Outline a research plan to diagnose low adoption despite positive usability tests

WHAT THIS TESTS: This question tests whether you understand that usability and adoption are distinct outcomes. Usability testing validates whether users can complete a task in a controlled setting; adoption depends on discoverability, motivation, timing, and organizational friction. The interviewer wants to see structured hypothesis generation and a mixed-methods research plan that moves from cheap behavioral data to expensive qualitative inquiry.

A GOOD ANSWER COVERS: First, frame hypotheses before choosing methods. Strong candidates propose three categories: discoverability failures, meaning the feature is buried or poorly announced; motivation gaps, meaning the problem is not urgent or the value proposition is weak; and ecological mismatches, meaning real workflows differ from the lab scenario. Second, sequence methods by cost and confidence. Start with quantitative triangulation: map the awareness-to-usage funnel, analyze clickstream data, and segment by user tenure or role. Third, add qualitative depth: conduct contextual inquiry with users who saw the feature but did not adopt it, and run a short Jobs-to-be-Done survey to quantify priority shifts. Fourth, define decision criteria explicitly, such as declaring the hypothesis confirmed if fewer than fifteen percent of active users see the entry point.

COMMON WRONG ANSWERS: A major red flag is conflating usability and adoption by suggesting another round of usability tests. Another is blaming users by claiming they are resistant to change without evidence. Proposing a full redesign before diagnosis is also weak. Finally, suggesting only one method such as a broad survey without behavioral triangulation shows shallow research design.

LIKELY FOLLOW-UPS: The interviewer may ask how you would prioritize if leadership demands an answer in one week. They might also probe how you would distinguish low adoption from low retention, or how you would validate a discoverability fix with an A/B test rather than another qualitative round.

ONE CONCRETE EXAMPLE: Imagine a new dashboard widget launched after usability tests scored ninety percent task success. Post-launch adoption is eight percent. Your plan: check the entry point impressions versus page views to test discoverability; interview five power users who ignored the announcement to test motivation; and compare usage timing between weekly and daily active users to test workflow fit. If impressions are high but clicks are low, you have a motivation or comprehension problem. If impressions are low, you have a discoverability problem.