Working Through the Average Order Value Formula Step by Step

What is the average order value formula?

The average order value formula divides total revenue by the number of orders placed in the same period, producing one figure that shows how much a typical order is worth. An order is a completed transaction, not a visit or a cart, so a store with $50,000 in revenue and 400 completed orders in a month has an AOV of $125.

average order value = total revenue / number of orders

Reading the formula correctly means treating both sides as counts from the same window. Revenue from a March order that ships in April still belongs to March's AOV, because the transaction closed in March, and mixing periods on either side of the division produces a number that matches neither.

What counts as an order in the AOV calculation?

An order counts as one completed, paid transaction, and a store decides upfront whether that means gross revenue before refunds or net revenue after them. A customer who buys three items in one checkout is one order, not three, so AOV rises when customers add items to an existing cart and falls when the same revenue splits across more separate checkouts. Define the cutoff for what counts as "completed" once, in writing, and apply it the same way every month so the number stays comparable across periods.

How is average order value different from revenue per visitor?

Average order value divides revenue by orders, while revenue per visitor divides the same revenue by total site visitors, so the two metrics answer different questions about the same money. AOV tells a team how much a buyer spends once they commit to checkout; revenue per visitor tells a team how much the whole audience is worth, including everyone who looked and left. A site can raise AOV by pushing bundles at checkout while revenue per visitor stays flat, if the conversion rate drops by the same amount that order size grew.

How do discounts and returns distort average order value?

Discounts lower the revenue side of the formula without lowering the order count, so a storewide 20% off sale can drop AOV even while the number of items per order goes up. Returns work on a delay: an order counted in March's revenue can be refunded in April, so a store measuring AOV on gross revenue overstates March and a store measuring it on net revenue restates March lower after the fact. Recalculate AOV on net revenue after a return window closes, and track gross and net separately so a promotion's effect on order size doesn't get read as a change in customer behavior.

How should average order value be segmented?

Average order value hides more than it shows when it is reported as one number for the whole business, because new customers, repeat customers and different acquisition channels rarely spend the same amount per order. Split AOV by channel to see whether paid traffic buys differently from organic traffic, and split it by new versus returning customer to see whether loyalty actually raises order size or just order frequency. A funnel report segmented the same way shows where in the checkout path the order size gets decided, such as at the cart page versus a post-purchase upsell.

What is a good average order value?

There is no AOV figure that applies across industries, since a grocery delivery order and a furniture order start from completely different price points and margins. Compare your own AOV against your own trailing months and against your own conversion rate rather than an outside benchmark, because a rising AOV paired with a falling conversion rate can mean the same customers are simply buying less often for more money each time.

How does average order value connect to revenue tracking?

Average order value is only as accurate as the revenue and order counts feeding it, and those numbers usually live in a payment processor rather than a spreadsheet someone rebuilds every month. Flowsery pulls revenue by source directly from Stripe, Paddle, Polar, Lemon Squeezy and Shopify, so revenue and order counts for the AOV formula come from the same billing data instead of two systems that drift apart. Tagging campaigns with UTM parameters before pulling the revenue side lets the same AOV formula run per channel without a second export.

Frequently Asked Questions

What is the average order value formula?

Average order value equals total revenue divided by the number of completed orders in the same period. A store with $50,000 in revenue and 400 orders in a month has an AOV of $125, and both sides of the division have to come from the same window.

Does average order value include tax and shipping?

That depends on what a store decides revenue means before calculating AOV, and the decision has to stay consistent across periods. Most teams calculate AOV on product revenue only, since including shipping charges inflates the figure for stores that pass shipping cost straight through to the customer.

Why did my average order value drop after a sale?

A storewide discount lowers the revenue side of the formula without lowering the order count, so AOV falls even when the number of items in each cart stays the same or grows. Check units per order alongside AOV during a sale, since a lower AOV with more items per order means the discount worked as intended.

Is a higher average order value always better?

No, a higher AOV paired with a lower conversion rate can mean fewer customers are checking out at all, which can lower total revenue even as the average order gets bigger. Read AOV next to conversion rate and total order count before treating a rising average as good news on its own.

How is average order value different from customer lifetime value?

Average order value measures one transaction, while customer lifetime value measures everything a customer spends across every order over the whole relationship. A customer can have a low AOV and a high lifetime value if they place small orders frequently over a long period.

Should average order value be calculated on gross or net revenue?

Both, tracked separately, since gross revenue shows AOV before returns and net revenue shows it after refunds have been processed. Reporting only gross revenue overstates AOV in any period with a meaningful return rate, because the refunded orders stay in the count.

Can average order value rise while revenue per visitor stays flat?

It can, when a store pushes bundles at checkout that raise the size of each order while the conversion rate drops by a matching amount. Revenue per visitor divides revenue by total site visitors, including everyone who never checks out, so it moves independently from AOV. Read the two metrics together rather than assuming a rising AOV means the whole audience is spending more.

Why should average order value be segmented by channel?

Paid traffic and organic traffic rarely spend the same amount per order, so one blended AOV figure hides which channel actually buys more. Splitting AOV by channel also separates new customers from returning ones, showing whether loyalty raises order size or just how often someone orders. A funnel report segmented the same way points to where in the checkout path the order size gets decided.

Is there an industry benchmark for a good average order value?

No single figure applies across industries, since a grocery delivery order and a furniture order start from different price points and margins entirely. Compare your own AOV against your own trailing months and your own conversion rate instead of an outside number. A rising AOV next to a falling conversion rate can mean customers are buying less often for more money each time, not that the business is healthier.

How does Flowsery calculate average order value automatically?

Flowsery pulls revenue by source directly from Stripe, Paddle, Polar, Lemon Squeezy and Shopify, so the revenue and order counts behind the AOV formula come from the same billing data. Tagging campaigns with UTM parameters before pulling the revenue side lets the same formula run per channel without a separate export. That removes the drift that shows up when revenue and order counts are pulled from two different systems.