Metrics question

What metrics would you track for Windsurf's developer engagement?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Metrics design for developer engagement, distinguishing surface-level activity from meaningful, sustained use.

Define core engagement: daily and weekly active developers using Cascade or other AI-assisted features, not just IDE opens.
Add depth metrics: number of AI-assisted actions accepted and retained per session, similar to the acceptance-and-retention pattern used for autocomplete tools.
Add breadth metrics: percentage of a developer's total coding sessions that involve AI assistance, showing how embedded Windsurf has become in daily work.
Add retention cohorts: week-over-week and month-over-month active usage by signup cohort, to see if engagement holds or decays.
Add a satisfaction guardrail: periodic in-product surveys or a simple thumbs up/down on AI suggestions to catch quality issues that raw usage numbers might miss.
Segment all of the above by whether the developer is on an individual, team, or enterprise plan.

Distinguishes surface activity (IDE opens) from real engagement (AI suggestions accepted and retained).
Adds a breadth metric measuring how embedded AI assistance is in the developer's overall workflow, not just isolated use.
Uses retention cohorts to detect decay, which is critical after the acquisition turmoil mentioned in related questions.
Pairs quantitative usage with a lightweight satisfaction signal as a guardrail against declining quality.
Segments by account type since enterprise and individual developers likely show very different engagement patterns.

Using raw daily active users as the only engagement metric without checking depth or retention.
Ignoring quality guardrails, risking a metric that rewards usage even if suggestions are poor.

Chapters of the AI PM course that teach what this question tests.