From Opinion to Evidence: A Go/No-Go Framework for Product Launches

Overview

Candid was preparing to launch a major product that merged two legacy tools into a unified search experience. A previous migration had shipped without structured UX validation and resulted in significant usability issues post-launch. Leadership mandated formal UAT before future releases, and UXR was responsible for designing a scalable, decision-oriented validation framework.

The Challenge

Senior leadership requested a formal user acceptance testing process without any formal definition. The research team was already at capacity with ongoing discovery and evaluative work, so we couldn't build a heavy, bespoke process for every feature launch. We needed a safeguard that prevented problematic releases, worked within tight product timelines, and didn't require custom research setup each time.

Constraints

• Capacity: Research team fully allocated with ongoing discovery and evaluative work
• Speed: Validation had to fit within active sprint cycles without blocking releases
• Clarity: Executives needed binary go/no-go signals, not nuanced findings reports
• Scalability: Framework had to apply across repeated launches with minimal setup
• Risk: Another failed launch would carry material business impact

Approach and Methodology

Method: Task-based usability testing and in-app surveys.

I designed a repeatable user acceptance system tied to explicit launch thresholds, creating both a release gate and a longitudinal quality benchmark.

Why task-based usability testing? We needed behavioral validation that features met user needs. For each launch, I ran unmoderated Maze tests with 30+ participants completing persona-based core workflows. This gave us task success rates, drop-off points, and satisfaction scores without requiring researcher time per session.

Why in-app beta surveys? Usability testing captured behavior, but the team also needed some scale to generalize satisfaction and usability to larger sets of users ahead of launch. During limited beta releases, we deployed short surveys that collected roughly 300 responses per feature, measuring ease of use, satisfaction, and friction themes from actual users in context.

More details: Subjective debates about "ready" had contributed to the original failed launch. I established explicit go/no-go criteria: task success rate of 80% or higher, and average satisfaction of 4.0/5 or higher. If criteria weren't met, launch paused, issues were prioritized, and retesting was required. Because these metrics were consistent across launches, they also created a stable baseline we could track over time to evaluate whether product changes were improving or degrading the user experience.

Execution

1. Partnered with product teams to establish limited beta launches as a prerequisite before full rollout
2. Designed and deployed Maze usability studies with persona-relevant task flows for each feature
3. Created and launched in-product surveys during beta periods to capture real-user feedback at scale
4. Compiled results against predefined thresholds in reports and shareouts
5. Presented go/no-go recommendations to leadership with supporting data
6. When criteria were not met, facilitated prioritization of issues and enforced iteration before release
7. Tracked threshold metrics across launches to build a longitudinal quality baseline

Key Findings

The framework's value showed up in its first applications. High-severity usability risks were identified and resolved in beta before reaching the broader user base, including workflow breakdowns and navigation confusion that would have scaled disastrously.

Launch readiness shifted from opinion-based to metric-based. Features that didn't meet the 80%/4.0 bar were held back and iterated on, which wouldn't have happened under the old process where decisions were driven by timeline pressure.

The consistent measurement created a reference point for evaluating future releases, turning UXR from a one-time checkpoint into an ongoing quality signal.

Impact

Product: Prevented another large scale usability failure. Critical issues were caught and fixed in beta before broad rollout.

Business: Established baseline usability metrics tied to launch readiness. Over time, teams could compare launches against historical performance to assess whether experience quality was improving, holding steady, or regressing.

Organizational: Repositioned UXR from reactive feedback to proactive risk management. Teams began engaging research earlier to ensure they would meet thresholds, embedding usability quality upstream rather than treating it as a final checkpoint.

Reflection

This project reinforced that rigor must match the decision context. I used pragmatic sample sizes sufficient to detect high risk usability issues without slowing delivery. The consistency of measurement mattered as much as the precision, because it enabled trend tracking across launches rather than just one off validation.

The most impactful research systems are not the most complex. They are the ones embedded into how decisions get made and measured over time.