Month-over-Month Revenue Growth
For every month that has completed revenue, show the revenue, the previous month's revenue and the percentage growth versus that previous month. The first month has no previous value.
Expected output
month (YYYY-MM), revenue, prev_revenue (both rounded to 2 decimals), growth_pct (rounded to 1 decimal; NULL for the first month). Sorted by month.
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
- Monthly Revenue Trend (easy)
- Quarterly Cancellation Rate (medium)
- Top 2 Products in Each Category (medium)
- Days from Signup to First Order (medium)
- Running Revenue Total for 2025 (medium)