Customer Segmentation Analysis in Online Fashion Store using Clustering Large Application and K-Means
Muthia Ramadhani (a*), Rezzy Eko Caraka (b)

a) Master of Management, Faculty of Economics and Business, University of Indonesia
Jl. Salemba Raya No. 4, Jakarta Pusat 10430, Indonesia
*muthia.ramadhani[at]ui.ac.id
b) National Research and Innovation Agency (BRIN)
BJ Habibie Building, DKI Jakarta 10340, Indonesia

Abstract

As the competition in the online fashion industry is increasing at this time, companies have to prepare the best strategy to compete in the industry. One of the strategies is a marketing strategy to attract customers by segmenting customers to ensure the marketing strategies carried out by the company are effective to perform appropriate services and maintain loyal customers. This research aims to do a customer segmentation analysis for The Blouse, which is an online fashion store that sells clothes through e-commerce, to be able to find out the segmentation and profile of their customers. Therefore, the marketing strategy they plan can be following the needs of their customers. This research is conducted by doing the RFM analysis of the customers and also cluster analysis for each customer^s RFM values using the k-means and CLARA clustering methods. After the best clustering is determined through silhouette analysis and Dunn index, the customer clusters owned by The Blouse will be known and analyzed to find out their profile. The results show that k-means is the best clustering method to cluster RFM values of the customers. The optimum cluster number that is obtained is four with two clusters that have the best customer profile based on the RFM values.

Keywords: E-commerce- Online fashion- Customer segmentation- Customer profiling- RFM model- Cluster analysis- K-means- CLARA- Silhouette analysis- Dunn index

Topic: Marketing Management