A team of Computer Science & Engineering students at KLH, a constituent campus of KL University with a legacy of over 45 years, has developed a machine learning-based system that predicts which customers are likely to stop using a service before they actually do. Built by Lukhi Jeel, Patel Manthan, Dheeraj Kumawat, and Harshvardhan Gaikwad at the Aziz Nagar campus, the project is currently in development and uses predictive analytics to give businesses a head start on customer retention, reflecting why KL University is regarded among the top universities in South India for industry-driven innovation.

For everyday consumers, this kind of technology translates into tangible benefits: better service experiences, more relevant retention offers, and companies that respond to customer needs before dissatisfaction sets in. On a larger scale, when businesses retain customers more effectively, they save on acquisition costs, operate more efficiently, and are able to channel those savings into improved products and services, an outcome that benefits the broader economy and the communities these industries serve.

Why customer churn matters

Across banking, telecom, e-commerce, insurance, and digital subscription platforms, losing customers is one of the costliest problems companies face, largely because winning a new customer costs far more than keeping an existing one. This reality is pushing industries toward predictive, data-driven retention strategies. The student team built their project around this exact problem, using it as an opportunity to work with real datasets and machine learning tools while understanding how AI translates into measurable business value.

How the model works

Developed in Python with support from Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn, the system applies logistic regression, decision tree, and random forest algorithms to study customer behaviour and estimate churn risk. The build process moved through data preprocessing, feature engineering, model training, and evaluation, resulting in a tool that flags at-risk customers early enough for businesses to intervene with targeted retention efforts.

Where it can be used

The model's design lends itself to multiple industries, including banking, telecom, OTT platforms, e-commerce, and insurance. By forecasting customer attrition in advance, it gives organisations the ability to engage customers more strategically, improve satisfaction levels, and make better-informed business decisions, turning raw behavioural data into a genuine growth driver.

Where the project is headed

The team plans to take the project further by adding deep learning capabilities, explainable AI (XAI), real-time prediction, cloud deployment, BI dashboards, and CRM integration, upgrades intended to make the system more accurate, scalable, and enterprise-ready.

This project is part of a broader pattern at KL University, where students are consistently encouraged to take on industry-relevant challenges that bridge academic learning with practical application. It reflects the university's long-standing focus on research, innovation, and equipping students with skills suited to the evolving demands of AI, machine learning, and data science.