Metrics question
A large health system launches Abridge with strong week-1 adoption, but by week 6 both retention and encounter share flatten. How would you break down the funnel by site, specialty, clinician cohort, and visit type to isolate the cause, and what experiments would you run first to improve sustained usage?
- Abridge
- Metrics
- Hard
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What this question tests
Tests structured funnel diagnosis for a clinical documentation product, isolating a retention drop across site, specialty, and visit type.
How to approach it
- Restate the pattern: strong week one adoption then flattening retention by week six, pointing to a habit problem, not an initial usability issue.
- Break the funnel down by site, since one site's rollout support can dominate an aggregate number across a health system.
- Segment by specialty and visit type, since a note template working for primary care may fail for a complex specialty encounter.
- Segment by clinician cohort, comparing early champions against the broader rollout group, since champions often mask a harder majority.
- Check leading indicators in lagging cohorts, like note edit rate, then run experiments on the highest volume, clearest failure segment first.
What a strong answer includes
- Hypothesizes specific causes before pulling data, for example specialty mismatch or a float clinician training gap, rather than diagnosing everything at once.
- Uses encounter share, not just active users, as the core retention metric, since logging in without using Abridge on most visits is common.
- Proposes segment specific fixes, for example a specialty specific template pilot instead of one blanket change for the whole system.
- Sets a concrete recovery target, for example returning a lagging specialty's encounter share to seventy percent of its peak within four weeks.
Common mistakes
- Looking only at aggregate retention and missing that one specialty or site is dragging the whole number down.
- Jumping to a product fix before checking whether the drop is a training or rollout issue at specific sites.
Likely follow-up questions
- Which segment would you dig into first if you could only pick one?
- How would you distinguish a training problem from a product quality problem?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop