Avg. Purchase Revenue
Your typical order size, as Google Analytics counts it.
What it means
Avg. Purchase Revenue is the revenue Google Analytics recorded divided by the number of purchases it tracked, in your store currency. Both halves are built from purchase events your site sent, not from your order list, so a purchase that never reached Google Analytics is missing from the revenue and from the count. That's why it rarely matches the order value your store reports, and why the two disagreeing isn't in itself a fault.
Show the math
Formula and a worked example
Revenue is the value carried on the tracked purchase events for the period — the same money that adds up to Total RevenueTotal RevenueCross-check against Shopify to confirm tracking is firing.. Purchases is the count of those events, shown elsewhere on the page as Transactions.
Worked example. €96,000 of tracked revenue across 1,200 purchases. 96,000 ÷ 1,200 = €80.
The store's own records show 1,260 orders for the same month. Those 60 missing purchases are a tracking gap — consent declined, a blocked script, an order taken somewhere other than the website — and they take their revenue with them. The average survives a gap like that reasonably well, because both halves shrink together. The totals don't.
It answers the question
When someone does buy on the site, how much do they spend? It's the size of a sale, with nothing about how many sales there were.
Why it matters
Google Analytics revenue is only ever two things multiplied: how many purchases, and how big each one was. When revenue moves, this column tells you which half did it — and more buyers and bigger baskets need completely different work.
It's also the quickest check that your tracking still means what it did last month. A figure that jumps or halves overnight, with no promotion behind it, is almost always a change in what your purchase event reports as its value rather than a change in what customers bought.
What good looks like
There's no standard worth publishing: €80 is unremarkable for furniture and extraordinary for a refill store, and two shops in the same category can sit far apart on product mix alone. Judge it against your own trend, and against your own order value in your store's records.
The most useful test isn't the level at all — it's whether the gap between this figure and your store's own average order value stays the same size. A stable gap means two systems counting slightly different things, which is normal. A gap that suddenly widens is a tracking change, and it's worth finding before you read another month of this page.
How to improve it
| Lever | What you do | Expect | How long | Watch out for |
|---|---|---|---|---|
| Fast Make the purchase event report the money your store reports | Check whether the value sent includes shipping and tax, and match it to how you read the business | The figure stops disagreeing with your own records for reasons nobody can explain | 1 week | It usually moves the number, often downward, and every comparison with the period before the change is broken from that day on. |
| Fast Set the delivery threshold above the current average | Move free delivery a little above this figure rather than well above it | Average up within a couple of weeks | 1–2 weeks | Baskets that would have converted at the old threshold get abandoned instead, so Transactions can dip while the average climbs. |
| Slow Sell the second item | One relevant cross-sell on the product page and one at the cart, chosen from what sells together | More units per purchase | 4–6 weeks | Cross-sells are usually carried by a bundle discount, so the average rises while the margin on each order thins. |
| Slow Offer the bigger size of what people re-order | Multipacks or a subscription on the consumables customers come back for | Average up, purchase count down | 1 quarter | You pull next month's order into this one. The average improves while the purchase count and the repeat rate both fall, and stock cash goes up. |
Every lever costs something somewhere. The last column is the one to read twice.
Read it with
Revenue is purchases multiplied by their size. Reading the two together is the only way to know which one moved.
Both halves growing
More purchases and bigger ones. Uncommon without a deliberate cause behind it — a new product, a threshold change, a better-matched audience — and worth identifying so you can do it again.
Fewer, bigger orders
The average rose because the small purchases stopped. That's a threshold or a bundle working, or entry-level stock running out — and only the revenue total says which side you came out on.
Volume at a smaller basket
More people buying less each. Normal during a promotion. It's a problem only if the discounting outlives the campaign that justified it.
Smaller and fewer
Both halves falling at once is the shape a partly broken purchase tag makes. Rule that out first, then look at stock on the products that carry the basket.
This pair prices the trade you make when you push basket size. Thresholds and bundles lift the average by discouraging small orders, so the honest read is the average and the conversion rate side by side — a bigger basket bought with a lower conversion rate can leave you with less money than you started with.
One is per buyer, the other is per visitor, and the distance between them is conversion. The average holding steady while value per user falls means the people who buy still spend the same and fewer of them are buying — a funnel problem that the order-size column alone will never show.
Common misreads
Different sources counting slightly different things. AOVAOVYour average order size, and a direct lever on revenue. is built from your store's own orders and takes discounts off gross sales; this is built from tracked purchase events and carries whatever value those events send. Expect a gap, and watch the size of it rather than the fact of it.
Product mix moves this without anyone spending differently. A cheap bestseller going out of stock lifts the average by removing the small orders, and so does a campaign that happened to point at your expensive range.
They're the purchases that reached Google Analytics. Anything the tag missed is absent from the count and from the revenue, so treat this as a well-behaved sample of your orders rather than a record of them.
Also called
Average purchase revenue · average transaction value · GA4 order value
See yoursYour average purchase size for the period with the change on the period before, and the same figure broken out by source and region.
Open Google Analytics Overview →