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

Abstrak

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