Customer Churn Prediction Using Machine Learning: Main Approaches and Models

Customer retention is one of the primary growth pillars for products with a subscription-based business model. Competition is tough in the SaaS market where customers are free to choose from plenty of providers even within one product category. Several bad experiences – or even one – and a customer may quit. And if droves of unsatisfied customers churn at a clip, both material losses and damage to reputation would be enormous. For this article, we reached out to experts from HubSpot and ScienceSoft to discuss how SaaS companies handle the problem of customer churn with predictive modeling. You will discover approaches and best practices for solving this problem. We'll discuss collecting data about client relationship with a brand, characteristics of customer behavior that correlate the most with churn and explore the logic behind selecting the best-performing machine learning models. Customer churn (or customer attrition) is a tendency of customers to abandon a brand and stop being a paying client of a particular business. The percentage of customers that discontinue using a company's products or services during a particular time period is called a customer churn (attrition) rate. One of the ways to calculate a churn rate is to divide the number of customers lost during a given time interval by the number of acquired customers, and then multiply that number by 100 percent.