Eight weekly questions retail and e-commerce teams can ask their data

Formulas are standard definitions and there are no benchmark ranges, because good values depend on your category and customers. Always reconcile numbers that leave the building to your source of record.

Pre-flight checklist for your data

• List the distinct order status values (paid, refunded, cancelled, failed) and decide which count as sales.
• Find out how refunds are stored: as a status, a separate table or negative orders.
• Check for duplicate order rows, which appear when an order has several payment attempts.
• Check that product variants (size, colour) can be grouped under one product.
• Check what share of orders have no customer ID (guest checkout).

1. What is revenue by month, and how does it compare with last month?

• Why it matters: the basic health line for everything else.
• Data needed: order date, order total, status.
• A good answer shows: months in calendar order, status filter stated, percent change.
• The trap: a partial current month compared with a full one.

2. What is the return rate by product, and how much revenue is that?

• Why it matters: high-return products quietly destroy margin.
• Data needed: order lines, returns, product.
• A good answer shows: return rate and revenue share together, with units.
• The trap: returns stored as negative orders and counted twice.

3. What is gross margin by category after returns?

• Why it matters: revenue growth with falling margin is a warning.
• Data needed: revenue, cost of goods, returns.
• A good answer shows: margin per category with the formula used.
• The trap: products with no cost recorded look as if they cost nothing to sell.

4. What share of customers have ordered more than once?

• Why it matters: repeat customers cost less to serve than new ones.
• Data needed: customer ID, order date.
• A good answer shows: customers with two or more orders over customers, within a fixed window such as 90 days.
• The trap: guest checkouts with no customer ID are dropped, so say what the rate covers.

5. Which products are in the top 10% of revenue?

• Why it matters: it shows where a stock-out or price change would hurt most.
• Data needed: product, revenue.
• A good answer shows: a ranked list with cumulative share.
• The trap: variants of one product counted separately.

6. What is average order value by customer segment or channel?

• Why it matters: it separates a traffic problem from a basket-size problem.
• Data needed: segment or channel, order total.
• A good answer shows: average and median with order counts.
• The trap: a few large orders lifting the average.

7. Which products sold last month but have no sales this month?

• Why it matters: finds stock-outs, delistings and broken product pages.
• Data needed: product, order date, stock level if available.
• A good answer shows: the list with last month's units and revenue.
• The trap: seasonal products flagged as problems.

8. Which channels have the highest refund rate?

• Why it matters: a channel can look profitable until refunds are subtracted.
• Data needed: channel, refunds, orders.
• A good answer shows: rate and counts for each channel.
• The trap: channels with very few orders show extreme rates.

How to use these

Run the pre-flight checklist once. Then ask the eight questions on the same day each week and keep the numbers in a simple log. Changes matter more than single values.