Involuntary churn significantly impacts subscription revenue, so it’s critical to address — and easier than ever. Machine learning can automatically reduce involuntary churn and boost monthly recurring revenue by an average of 9 percent. Want to know more? Join our latest VB Live event!
One of the most important factors in subscription commerce is subscriber retention. Every billing event needs to occur flawlessly to avoid involuntary churn — and avoid losing a customer because of a credit card decline or a failed transaction. Even slight variations in a subscription business’s churn rate can have significant impact on revenues — yet some credit card declines are inevitable.
There are a wide variety of reasons for a recurring transaction to be declined, from insufficient funds to circumstances beyond the customer’s control like credit or debit card restrictions, technical issues, and more.
The key to maximizing revenue is transforming failed transactions into successful payments. Subscription technology today makes it easier than ever to do that, combating involuntary churn using statistical models and machine learning to improve collections for recurring credit card- and debit card-based transactions.
In subscription commerce, a transaction is considered recurring when a customer’s card is charged again at the start of every billing cycle. Subscription technology company Recurly discovered that on average, 13 percent of recurring transactions are declined — and every one of those declines directly or indirectly impacts your bottom line.
Every declined transaction is different, and every declined transaction is a sticking point in the customer/company relationship. The traditional method of addressing a decline is usually a standard retry schedule, in the meantime suspending the customer’s account while you try to resolve the payment issue, getting in touch with the customer about the problem, and asking them to update their billing information.
This is less than effective, less than efficient, and when the issue is not on the customer’s side, it becomes a customer service fail.
With a machine-learned algorithm, fed on payment data from millions of subscription transactions across the world, machine learning subscription technology can create intelligent retry schedules that are specifically tailored to each individual declined transaction, which means credit and debit card payment issues can be resolved more quickly and more efficiently — with less negative customer interaction, than a static schedule can.
That means you gain access to cash sooner and on a more reliable schedule and subscribers retain uninterrupted access to your product or service. And since your subscription business relies on recurring revenue, every billing cycle in which you improve your involuntary churn rate compounds the positive effect over time on subscriber retention and total recurring revenue.
Subscription billing can and should be a competitive advantage. Learn more about how you can make a positive impact on revenue by optimizing decline management and revenue recovery strategies based on your own unique business needs when you join our latest VB Live event.
Plus get a first look at the latest Revenue Recovery Benchmarks, which reveal the powerful impact of machine learning.
Don’t miss out!
In this webinar, you’ll learn…
- The power of dynamic retry logic, optimized for each individual invoice
- The incremental lift that a well-designed dunning strategy can have on revenue
- The key metrics every subscription business should understand to prevent churn
- How to develop a comprehensive decline management and revenue recovery plan using proven strategies for successful transactions
- Emma Clark, Director of Product, Recurly
- Devin Brady, Data Scientist, Recurly
- Stewart Rogers, Analyst-at-Large, VentureBeat
- Rachael Brownell, Moderator, VentureBeat
Sponsored by Recurly