Case Study

How an EdTech platform decreased overall churn by -20%

20%
Decrease in churn
18%
Increased payment recovery rate
$1.4M
Incremental revenue recovered

The Situation

An online learning platform with over 2M learners aiming to build skills via virtual interactions and projects was suffering from artificially inflated rates of payment failure, with more than half of all attempted payment transactions on the platform failing within a given year. The end result — millions of potential learners missing out on the opportunity to build their own skills, and millions in lost ARR.

The company sought guidance with respect to two key problems:

  • How to reduce the number of failing payments from legitimate users
  • How to ensure that users who want to pay for access to premier educational resources can
In the course of a single year prior to engagement, more than 1.2M legitimate payment attempts failed.

The Challenge

The company needed a solution to address the ‘leaky bucket’ of silently churning users. Silently in the sense that these users were not churning through traditional 'unsubscribe’ methods, but rather their payments were automatically failing.

For every 100 payment attempts, only 44 were successfully completed.

The company was bleeding users who wanted to pay to learn—causing pain both for the company, and for its customers who wanted to better themselves by learning new and valuable skills on the platform.

The Solution

The EdTech company engaged Butter to handle the problem of artificially inflated failed payment rates due to improper payment configuration - timing, cadence, and presentation.

First, Butter’s proprietary algorithm sorted between legitimate failed payments and true failed payments. It was identified that ~60% of all failed payments on the platform were indeed legitimate attempts to pay — indicating willingness of an end user to use the platform, but inability to access it due to the underlying billing infrastructure.

Butter then introduced an ML-powered approach to scheduling payment collection attempts.

The company is now able to proactively prevent subscriber churn, identifying at-risk billing infrastructure and updating payment presentation accurately to ensure everyone who wants to pay to learn can.

  • Dynamic decline handling based on the error codes provided by the underlying issuing banks
  • Optimal time of day billing for charge attempts; and,
  • Personalized recovery schedules sorted by card scheme, funding source, and other high-value parameters.

The Results

Targeted interventions led to a massive decline in involuntary churn at the company—totaling nearly 5% of top line revenue. The company realized massive improvements in the rate of payment updating for lapsed users—approximately 20% more users.

As a result of these cumulative changes, the company realized a massive benefit in revenue saved that accounted for more than $1M in revenue savings.

The company is now able to better serve recurring customers, and spare users the inconvenience of being involuntarily kicked off a service they rely upon for everyday learning and productivity.

  • Payment recovery rates rose by 18%
  • Incremental revenue recovered totaled $1.4M
  • The company was able to deliver 4.5% of top line ARR

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