Shopify store benchmarks
Enter your order value, repeat purchase rate, and refund rate for the last 90 days. See where each one lands against stores in your category, quartile by quartile rather than as a single vague verdict.
Comparisons are only useful inside a category. Pick the closest fit.
Enter each figure for the last 90 days and leave out canceled orders. The peer percentiles cover the same window.
Order totals with tax and shipping, before refunds, divided by orders. In a Shopify orders export, average the Total column. Shopify's own AOV report leaves out tax and shipping, so it reads lower.
Of customers who ordered in the last 90 days, the share who ordered at least twice in that time. Count customers by email in a Shopify orders export.
Refunds on orders placed in the last 90 days, divided by those orders' totals. In a Shopify orders export, divide the sum of Refunded Amount by the sum of Total.
Enter a metric to compare
Fill in any field and we will place it in the all stores distribution for the last 90 days.
Reading a distribution
A percentile answers a narrower question than most people want it to. It says where your last 90 days sit among stores loosely like yours. It does not say what is achievable for your catalog, your price point or your channel mix. A store in the ninetieth percentile can still be losing money if its margin is thin.
Use the spread between your own metrics rather than their absolute positions. The gap between your strongest and weakest is a better guide to next quarter's work than any single number, and it is unaffected by whether the category fits you perfectly. Building an ecommerce KPI dashboard covers keeping those definitions consistent, which is what makes a comparison honest.
For a single weighted reading across all three metrics, the store health score rolls them into one number. To price the gap in money instead of percentiles, the revenue leak calculator converts a conversion or refund difference into an annual figure.
Benchmark questions, answered
Where do the benchmarks come from?
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.
Why does my repeat purchase rate look low?
It covers the last 90 days, not a year. A customer counts only if they order twice within the last 90 days. Many repeat buyers take longer than that between orders. A yearly or lifetime rate gives them more time, so it reads higher. The percentiles here cover the same window, so the comparison is like for like.
Why is the result email-gated?
Because the breakdown draws on By the Numbers network data, not your inputs alone. The store health score gives a single score without an email. The comparison runs in your browser either way.
How should I use a percentile?
As a prompt, not a target. Being in the top quartile on AOV and the bottom on repeat rate tells you where the next quarter's work is. Chasing a median on every metric at once is how stores end up average everywhere.
My store does not fit any category.
Use All stores. The distribution is wider, so read it more loosely: the useful signal is which of your own metrics is relatively weakest, and that holds regardless of which distribution you place it in.
The most useful benchmark is your own last quarter.
By the Numbers tracks these metrics against your own history continuously, so you see movement rather than a one-off comparison.
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