The Donor Tipping A/B Test

Neon One, ~2021

Problem

The donation flow was partially subsidized through a "donor covers the fees" checkbox. The open question: would an optional donor tipping model out-earn the cover fees checkbox? Both sides had plausible intuitions, so we needed to measure it.

My role

Owned the experiment design, the read, and the rollout decision.

Outcome

Tipping out-earned "donor covers the fees." We rolled out tipping platform-wide as an optional revenue driver for every customer.

Situation

Our "donor covers the fees" option was the incumbent, familiar to donors across the nonprofit space. Tipping was gaining ground elsewhere but felt riskier: would donors tip a platform on top of a charitable gift? This was a monetization change touching every donation form on a platform processing $200M a year. Guessing wrong at that scale is expensive: either we're shipping a worse model or we're leaving a better one unshipped.

The decision

Run the experiment properly. We A/B tested multiple versions of each model across donor cohorts and measured the effect on transaction volume and revenue.

Outcome

The tipping model won on revenue. We rolled it out platform-wide as an option for every customer to reap the benefit of the experiment, and the business got a new monetization model chosen on evidence.

What it shows

Growth mechanics decided by experimentation rather than taste, and the decision to ship the winner everywhere, which is the half of A/B testing most teams skip.