Scaling beta adoption by 35%
Customers were wary of a big change, so we redesigned the opt-in flow and built a beta playbook the whole company now reuses.
Overview
As a CX platform, we regularly run betas for big features before general availability.
The problem
For a specific feature going live on the platform soon, we needed a certain number of beta participants to be able to collect data and fix issues before GA. Because of sweeping changes and customers being risk-averse, the product team struggled to find the right number of people willing to participate in this beta.
Challenges
Time pressure from external commitments
The feature had already been announced publicly, with a delivery date shared across teams, so there was little room for delay.
Unclear scope
There was no internal alignment on how much we should prioritize customer needs versus end-user needs, especially given our limited resources.
Goal misalignment
Legacy business requirements prioritized retention, creating a complex opt-out process. My goal was to simplify this flow to build user trust.
Complex dependencies
The feature had broad UI and UX impacts, requiring tight collaboration across multiple teams to maintain consistency and minimize disruption.
What I did
- Looked at why customers weren't signing up. Legacy rules favored retention and made opting out hard, which made people nervous about opting in.
- Redesigned the opt-in and opt-out flow so leaving was as easy as joining. Trust went up as a result.
- Got Product, Engineering, and Research aligned on one beta plan, with a clear owner for each part.
- Wrote up the process as a repeatable Beta Rollout playbook for future AI feature releases.
Result
Beta participation went up 35%, with more than 100 beta participants. We reached the number of testers we needed to gather data and fix issues before GA, within 60 days. The playbook has since been used for later releases across the whole company.
What I learned
Making it easy to leave made more people willing to join.