Klasterisasi Pelanggan Tenant Inkubator Bisnis STIKOM Bali Untuk Strategi Manajemen Relasi Dengan Menggunakan Fuzzy C-Means

I Gede Harsemadi; Dedy Panji  Agustino; I Gede Bintang Arya Budaya

doi:10.35746/jtim.v4i4.293

I Gede Harsemadi Institute of Technology and Business STIKOM Bali
Dedy Panji Agustino Institute of Technology and Business STIKOM Bali
I Gede Bintang Arya Budaya Institute of Technology and Business STIKOM Bali

DOI: https://doi.org/10.35746/jtim.v4i4.293

Keywords: CRM, , RFM, Fuzzy Partition Coefficient, Business Intelligence, Tenant

Abstract

Business Incubator is an institution that assists start-up businesses as their tenants that are still newly established or are growing. The main goal of a business incubator is to explore the most appropriate ways of assistance for tenants, from the process of starting a business, developing a business, and scaling up a business so that tenants can succeed in their business. Based on existing assistance data and the results of interviews with managers of the STIKOM Bali business incubator, one of the challenges for tenants is the ineffectiveness of the marketing process and strategic schemes in terms of maintaining customer loyalty. Ineffective and efficient plans can result in wasted use of resources. The customer relationship management (CRM) strategy can be applied by tenants as the solution, but the basis is that tenants need to know how to find out the right treatment for customers. So a strategy is needed to find out the characteristics of customers. In this case, it is done by using a business intelligence approach through customer clustering using fuzzy c-means. The dataset comes from the transaction of one of the tenants who is engaged in education technology. Based on values of the fuzzy partition coefficient (FCP) for the scenarios from clusters 2 to 10, it was found that 7 is the most optimal number of clusters (customer category) with the highest FCP value = 0.793. The main strategy that can be implemented based on tenant business for CRM is the pricing of subscription and engagement packages to customers regarding the information on both recently released and upcoming learning content.

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References

C. Lin-Lian, C. De-Pablos-Heredero, and J. L. Montes-Botella, “Value creation of business incubator functions: Economic and social sustainability in the covid-19 scenario,” Sustainability (Switzerland), vol. 13, no. 12, 2021, doi: 10.3390/su13126888.

A. Ghina and I. Sinaryanti, “The Learning Evaluation of Business Incubator’s Role in Developing Technology-Based Startups at Technology Business Incubator,” The Asian Journal of Technology Management (AJTM), vol. 14, no. 1, pp. 35–56, 2021, doi: 10.12695/ajtm.2021.14.1.3.

K. Khalili-Damghani, 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,” Appl Soft Comput, vol. 73, pp. 816–828, 2018.

M. Khajvand, K. Zolfaghar, S. Ashoori, and S. Alizadeh, “Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study,” Procedia Comput Sci, vol. 3, pp. 57–63, 2011.

P. Kolarovszki, J. Tengler, and M. Majerčáková, “The new model of customer segmentation in postal enterprises,” Procedia-Social and Behavioral Sciences, vol. 230, pp. 121–127, 2016.

P. Anitha and M. M. Patil, “RFM model for customer purchase behavior using K-Means algorithm,” Journal of King Saud University - Computer and Information Sciences, no. xxxx, 2020, doi: 10.1016/j.jksuci.2019.12.011.

C. D’Arconte, “Business Intelligence applied in Small Size for profit companies,” Procedia Comput Sci, vol. 131, pp. 45–57, 2018.

Y.-H. Hu and T.-W. Yeh, “Discovering valuable frequent patterns based on RFM analysis without customer identification information,” Knowl Based Syst, vol. 61, pp. 76–88, 2014.

A. Griva, C. Bardaki, K. Pramatari, and D. Papakiriakopoulos, “Retail business analytics: Customer visit segmentation using market basket data,” Expert Syst Appl, vol. 100, pp. 1–16, 2018.

A. J. Christy, A. Umamakeswari, L. Priyatharsini, and A. Neyaa, “RFM ranking–An effective approach to customer segmentation,” Journal of King Saud University-Computer and Information Sciences, vol. 33, no. 10, pp. 1251–1257, 2021.

P. W. Murray, B. Agard, and M. A. Barajas, “Market segmentation through data mining: A method to extract behaviors from a noisy data set,” Comput Ind Eng, vol. 109, pp. 233–252, 2017.

P. Q. Brito, C. Soares, S. Almeida, A. Monte, and M. Byvoet, “Customer segmentation in a large database of an online customized fashion business,” Robot Comput Integr Manuf, vol. 36, pp. 93–100, 2015.

D. H. Nguyen, S. de Leeuw, and W. E. H. Dullaert, “Consumer behaviour and order fulfilment in online retailing: A systematic review,” International Journal of Management Reviews, vol. 20, no. 2, pp. 255–276, 2018.

S. Hwang and Y. Lee, “Identifying customer priority for new products in target marketing: Using RFM model and TextRank,” Marketing, vol. 17, no. 2, pp. 125–136, 2021.

Taqwim and Dkk, “Analisis Segmentasi Pelanggan Dengan RFM Model Pada Pt . Arthamas Citra Mandiri Menggunakan Metode Fuzzy C-Means Clustering,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 3, no. 2, pp. 1986–1993, 2019.

J.-T. Wei, S.-Y. Lin, and H.-H. Wu, “A review of the application of RFM model,” African Journal of Business Management, vol. 4, no. 19, pp. 4199–4206, 2010.

K. Coussement, F. A. M. van den Bossche, and K. W. de Bock, “Data accuracy’s impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees,” J Bus Res, vol. 67, no. 1, pp. 2751–2758, 2014.

Y. Huang, M. Zhang, and Y. He, “Research on improved RFM customer segmentation model based on K-Means algorithm,” in 2020 5th International Conference on Computational Intelligence and Applications (ICCIA), 2020, pp. 24–27.

M. A. M. Pamungkas, “Perbandingan Fuzzy C-Means dan K-Means untuk Mengelompokkan Tingkat Buta Huruf Berdasarkan Provinsi di Indonesia,” 2021.

D. P. Agustino, I. G. Harsemadi, and I. G. B. A. Budaya, “Edutech Digital Start-Up Customer Profiling Based on RFM Data Model Using K-Means Clustering,” Journal of Information Systems and Informatics, vol. 4, no. 3, pp. 724–736, 2022.