Online Consumers Segmentation Using RFM and ARM Analysis

Abstract

The apparel and fashion industry of local brands in Indonesia has been growing rapidly. The increase of e-commerce usage by the apparel and fashion industry has resulted in a stiff competition between local and global fashion brands. Therefore, a good and strategic marketing strategy is needed to maintain the industry growth and sustain the local industry. This research aims to build customer segmentation to help local fashion brands build marketing strategy in e-commerce. The study utilized Machine Learning using Association Rules Mining (ARM) and Consumer segmentation of RFM models. The ARM is one of the most popular techniques to learn a pattern or associations of attributes of customers. Consumer segmentation of RFM models was used to understand about consumer’s behaviors. The data was collected from local fashion brands in e-commerce platforms. After data was collected, the data was preprocessed, and then analyzed using the RFM model. After the RFM model concluded, the model was used to associate consumers type, discount and delivery promotion by using ARM to understand the relations about the sales promotions and the consumers type. The segmentation is done by clustering with the k-means algorithm.

Keywords: Association Rules Mining, Consumer’s Segmentation, RFM