Reseller Segmentation for a Strategy to Increase The Purchase Deposit of a Telecommunications Company Using The LRFM and K-Means Algorithm

Telecommunications companies that provide provider services sell credit vouchers through resellers. Reseller service providers are spread throughout Indonesia. In 2019, the company had resellers who deposit approximately 7000 data per month. The problem for the company was a decrease in resellers making deposits even though reseller data continued to increase daily, resulting in a reduction in company income. So, finding the right strategy to increase deposit purchase transactions is necessary. To determine the right strategy, companies need to understand the characteristics of resellers. The K-Means clustering method can be used in this research to group resellers into groups with similarities based on Length, Recency, Frequency, and Monetary (LRFM) attributes. The data used in this research is reseller deposit data in 2019. The attribute model was used. The cluster results were 3 clusters: cluster 1 containing 2917 resellers, cluster 2 containing 3169 resellers, and cluster 3 containing 3914 resellers. The results of each cluster formed have different reseller characteristics based on LRFM attributes, which will later be used by management in creating strategies to increase reseller deposit purchases.