Metrics question
Perplexity wants this PM to build the SMB design partner program for Computer from scratch. How would you define the ideal partner profile, pick the first 2-3 workflows to support, and set clear entry/exit criteria so each engagement produces product learning rather than bespoke services work?
- Perplexity
- Metrics
- Hard
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What this question tests
Whether you can design a design-partner program from scratch with clear structure so it produces reusable product learning instead of one-off services work.
How to approach it
- Define the ideal partner profile: SMB operators actively running a specific workflow (paid acquisition, lifecycle, or SEO) with enough volume to generate meaningful usage data, and willingness to give structured feedback.
- Pick the first 2-3 workflows to support based on where Computer already has partial capability and where SMB pain is highest and most frequent, rather than spreading across every possible workflow.
- Set entry criteria: a partner must commit to a fixed engagement window and a defined check-in cadence, not open-ended support.
- Set exit criteria: the engagement ends when either the workflow is validated and generalized into the core product, or a fixed time period elapses with clear learnings documented either way.
- Build a lightweight artifact requirement per engagement, for example a structured learning summary at exit, so each engagement produces a comparable output regardless of the individual partner's specifics.
- Avoid scope creep by explicitly declining bespoke requests that fall outside the chosen workflows, redirecting them to a backlog for future consideration rather than solving them ad hoc.
What a strong answer includes
- Defines both entry and exit criteria explicitly, which is what prevents a design-partner program from turning into indefinite bespoke services work.
- Picks the first workflows based on existing partial capability plus pain frequency, a concrete prioritization logic rather than an arbitrary pick.
- Requires a standard learning artifact per engagement, ensuring comparable, reusable output across partners.
- Explicitly protects scope by declining and logging out-of-scope requests rather than accepting scope creep to keep a partner happy.
Common mistakes
- No exit criteria, letting engagements run indefinitely and turn into bespoke services.
- Picking workflows without a clear reason tied to existing capability or pain frequency.
- No standard artifact or learning output, making it hard to compare or generalize across partners.
Likely follow-up questions
- How would you handle a partner who wants to continue past the exit criteria?
- What would you do if a valuable partner's workflow does not fit any of the first 2-3 chosen?
- How would you measure whether the program is producing generalizable learning versus one-off wins?
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More questions from Perplexity
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Measuring AI Product Economics
- Chapter 2: Using SQL and Logs
- Chapter 14: Interviewing for AI PM Roles