Company Context
NotionFlow is a B2B SaaS collaboration platform used by 120,000 teams globally. The company has strong adoption among startups and mid-market companies, and is now investing in enterprise growth where buying cycles are longer and product decisions require higher confidence.
Problem
The product team is debating how to improve adoption of a new AI meeting notes feature. Early telemetry shows that 38% of users who try the feature use it only once, while enterprise admins report concerns about accuracy, privacy, and workflow fit. Leadership wants a clear approach for deciding which research method to use for different product questions instead of defaulting to the same playbook every time.
You are the PM responsible for the feature area. Your task is not to run all possible research, but to determine which research methods best fit specific product questions at different stages of decision-making.
Deliverables
- Define the key product questions you would answer before making roadmap decisions for the AI meeting notes feature.
- Recommend the most appropriate research method for each question (for example: user interviews, usability testing, surveys, diary studies, product analytics, support ticket analysis, or experiments).
- Explain the trade-offs behind your method choices, including speed, confidence level, cost, and risk of bias.
- Propose a lightweight research plan for the next 6 weeks, including sequencing and decision points.
- Define how you would know whether the research generated actionable insight rather than just more data.
Constraints
- You have 1 UX researcher shared across 4 teams and limited data science support.
- You must recommend a plan within 6 weeks to inform the next quarterly roadmap.
- Enterprise customers require privacy review before any new data collection.
- Engineering can only support one major product change next quarter, so research must help narrow priorities rather than expand scope.
