Segmentation of Online Customers Based on Household Panel Data Using Unsupervised Learning

Marketing departments of companies often struggle with cost-related issues arising from insufficient and inaccurate definition of their target customers. They desire to focus on customers who are highly profitable and loyal. The aim of this study is to enable companies with e-commerce activities to precisely define their target customer segments and understand their characteristics better. For this purpose, the e-commerce transaction dataset provided by a household panel company operating in Türkiye is used. In this project, where a real-life case is analysed, unsupervised machine learning algorithm, K-Means, is used. By doing cluster analysis, the eventual aim is to reach the right number of clusters having similar customer behaviours. The result demonstrates successful modelling, achieving distinct segments consisting of homogenous personas. Consequently, enterprises with e-commerce activities will be able to identify different customer types more effectively, which paves the way for the development of customised marketing strategies, increasing loyalty and profitability.