Calculate the payment success rate for each country. Success rate is the number of successful payments divided by total payment attempts. Round to 2 decimal places and only show countries with at least 5 payment attempts.
This metrics calculation problem reflects Stripe's core business of payment processing analytics. Understanding success rates by geography is critical for identifying fraud patterns, optimizing routing, and improving authorization rates. This question tests your ability to perform conditional aggregation, calculate percentages correctly, and apply filtering at the right stage of query execution.
Key concepts: CASE WHEN for conditional counting, GROUP BY for country-level aggregation, HAVING clause for post-aggregation filtering, ROUND() for precision, and division with proper type casting (1.0 for float division). Understanding the difference between WHERE (pre-aggregation) and HAVING (post-aggregation) is crucial.
Stripe's analytics teams use similar queries to: generate country-specific success rate dashboards for merchants, identify regions requiring payment method optimization, calculate SLA compliance metrics, detect anomalies in authorization patterns, feed machine learning models for fraud detection, and power automated alerts when success rates drop below thresholds.
When tackling this Stripe problem, the key is to understand the grain of the result. Are you returning one row per user, or one row per category? Always start by identifying your unique join keys and consider if filtered aggregations (CASE WHEN) are more efficient than multiple subqueries.
Be careful with NULL values in your JOIN conditions or aggregate functions. In interview scenarios, datasets often include edge cases like zero-count categories or duplicate entries that can throw off a simple COUNT(*) if not handled with DISTINCT.
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