Ecommerce Benchmarks: Data From Shopify Stores

Ecommerce benchmarks show how your store compares with other stores, but the useful comparison starts with similar products and matching measurements. Use category benchmarks for order value and refunds, then check repeat buying separately. A gap helps you choose an investigation; it does not explain the cause.

What do ecommerce benchmarks tell you?

A benchmark is a reference from other businesses, rather than a target your store must reach. These benchmarks describe the middle store and the range around it. They help you ask whether your result is unusual before deciding what to change.

Start with the question behind the number. Order value describes basket spending, refunds describe money returned, and repeat buying describes customer behavior. None tells you whether the store is profitable on its own.

The By the Numbers references below cover about the last 90 days, roughly July 7 through October 7, 2026. Each sample counts the stores behind that measure.

The median is the middle store, so a large merchant does not dominate the result through its order volume. The range in the charts shows the middle half of stores. A wide range is a reason to investigate your business model before chasing the midpoint.

For the broader choice of measures, see our guide to ecommerce metrics and KPIs for Shopify.

Choose the closest peer group

Choose a category close to what you sell, then consider product prices and how customers use the products. A furniture store and a replenishment brand can have different baskets and buying cycles. The same result can mean different things for each.

Store size and geography also matter. A wholesale order can raise basket value, while cross-border shipping changes the amount paid at checkout. These category charts do not control for revenue tier, country, or wholesale activity, so use them as an initial reference.

Keep your own comparison consistent too. Compare equivalent dates, separate major promotions from normal trading, and check mobile and desktop conversion separately. A change in traffic mix can move a storewide rate even when the shopping experience stays the same.

Compare order value within your category

Compare order value within your category before using the overall median. Across 1,403 Shopify stores, the median store's average order value was $91. This covers approximately July 7 through October 7, 2026.

Revenue here includes tax and shipping, after discounts and before refunds. If your report uses merchandise revenue alone, its order value will not be directly comparable.

Electronics has a higher median than food and drink in this sample. Its wider range also leaves more room for differences in product price and order mix. Neither result shows that an electronics merchant sells more effectively.

If your basket is smaller than comparable stores, check items per order, discounts, and shipping charges separately. A bundle might raise the basket while reducing the money left after product costs. Measure both before expanding the offer. Check the chart's data table for category store counts, and treat smaller groups cautiously.

Separate refunds from product returns

Refund rate measures money refunded, rather than the share of items or orders sent back. Across 1,402 stores, the median refunded share was 0.8%. The same approximate July 7 through October 7, 2026 window applies.

A partial refund and a fully refunded order affect this measure differently. An exchange may also differ from a refund, depending on how it is recorded. Match the money-based definition before comparing your store.

Clothing and fashion has a higher median refunded share than food and drink in this sample. Category differences do not identify the reason. Start with refund reasons and products before deciding that checkout, fulfillment, or product quality needs changing.

Refunds can arrive after the order window ends, so recent orders have had less time to accumulate them. A drop immediately after a promotion may reflect that delay. Check older order groups before calling it an improvement.

Measure repeat buyers and returning buyers separately

Repeat purchase rate and returning customer rate answer different questions, even over the same dates. Among 1,403 stores, the median repeat purchase rate was 10.9% over the last 90 days. That covers approximately July 7 through October 7, 2026.

Repeat purchase rate counts buyers who ordered at least twice inside the window, among buyers who ordered during it. Returning customer rate counts buyers whose first order happened before the window.

Consider a customer who first bought last winter and buys once this summer. That customer is returning, but has not bought twice inside the summer window. A new customer who buys twice this summer counts toward repeat purchasing, but not toward returning customers.

Longer periods give customers more opportunities to buy again. Compare this repeat purchase measure with another 90-day measure, rather than an annual or lifetime rate. Durable products can also have a buying cycle longer than the window.

Check costs before using a margin benchmark

Check product cost coverage before treating a gross-margin benchmark as a target. This benchmark covers 277 stores that track product costs, over the same approximate July 7 through October 7, 2026 period. That is a smaller and differently selected group than the order-value sample.

Gross margin is revenue minus product costs, as a share of revenue. Revenue includes tax and shipping, before refunds. A store with costs on only part of its catalog can show an inflated margin.

Partial cost coverage is a limitation. These figures cannot establish a healthy profit target. They also leave out expenses such as advertising, payroll, and fulfillment that affect the money your business keeps.

Review costs across the catalog before using a margin comparison to justify more spending. In By the Numbers, Copilot analytics returns answers as charts or tables; use those views to investigate your store's results. A benchmark still needs the measurement checks described here.

Use separate references for conversion and advertising

Use a source that measures the funnel stage you want to investigate. Dynamic Yield's ecommerce benchmarks provide conversion, add-to-cart, and cart-abandonment references with industry, region, or device filters. They come from a different dataset, so keep them separate from the By the Numbers figures above.

Conversion describes purchases relative to visits or visitors, depending on the source. Add-to-cart describes an earlier action; checkout completion describes what happens after checkout begins. Cart abandonment describes carts that do not become purchases, rather than visits that never created a cart.

If carts look healthy but purchases do not, inspect delivery charges, payment failures, and checkout steps. If few shoppers add products, inspect the offer, product information, and traffic quality. Match each source's starting point before comparing rates.

Advertising comparisons need similar care. Polar Analytics' benchmark dashboard includes acquisition cost and return on ad spend alongside basket and funnel measures. Check its definitions and merchant groups before carrying a reference into your budget.

Acquisition cost can count ad spending alone or broader marketing expenses. Return on ad spend depends on which sales receive credit and which spending is included. Platform attribution and a blended store measure can therefore tell different stories.

Lifetime value describes value over a customer's relationship with the store, and some reports predict future spending. Revenue-based lifetime value is different from lifetime margin. Start with the money left from an order after product costs, refunds, and fulfillment, then compare it with acquisition spending. If you depend on repeat purchases to recover that spending, check when those purchases happen. Predicted lifetime revenue does not pay today's bills.

Turn the gap into one test

Choose one gap with a clear business consequence, then verify the measurement before changing the store. Use the checks in our guide to common Shopify analytics mistakes if reports disagree. A tracking discrepancy can look like a performance problem.

For example, if baskets are small but repeat buying is healthy, test a relevant bundle before increasing acquisition spending. If refunds concentrate in one product, investigate its descriptions and fulfillment before changing the whole catalog. These are starting hypotheses, rather than conclusions from the benchmark.

Record the metric, the change, and the outcome you need to see. Where practical, compare customers who receive the change with a similar group who do not. Check refunds and money left after costs alongside the headline result.

Keep the comparison in an ecommerce KPI dashboard with its source and dates visible. Review your own trend alongside the peer reference. Improvement that survives those checks matters more than crossing a median.

How to use these benchmarks

Dollar values are in US dollars. Each category combines businesses with different prices, markets, and purchase cycles. Use the benchmarks to choose what to examine, then use your store's own evidence to decide what to change.

How we measured these benchmarks

These benchmarks use anonymized, aggregated data from Shopify stores in the By the Numbers network. No individual store is identifiable from any figure. Stores that use an analytics app are not a random sample of all Shopify stores, so read the figures as a reference point, not as an average for the whole market.

Keep reading

If you want the full picture on ecommerce metrics and KPIs, start with the KPIs that actually move Shopify revenue.

Related reading: How to Build an Ecommerce KPI Dashboard for Shopify, Best Shopify Analytics Plan For Store Growth and 7 Common Shopify Analytics Mistakes to Avoid.

By the Numbers builds this into the dashboard. See Shopify ecommerce reporting.