What it is
Using analytics tools (such as LogRocket, Pendo, FullStory, or Amplitude) to measure adoption, usage, and drop-off after launch against metrics defined beforehand.
Why use it
Quantitative post-launch data shows what happened at scale against the metrics you set, and where users succeed, struggle, or abandon.
Use this when
- A feature has launched and instrumentation is in place
- You need to measure adoption, engagement, or funnel performance
- You want to find where users drop off in the new flow
You may not need this when
- The feature is too new for meaningful data
- Analytics are not instrumented for the relevant flows
Dependencies — what you need
- Instrumentation deployed for the feature
- Metrics and baselines defined before launch
- Enough time after launch for data to accumulate
People involved
- Design
- Product
- Analytics
- Engineering
Typical outputs
- Adoption and engagement metrics
- Drop-off points and friction areas
- Evidence on whether success metrics were met
- Backlog items or research questions
Template
Analytics Review 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
- Help interpret metric changes and list questions about why, to test
- Draft a monitoring plan and KPI dashboard brief
- Draft an analytics summary for stakeholders
- Suggest follow-on research questions
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.