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.

MediumTechnical

0 practiced

Write a SQL query (BigQuery/Postgres-style) that computes, for each user who reached step 2 of a funnel, the average time in minutes between step 1 and step 2 and between step 2 and step 3. Use the events table below and explain how you would treat outliers and sessions spanning midnight.

Schema:

sql

events(user_id STRING, event_name STRING, occurred_at TIMESTAMP)

HardTechnical

0 practiced

You discover two weeks of missing client-side events due to a release bug that went unnoticed. Describe methods to recover or correct analytics that rely on those events: options for backfill, using server-side alternatives, statistical adjustments, and how you would report the uncertainty to stakeholders and decision-makers.

MediumTechnical

0 practiced

Given this funnel: 10,000 visits -> 2,000 signups -> 400 purchases. If you improve the signup->purchase conversion from 20% to 30% (a 10 percentage-point absolute increase), calculate the new number of purchases and percent uplift in purchases. Describe how to generalize this multiplication approach to estimate impact for other funnel changes and how to propagate uncertainty.

HardTechnical

0 practiced

Write pseudocode or describe a query pattern to compute funnels on a very large dataset in BigQuery minimizing bytes scanned and cost. Include strategies such as partition pruning, clustering, pre-aggregated tables, approximate distinct counts, and using event-date vs ingestion-date. Discuss trade-offs in freshness and accuracy.

MediumTechnical

0 practiced

When designing experiments that may increase conversions but also increase returns or refunds, what guardrail metrics would you define and why? Provide at least five guardrails (quantitative), their acceptable thresholds or monitoring approach, and action playbooks for when guardrails are violated post-deployment.

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