The RFM Model Analysis for VIP Customer: A Case Study of Golf Clothing Brand

Numerous firms accumulate large quantities of data or transactions after importing information systems and services, which leads to troubles with data procedure. Firms also have demands to find customers' information from large datasets and to understand how to develop marketing strategies accurately to adjust their operational methods. Therefore, this study proposed customer ranking combined big data process based on the RFM model (recency, frequency, monetary) to develop a recommendation algorithm using an association rule, which finds greater recommendation to promote operational effects of firms. The authors adjust the weight of potential information to perform the customer ranking, which is conducted by using agglomerate hierarchical clustering. Finally, they present the recommendation by the association rule for each customer level. The datasets in this study use actual sales data; therefore, they are authentic and have been practically applied. The metrics of evaluation showed that the recommended system this study proposes is highly accurate.