### Tracking Down eCommerce Drops Retail stores and e-commerce setups process thousands of transaction events daily. If revenue starts dropping, the analyst team must instantly slice transaction status categories to find the leak. #### 💡 The Real-World Scenario ShopMart noticed a sudden 15% revenue contraction in Q2. Reviewing raw sales logs showed thousands of customer baskets, but many remained unpaid or processing. We must track order totals categorized by user to isolate low-value cohorts. ---\n\n### 🛠️ Step-by-Step SQL Resolution To modularize this multi-stage search, we employ a **Common Table Expression (CTE)**: * We declare a temporary view called `order_pricing` using the `WITH` statement. This allows us to handle empty order values using `COALESCE` before performing aggregations. * We execute a **`LEFT JOIN`** between `users` and `order_pricing` to preserve every customer profile—even those who have placed zero orders. This quickly exposes inactive users. ---\n\n### ⚠️ Common Pitfalls for Beginners * **Incorrect Table Ordering in Left Joins:** Writing `order_pricing LEFT JOIN users` completely reverses the logic, dropping users who have never placed orders! Always put the core entity table (users) on the left side of your join to keep all users. * **Assuming SUM aggregates ignore NULLs:** While SQL aggregates naturally skip NULL cells, writing calculations like `total_amount + discount` where either column contains `NULL` will yield a `NULL` result! Always use `COALESCE(total_amount, 0)` to supply safe math defaults.