A few months ago, we laid out Recurly’s approach to optimizing subscription billing using machine learning. Today, we have a follow-up with more details about our approach, answers to common questions, and discussion about the improvements we’re gaining for our customers.

As a data scientist at Recurly, my job is to use the vast amount of data that we have collected to build products that make subscription businesses more successful. One way to think about data science at Recurly is as an extended R&D department for our customers. We use a variety of tools and techniques, attack problems big and small, but at the end of the day, our goal is to put all of Recurly’s expertise to work in service of your business.

“Insufficient Funds”, “Invalid Card Number”... phrases that merchants fear most. Especially the mysterious, ambiguous “Declined”.

These are messages that accompany a declined credit card. But the decline is not the end of the road for merchants using Recurly. Our service automatically retries many failed transactions. These retries are frequently successful, recovering otherwise-lost money and preventing subscribers from churning.

In 2004, Netflix was sued by its shareholders over its reported churn rates. The shareholders argued that Netflix “[used] an improper calculation of the rate that produced an artificially low churn rate.” A judge threw out the case, ruling that there is no single industry-wide definition of churn rate.

Recurly has processed tens of millions of transactions, between merchants and customers around the world.

While 73% of our revenue comes from Americans purchasing subscriptions in US dollars, we process many international transactions and currencies (and we’re always adding more).