Analisis Segmentasi Recency dan Customer Value Pada AVANA Indonesia Dengan Algoritma K-Means dan Model RFM (Recency, Frequency and Monetary)

Abstract

Avana Indonesia is a social commerce startup headquartered in Malaysia. Wanting to expand their business and enter the Indonesian market, they still don't have the best marketing strategy in place, so a service sales deal is not enough. That's why we need a marketing strategy that focuses on customers with customer relationship management, one of which is customer segmentation. Customer segmentation can be done by implementing a data mining process which is carried out using the K-Means clustering algorithm based on the RFM (Recency, Frequency, Monetary) model. The number of clusters in the clustering process is determined using the elbow method. Cluster analysis based on customer value using the recency clustering method reveals active, warm, cold, and inactive customers. Then the two from recency frequency (customer value) segmentation produce common, ultra-high, low, and high clusters.