Implementation of Unsupervised ML Techniques for Behavioural-Based Credit Card Users Segmentation in Africa

Abstract

A strategy used in marketing is segmentation, which is used to categorise consumers or other entities based on traits like behaviour or demographics. Finding consumer groups that may react similarly to particular marketing strategies, including such email subject lines or display ads, is beneficial. Due to the fact that it allows firms to customise marketing messaging and timing to increase response rates and enhance customer experiences. Customers often exhibit a wide range of behaviours. Businesses frequently employ segments that are based on thresholds. A scientifically based approach to categorise clients is required due to the expanding amount of characteristics and the overall theme of tailored products. The solution is clustering based on behavioural data. The goal of this study is to appropriately classify credit card users so that we can better understand their requirements and behaviour and provide them with relevant marketing offers. We must use the credit card segmentation dataset in our procedure. The input dataset was gathered from a repository of datasets. The pre-processing procedure must then be carried out. After that, feature extraction must be used. The system is then created using the k-means method to come up with the best segmentation plan. We must compute the silhouette score using the k-means technique. The findings of the experiment indicate that the visualisation and customer segmentation based on clustering algorithm.