Customer Experience and Data Driven Thinking Questions

Covers the ability to understand and improve customer experience using quantitative and qualitative evidence. Interviewers look for candidates who analyze user behavior and funnel metrics, identify drop off points, use experiments or controlled tests to validate hypotheses, and balance data signals with user research and empathy. This topic includes awareness of data quality and measurement limitations, selecting appropriate success metrics, interpreting results responsibly, and using insights to prioritize and influence product or process changes that improve customer outcomes. Candidates should show structured thinking about measurement, trade offs when data is incomplete, and how to communicate data driven recommendations to technical and non technical stakeholders.

HardTechnical

90 practiced

Explain intent-to-treat (ITT) versus per-protocol analyses in experiments. For a rollout where some users assigned to treatment do not receive the change due to rollout issues, describe which analysis you would present to executives and which to engineers, explaining the reasons and implications for interpretation.

MediumTechnical

90 practiced

Given table events(user_id, event_type, event_date) where event_type includes 'signup' and 'open' (user returned), write a SQL query (standard SQL) to produce a 7-day retention matrix: for each signup_date cohort, the percentage of users active on day 1..7 after signup. Explain assumptions about time zones, deduplication, and how you would pivot or present the output.

MediumTechnical

77 practiced

An experiment shows a statistically significant 1% relative lift in conversion. As the PM, describe how you would assess whether this lift is worth a full rollout. Cover business impact calculations, downstream metric considerations (retention, ARPU), sample reliability, engineering and operational costs of rollout, and how you'd present a recommendation to stakeholders.

HardTechnical

68 practiced

You're responsible for prioritizing 20 growth experiments across three product areas for the next quarter with limited engineering capacity. Design a decision process that handles dependencies, sequencing, measurement capacity, and fairness across teams. Explain how you will quantify expected ROI, manage risk, and present trade-offs to the executive team.

MediumTechnical

88 practiced

Create a prioritization rubric (RICE or similar) to score experiments balancing reach, impact, confidence, and effort. Provide concrete example scores and justification for three hypothetical experiments (low-effort bugfix, medium-effort UX test, high-effort new feature) and explain how you would calibrate scores across teams to reduce bias.

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