Churn rate describes the rate at which customers abandon a product or service. Churn rates are studied most closely in subscription-based products and services — everything from traditional cable or gym memberships to streaming entertainment and gaming devices. For example, churn is a top-of-mind metric for wireless carriers, especially as they go head-to-head with wireline companies offering internet access and video.
The antidote for churn is high customer satisfaction. But different things satisfy different customers. You can’t prevent every churning customer from departing, but you can try to better understand why they’ve departed in the hope of preventing more churn by other customers. Unless you can use data to reliably determine what prompts customers to stay or leave and which groups of customers are likely to churn in the future, your company is at a competitive disadvantage.
The role of analytics in addressing customer churn is to use historical data to predict which users are likely to leave so the company can induce them to stay. The best model connects the right inducement with the right customer at the right time, using algorithms such as support vector machines, random forest, or k-nearest neighbors. It also balances the trade-off between precision (correctly predicting a churning customer) and recall (number of predictions that were actually successful).
Alteryx Designer allows the creation of a churn prediction model directly in a workflow. Utilizing Alteryx for data science with Python, users can train models using existing customers that have already left. Logistic regression can be used to separate out customers that churned based on business failures. This model can then be used to create a list of high-risk customers and action can be taken to prevent churn.