AOV calculator

Average order value (AOV) is the average amount a customer spends in a single transaction. It's a store economics number more than an ad number, but it sets the ceiling for what you can afford to spend acquiring a customer in the first place.

AOV is one of only three levers that decide store revenue — the others being traffic and conversion rate — and it's often the most overlooked. A 10% lift in AOV moves total revenue exactly as much as a 10% lift in traffic, but usually costs far less to achieve.

AOV also directly sets how much room your ads have to work with. A higher AOV means a single sale can absorb a higher cost per acquisition and still turn a profit — which is why AOV and ad spend need to be read together, not separately.

AOV also interacts with your fulfillment and shipping costs in a way ROAS doesn't capture — a $200 order and a $40 order can cost very different amounts to pack, insure, and ship, which is another reason AOV alone doesn't translate directly into profit without checking margin and fulfillment cost alongside it.

Revenue is total sales over your chosen window — usually from your store's own reporting rather than your ad platform, since AOV should reflect every order, not just the ones your ads are credited with.

Orders is the count of individual transactions, not units sold — a single order with three items still counts once. Keep the date range consistent with the revenue figure you're using.

As a worked example: $9,600 in revenue across 120 orders gives an AOV of $80 — enough room for most stores to support a moderate acquisition cost. Enter your own revenue and order count in the calculator above to check your figure.

There's no universal "good" AOV — a $35 average is healthy for a low-cost consumable and disappointing for a $200 product line. Read your AOV against your own product pricing and category, not a generic benchmark.

What matters more is what your AOV can support. Divide your average margin per order by your AOV, and you get a rough ceiling for how much you can spend acquiring a customer while staying profitable on the first sale alone.

AOV also tends to move with small, deliberate changes — a bundle, a free-shipping threshold set just above your current average, or an upsell at checkout — more than it moves with ad spend itself, which mostly affects order count rather than order size.

It's also worth tracking AOV alongside order frequency, not instead of it. A rising AOV paired with a falling order count can mean total revenue is actually flat or shrinking — the two numbers together tell a more complete story than either one on its own. Pull both figures from the same date range each time you check, so a change in one isn't mistaken for a change in the other.

AOV says nothing about margin. A $150 order on a product with thin margin can be less profitable than a $60 order on a high-margin one — a rising AOV feels good but doesn't guarantee the business is making more money on each sale.

AOV also averages away the shape of your orders. A store with many small orders and a few very large ones can show the same AOV as one where every order is close to that average — and those two businesses need very different ad and pricing strategies.

A rising AOV can be a discounting artifact rather than a genuine win — a threshold-based free-shipping offer can lift AOV by nudging people to add one more item, without necessarily increasing what they actually wanted to buy or their lifetime value. The same nudge often shows up directly in the ad creative itself, not just the checkout page.

And AOV only measures a single transaction — it says nothing about whether that customer returns. A store optimizing purely for AOV can end up with high-value one-time buyers instead of the repeat customers that usually matter more to long-term profit.

And AOV calculated store-wide can hide real differences between customer segments — new customers and returning customers often have meaningfully different average orders, and blending them into one figure can make a real change in either group harder to spot until it's already affected your overall numbers for a while. Splitting the calculation by segment, even roughly, usually surfaces the shift far sooner than watching the blended figure alone.