Did you know that according to the authors of "Leading on the Edge of Chaos", an increase in customer loyalty (or a reduction in customer churn) of 2% means a reduction in costs of 10%? This is why churn analysis deserves so much attention. Companies often use backward-looking statistical models, which usually do not bring the desired success. Customer churn refers to the customer churn rate in a company; in other words, customers who cancel their subscription, shop in another store or switch to another service provider.
The reasons for a customer churn are diverse. It is important for companies to find out why and when a customer wants to migrate. The end goal of churn prediction is to predict the customer migration and take appropriate measures to stop it before it happens. We develop churn models for our clients in order to predict the migration of their customers. Our latest churn model for a client has achieved an accuracy of 96.7% in predicting customer churn.