Calculate the signup activation rate. A user is activated if they confirmed their signup via SMS. Return the rate rounded to 2 decimal places.
This funnel analysis problem reflects TikTok's focus on growth metrics and user activation. Understanding conversion rates at each step of the user journey is critical for product teams. This question tests your ability to calculate rates correctly using LEFT JOINs to preserve the denominator (all signups) while counting only successful conversions in the numerator—a common pattern in A/B testing and funnel analysis.
Key concepts: LEFT JOIN to preserve all base records, conditional aggregation with COUNT() and filtering, division with proper type casting (1.0 for float division), ROUND() for precision control, and understanding the difference between INNER JOIN (would exclude non-confirmed users) and LEFT JOIN (keeps all signups). This tests your grasp of join semantics and their impact on metrics.
TikTok's growth team uses similar queries to: calculate conversion rates across signup funnels, measure email/SMS verification success rates, track feature adoption metrics across user cohorts, generate daily KPI dashboards for product managers, identify drop-off points in onboarding flows, and power automated alerts when activation rates fall below thresholds.
When tackling this TikTok 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.
Share your approach, optimized queries, or ask questions. Learning from others is the fastest way to master SQL.