SHOP_VISIBILITY_HISTORY +----------+----------------+---------------------+-------------------+---------+ | user_id | user_signup_dt | action_timestamp | visibility_status | shop_id | +----------+----------------+---------------------+-------------------+---------+ | 101 | 2020-03-10 | 2023-07-01 10:04:11 | invisible | 555 | | 102 | 2023-06-20 | 2023-07-02 08:22:37 | visible | 556 | | 103 | 2021-11-05 | 2023-07-01 14:15:02 | invisible | 557 | | 104 | 2023-07-01 | 2023-07-02 16:45:09 | invisible | 555 | | 105 | 2020-01-18 | 2023-07-03 09:30:00 | visible | 558 | +----------+----------------+---------------------+-------------------+---------+ ##### Scenario E-commerce platform wants to know whether existing users are more likely than newly registered users to set a shop profile to invisible. ##### Question Given historical shop visibility actions, write a SQL query that computes a metric of your choice that compares the propensity of old users versus new users to switch a shop profile to invisible. Briefly justify why the metric you chose is appropriate. ##### Hints Define "old" vs "new" users, then calculate the proportion of invisible actions per group.