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Choose Research Methods for Product Decisions

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Your question is Choose Research Methods for Product Decisions. Take a moment with it on the right.

Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).

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Problem

Company Context

NotionFlow is a B2B SaaS collaboration platform used by 60,000 teams globally. The company is growing quickly in the mid-market segment, but product leaders believe research quality is inconsistent: teams often default to customer interviews even when the question may require behavioral data, surveys, usability testing, or experiments.

Problem

The Head of Product wants a repeatable approach for choosing the right research method for different product questions. Recent examples have created confusion: the onboarding team wants to understand why activation dropped from 48% to 41% after a redesign, the AI assistant team wants to test whether users trust auto-generated summaries, and the pricing team wants to know whether a new seat-based packaging model would hurt expansion revenue. Research bandwidth is limited, and choosing the wrong method has delayed decisions by weeks.

Your task is not to run the research itself, but to explain how you would decide which method is appropriate for a given product question and how that choice should vary by decision type, risk, and stage of product development.

Deliverables

  1. Define a framework for selecting research methods based on the product question being asked.
  2. Apply your framework to the three example decisions: activation drop, AI summary trust, and pricing/packaging change.
  3. Explain when to use qualitative vs. quantitative methods, and when to combine them.
  4. Recommend how you would prioritize research if the team can only support two studies this quarter.
  5. Define success criteria for whether the research produced decision-ready insight.

Constraints

  • Central research team has capacity for only 2 major studies and 1 lightweight study this quarter.
  • Engineering can instrument new events within 3 weeks, but cannot support a large rebuild of analytics pipelines.
  • Leadership expects recommendations within 4 weeks for onboarding and AI assistant, and within 8 weeks for pricing.
  • The company sells to both self-serve teams and enterprise admins, so some questions involve multiple user personas and buyers.