral Sciences, 62, 766?774. https://doi.org/10.1016/j.sbspro.2012.09.129
  12. Hughes, A. M. (1996). Boosting response with RFM. Marketing Tools, 4?8.
  13. Kabasakal, İ. (2020). Customer Segmentation Based On Recency Frequency Monetary Model: A Case Study in E-Retailing. Bilişim Teknolojileri Dergisi, 13(1), 47?56.
  14. Maryani, I., & Riana, D. (2017). Clustering and profiling of customers using RFM for customer relationship management recommendations. 2017 5th International Conference on Cyber and IT Service Management (CITSM), 1?6.
  15. Namvar, M., Khakabimamaghani, S., & Gholamian, M. R. (2011). An approach to optimised customer segmentation and profiling using RFM, LTV, and demographic features. International Journal of Electronic Customer Relationship Management, 5(3?4), 220?235. https://doi.org/10.1504/IJECRM.2011.044688
  16. Pakyurek, M., Sezgin, M. S., Kestepe, S., Bora, B., Duzagac, R., & Yildiz, O. T. (2018). Customer clustering using RFM analysis. 26th Signal Processing and Communications Applications Conference (SIU), 1?4. https://doi.org/10.1109/SIU.2018.8404680
  17. Peker, S., Kocyigit, A., & Eren, P. E. (2017). LRFMP model for customer segmentation in the grocery retail industry: a case study. Marketing Intelligence & Planning.
  18. Piersma, N., & Jonker, J.-J. (2004). Determining the optimal direct mailing frequency. European Journal of Operational Research, 158(1), 173?182.
  19. Sarvari, P. A., Ustundag, A., & Takci, H. (2016). Performance evaluation of different customer segmentation approaches based on RFM and demographics analysis. Kybernetes.
  20. Sohrabi, B., & Khanlari, A. (2007). Customer Lifetime Value (CLV) Measurement Based on RFM Model. Iranian Accounting & Auditing Review, 14(47), 7?20.
  21. Tsiptsis, K. K., & Chorianopoulos, A. (2011). Data mining techniques in CRM: inside customer segmentation. John Wiley & Sons.
  22. Wei, J.-T., Lin, S.-Y., & Wu, H.-H. (2010). A review of the application of RFM model. African Journal of Business Management, 4(19), 4199.