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.

HardTechnical

0 practiced

Case study (hard): Your company's growth has plateaued. You find the onboarding funnel has a 40% drop between activation and first-success. Outline an end-to-end plan: instrumentation checks, exploratory analysis to diagnose causes, prioritized experiments to run, expected measurement plans for each experiment, and how you'd report a recommended roadmap to the executive team.

HardSystem Design

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Experimentation platform (hard): Design a platform to run concurrent funnel experiments while preventing cross-experiment contamination. Address assignment hashing, namespace management, overlapping audiences, sequential experiments, logging, and statistical detection of interactions. Provide examples of policies to allow or prevent overlapping experiments.

MediumTechnical

0 practiced

You have touch events table and conversion events table. Design a SQL approach to attribute each conversion to the last marketing touch within a 7-day lookback window (last-touch). Discuss edge cases such as multiple touches at the exact same timestamp and touches outside the lookback window.

Schemas:

touches(user_id, channel, touched_at TIMESTAMP)
conversions(user_id, conversion_id, converted_at TIMESTAMP)

MediumTechnical

0 practiced

When running several concurrent experiments across funnel steps and segments, how do you handle multiplicity and false discovery risk in analysis? Discuss both statistical corrections and practical organizational strategies to keep experimentation reliable at scale.

EasyTechnical

0 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.

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