A Sample Strategic Marketing Application: Patient Segmentation And Channel Analysis With The LRM Model

Year 2024, Volume: 8 Issue: 2, 109 - 117

Mustafa Şehirli , Samet Aydın

Abstract

This research aims to develop a new customer segmentation method and to propose strategies for acquiring new customers accordingly. To this end, data from 48,870 patients of a healthcare institution were segmented using the K-Means method. Patients were classified based on their longevity (L), recency (R), and monetary return (M) status and analyzed according to acquisition channels. The findings revealed that the total patients were divided into four distinct clusters. Two clusters containing 2,981 patients, representing 6% of the total, were identified as the ideal segments. While evaluating the clusters, a new indicator based on the Profitability Ratio per Patient was also utilized. The research concluded that the hospital primarily acquires patients through referral channels, with search engines and the website as the second most effective channel. At the same time, social media advertising had a comparatively lesser impact on patient acquisition. Furthermore, it was found that there were no significant differences among customer acquisition channels between the clusters. Recommendations for managers at the end of the study include maintaining more comprehensive customer data, developing profiles for cluster patients for similar sales activities, organizing "refer a friend" campaigns, and conducting evaluations between channel costs and profits.

Keywords

segmentation, LRFM model, K-means method, clustering, strategic marketing

References

• [1] M. Alves Gomes, & T. Meisen, (2023). “A review of customer segmentation methods for personalized customer targeting in e-commerce use cases”, Information Systems and e-Business Management, 21(3), 527-570.M.
• [2] H.H., Channg., S.F., Tsay., ‘’ Integrating SOM and K-man in Data Mining Clustering: An Empirical Study of CRM and Profitability Evaluation’’. Journal of Information Management, 11(4), 161–203 (2004).
• [3] Chen X, Fang Y, Yang M, Nie F, Zhao Z, Huang J.Z. Purtreeclust: a clustering algorithm for customer segmentation from massive customer transaction data. IEEE Trans Knowl Data Eng 30(3):559–572. https://doi.org/10.1109/TKDE.2017.2763620 (2018)
• [4] Y.S., Chen, C.H., Cheng, C.J., Lai, , C.Y., Hsu, & Syu, H.J. Identifying patients in target customer segments using a two-stage clustering-classification approach: A hospital-based assessment. Computers in biology and medicine, 42(2), 213-221 (2012).
• [5] A.J., Christy, A., Umamakeswari, L., Priyatharsini, & A., Neyaa. ‘’RFM ranking–An effective approach to customer segmentation.’’ Journal of King Saud University-Computer and Information Sciences, 33(10), 1251–1257, (2021).
• [6] S.,Dolnicar, B. Grün, & F., Leisch. Market segmentation analysis: Understanding, doing, and making it useful. Springer Nature.(2018).
• [7] S.,Dolnicar, & K., Lazarevski,. Methodological reasons for the theory/practice divide in market segmentation. Journal of Marketing Management, 25(3-4), 357–373. https://doi.org/ 10.1362/026725709X429791, (2009).
• [8] D., Dryglas, & M., Salamaga,. Segmentation by push motives in health tourism destinations: A case study of Polish spa resorts. Journal of Destination Marketing & Management, 9, 234–246. https://doi.org/10.1016/j.jdmm.2018.01.008, (2018).
• [9] O., Ergun, ‘’Makine Öğrenmesi Algoritmaları İle Müşteri Segmentasyonu Ve Hepsiburada E-Ticaret Platformu Üzerine Bir Uygulama.’’ Uludağ Üniversitesi, Sosyal Bilimler Enstitüsü, Yüksek Lisans Tezi, (2023).
• [10] Z.A., Hallab, Y., Yoon, & M., Uysal. Segmentation based on the healthy-living attitude: A market’s travel behaviour. Journal of Hospitality and Leisure Marketing, 10(3/4), 185–198. https:// doi.org/10.1300/J150v10n03_12 , (2003).
• [11] S., Han, Y., Ye, X., Fu, & Z., Chen. Category role aided market segmentation approach to convenience store chain category management. Decision Support Systems, 57, 296–308. https:// doi.org/10.1016/j.dss.2013.09.017 , (2014).
• [12] A.M., Hughes, AStrategic database marketing. Chicago: Probus Publishing Company, (1994).
• [13] S., Kanca, T., Özcan, & Y., Çelikbilek. Bir Tekstil Perakendecisinin Müşterileri İçin RFM Modeli ile Müşteri Segmentasyonu. The Journal of International Scientific Researches, 8(3), 393-409, (2023).
• [14] U., Kaymak. ‘’Fuzzy target selection using RFM variables, in Proceedings of the IFSA World Congress and 20th NAFIPS International Conference,’’ vol. 2, 2001, pp. 1038–1043, (2001).
• [15] U., Konuş, P.C, Verhoef, & S.A., Neslin. Multichannel shopper segments and their covariates. Journal of Retailing, 84(4), 398-413, (2008).
• [16] P., Kotler & G., Armstrong. Principles of Marketing, 7Th ed., Englewood Cliffs, NJ: Prentice-Hall, (1996).
• [17] J.B., MacQueen, Some methods for classification and analysis of multivariate observations, in: Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1967, pp. 281–297, (1967).
• [18] F., Marisa, S.S.S., Ahmad, Z.I.M., Yusof, F., Hunaini and T.M.A., Aziz. Segmentation model of customer lifetime value in small and medium enterprise (SMEs) using K-means clustering and LRFM model. International Journal of Integrated Engineering, 11(3), (2019).
• [19] G., McKernan. Customer Segmentatıon Approaches:’’ A Comparıson Of Methods Wıth Data From The Medıcare Health Outcomes Survey ‘’ (Doctoral Dissertation, University Of Pittsburgh), (2018).
• [20] J.A., McCarty, & M., Hastak. Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression. Journal of Business Research, 60(6), 656–662, (2007).
• [21] A., Moghaddam, & Harandi. ‘’An RFMV Model and Customer Segmentation Based on Variety of Products,’’ 155-161, (2017).
• [22] S., Nakano, & F.N., Kondo. Customer segmentation with purchase channels and media touchpoints using single source panel data. Journal of Retailing and Consumer Services, 41, 142-152, (2018).
• [23] U.O., Nnaji, L.B., Benjamin, N.L., Eyo-Udo, & E.A., Etukudoh. A review of strategic decision-making in marketing through big data and analytics. Magna Scientia Advanced Research and Reviews, 11(1), 084-091, (2024).
• [24] N.T., Nwosu, S.O., Babatunde, & T., Ijomah. Enhancing customer experience and market penetration through advanced data analytics in the health industry. World Journal of Advanced Research and Reviews, 22(3), 1157-1170, (2024).
• [25] P., Özkan, & İ.D., Kocakoç. Sağlık sektöründe LRFM analizi ile pazar bölümlendirme. Kuşadası: Türkiye, (2019).
• [26] E., Sarıoğlu, & M., İnel. Müşteri Segmentasyon Modellerinin Karşılaştırılması Üzerine Ampirik Bir Araştırma. Öneri Dergisi, 19(62), 130-145, (2024).
• [27] J.M.C., Schijns, G.J., Schroder. Segment selection by relationship strength, J. Direct Mark. 10 (3) (1996) 69–79.), (1996).
• [28] T.T., Shi, X.R., Liu, & J.J., Li. Market segmentation by travel motivations under a transforming economy: Evidence from the Monte Carlo of the Orient. Sustainability, 10(10), 3395. https://doi.org/10.3390/su10103395 , (2018).
• [29] L., Torkzadeh, H., Jalilian, M., Zolfagharian, H., Torkzadeh, M., Bakhshi, & R., Khodayari-Zarnaq. Market segmentation in the health tourism industry: ‘’A systematic review of approach and criteria.’’ Journal of Policy Research in Tourism, Leisure and Events, 16(1), 69-88, (2024).
• [30] Y.C., Tsao, P.V.R.P., Raj, & V., Yu. Product substitution in different weights and brands considering customer segmentation and panic buying behaviour. Industrial Marketing Management, 77, 209–220, (2019).
• [31] M., Wedel, W., Kamakura.: Market Segmentation: Conceptual and Methodological Foundations. Kluwer Academic Publishers, Boston, 2nd edn. (2000)
• [32] J.T., Wei, S.Y., Lin, & H.H., Wu. A review of the application of RFM model. African journal of business management, 4(19), 4199, (2010).
• [33] J.T., Wei, S.Y., Lin, C.C., Weng ve H.H., Wu, : “A Case Study of Applying LRFM Model in Market Segmentation of A Children's Dental Clinic,”. Expert Systems With Applications, 39(5), 5529-5533, (2012).
• [34] J.-T.,Wei, M.-C., Lee, H.-K., Chen, ve H.-H., Wu. ‘’Customer Relationship Management in the Hairdressing Industry: An Application of Data Mining Techniques. Expert Systems with Applications,’’ 2013, 40(7), 7513- 7518, (2013).
• [35] H.F., Witschel, S., Loo, & K., Riesen. How to support customer segmentation with useful cluster descriptions. In Advances in Data Mining: Applications and Theoretical Aspects: 15th Industrial Conference, ICDM 2015, Hamburg, Germany, July 11-24, 2015, Proceedings 15 (pp. 17-31). Springer International Publishing, (2015).
• [36] H.-H., Wu, S.-Y., Lin, & Liu, C.-W., Liu. Analyzing patients’ values by applying cluster analysis and LRFM model in a pediatric dental clinic in Taiwan. The Scientific World Journal, 2014. https://doi.org/10.1155/2014/685495 , (2014).
• [37] A.X., Yang. How to develop new approaches to RFM segmentation. Journal of Targeting, Measurement and Analysis for Marketing, 13, 50–60, (2004). I.C., Yeh, K.J., Yang ve T.M., Ting: “Knowledge Discovery on RFM Model Using Bernoulli Sequence”. Expert Systems with Applications, 36:5866–5871, (2008).