How Recurly Uses Machine Learning to Reduce Transaction Declines

One of the significant benefits of subscription commerce is the amount of data and related insights this model generates compared to one-time purchases. New data related to marketing, payments, and customer lifecycle events is generated regularly, at each new billing cycle. This data is invaluable for gaining insights and making decisions that help you to optimize your business.

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Strategies to Understand Decline-Rate Data and Reduce Involuntary Churn

In our previous blog post, we summarized the common decline reasons for failed transactions and the messages that the gateway delivers. We also talked about Recurly’s Revenue Optimization Engine which helps recover failed transactions. In this blog, we want to discuss some strategies that subscription businesses can utilize to avoid payment failures in the first place.

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Revenue Optimization Engine’s New Machine Learning Model Improves Prediction Accuracy

In a previous blog post, we talked about how Recurly uses machine learning to optimize subscription billing for our customers and prevent involuntary churn. As part of our goal to help our customers maximize their subscription revenue, we introduced the Revenue Optimization Engine in 2018. When a recurring transaction fails, this technology creates a customized retry schedule, so subsequent retries of that transaction have a higher chance of succeeding. This technology is driven by machine learning which relies on models based on Recurly’s incredible breadth of historical subscription data which identifies factors that are highly correlated with successful transaction processing.

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Predicting Recurring Transaction Success

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.

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Improve Transaction Success Rates and Reduce Churn With Recurly’s Revenue Optimization Engine

Every subscription business encounters credit card declines. These declines increase churn, reduce your revenue, and can negatively impact your subscriber relationships. But, with the right subscription management platform, you can minimize their impact.

Recurly has the advantage of working with thousands of subscription businesses which come from a wide range of industries. Many of these are ‘high-velocity’ businesses with large subscriber bases generating high transaction volumes annually. This gives us access to hundreds of millions of data points that encompass billions of attributes from many different types of companies in both B2B and B2C categories.

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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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