Metrics question
What metrics would you define for Command Center across adoption, admin efficiency, and governance coverage, and how would you use those metrics to decide whether the product is actually improving Harvey’s enterprise land-and-expand motion?
- Harvey
- Metrics
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Tests defining metrics across three distinct admin outcomes and using them as a real decision tool for the land-and-expand motion, not just reporting.
How to approach it
- Adoption metrics: percent of licensed seats active weekly, and percent of teams using Harvey for their core matter types.
- Admin efficiency metrics: time admins spend on user provisioning and access changes, and support tickets per active admin.
- Governance coverage metrics: percent of workspaces with audit logging enabled, and percent of matters under enforced access policies.
- Define a composite health signal combining the three, since a firm with high adoption but weak governance coverage is actually an expansion risk, not a win.
- Use governance coverage as a leading indicator for renewal risk: firms with low coverage are more likely to stall at legal or security review during expansion.
- Review these quarterly with customer success to flag accounts where governance lags adoption before it becomes a blocker.
What a strong answer includes
- Names a composite health view instead of three siloed metrics, since strong adoption with weak governance is a hidden expansion risk.
- Uses governance coverage specifically as a leading indicator for renewal risk, tying metrics to revenue outcomes.
- Gives concrete metric examples, like tickets per active admin, rather than a vague 'admin satisfaction' measure.
Common mistakes
- Tracking adoption alone as the health signal while ignoring governance gaps.
- No connection between the metrics and actual expansion or renewal decisions.
Likely follow-up questions
- How would you weight the composite score if two metrics disagree?
- What would you do if governance coverage is low but the customer refuses to invest time in it?
More metrics questions
- What metrics prove Harvey's value to a firm like PwC or Paul Weiss?Harvey · Metrics · Hard
- What KPI hierarchy would you use for Vault, from account-level adoption and active matters to search success, document coverage, and workflow outcomes, to measure value for law firms and enterprises? How would those metrics change your roadmap if usage is high but repeat usage in critical workflows is low?Harvey · Metrics · Hard
- Harvey is considering expanding its platform to additional regions. How would you decide whether multi-region expansion is the right product investment now versus later? Explain the customer signals, business considerations, and success metrics you would use, and how you would compare this investment against competing platform priorities.Harvey · Metrics · Hard
- For Vault’s search and Q&A experience, what metrics would you define for adoption, engagement, trust, and business value at the workspace and firm level? Which leading indicators would you watch in the first 90 days, and how would changes in those metrics alter your product decisions?Harvey · Metrics · Hard
- A pilot agent deployment has strong qualitative feedback from senior stakeholders, but end-user adoption is inconsistent and the customer’s data environment is messy. How would you structure the deployment plan, define the success metrics, diagnose whether the issue is workflow fit vs data readiness vs change management, and decide if this should graduate into a repeatable product for the broader vertical?Harvey · Metrics · Hard
- If you joined Harvey, what 2 or 3 AI-assisted workflows would you implement first for the product communications team, and why those first? For each workflow, explain the job to be done, the human review points, the main failure modes or brand risks, and the metrics you would use to decide whether it improved speed or quality enough to scale.Harvey · Metrics · Hard
More questions from Harvey
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