Customer and User Obsession Questions

Demonstrating a deep commitment to understanding and advocating for customers and end users. Candidates should show how they prioritize user needs in decision making, even when it conflicts with other priorities, and provide concrete examples of advocating for users internally. Topics include using qualitative and quantitative research to surface user pain points, validating assumptions with user evidence, designing or improving experiences to solve real problems, maintaining ongoing connection to users through feedback loops, and influencing stakeholders to keep the organization user focused. Examples may range from entry level empathy and direct customer learning to strategic changes driven by user insight.

MediumTechnical

74 practiced

You need to measure the impact of a UX change but cannot run a randomized experiment. Describe three quasi-experimental methods you could use (e.g., difference-in-differences, synthetic controls, propensity score matching), explain assumptions for each, and detail how you'd validate those assumptions with BI data.

HardTechnical

95 practiced

Design an approach to reduce false-positive anomaly alerts in automated BI monitoring so that engineers and PMs retain trust in alerts. Include statistical techniques, alert scoring, human-in-the-loop validation, and processes for alert review and tuning.

EasyTechnical

70 practiced

You have an events table with schema: events(event_id, user_id, event_name, occurred_at timestamp, platform). Write a PostgreSQL query to compute 7-day active users (DAU where a user appears at least once) for each day during the last 30 days. Output columns: date, dau. Explain assumptions about time zones and deduplication.

EasyTechnical

83 practiced

Explain the difference between qualitative and quantitative user research in the context of BI work. Give two examples of how a BI Analyst would use qualitative insights and two examples of quantitative analyses to validate an assumption about a product feature.

HardTechnical

77 practiced

A post-launch in-app survey shows very positive satisfaction, but you suspect sampling bias because high-value customers respond more often. Describe statistical techniques to correct or account for this bias (weighting, post-stratification, raking, propensity scores) and how you would implement the correction in BI reports.

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