Product Case: Define Success Metrics for a Product, Using Enterprise Search as Example
GleanData Scientist · Onsite
Last updated
A data scientist product case: define how to measure the success of a product, practiced on an enterprise search tool. Tests building a metric hierarchy with a north star, input metrics and guardrails, separating users from paying customers, defining a successful search and attributing changes causally.
In a hiring-manager interview for a data scientist role, one question was a product case: how would you measure the success of a product?
The report does not say which product. For practice, assume the interviewer meant the company's own product: an enterprise search tool that employees of customer companies use to find documents, people and answers across their workplace apps. In a real interview, confirm the product before answering.
Clarifying Questions
- Is the goal to define success for the whole product, or for one feature or launch?
- What decision will the metrics inform: roadmap prioritization, a launch decision, or reporting to customers?
- Which data is available: search logs with clicks, per-customer seat counts, renewals, user surveys?
Hints
Hint 1
Two kinds of customer: In a business-to-business product, the person who uses it and the organization that pays for it are different. Decide what success means for each.
Hint 2
Guard the headline metric: For each metric you propose, ask how it could go up while the product actually gets worse.
Follow-up questions
- Queries per user rose after a release. Is that good news?
- How would you run an A/B test when users in the same company share documents and influence each other?
- Which single metric would you put on the executive dashboard, and why?
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