# Campaign Efficiency Metrics and Below-Average CTR You are given an `ad_campaigns` table with one row per campaign: | Column | Type | Meaning | | --- | --- | --- | | `campaign_id` | INTEGER | Unique campaign identifier | | `campaign_name` | VARCHAR | Campaign name | | `start_date` | DATE | First active date, inclusive | | `end_date` | DATE | Last active date, inclusive | | `daily_budget` | DECIMAL | Planned budget per active day | | `impressions` | INTEGER | Recorded impressions | | `clicks` | INTEGER | Recorded clicks | | `conversions` | INTEGER | Recorded conversions | For this console, use PostgreSQL and the following explicit assumptions: campaign dates are inclusive, each row covers one entire campaign, and `daily_budget` is constant across its active dates. Write one query that returns only campaigns whose campaign-level click-through rate is below the arithmetic mean of the valid campaign-level click-through rates. Return these columns: - `campaign_id` - `campaign_name` - `total_cost`: `daily_budget` multiplied by the inclusive number of active dates - `ctr`: `clicks / impressions` - `conversion_rate`: `conversions / clicks` - `cost_per_conversion`: `total_cost / conversions` Use decimal division. When a denominator is zero, return `NULL` for that rate rather than raising an error. Campaigns with zero impressions do not contribute to the average CTR and cannot be classified as below it. Order the final rows by `campaign_id`. After writing the query, be prepared to explain whether `daily_budget × active_days` represents actual spend or only planned budget.