How to Analyze Ecommerce Data
Knowing how to analyze ecommerce data starts with an order-level export that records revenue, product, customer, and channel dimensions at the transaction level — the four dimensions that let you answer every ecommerce question from product mix to customer lifetime value to channel attribution. Most platforms export these columns but the format varies significantly between Shopify, WooCommerce, and enterprise OMS systems.
See exactly which export columns matter, how to handle returns and discounts correctly, and how to build a product and channel performance summary from raw order data.
Drop your file in and ask a question — no account needed.
Upload your spreadsheet.xlsx .xls .csv — free to try, no accountOr start with
What this export contains
| order_id |
| order_date |
| customer_id |
| product_sku |
| product_name |
| quantity |
| unit_price |
| discount_amount |
| channel |
| return_flag |
What usually goes wrong with it
Returns reduce revenue but appear as separate rows
Return transactions appear as negative-value rows or separate refund records rather than adjustments to the original order — net revenue requires matching and subtracting returns from gross order value.
DataMimi Datamimi matches return rows to original orders by order_id and subtracts them automatically so net revenue is correct without manual row manipulation.
Discount amounts inflate order counts
discount_amount is applied per line item, but some exports allocate cart-level discounts across every SKU row — summing discount_amount without deduplication overstates the total discount given.
DataMimi It deduplicates cart-level discounts by order_id before summing so total discount_amount reflects the actual promotional spend.
Customer ID not present on guest orders
Guest checkout orders have no customer_id, making repeat purchase and LTV analysis undercount returning customers who didn't create an account.
DataMimi It groups guest orders by email or shipping address when customer_id is absent to improve repeat purchase identification.
Multi-channel attribution is incomplete
The channel column captures the last-click source but not the full acquisition path — revenue attributed to direct traffic often includes orders that were originally sourced by paid campaigns.
DataMimi It flags direct-channel orders with a short session window as likely cross-channel conversions so the attribution picture is more accurate.
Common questions
How do you analyze ecommerce data in a spreadsheet?
Load your order export with order_id, order_date, customer_id, product_sku, quantity, unit_price, discount_amount, and channel; filter out returns; pivot by product and channel to see revenue, order count, and average order value.
What are the most important ecommerce metrics?
The core metrics are revenue, average order value, conversion rate, repeat purchase rate, and customer lifetime value; channel and product breakdowns of these metrics drive most optimization decisions.
How do I calculate net revenue after returns?
Sum gross order revenue from positive-value rows; sum refunds from return_flag rows or negative-value rows; subtract total refunds from gross revenue to get net revenue.
How do I identify my most valuable customers from order data?
Group by customer_id and sum revenue; count orders per customer; sort by total revenue descending; customers in the top 20% by revenue who have 3+ orders are your highest-value repeat buyers.
How much does Datamimi cost?
Datamimi offers a Free plan at $0/month (40 credits, no credit card required), Lite at $9/month (400 credits), Starter at $24/month (1,500 credits, up to 3 simultaneous files), Pro at $59/month (5,000 credits with rollover), and Team at $199/month (20,000 credits, 5 users).
Try it with your own file
DataMimi reads the file you actually have — merged cells, headers below row one, totals pasted at the bottom — and shows which rows and columns every number came from.
Ask about your file