Case Study

How a file sharing subscription business cut accidental churn in half

61%
Decrease in churn
176%
Increase in payment cure
$22M
Incremental annual revenue

The Situation

The company is a file-sharing subscription service with over 10Mpaying subscribers and 500M users spread across its consumer and enterprise offerings. The company suffered from high rates of accidental churn—subscriber drop-off caused by a failure to collect payments. Despite a healthy user acquisition funnel, the company struggled with user retention on its platform with rates hovering below 85%monthly.

3 Goals

  • Reduce the number of users failing payments
  • Appropriately handle users in a delinquent payment state
  • Increase the likelihood that a lapsed user would successfully update their payment information
In one year, more than 750,000 paying users and 65,000 paying enterprise accounts failed at least one payment.

The Challenge

The company needed a solution to address the leaky bucket of silently churning users. For every 100 previously paid users, only 35 had explicitly asked to cancel their subscriptions. The company was bleeding users who had not actively requested to stop using their product—causing pain both for the company, and for its customers who were unaware that their access to the service was at risk.

The Solution

The company proactively prevented payment lapse by identifying at-risk users and updating payment information prior to scheduled billing dates. An ML-based approach to scheduling payment collection attempts was implemented—including:

  • Dynamic decline handling based on error codes provided by issuing banks
  • Optimal time of day billing for charge attempts; and,
  • Personalized Dunning (payment retry) schedules sorted by card scheme, funding source, and other high-value parameters
Accidental churn fell from 65% to 25%, driving $14M from the enterprise segment and $8M from the consumer side of the business.

The Results

Targeted interventions led to a massive decline in accidental churn at the company—from 65% of overall churn to just 25% for its enterprise product. The company realized massive improvements in the rate of payment updating for lapsed users—from 10.9% to 33.5% on Day 0 and from 15.7% to 43.4% on Day 24. As a result of these cumulative changes, the company realized a massive benefit in revenue saved that accounted for $22M in top-line annualized recurring revenue—$14M on the enterprise side and $8M on the consumer side of the business.

  • Accidental churn fell from 65% to 25% of overall volume
  • Payment cure rose from 15.7 to 43.4%
  • Incremental revenue totaled $22M annually

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