Dynamic Churn Prediction Using Ann- Predict Your Customer Behavior

Dynamic Churn Prediction Using Ann- Predict Your Customer Behavior

A. Amulya1, Afifa Butool2, Sedmaki Aishwarya3

1Professor, Mahatma Gandhi Institute of Technology
2,3UG Student, Mahatma Gandhi Institute of Technology

Abstract- In today’s competitive landscape, customers prioritize high-quality services, compelling organizations to focus on technologically advanced solutions to meet customer expectations. Amid this growing competition, effective customer relationship management (CRM) systems have become essential for organizations to attract new customers, strengthen existing relationships, and enhance customer retention. This, in turn, contributes to business profitability and sustainability. Leveraging machine learning models such as Artificial Neural Networks (ANN), Support Vector Machines and Random Forest Classifier techniques offers significant potential in understanding and predicting customer behavior. These advanced models enable organizations to identify churn risks, make data-driven decisions, and develop proactive strategies to improve customer satisfaction and loyalty. By integrating ANN, businesses can gain deeper insights into customer behavior, ensuring transparency and fostering trust, ultimately leading to improved retention and sustained growth.

Keywords: Churn analysis, Data Processing, Artificial Neural Networks, machine learning, Deep Learning, Support Vector Machines, Random Forest, KNN, XG Boost, SHAP, Explainable AI, CRM.