Penerapan Algoritma K-Means Menggunakan Model LRFM Dalam Klasterisasi Nilai Hidup Pelanggan - Afifah, Tiara Afrah; Novita, Rice; Ahsyar, Tengku Khairil; Zarnelly, Zarnelly (JURNAL MEDIA INFORMATIKA BUDIDARMA, 2024)
In implementing customer relationship management, there are still many companies that have not utilized CRM optimally as part of their business strategy. As is the case with UD Sandeni. UD Sandeni still has problems in managing its relationships with customers because UD Sandeni does not fully understand the difference between customer information that is profitable and unprofitable for the company's sustainability. UD Sandeni has used a system to manage customer transaction data. However, this system is only used to calculate profits and create bookkeeping for registered agents so that UD Sandeni does not have an in-depth understanding of the characteristics of its customers. To overcome this problem, the solution that can be applied is to use customer grouping techniques, such as clustering. Customer transaction data is processed using a clustering process with K-Means and LRFM. Test the validity of cluster results using DBI and calculate CLV values using AHP weights to produce cluster rankings. The results of this research obtained customer clustering which consists of 2 segments, namely cluster 1 which has the highest CLV value of 0.3171156 with a total of 298 customers and includes the High Value Loyal Customers segmentation, and cluster 2 with a CLV value of 0.1434054 with a total of 72 customers. which is included in the segmentation of uncertain new customers (uncertain lost customers).