The global subscription industry continues to grow with a projected market value of £1.2trn ($1.5trn) by 2025. But the challenge of subscriber churn continues to impact many leading subscription companies.
To understand the potential economic impact of involuntary churn, Recurly analysed billions of data points to identify its impact.
Involuntary churn is when a subscription payment stops due to a payment error. Examples include an expired or reported lost card or gateway failure. It is one of the top but manageable, challenges facing the subscription industry today.
Average 8.6% revenue lift: Recurly
Recurly estimates that potential lost revenue arising from the complexities of recurring billing and payments, could hit £102bn by 2025. This is calculated from an average 8.6% revenue lift that subscription businesses experience when implementing Recurly’s data-driven churn management solutions.
“Subscriber churn is the enemy of every brand we speak to. Many businesses have basic, manual solutions in place to manage churn, specifically involuntary. But they don’t realise how much they can move the needle with a better strategy and automated tools,” said Jonas Flodh, CPO at Recurly.
“Recurly is the only company that addresses churn pre- and post-transaction, leveraging machine learning and data models trained for over a decade. We have the smartest churn management solutions in the industry, based on vast amounts of data powering our churn algorithms. Some of our customers have reduced churn to as low as 1%. That translates to material revenue retention and happier subscribers.”
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He argues that subscription businesses must apply both proactive and reactive techniques that deploy a full array of strategies. These include advanced automation and machine learning to remove friction in the payment experience. Some subscription platforms leverage static rules. But he says that the best combine years of data with AI-powered transaction retry models to refine and improve. Even optimising for a specific data set can optimise automated retries.