Metrics question
LinkedIn has introduced a new feature for job applicants called the ‘Match Calculator.’ This feature allows users to click on a job post and see a percentage indicating how well their profile matches the job description. How would you measure its success?
- Metrics
- Medium
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What this question tests
Metric design for a discovery feature: connecting a new UI element to downstream application and hiring outcomes.
How to approach it
- Clarify the feature's goal: help candidates self select into jobs they are a strong fit for, saving time for both sides.
- Define a primary metric, such as application rate among users who see a high match score, for example above 80%.
- Add a quality metric downstream, like recruiter response rate or interview rate for match driven applications, to check the score is meaningful.
- Add a guardrail: application rate among low match score users, to ensure the feature is not discouraging valid candidates from applying.
- Track adoption, such as percentage of job views where users open the match calculator.
What a strong answer includes
- Chooses a metric tied to real outcomes, like interview rate for high match applicants, not just clicks on the feature.
- Adds a fairness guardrail, checking that the score does not systematically discourage qualified candidates from underrepresented backgrounds.
- Proposes a concrete illustrative target, for example a 10% lift in recruiter response rate for match scored applications versus unscored ones.
Common mistakes
- Measuring only feature engagement, like clicks, without connecting it to hiring outcomes.
- Ignoring the risk that a low score discourages otherwise qualified candidates from applying.
Likely follow-up questions
- How would you validate that the match score itself is accurate?
- How would you detect if the feature is reducing applications overall?
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More questions from LinkedIn
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