Telecom Customer Segmentation Using Deep Embedded Clustering Algorithm

Abstract

Telecom companies record customer’s actions, which generates a large amount of data that can lead to crucial insights on customer behaviour and demands. Most telecom companies use customer segmentation to increase customer satisfaction, which entails dividing targeted customers into different groups based on demographics or usage perspectives such as gender, age group, buying behaviour, usage pattern, special interests, and other characteristics that represent the customer. With more number of attributes and great sparsity of telecom data, identifying targeted customers become difficult and various machine learning algorithms have been proposed for the same. Deep learning has gained huge popularity in various business analytics and operations. However, use of deep learning for customer segmentation is very limited. This paper aims to segment Telecom customer data using deep embedded clustering algorithm. For experimental purpose, Kaggle’s telco customer churn dataset is considered. Results of our study indicate that deep embedded clustering algorithm is able to attain better segmentation results as compared to traditional clustering algorithms such as K-means and Hierarchical clustering approaches.