Customer Segmentation Analysis Using LRFM Based Product and Brand Dimensions

The development of information technology (IT) has encouraged transaction activities in companies so they can grow rapidly and be competitive. One of them is the utilization of customer behavior, which is widely used in helping companies make important marketing decisions and be competitive. One of them is the utilization of customer behavior, which is widely used in helping companies make important marketing decisions. However, the company considers that customer behavior is only limited to data recording, while transaction data can also be further analyzed by the company to gain knowledge about its customers. To overcome these problems, customer segmentation can be carried out to assist companies in adjusting marketing strategies and describing the relationship between products and customers. Customer segmentation is carried out using the LRFM/product and LRFM/product methods with the fuzzy C-Means algorithm. The results of the study show that customers who are in cluster 1 have good loyalty to the brand and product. This is characterized by a long duration, low recency, high frequency, and a large monetary value for the product or brand. And cluster 2 is a customer cluster with minimal loyalty or that does not yet have loyalty to the product and brand.