COMPARISON OF RECENCY, FREQUENCY, MONETARY (RFM) ANALYSIS AND K-MEANS CLUSTERING: CASE STUDY OF CORNER'S CARD MALL 23 PASKAL CUSTOMER DATA

• Main Author: Chan, Vivian
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/45322
• Institution: Institut Teknologi Bandung

23 Paskal mall is one of the shopping centers in the city of Bandung which has corner’s card as a customer loyalty program. This program allows customers to collect points from transactions therefore that customer transaction activities can be tracked through collected data. These data can be used to analyze customers and customer segmentation to develop marketing strategies. Customer segmentation can be done with Recency, Frequency, Monetary (RFM) analysis and k-means clustering. Based on those, the two analyzes can be compared in the case study of corner’s card 23 Paskal Mall customer data. Customer segmentation with RFM analysis utilizes the existing score provisions, to produce 10 segmentations. On the other hand, customer segmentation with k-means clustering is carried out several times in a simulation which results in 4 segments. The main differences between RFM analysis and k-means clustering are the basic concept, the process carried out, the type of output, the results of segmentation along with the advantages and disadvantages. Customer segmentation with k-means clustering is better used than RFM analysis because there are advantages in segmentation results and the goodness of analysis results. This research can be useful to underpin the development of marketing strategies for each corner’s card 23 Paskal Mall customer segment effectively and efficiently.