Next-Wave of E-commerce: Mobile Customers Churn Prediction using Machine Learning

  • Asif Yaseen Department of Commerce, Bahauddin Zakariya University Multan
Keywords: Predictive Analytics, kNN, Decision Tree, Naive Bayes, Telecom Customer Churn and Retention, Logistic Regression, PSO.


With the swift increase of mobile devices such as personal digital assistants, smartphones and tablets, mobile commerce is broadly considered to be a driving force for the next wave of ecommerce. The power of mobile commerce is primarily due to the anytime-anywhere connectivity and the use of mobile technology, which creates enormous opportunities to attract and engage customers. Many believe that in an era of m-commerce especially in the telecommunication business retaining customers is a big challenge. In the face of an extremely competitive telecommunication industry, the value of acquiring new customers is very much expensive than retaining the existing customer. Therefore, it has become imperative to pay much attention to retaining the existing customers in order to get stabilized in a market comprised of vibrant service providers. In the current market, a number of prevailing statistical techniques for customer churn management are replaced by more machine learning and predictive analysis techniques. In this study, we employed the feature selection technique to identify the most influencing factors in customer churn prediction. We adopt the wrapper-based feature selection approach where Particle Swarm Optimization (PSO) is used for search purposes and different classifiers like Decision Tree (DT), Naïve Bayes, k-NN and Logistic regression is used for evaluation purposes to assess the enactment on optimally sampled and abridged dataset. Lastly, it is witnessed through simulations that our suggested method accomplishes fairly thriving for forecasting churners and hence could be advantageous for exponentially increasing competition in the telecommunication sector.

How to Cite
Asif Yaseen. (2021). Next-Wave of E-commerce: Mobile Customers Churn Prediction using Machine Learning. Lahore Garrison University Research Journal of Computer Science and Information Technology, 5(2), 62-72.