A Case Study of Applying Customer Segmentation in A Medical Equipment Industry

Iqbal Grady Favian, Erma Suryani

Abstract


The purpose of this paper is to apply LRFM (length, recency, frequency, monetary) for customers in the medical equipment industry and identify differences in each customer segment. This study uses LRFM and clustering to segment its customers. This research uses transaction data of the medical device industry in Indonesia. This data will be extracted for the length, recency, frequency, and monetary (LRFM). The optimal cluster obtained from the validation process is four which will be used as a basis for customer segmentation. This study uses the K-Means algorithm as a clustering method and Decision Tree as a classification method and the application of IF-THEN rules. The segmentation process will be identified based on LRFM criteria in each segment that has been formed and will form a marketing strategy that is appropriate for the company. The results obtained from this study are four customer segments based on LRFM with each segment given a profile name as: Best, Frequent, Low and Uncertain. This study provides guidance on customer identification based on LRFM that can be used by medical equipment companies to develop strategies that are in accordance with the criteria of each segment that has been obtained to improve customer relationships management system and new ways of marketing products.

Keywords


LRFM; K-Means; Medical Marketing; Segmentation; Customer Relationship Management

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References


. M. M. Tseng, and F. Piller, “The customer centric enterprise-advances inmasscustomization and personalization”: Springer. 2003.

. Z. Yao, "Visual Customer Segmentation and Behavior Analysisa SOM-Based Approach", TUCS Dissertations, No. 163, October 2013.

. R. Koch, The 80/20 principle: the secret to achieving more with less, Crown Business, New York, 1999.

. D. W. Craven, Strategic Marketing, TrivusMirris Higher Education Group Inc Company, 2003.

. A. A. Rahman, A.Supaidi, I. Aslamiah, A. Ibrahim, "Implementasi Customer Relationship Management (Crm) Pelayanan Pelanggan (Corporate) Divisi Bges Pada PT Telkom Witel Sumsel", Jurnal Riset Manajemen Sains Indonesia (JRMSI), Vol. 9, No. 1, 2018.

. B. Santosa, Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis, Yogyakarta : Graha Ilmu, 2007.

. I. H. Witten, and E. Frank, Data mining: Practical machine learning tools and techniques (2nd ed.), USA : Morgan Kaufmann Publishers, 2005.

. D. C. Li, W. L. Dai, and W. T. Tseng, "A two-stage clustering method to analyze customer characteristics to builddiscriminative customer management: A case of textile manufacturing business", Expert Systems with Applications, 38, pp. 7186–7191, 2011.

. J. T. Wei, S. Y. Lin, C. C. Weng, and H. H. Yu, “A case study of applying LRFM model in market segmentation of a children’s dental clinic”, Expert Systems with Applications, vol. 39, pp. 5529–5533, 2012.

. C. Y. Chiu, Y. F. Chen., I. T. Kuo, and H. C. Ku, "An intelligent market segmentation system using K-means and particle swarm optimization", Expert Systems with Applications. 36, hal. 4558–4565, 2009

. C. H., Cheng and Y. S. Chen, "Classifying the segmentation of customer value via RFM model and RS theory", Expert Systems with Applications”, 2009, 36 , pp. 4176 – 4184.

. K. K. Dhamgani, F. Abdi, and S. Abolmakarem, "Hybrid soft computing approach based on clustering, rule mining, and decision tree analysis for customer segmentation problem: Real case of customer-centric industries", Applied Soft Computing Journal, 73, pp. 816–828. 2018.

. A. Dursun, and M. Caber, "Using data mining techniques for profiling profitable hotel customers: Anapplication of RFM analysis", Tourism Management Perspectives, vol. 18, pp. 153- 160, 2016.

. J. Miglautsch, “Thoughts on RFM scoring”. The Journal of Database

. Marketing, vol. 8(27), pp. 1–7, 2000.

. C. Marcus, A Practical Yet Meaningful Approach to Customer Segmentation. Journal of Consumer Marketing. Vol. 15, No. 5, pp. 494-504. 1998.

. H. H. Chang and S. F. Tsay, “Integrating of SOM and K-mean in data mining clustering: an empirical study of CRM and profitability evaluation,” Journal of Information Management, vol. 11, no. 4, pp. 161–203, 2004.

. A. Wong, and A. Sohal “An examination of the relationship between trust, commitment and relationship quality”, International Journal of Retail & Distribution Management, Vol. 30 No. 1, pp. 34-50, 2002.

. D. Grewal, K. L. Ailawadi, D Gauri, K. Hall, P. Kopalle, and J. R. Robertson, “Innovations in retail pricing and promotions”, Journal of Retailing, Vol. 87, pp. S43-S52. 2011.




DOI: http://dx.doi.org/10.12962/j23546026.y2020i3.11139

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