Average Order Value by Country
Compute the average order value (AOV) of completed orders for each customer country. AOV is the average of order totals, where an order total is the sum of its lines.
Expected output
country, orders (number of completed orders), aov (rounded to 2 decimals). Highest AOV first.
Row order is checked. Column names are not checked; numbers are compared to 2 decimals.
Dataset: E-commerce store
An online store selling electronics, home goods, books, apparel and beauty products to customers in six countries (Jan 2025 to Mar 2026).
customers
One row per registered customer.
| customer_id | INTEGER | primary key |
| name | TEXT | |
| country | TEXT | US, UK, DE, IN, BR or CA |
| signup_date | TEXT | YYYY-MM-DD |
| referred_by | INTEGER | customer_id of the referrer, NULL if none |
products
Product catalog with current list price.
| product_id | INTEGER | primary key |
| name | TEXT | |
| category | TEXT | |
| price | REAL | list price in USD |
orders
One row per order. Only completed orders count as revenue.
| order_id | INTEGER | primary key, increases with order_date |
| customer_id | INTEGER | |
| order_date | TEXT | YYYY-MM-DD |
| status | TEXT | completed, cancelled or returned |
order_items
Line items. Revenue of a line = quantity * unit_price (unit_price may include a discount).
| order_id | INTEGER | |
| product_id | INTEGER | |
| quantity | INTEGER | |
| unit_price | REAL | price actually charged per unit |
Engine: SQLite. Dates are text (YYYY-MM-DD); use strftime(), date() and julianday() for date math.
Related problems
- Revenue by Product Category (easy)
- Monthly Revenue Trend (easy)
- Repeat Purchase Rate (medium)
- Days from Signup to First Order (medium)
- Referral Leaderboard (medium)