Cosmetics Customer Segmentation and Profile in Indonesia Using Clustering and Classification Algorithm

Abstract

The cosmetics business competition in Indonesia is currently increasing so rapidly, cosmetics customers have spread to various brands, and according to taste. The customer for the company is an asset that is very important for business continuity, so that good customer management can increase company revenue. However, it is not easy to manage customers if they cannot read the characteristics of customers, to carry out appropriate business strategies. So that requires a customer analysis method that can provide recommendations for the company. RFM is one of the most widely used analytical methods for analyzing customers through segmentation and profiling of customers. In addition to segmenting, the customer profile is also a very important factor in analyzing customers, ALC is a form of a customer profile that can be used. RFM + ALC method is not easy to do with very large customer history data, so data mining is needed to help conduct the RFM + ALC analysis. Data mining methods using the clustering function with K-Means and the use of the Elbow method to get the most optimal amount of K in the clustering process can be a model used to segment with RFM, as well as the Naïve Bayes and Decision Tree classification methods to determine ALC profile factors the most influential customer. The results of clustering modeling carried out produce two dominant customer segments. While the Naïve Bayes classification model of the ALC factor can provide recommendations for the most influential customer profiles, with the highest level of accuracy with an accuracy value of 65.87% when compared to the Decision Tree.