<![CDATA[Customer churn, also known as attrition, occurs when subscribers or customers stop doing business with an enterprise or organization by unsubscribing to a service, discontinuing membership or simply stopping payment. Churn is a critical metric because it is more cost-effective to retain existing customers than it is to acquire new ones. Since churning impedes growth, companies usually use a defined method for calculating customer churn in a given period. By monitoring churn rate and the various factors affecting it, organizations determine their customer retention success rates and identify strategies for improvement. The model, “Artificial Intelligence based Predictive Analysis of Customer Churn” is aimed at predicting a customer’s likelihood of discontinuing the usage of the products or services extended by a company, using ML & DL algorithms. It is built for the example of a US based telephone service provider’s services. This model uses machine learning’s capability to identify patterns and build prediction algorithms using provided data, blended with the adoption of complex algorithms of deep neural networks, which form deep learning. Accuracy tests, comparative graphs and illustrations equip this project with higher efficiency and good performance by estimating which is the best model from the suite of models created]]>
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