Customer Churn Predictions Using Classification Analysis

Churn refers to the number of customers who stop using a product or service over a given period of time. Customers churn for various reasons such as poor customer service, product dissatisfaction, price sensitivity, better alternatives, and changes in circumstances, e.g. relocation. A data analyst finds the factors causing churn in data and works towards preventing it. Churn prediction is the process of using data and analytical models to identify which customers are most likely to stop doing business with or using a company’s product or service in the near future. With churn prediction, a company can take proactive measures to retain customers who are at risk of leaving. Churn prediction helps them to focus more on the customers that are at a high risk of leaving. Examples: Netflix subscription, Network service providers Classification analysis is a data analytics technique that can be used to predict customer churn. Data analytics professionals typically use machine learning algorithms such as logistic regression, decision trees, and support vector machines to predict customer churn using classification analysis. These algorithms analyze data such as customer demographics, purchase history, and interactions with the company to identify patterns that can predict customer churn. <a href="https://medium.com/@lekileki/customer-churn-predictions-using-classification-analysis-e0f0a038f2fd">Website</a>