Edutech Digital Start-Up Customer Profiling Based on RFM Data Model Using K-Means Clustering

Authors

  • Dedy Panji Agustino Institut of Tehcnology and Business STIKOM Bali, Indonesia
  • I Gede Harsemadi Institute of Technology and Business STIKOM Bali, Indonesia
  • I Gede Bintang Arya Budaya Institut of Technology and Business STIKOM Bali, Indonesia
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DOI:

https://doi.org/10.51519/journalisi.v4i3.322

Keywords:

Customer Segmentation, Silhouette Coefficient, Elbow Method, Davies Bouldin Index, Business Intelligences

Abstract

Digital start-up is companies with a high risk because they are still looking for the most fitting business model and the right market. The company's growth is the primary goal of the start-up. As a newly established company, digital start-ups have one challenge, it is the ineffectiveness of the marketing process and strategic schemes in terms of maintaining customer loyalty, the same goes for edutech digital start-ups. Ineffective and inefficient plans can waste resources. Hence, a method is needed to find out the optimal solution to understanding the customer characteristic. Business Intelligence is needed, with the customer profiling process using transaction data based on the RFM (Retency, Frequency, Monetary) model using the K-Means algorithm. In this study, the transaction data comes from an education platform digital start-up assisted by the STIKOM Bali business incubator. Based on three metrics, namely the Elbow Method, Silhouette Scores, and Davis Bouldin Index, transaction data for sales retency, sales frequency, and sales monetary can be analyzed and can find the optimal solution. For this case, K = 2 is the optimum cluster solution, where the first cluster is the customer who needs more engagement, and the second cluster is the best customer

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Published

2022-09-01

Issue

Vol. 4 No. 3 (2022): Journal of Information Systems and Informatics

Section

Articles

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How to Cite

[1]
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”, journalisi, vol. 4, no. 3, pp. 724–736, Sep. 2022, doi: 10.51519/journalisi.v4i3.322.