End To End Case Study: Measurement Frameworks Questions
Practice designing measurement frameworks for new products, features, or business models. Include defining the success criteria, identifying key user segments, setting up tracking, and planning for ongoing analysis.
MediumTechnical
0 practiced
Given an events table with columns (user_id, event_name, occurred_at, session_id, platform), write a PostgreSQL query that computes funnel conversion counts and rates from the sequence ['visit', 'signup', 'activate', 'purchase'] where each step must occur within 14 days of the user's first 'visit'. Return counts and conversion rates per platform and include 95% confidence intervals for each step's conversion rate. Assume millions of rows and aim for readable, maintainable SQL.
EasyTechnical
0 practiced
Explain what cohort retention is, how it differs from a point-in-time retention metric, and provide an outline (and brief SQL pseudocode) to compute 7-day retention for weekly cohorts using a simple events table: events(user_id, event_name, occurred_at). State assumptions and common limitations of cohort retention analysis.
EasyTechnical
0 practiced
Design an event schema for a 'checkout' event that will be tracked across web, iOS, and Android. For each field include: field name, data type, required vs optional, and a short explanation. Include fields necessary for revenue calculations, product line, item-level details, campaign attribution, and user identity while noting PII concerns.
EasyTechnical
0 practiced
A photo app will add a freemium 'premium filter' upgrade. Define at least three primary and three secondary success metrics you would propose to evaluate the feature. For each metric, specify whether it's a leading or lagging indicator, the expected direction of movement (increase/decrease), and why it matters to the business.
HardTechnical
0 practiced
Revenue per user is heavy-tailed (few users account for most revenue). Standard mean-based A/B analyses are unstable. Propose robust experiment design and analysis techniques suitable in this setting (e.g., log-transformations, median or trimmed-mean metrics, bootstrap, quantile tests), discuss trade-offs, and explain how to compute power or sample size for robust metrics.
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