Customer Churn Prediction Model Using Deep Learning

Retaining existing customers is a huge challenge for businesses. As customer retention can improve profit margins. Predicting the churn can help with that. Customer churn prediction is a data-driven approach to help companies reduce customer churn and increase customer retention. Machine learning algorithms are applied to analyze past customer data in order to find trends and behaviors which indicate to a high risk of churn. These can include factors such as reduced engagement, complaints, and service usage patterns. By proactively identifying and addressing these issues, companies can retain more customers, reduce churn, and increase revenue. With the emergence of deep learning paradigms, it has been observed that algorithms give a new perspective to this business problem. Conventional algorithms have been utilized to predict churn and then develop a number of client retention strategies. This paper involves data collection, data exploration, data visualization, and creation of a churn prediction model that can be integrated into a company's existing customer retention strategy. The churn prediction model works on customer information, their usage of the service, and contextual features. The deciding factors along with the probability of churn are determined. churn factors are also demonstrated for the companies to take action.