A predictive churn model provides a repeatable framework for using data to understand when and why your customers stop interacting with your business. Standard churn rate calculations give you an idea of how many of your customers have churned in a given period; a model, on the other hand, can make predictions on an individual-customer level. Building one requires knowledge of feature engineering, statistical concepts, and other data science techniques.
Having information about the customer behaviors that precede churn, as well as churn risk ratings for individual customers, can help you take targeted action to retain your most valuable clients. A well-constructed model can inform a wide range of decisions — like which customers to target with an email campaign or discount — and flow that information into customer service and marketing tools.
The DataScience.com Churn Playbook is a comprehensive repository of instructional content and code designed to help your data science team build a churn model or level up the capabilities of an existing one. Created by our internal team of experienced data scientists, the Churn Playbook includes code libraries, notebooks, and more that have been curated to help teams of every level effectively predict churn.