1 min lesson
Reading product analytics
Pick two rows from the table in "Reading product analytics" and explain the choice each one supports.
Step 1 of 3
Reading product analyticswhat the logs tell you
- Metric family
- Funnel / activation
- Question it answers
- Where do new users drop before first value?
- What it can't tell you
- Why they dropped or what they expected instead.
- Metric family
- Retention
- Question it answers
- Do developers keep coming back week over week?
- What it can't tell you
- Whether they're retained out of value or lock-in.
- Metric family
- Feature adoption
- Question it answers
- How many try the agent and how many keep using it?
- What it can't tell you
- What stopped the ones who tried it once and left.
- Metric family
- Acceptance rate
- Question it answers
- What share of suggestions get accepted?
- What it can't tell you
- Whether accepted code was actually correct or kept.
| Metric family | Question it answers | What it can't tell you |
|---|---|---|
| Funnel / activation | Where do new users drop before first value? | Why they dropped or what they expected instead. |
| Retention | Do developers keep coming back week over week? | Whether they're retained out of value or lock-in. |
| Feature adoption | How many try the agent and how many keep using it? | What stopped the ones who tried it once and left. |
| Acceptance rate | What share of suggestions get accepted? | Whether accepted code was actually correct or kept. |
Behavioral data is excellent at “what” and “how many,” mostly silent on “why.”
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Full explanation
Metrics that matter for an AI editor
Metrics that matter for an AI editorCursor-flavored fluency
- Acceptance rate of suggestions - share of completions or agent edits a developer keeps. High value, but acceptance isn't correctness; pair it with downstream signals.
- Time-to-first-value - how fast a new user gets a genuinely useful result, a leading indicator for activation and retention.
- Retained daily use - does Cursor become part of the everyday loop, the strongest signal that the product earns its place in a developer's day.
- Revert / undo rate - a guardrail; suggestions accepted and then ripped back out are a quiet quality problem acceptance rate alone hides.
An experiment can prove the diff-preview raised acceptance. It cannot tell you that developers accept more because they finally trust what the agent is about to change. That causal story is qual's job and it's the part that shapes the next three features rather than just this one.
When a panel hands you a clean experiment win, don't just celebrate it. Ask “what does this not explain?” and propose the follow-up interviews. Showing you know the boundary of an experiment signals senior mixed-methods judgment, which is exactly the bar this role is hired against.
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Optional practice