You work at a subscription company and are given a user-level table. ## Table `company_users` - `id` (INT, PK) — user/customer id - `signup_date` (DATE) — date the user signed up (created an account) - `subscription_date` (DATE, NULL) — date the user first started a subscription (NULL if never subscribed) - `termination_date` (DATE, NULL) — date the subscription ended (NULL if still active) - `subscription_type` (VARCHAR) — `'free'` or `'paid'` (assume this reflects the first subscription started) Assumptions: - Dates are in UTC. - A “subscription” means `subscription_date IS NOT NULL`. - “Conversion rate by month” is defined as: for users who signed up in a given calendar month, the fraction who ever subscribed (at any later time). - Report results for months starting **2017-01-01** (inclusive). ## Tasks 1. For each calendar month (by `signup_date` month), return: - `month` (e.g., `2017-01-01` for Jan 2017) - `signups` (count of users who signed up that month) - `subscribers` (count of those signups who ever subscribed) - `conversion_rate` = `subscribers / signups` 2. Compute **YoY subscription rate growth** for each month as: - `yoy_subscription_rate_growth` = \(\frac{conversion\_rate - conversion\_rate\_{same\_month\_last\_year}}{conversion\_rate\_{same\_month\_last\_year}}\) - Return NULL if the prior-year month is not available. 3. Now assume the dataset also includes free subscriptions (`subscription_type='free'`). Recompute (2) but **only for paid subscriptions** (i.e., treat a user as “subscribed” only if `subscription_type='paid'`). ## Output A monthly table with columns: - `month`, `signups`, `subscribers`, `conversion_rate`, `yoy_subscription_rate_growth` (And a paid-only version for task #3.)