Creating a churn propensity model is now pretty standard for data scientists. Today, churn is the most common data science problem in the world, because every company wants recurring revenue. But how do you go from a churn model to churn prevention? It is much harder than it sounds. Suppose you have a machine learning model that can predict churn.

Running a business optimally is not only about finding and convincing customers to buy your product. It's also about retaining customers who've already bought your product, so they will remain loyal to your business. Stopping customer "churn" has long been a big business behind the scenes, but historically it's been a slow, tedious process. Boston, Massachusetts-based marketing-tech startup OfferFit, which announced $14 million in series A funding today, has made halting customer churn its life's ambition in part by replacing standard manual A/B testing with an AI implementation that's faster to respond to customers, more accurate, and more efficient to use. A/B testing (also known as split-testing) is the process of comparing two versions of a web page, email, or other marketing asset and measuring the difference in performance.