Clustering and profiling of customers using RFM for customer relationship management recommendations

The problem faced by the company is how to determine potential customers and apply CRM (Customer Relationship Management) in order to perform the right marketing strategy, so it can bring benefits to the company. This research aims to perform clustering and profiling customer by using the model of Recency Frequency and Monetary (RFM) to provide customer relationship management (CRM) recommendation to middle industrial company. The method used in this study consists of four steps: data mining from transaction history data of customer sales, data mining modeling using RFM with K-Means algorithm and customer classification with desicion tree (J48), determination of customer loyalty level and recommendation of customer relationship management (CRM) on the medium-sized industry. This research produces RFM data mining model for medium industrial companies. In addition, the results of the study provide recommendations of four customer segments and characteristics of each customer to perform customer relationship strategy.