Subscription businesses of all types from B2B and B2C and all industries face the challenges of churn and declines, as well as the reality of revenue recovery.
Every subscription business deals with the challenges of churn, or what percent of subscribers cancel each month. Different industries have different factors that affect churn. Understanding these elements can help subscription businesses formulate effective strategies to combat churn because even slight fluctuations in churn can make a significant impact on the bottom line.
Different factors lead to different kinds of churn—each requiring a specific approach. Improving customer satisfaction reduces cancellations that result in voluntary churn while using decline management techniques minimizes payment declines that lead to involuntary churn.
Price has an obvious effect on churn. Higher-priced subscriptions experience less churn, possibly because the purchase is more considered. Subscribers both sign up and cancel more readily in categories with lower price points.
There are over 2,000 things that can go wrong with a credit card transaction: out-of-date or inaccurate card information, insufficient funds or temporary hold, gateway issues, and fraudulent activity, to name just a few. When payments are declined, subscription businesses can lose subscribers to involuntary churn. The better you understand why recurring payments are being declined, the more effective your decline management strategy can be, leading to higher MRR (Monthly Recurring Revenue).
The top 3 failure types are the same across all geographic regions but each region sees a unique combination of failure types for the 4th and 5th most prevalent.
05Invalid Card Number
05Fraud Stolen Card
04Invalid Card Number
The realities of churn, declines, and revenue recovery might not be going away, but armed with the right data you can take strategic action to drive improvements for your business.
We examined a sample of over 1850 subscription sites that used the Recurly platform. The study encompassed a period of twelve months, from January 2019 to December 2019. All data was aggregated and anonymized. Our study uses median, 25th, and 75th percentile values which eliminate outliers and provide a more accurate representation of the data.