You are analyzing an A/B test for a marketing campaign that offers a **free 1‑month trial**. Assume all timestamps are in **UTC**. ## Tables ### `experiment_assignments` One row per user assignment. - `user_id` BIGINT - `experiment_id` STRING - `variant` STRING -- 'control' or 'treatment' - `assigned_at` TIMESTAMP -- first time user was assigned (sticky) ### `trial_starts` One row per trial start. - `user_id` BIGINT - `trial_started_at` TIMESTAMP ### `sessions` One row per user session. - `user_id` BIGINT - `session_start_at` TIMESTAMP ## Metric definitions - Experiment window: users assigned between **2025-01-01** and **2025-01-31** inclusive. - **Signup (trial start) conversion**: user starts a trial within **7 days** after `assigned_at`. - **D30 retention after signup**: for users who converted (started a trial within 7 days), the user is retained if they have **≥ 1 session** with `session_start_at` in the window **[trial_started_at + 30 days, trial_started_at + 37 days)**. ## Task Write a single SQL query that outputs one row per `variant` with: - `variant` - `assigned_users` - `converted_users` - `signup_rate` - `retained_users` - `retention_rate_among_converted` Notes: - Use distinct users (not event counts). - If a user has multiple trial starts, use the **earliest** one that satisfies the 7‑day conversion window after assignment. - A user can have many sessions; any qualifying session counts as retained.