Activation & onboarding review
Can the model find the friction that matters most and prioritise the fixes?
The PM job
Reviewing a signup and onboarding flow that is losing users.
Why it matters
Anyone can list fifty UX nits. The job is finding the two that explain the drop-off, backed by the funnel data supplied.
What good looks like
- Ties each issue to the funnel data
- Prioritises by likely impact
- Distinguishes activation from mere completion
Deliberately not measured
- Accessibility audit completeness
- Visual redesign
Consequential critique
A generic UX checklist
Decision model, LLM judge and blind PM review
Results
Every setup we’ve tested on this task, across all cases and repeats.
| # | Model · Harness | Task score | Decision model | LLM judge | PM review | Runs | Critical failures | Cost / run | Latency |
|---|
Case viewer
Read the brief, then put up to three outputs side by side. The outputs are the point; the scores just tell you where to look.
Review our onboarding flow and funnel below. Write a memo to the onboarding squad saying where we are losing people and what to fix, in the order you would fix it, with the reason for each. Keep it under 500 words.
The data-source connection step (screens 5–8) is the activation bottleneck; prioritise a sample-data or CSV-first path to a first chart, then non-admin credential handoff and a sync that survives leaving the page, ahead of cosmetic fixes.
v1.2 · anonymised real · B2B SaaS, data