Analysis Outlier Data on RFM and LRFM Models to Determining Customer Loyalty with DBSCAN Algorithm

The aims of this study obtain to outlier data on RFM (Recency, Frequency, Monetary) and LRFM (Length, Recency, Frequency, Monetary) models. The outlier have found analyzed to determining customer loyalty. There five step in this study. First is determining data based on RFM and LRFM models attributes. Second is normalizing the data with min-max method. Third is clustering data with DBSCAN algorithm after determining best cluster with Dunn Index method. Last is analizing the data to determining customer loyalty. This study found that there are 8 outliers on RFM and 9 outliers on LRFM. Based on RFM and LRFM outliers found that the customers have lost customer groups, low consumption and uncertain new customers becoming loyal customers.