Customer Attrition 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. Companies usually make a distinction between voluntary churn and involuntary churn. Voluntary churn occurs due to a decision by the customer to switch to another company or service provider, involuntary churn occurs due to circumstances such as a customer's relocation to a long-term care facility, death, or the relocation to a distant location. In most applications, involuntary reasons for churn are excluded from the analytical models. Analysts tend to concentrate on voluntary churn, because it typically occurs due to factors of the company-customer relationship which companies control, such as how billing interactions are handled or how after-sales help is provided. 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.

Churn prediction is one of the most well known applications of machine learning and data science in the Customer Relationship Management (CRM) and Marketing fields. Simply put, a churner is a user or customer that stops using a company’s products or services. Churn applications are common in several sectors: Subscription business companies (think internet and telephone services providers): customers that are most likely to churn at the end of their subscription are contacted by a call center and offered a discount. E-commerce companies (think Amazon and the like): automatic e-mails are sent to customers that haven’t bought anything for a long time, but may respond to a promotional offer.

Telephone Customer Churn Prediction uses Artificial Neural Networks (ANN) to predict which customers are likely to leave a telecom service. By analyzing customer data, the model identifies patterns that help businesses improve retention strategie

This repository consists of Jupyter notebook for Churn Prediction.

Customer churn prediction on telephone billing data

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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. Companies usually make a distinction between voluntary churn and involuntary churn. Voluntary churn occurs due to a decision by the customer to switch to another company or service provider, involuntary churn occurs due to circumstances such as a customer's relocation to a long-term care facility, death, or the relocation to a distant location. In most applications, involuntary reasons for churn are excluded from the analytical models. Analysts tend to concentrate on voluntary churn, because it typically occurs due to factors of the company-customer relationship which companies control, such as how billing interactions are handled or how after-sales help is provided. 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.

This repo is all about the work done of various Machine Learning and Data Science projects like Titanic Survival, Amazon Customer Category Prediction, Telephone Churn Prediction

Telephone service companies often prefer to use customer attrition analysis and customer attrition rates as one of their key business metrics as the cost of retaining an existing customer is far less than acquiring a new one. Predictive analytics use churn prediction models that predict customer churn by assessing their propensity of risk to churn.

Customer churn, also known as customer attrition, 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. 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. For this project, we will be exploring the dataset of a telecom company and try to predict the customer churn

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.

Customer churn, also known as customer attrition, 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. 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. For this project, we will be exploring the dataset of a telecom company and try to predict the customer churn Problem Statement Using the method of Boosting, classify whether or not the customer will churn

With the enormous increase in the number of customers using telephone services, the marketing division for a telcom company wants to attract more new customers and avoid contract termination from existing customers. This churn prediction model would be able to provide clarity to the telcom company on how well it is retaining its existing customers and understand what are the underlying reasons that are causing existing customers to terminate their contract (high churn rate).