How Data Science Work Reveals Hidden Trends in Payment Success Rates

In a previous blog post, we outlined how Recurly uses machine learning in our Revenue Optimization Engine to predict transaction success and maximize your revenue. This unique Recurly technology is an example of how machine learning, if handled correctly, can be a powerful tool for preventing involuntary churn. 

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Using Machine Learning to Optimize Subscription Billing

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.

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Minimizing the Impact of Declined Transactions

In today's blog post, we'll explore best practices to minimize the impact of declined transactions. We’ll also share tips for benchmarking and iterating on site configurations to improve transaction decline recovery and drive revenue.

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Benchmarking & Minimizing Credit Card Transaction Decline Rates

Many merchants are concerned about their transaction decline rates and want to ensure that the rates are within what is normal for their industry or segment. Their concerns can be amplified by seeing large blocks of declined transactions, multiple attempts to collect on a single invoice, and numerous customer updates to billing information. It's important to note that with subscription billing, declines are normal. For most customers, a decline rate of 5-14% of monthly transactions for business-to-business (B2B) and 6-18% for business-to-consumer (B2C) is about right—but that number will vary greatly depending on the composition of a user base.

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