Customer Profiling and Market Basket Analysis Using K-Means Algorithm and Association Rule Mining: Evidence from Indonesia E-commerce Company Pratiwi Arizona, Arga Hananto
Magister Manajemen, Universitas Indonesia
Abstract
Online customers’ segmentation could be valuable research topic of marketing strategy. Previous literatures mainly studied the differences between non-purchasers and purchasers, lacking further segmentation of online customers itself. This paper focuses on online customers segmentation based on large volume of real transaction data in one of Indonesia e-commerce website. This research proposed the clustering method of customers using K-Means algorithm, and RFM Patterns as an analysis of customer profiling; then we perform the market basket analysis using Apriori algorithm for every cluster in order to get more information of frequently purchased products by customers. Later on, the analysis could be an input for respective company in designing their product recommendation system for segmented customers.
