Churn Buster allows companies more control over delinquent payments than ever before possible. 

However, beyond the customization and convenience of this platform, sometimes you'll want to know exactly how much CB is outperforming you previous setup.

Comparing before and after performance can be challenging. 

Using the same data sources, and the same methodology, is critical for accuracy of results. 

Also, you need to use a long enough time frame to average out any inconsistencies. For a company with thousands of failed payments each month, this could be as few as 60 days before and 60 days after. For smaller companies 90+ days of data is typically required.

The Data

First, let's decide where the data will be coming from.

For example, if you use Stripe with Stripe Billing, that will be the best source of subscription-related data. However if you use Stripe with subscriptions managed elsewhere, with a service like ReCharge or an in-house billing system, you may want to export data from the subscription manager itself. This will help you weed out one-time payments if you have any, and charge reattempts to the same past-due invoice. 

We ONLY want to be looking at customers that go delinquent, filtering out any repeat charge attempts to the same customer.

Next, run a data export for the desired time range. Export all the customers who went delinquent during that range. 

This part can get a little tricky: if you were doing a 90-day analysis before/after, you would run a delinquency report ranging 90 days before starting with Churn Buster until 30 days before starting with Churn Buster. 

So the range is 90–30 days before CB. This allows a 30 day buffer, for campaigns to complete without overlapping into the "after" cohort you'll do next. 

Similarly, the "after" cohort of delinquencies will only be for days 0 to 60 with CB, allowing 30 days for campaigns to run to completion.

To tie all this together, you'll need to run one more report before we can start crunching numbers. 

Generate a list of active subscriptions on the day that CB was activated. Generate another list of active subscriptions 90 days after CB was activated. With these lists we can match against the other reports to see which delinquencies resulted in a successful payment, calculating a percentage-based recovery rate.

The Calculation

  1. Tally up how many customers went delinquent in the "before" cohort.
  2. Tally up how many customers went delinquent in the "after" cohort.
  3. Match with the before/after active subscriptions lists to find how many delinquent customers went on to issue payment.
  4. Divide the number of successful outcomes into the total number of delinquencies.
  5. Multiply by 100, and view your before/after recovery rates.

Here's a quick example:

Let's assume 1,000 customers went past due in the range 90–30 days before starting with CB. 

From the list of active customers on day 0 (when CB was turned on), we find that 250 of them match with this 90–30 delinquency list. 250 divided into 1000 is 0.25, giving us a 25% recovery rate.

Now, let's assume 1,000 customers also went past due in the range 0-60 days after starting with CB.

From the list of active customers on day 90 (with all campaigns having run to completion), we find that 400 of them match with the 0–60 delinquency list. 400 divided into 1000 is 0.40, giving us a 40% recovery rate.

That's a 60% increase in recovery rate—hypothetically outstanding! 😁



As a company that is focused on solving payment problems, we are confident that you will find a significant improvement. To-date we have not seen a payments setup we couldn't improve. The devil is in the details, and our team spends every workday looking at ways to improve campaigns, reduce customer friction, and make sure no customer churns involuntarily.

With that said, please share your results with us! We love to hear success stories from our customers. 

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