You shipped it. Adoption didn't follow. This finds out why — and leaves you with a fix list you can act on this sprint.
When you need this
Usage numbers are flat or falling after launch, and nobody can say exactly why.
A feature that tested well in a demo isn't sticking in real, everyday use.
Leadership is asking "fix or kill?" and the team doesn't have evidence to answer with.
What's included
Analytics review of the adoption funnel to find where users drop off
Heuristic analysis of the flow to name specific friction points
Interviews with real users who tried the feature and didn't stick
A prioritised fix list, scoped to what's achievable this sprint
This mirrors the Optimization phase of the AI Troubleshooting engagement — analytics, a heuristic adoption audit, and evaluative testing, run to diagnose and close an adoption gap after launch. View that case study →
