B2B Customer Segmentation Based on Customer Lifetime Value Concept and RFM Modeling

Fakhruddin Agung Laksono; Basuki Rachmat; Yudi Sutasrso

doi:10.37385/ijedr.v5i1.4295

Authors

Fakhruddin Agung Laksono Universitas Hayam Wuruk Perbanas
Basuki Rachmat Universitas Hayam Wuruk Perbanas
Yudi Sutasrso Universitas Hayam Wuruk Perbanas

DOI:

https://doi.org/10.37385/ijedr.v5i1.4295

Keywords:

RFM, Customer Lifetime Value, K-means Clustering

Abstract

The company has limited resources to use in implementing marketing strategies for its customers. The first step to be able to develop an effective and efficient marketing strategy is to divide customers into several large groups based on their similarities. The company needs to allocate its limited resources proportionally to groups of customers based on the value and benefits that those customers can contribute to the company. One of the bases for customer grouping is based on the concept of Customer Lifetime Value (CLV) with Recency, Frequency, and Monetary (RFM) modeling. CLV ratings show how much value and benefits customers can bring to a company. This study conducted a cluster analysis with the K-means algorithm on 351 customers based on their RFM value. The number of clusters is most effectively obtained through the elbow method. Cluster analysis produces 4 customer clusters that have different characteristics and are ranked based on their CLV values. Cluster names and marketing strategy recommendations for each cluster are arranged based on their characteristics and CLV rating. The four clusters formed are the Non-Valuable Customers cluster, VIP Customers cluster, Valuable Customers cluster, and Potentially Valuable Customers cluster.

References

Aggarwal, A. G., & Yadav, S. (2020). Customer Segmentation Using Fuzzy-AHP and RFM Model. ICRITO 2020 - IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), pp 77–80. DOI: https://doi.org/10.1109/ICRITO48877. 2020.9197903.

Buttle, F., & Maklan, S. (2019). Customer Relationship Management: Concepts and Technologies: Fourth Edition. London: Routledge.

Christy, A. J., Umamakeswari, A., Priyatharsini, L., & Neyaa, A. (2021). RFM Ranking – An Effective Approach to Customer Segmentation. Journal of King Saud University - Computer and Information Sciences, 33(10), pp 1251–1257. DOI: https://doi.org/10.1016/j.jksuci.2018.09.004

Cuadros, A. J., & Domínguez, V. E. (2014). Customer Segmentation Model Based on Value Generation for Marketing Strategies Formulation. Estudios Gerenciales, 30 (130), pp 25–30. DOI: https://doi.org/10.10 16/j.estger.2014.02.005.

Dachyar, M., Esperanca, F. M., & Nurcahyo, R. (2019). Loyalty Improvement of Indonesian Local Brand Fashion Customer Based on Customer Lifetime Value (CLV) Segmentation. IOP Conference Series: Materials Science and Engineering, 598(1). DOI: https://doi.org/10.1088/1757899X/59 8/1/012116.

Do?an, O., Ayçin, E., & Bulut, Z. A. (2018). Customer Segmentation by Using RFM Model and Clustering Methods: A Case Study in Retail Industry. International Journal of Contemporary Economics and Administrative Sciences, 8(1), pp 1–19. DOI: http://www.ijceas.com/index.php/ijceas/issue/view/23.

Dursun, A., & Caber, M. (2016). Using Data Mining Techniques for Profiling Profitable Hotel Customers: An Application of RFM Analysis. Tourism Management Perspectives, 18, pp 153–160. DOI: https://doi.org/ 10.1016/j.tmp.2016.03.001.

Gupta, S., Hanssens, D., Hardie, B., Kahn, W., Kumar, V., Lin, N., Ravishanker, N., & Sriram, S. (2006). Modeling Customer Lifetime Value. Journal of Service Research (Vol. 9, Issue 2, pp. 139–155). DOI :https://doi.org/10.1177/1094670506293810

Husein, A. M., Waruwu, F. K., Batu Bara, Y. M. T., Donpril, M., & Harahap, M. (2021). Clustering Algorithm for Determining Marketing Targets Based Customer Purchase Patterns and Behaviors. SinkrOn, 6(1), pp 137–143. DOI: https://doi.org/10.33395/sinkron.v6i1.11191

Imani, A., Abbasi, M., Ahang, F., Ghaffari, H., & Mehdi, M. (2022). Customer Segmentation to Identify Key Customers Based on RFM Model by Using Data Mining Techniques. International Journal of Research in Industrial Engineering, 11(1), pp 62–76. DOI: http://www.riejournal.com/article_138379.html.

Khajvand, M., & Tarokh, M. J. (2011). Estimating Customer Future Value of Different Customer Segments Based on Adapted RFM Model in Retail Banking Context. Procedia Computer Science, 3, pp 1327–1332. DOI: https://doi.org/10.1016/j.procs.2011.01.011.

Kotler, P., & Keller, K. L. (2016). Marketing Management Global Edition 15. Essex: Pearson Education Limited.

Liu, D. R., & Shih, Y. Y. (2005). Integrating AHP And Data Mining for Product Recommendation Based on Customer Lifetime Value. Information and Management, 42(3), pp 387–400. DOI: https://doi.org/10.1016/j.im.2004.01.008.

Monalisa, S., Nadya, P., & Novita, R. (2019). Analysis for Customer Lifetime Value Categorization With RFM Model. Procedia Computer Science, 161, pp 834–840. DOI: https://doi.org/10.1016/j.procs.2019.11.190.

Paul, L., & Radha Ramanan, T. (2019). An RFM and CLV Analysis for Customer Retention and Customer Relationship Management of A Logistics Firm. International Journal of Applied Management Science, 11(4), pp 333–351. DOI: https://doi.org/10.1504 /IJAMS.2019.103713.

Phu Son, N., Thi Mai Linh, T., Giang Thy, N., & Phuoc Toan Tu Van Binh, L. V. (2022). B2B and Its Market Segmentation Based On RFM with Clustering Method. American International Journal of Business Management (AIJBM) ISSN (Vol. 5, Issue 03). DOI: https/www.aijbm.com/b2b-and-its-market-segmentation-based-on-rfm-with-clustering-method.

Pratomo, Edwin Agung, Najib, M., & Mulyati, H. (2019). Customer Segmentation Analysis Based on The Customer Lifetime Value Method. Jurnal Aplikasi Manajemen, 17(3), pp 408–415. DOI: https://doi.org/10.21776/ub.jam.2019.017.03.04.

Qadadeh, W., & Abdallah, S. (2018). Customers Segmentation in the Insurance Company (TIC) Dataset. Procedia Computer Science, 144, pp 277–290. DOI: https://doi.org/10.1016/j.procs.2018.10.529.

Raiter, O. (2021). Segmentation of Bank Consumers for Artificial Intelligence Marketing. International Journal of Contemporary Financial Issues, 1(1), pp 39–54. DOI: https://doi.org/10.17613/q0h8-m266.

Ray, M., & Mangaraj, B. K. (2016). AHP Based Data Mining for Customer Segmentation Based on Customer Lifetime Value. International Journal of Data Mining Techniques and Applications, 5(1), pp 28–34. DOI: https://doi.org/10.20894/ijdmta. 102.005.001.007.

Safari, F., Safari, N., & Montazer, G. A. (2016). Customer Lifetime Value Determination Based on RFM Model. Marketing Intelligence and Planning, 34(4), pp 446–461. DOI: https://doi.org/10.1108/MIP-03-2015-0060.

Sarvari, P. A., Ustundag, A., & Takci, H. (2016). Performance Evaluation of Different Customer Segmentation Approaches Based on RFM and Demographics Analysis. Kybernetes, 45(7), pp 1129–1157. DOI: https://doi.org/10.1108/K-07-2015-0180.

Sheikh, A., Ghanbarpour, T., & Gholamiangonabadi, D. (2019). A Preliminary Study of Fintech Industry: A Two-Stage Clustering Analysis for Customer Segmentation in the B2B Setting. Journal of Business-to-Business Marketing, 26(2), pp 197–207. DOI: https://doi.org/10.1080/1051712X.2019.1603420.

Sutarso, Y., Ayu Sekarsari, L., Annisatul Hidayati, E., Andariksa, H., & Zafira Putri, M. (2022). Understanding The Attributes of Digital Wallet Customers: Segmentation Based on Perceived Risk During The Covid-19 Pandemic. Jurnal Ekonomi Dan Bisnis, 25(Oktober), pp 381–400. DOI: https://doi.org/10.24914/jeb.v25i2.5676.

Wei, J., Lin, S., & Wu, H. (2010). A review of the application of RFM model. African Journal of Business Management, 4(19), pp 4199–4206. DOI: https://doi.org/10.5897/AJBM. 9000026.

Wong, E., & Wei, Y. (2018). Customer Online Shopping Experience Data Analytics: Integrated Customer Segmentation and Customised Services Prediction Model. International Journal of Retail and Distribution Management, 46(4), pp 406–420. DOI: https://doi.org/10.1108/IJRDM-06-2017-0130.

Wu, I. L. (2013). The Antecedents of Customer Satisfaction and Its Link to Complaint Intentions in Online Shopping: An Integration of Justice, Technology, and Trust. International Journal of Information Management, 33(1), pp 166–176. DOI: https://doi.org/10.1016/j.ijinfomgt.2012.09.001.

Wu, J., Shi, L., Lin, W. P., Tsai, S. B., Li, Y., Yang, L., & Xu, G. (2020). An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K -Means Algorithm. Hindawi Mathematical Problems in Engineering, 2020. DOI: https://doi.org/10.1155/2020/ 8884227.