LRFM Model Analysis for Customer Segmentation Using K-Means Clustering

Customer segmentation is one method used by businesses to obtain a better knowledge of their consumers and improve the quality of their connections with them. To achieve good and quantifiable customer relationship management results, it is crucial to adopt systematic data analysis methodologies to understand client characteristics. Customer segmentation is used to develop a retention strategy in UD. Antar Berkah Group that matches the potential of each client segment. The LRFM (Length, Recency, Frequency, and Monetary) model is used, and the K-Means algorithm is used as a clustering technique. To segment customers, both methods are applied. The Elbow method is used to determine the ideal number of clusters. The value of the LRFM variable in each customer is multiplied by the weights previously obtained using the Analytical Hierarchy Process (AHP) method to produce the Customer Lifetime Value (CLV) for each cluster. The results of customer clustering are then used in cooperation with UD. Antar Berkah Group to create a retention strategy. Using daily transaction data, information on the number of visits made, and data on the amount of money spent by consumers, this study was successful in making it easier to identify UD. Antar Berkah Gorup customer characteristics. Two clusters based on the LRFM variable are the outcome of the clustering method. The retention strategy is developed for each cluster in accordance with its unique characteristics because the two clusters differ from one another in many ways.