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Customer Journey and Funnel Optimization Questions

Covers analysis and optimization of user conversion funnels and the broader customer journey from initial awareness through acquisition, onboarding, activation, monetization, retention, and advocacy. Core skills include mapping multichannel touchpoints, defining funnel stages and key metrics, constructing and querying funnels, creating funnel visualizations, measuring stage conversion rates and transition probabilities, and identifying friction points and drop off stages. Candidates should demonstrate cohort and segmentation analysis, calculation and use of lifetime value and customer acquisition cost, and diagnosis of root causes using both quantitative signals and qualitative research. Work also covers instrumentation and clean event design to ensure data quality, meaningful reporting that ties funnel improvements to business outcomes, and prioritization frameworks that weigh volume, expected lift, and downstream impact. Candidates should be able to design controlled experiments and split tests with appropriate measurement windows and power considerations, measure incremental and downstream effects, and recommend tactical interventions such as onboarding improvements, progressive disclosure, checkout and signup friction reduction, personalization, nurturing, and lead scoring. Finally, candidates should translate analytics into data driven roadmaps and product or marketing experiments that move business metrics such as revenue and retention.

MediumTechnical
78 practiced
List common biases and measurement errors that affect funnel analysis (e.g., selection bias, survivorship bias, attribution leakage, instrumentation gaps, cross-device identity loss). For each, explain how it would distort funnel metrics and one concrete mitigation strategy.
HardTechnical
100 practiced
Case study: Monthly revenue increased year-over-year, but net new MRR per cohort has declined for the last three cohorts. Provide a step-by-step analysis plan to reconcile these signals, listing specific queries, cohort comparisons, segmentation checks, and data quality checks you would run.
EasyTechnical
150 practiced
A spike in mobile checkout drop-off occurred on 2025-01-15. As the BI analyst on-call, outline the immediate 24-hour triage steps you would take: which dashboards and queries to run, slices to inspect, logs to request from engineering, and how you'd communicate status to product and ops teams.
HardTechnical
74 practiced
Case study: A freemium SaaS product has very low paid conversion from free users. Propose experiments and a measurement plan to increase paid conversion: list testable hypotheses (pricing, feature gating, onboarding changes), segmentation strategy, instrumentation required, experiment design, primary/secondary metrics, and success criteria.
HardSystem Design
76 practiced
Architect a funnel analytics platform that supports real-time metrics, daily cohort analyses, anomaly detection, experiment integration, and a REST API for dashboards. Describe the ingestion tier, canonical event schema, transformation layers (raw → canonical → aggregates), storage choices (OLAP, real-time serving), and trade-offs between latency, cost, and query flexibility.

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