jrdeco560

In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.

MIT000

Customer-Survival-Analysis-and-Churn-Prediction-for-an-Internet-Service-Provider

Customer Survival Analysis and Churn Prediction App: https://churn-prediction-app.herokuapp.com/ Customer attrition, also known as customer churn, customer turnover, or customer defection, is the loss of clients or customers. Telephone service companies, Internet service providers, pay TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics because the cost of retaining an existing customer is far less than acquiring a new one. Companies from these sectors often have customer service branches which attempt to win back defecting clients, because recovered long-term customers can be worth much more to a company than newly recruited clients. Predictive analytics use churn prediction models that predict customer churn by assessing their propensity of risk to churn. Since these models generate a small prioritized list of potential defectors, they are effective at focusing customer retention marketing programs on the subset of the customer base who are most vulnerable to churn. In this project I aim to perform customer survival analysis and build a model which can predict customer churn. I also aim to build an app which can be used to understand why a specific customer would stop the service and to know his/her expected lifetime value.

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