Defining and Using Success Metrics Questions
Learn to propose metrics that directly tie to business or product goals. Understand primary metrics (direct measure of success, like feature adoption rate or API call volume) versus secondary metrics (supporting indicators like latency, error rates, or user satisfaction). Practice proposing 2-3 realistic metrics for different scenarios. At entry-level, you don't need statistical sophistication, but you should understand how to measure whether something worked and why certain metrics matter.
MediumTechnical
0 practiced
You are planning an A/B test where the baseline conversion rate is 5%. The product team wants to detect a 10% relative uplift (i.e., from 5% to 5.5%) with 80% power and alpha 0.05 (two-sided). Describe the steps to compute the required sample size per variant and provide the approximate calculation or formula you would use (no need to compute exact number, but show how you'd get it).
HardTechnical
0 practiced
When comparing metrics across time, user population composition can change (seasonality, marketing campaigns). Describe three normalization or reweighting strategies to make time series comparable and give pros/cons for each. Include an example SQL or pseudocode for one approach.
EasyTechnical
0 practiced
Given the users table (user_id, signup_ts) and events table (user_id, event_name, occurred_at), write a SQL query to compute 7-day retention: percent of users who performed any event between day 7 and day 13 after signup, for signups during the last 30 days. Use PostgreSQL syntax and state any assumptions about timezones or multiple events per user.
MediumTechnical
0 practiced
Your web, iOS, and Android teams report slightly different values for 'daily active users' because of differences in event naming and filtering. Describe your approach to reconcile these differences and produce a single cross-platform DAU metric you can trust.
MediumTechnical
0 practiced
Explain step-by-step how you would run a cohort analysis to estimate 12-month Lifetime Value (LTV) for customers who joined in Q1. List required data, how to handle censored customers (less than 12 months of data), and one statistical assumption you must state.
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