What it is
Evaluating the live product or feature against success metrics defined beforehand, to see whether it did what they said it should. It can include moderated sessions with live users, task completion studies, or CSAT.
Why use it
Prototype and lab testing cannot fully predict real-world behavior. Post-launch evaluation compares outcomes with design intent.
Use this when
- A feature has launched and enough data exists
- Success metrics defined before launch can now be measured
- Support tickets, NPS, or analytics signal problems
You may not need this when
- Analytics alone answer the key success questions
- The change was minor, with low risk of user impact
Dependencies — what you need
- Live product data and early user feedback
- Success metrics agreed before launch
- Product and Engineering ready to act on findings
People involved
- Design
- Research
- Product
- Engineering
- Analytics
Typical outputs
- A post-launch evaluation report
- Whether each success metric was met, missed, or could not be judged
- An improvement backlog
Template
Post-Launch Testing template
A fill-in structure for running this method: what to prepare, what to do, what to record, and how to read the result.
How AI can help
AI as augmentation — not replacement
- Draft an evaluation plan from the original success criteria
- Summarize patterns in support tickets and feedback
- Compare pre- and post-launch metrics, noting other changes
- Draft recommendations, labeled as ideas
AI prompt
A prompt for any AI assistant. It works only from material you paste in, keeps assumptions apart from findings, and leaves the decisions to you.