Segmentation Analysis of Students in X Course with RFM Model and Clustering

Abstract

In the business world, the competition to maintain and obtain more customers has become tougher. The presence of new players entering the market is driven by the developments of internet and advertisement. The X guitar course is an institution engaged in the field of non-formal education services. The customers are the course student that has made the payment transaction. The map of customer segmentation is one of the most important components in finding the main needs of each customer. Know the main needs of each customer is expected to increase the customer’s loyalty. Customer segmentation can be done by using the clustering method through a data mining approach in the form of RFM (Recency, Frequency and Monetary) Models. Recency is the data of the last payment transaction date. Frequency shows the number of course payment transactions. Monetary comes from the nominal amount of the transaction. RFM data is combined with the Fuzzy Gustafson-Kessel and K-Means clustering method to produce output in the form of k-clusters of customer. The formed segment is expected to represent the need of customers that vary by using validation process with the Global Silhouette Index. The customer population of the course is 225 students. It has been concluded that the RFM score for each subject by using 3 FGK clusters is the optimum cluster model with the largest Silhouette Index, which is 0.523. This research is expected to provide an in-depth analysis of customer segmentation for X guitar course.

Keywords

• clustering
• customer segmentation
• x course
• rfm model
• silhouette index