A Review on Machine Learning based Customer Segmentation

Abstract

The process of gathering and grouping clients into groups of people who share similar traits is known as customer segmentation. To segment a company's client base, keep consumers, and make money from them, customer segmentation has gained popularity in recent years. This divide increases the likelihood that a potential customer will buy an item by enabling advertisers to target a specific group of consumers. It allows them to create and employ clear lines of communication to interact with different clients regarding their offerings and draw them in. A simple strategy would be for the organizations to use radio advertising to reach older listeners and online media posts to attract younger audiences. This aids in establishing stronger client relationships and the organization's overall image as an association. Created clusters assist the business in focusing on specific clients and promoting material to them via social media platforms and marketing campaigns that genuinely interest them.

One common application of unsupervised machine learning is customer segmentation. Our approach, which is based on the effective K-Means clustering technique for clustering unlabelled datasets, is presented in this study. The method involves displaying the data to identify essential characteristics that can be used to group the consumers and extract specific insights. Information about demographics, location, economic popularity, and behavioral patterns can be beneficial in determining an organization's goal when defining its clientele.