Analysis of Marketing Segmentation and Its Implementation on 7ps Erha’s Treatment Ultimate Acne Cure Using K-Means Clustering

Abstract

K-Means is a non-hierarchical data grouping method that separates existing data into two or more groups. This method separates existing data into groups so that data with the same character is included in the same group and data with different characters is grouped into other groups. This study aims to produce an analysis that can classify Erha Clinic product/treatment data for March 2022 period using software SPSS 25 IBM to make marketing strategies more targeted. This study divided Erha Clinic product/treatment data with attributes of age group, type of plan, gender, education and total purchase into three clusters (Cluster Metrosexual, Cluster Millenials and Cluster Generation Z). The clustering process is 10 iterations with a minimum distance between clusters of 8,746. The significance value indicates that there is a significant difference between cluster 1, cluster 2 and cluster 3 related to Gender as one of the attributes in the study. The results of clusters show that the marketing target chosen by Erha Clinic is in cluster 3 (Gen-Z Persona) due to acne problems are mostly experienced by young people and although the transaction price is cheap, in the cluster 3 has the most purchased acne products compared to the purchase of the Advance plan and Basic plan bundling in cluster 1 and cluster 2.