Funnel Analysis and Conversion Tracking Questions

Product analytics practice focused on analyzing user journeys and measuring how well a product or website converts visitors into desired outcomes. Core skills include defining macro and micro conversions, mapping multi step user journeys, designing and instrumenting event level tracking, building and interpreting conversion funnels, calculating step by step conversion rates and drop off, and quantifying funnel leakage. Candidates should be able to segment funnels by cohort, acquisition source, channel, device, geography, or user persona; perform retention and cohort analysis; reason about time based attribution and multi path journeys; and estimate the impact of optimization levers. Practical competencies include implementing tracking, validating data quality, identifying common pitfalls such as missing events or incorrect attribution windows, and using split testing and iterative analysis to validate hypotheses. Candidates should also be able to diagnose root causes of drop off, create mental models of user behavior, run diagnostic analyses and experiments, and recommend prioritized interventions and product or experience changes with expected outcomes and measurement plans.

EasyTechnical

66 practiced

For a SaaS trial-to-paid funnel, list the core KPIs you would include on a weekly executive report and explain how each KPI ties to business outcomes (e.g., activation rate, trial-to-paid conversion, time-to-conversion, churn of new trials). Include one suggested leading indicator and one lagging indicator.

MediumTechnical

73 practiced

Back-of-envelope estimation: a product change improves a funnel step conversion from 25% to 30% on a page with 100,000 monthly visitors. Downstream conversion (to paid) is currently 20% from the next step, and average revenue per new paid user is $120. Estimate additional monthly paid conversions and incremental monthly revenue. Show your calculations and assumptions.

EasyTechnical

109 practiced

List the top data-quality red flags you would monitor for a funnel reporting system (e.g., sudden drops, event count mismatches). For each red flag, give a first immediate check an analyst should run to diagnose whether the issue is instrumentation, ETL, or analysis-level.

EasyTechnical

60 practiced

SQL task (assume PostgreSQL): Given an events table with schema:

events(event_id UUID PRIMARY KEY, user_id VARCHAR, event_name VARCHAR, occurred_at TIMESTAMP, properties JSONB)

Write a SQL query to compute the number of unique users who fired each of three funnel events in March 2025: 'view_landing', 'start_signup', 'complete_signup'. Return a table with columns: event_name, unique_users.

MediumTechnical

56 practiced

Design strategies to detect and deduplicate duplicate events in an event stream. Describe both prevention (client/server-side idempotency) and post-ingest deduplication (SQL/ETL) approaches, and discuss trade-offs when using event_id, fingerprinting, or time-window based deduplication.

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