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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