What Is Average Order Value (AOV)? How to Calculate and Increase It

What is average order value (AOV)?

Average order value (AOV) is the average revenue you earn per order in an online store. You find it by dividing total sales revenue for a period by the number of orders in that same period. For example, $60,000 in revenue from 800 orders gives you an AOV of $75.

AOV is the third growth lever next to traffic and conversion rate. Most stores think about growth only as more visitors. However, earning a little more per order from the visitors you already have grows revenue without a bigger ad budget. I have worked with ecommerce accounts since 2012, and the stores that manage average order value on purpose almost always have more breathing room in their ad accounts.

In this guide I cover how to calculate AOV, which costs to include, where to find the metric in GA4 and Google Ads, and the three main levers you can pull: bundles, cross selling and threshold offers. The figures below are worked examples; swap in your own data and the logic still holds.

How do you calculate average order value?

The formula fits on one line: AOV = total revenue / number of orders. What matters is that both numbers come from the same period and the same source. If you divide a figure from your shop system by a figure from your ad platform, the result means nothing.

A few quick examples:

• September revenue of $90,000 from 1,200 orders: AOV = $75.
• One week with $16,800 from 140 orders: AOV = $120.
• A sale day with $12,000 from 300 orders: AOV = $40.

The last example deserves attention. On sale days, order count rises but baskets usually shrink, because shoppers grab the one discounted item and leave. That is why I recommend tracking AOV weekly or monthly rather than daily. In addition, reporting promotion periods on a separate line lets you compare normal weeks without the noise.

Also, do not confuse AOV with revenue per customer. If one customer places three orders in a month, AOV looks at each order, while revenue per customer adds all three together. They answer different questions.

Which costs belong in the calculation?

The most common problem I see is that every team defines AOV differently. Finance looks at net sales without tax, the ad platform shows the value you send it, and the warehouse works with figures after returns. As a result, three people discuss three different averages for the same month.

These are the items you need to decide on:

• Sales tax or VAT: For profit decisions, use revenue without tax.
• Shipping fees: What customers pay for delivery is not product revenue, so most teams leave it out.
• Discounts: Subtract coupon and promotion discounts from revenue.
• Returns: In categories with high return rates, track a second AOV after returns.

For example, Shopify explains in its sales report documentation that it takes gross sales minus discounts, excluding later adjustments, and divides that by the number of orders. In other words, it reflects product revenue at the moment of purchase and ignores later edits, exchanges and returns. Pick a similar definition and write it down. A shared definition matters more than a perfect one.

Where do you find average order value in GA4?

In Google Analytics 4, the English interface uses the name "Average purchase revenue". There is a trap, though. One of the ready made cards in the Monetization overview report is "Average purchase revenue per active user". According to Google's documentation, that card divides purchase revenue by active users. So it is not AOV at all, because visitors who never bought anything sit in the denominator too.

You have two ways to get revenue per order. First, build an Exploration that places purchase revenue and transactions side by side. Second, create a calculated metric. The GA4 help page places this feature under Admin, then Custom definitions, then the Calculated metrics tab, and notes that standard properties allow up to 5 calculated metrics.

If your GA4 setup is still shaky, fix the foundation first. When the purchase event lacks value and currency parameters, no basket report will ever be right. For the basics, read my guide to GA4.

How do you measure AOV with cart data in Google Ads?

Google Ads offers a feature called conversions with cart data. It adds the list of purchased products to each purchase conversion, so you can see revenue per order and basket size inside the ad platform as well.

According to the Google Ads help page, the feature gives you these metrics:

• Average order value: revenue from orders attributed to your ads divided by the number of orders.
• Average cart size: units sold divided by the number of orders.
• Cross sell revenue: revenue from products other than the one featured in the ad the customer interacted with.
• Gross profit: the profit from ad driven sales once you add cost of goods sold data.

Cross sell revenue teaches a lot. The product you advertise may act as a doorway that pulls other products into the basket. On the other hand, a product with weak ROAS on its own can turn profitable once you count what it sells alongside it. So do not judge ads only by the revenue of the advertised item.

How do AOV, ROAS and CPA connect?

All three belong to the same equation. ROAS divides ad revenue by ad spend. CPA divides ad spend by conversions. Put together, ROAS = AOV / CPA. In other words, if your cost per order stays the same while baskets grow, ROAS rises on its own.

Here is a quick example. With a CPA of $20 and an AOV of $60, your ROAS is 3. Lift the basket to $72 and, with the same CPA, ROAS climbs to 3.6. As a result, campaign efficiency improves without touching your bid strategy.

Instead of doing this by hand, you can test different basket scenarios with the ROAS calculator. I explain the CPA side in detail in my article on cost per acquisition. In short, AOV is the silent partner of ad efficiency: very often it is basket design, not a bid tweak, that makes a campaign profitable.

Why is raising average order value such a strong lever?

Because you pay to acquire a customer only once. By the time a shopper lands on your site, the money for ads, content and trust building is already spent. Each extra item added to that order comes in with little more than product cost and handling cost.

Let me build a simple example. Picture a store with 1,000 orders a month, a $60 AOV and a 30 percent gross margin. Gross profit per order is $18, or $18,000 in total. If AOV grows 10 percent to $66, gross profit per order rises to $19.80. With the same traffic, the store keeps an extra $1,800 in gross profit that month.

Moreover, as baskets grow, fixed costs per order such as shipping and packaging take a smaller share. That said, this math only works as long as the added items keep their margin. If you buy bigger baskets with heavy discounts, AOV can rise while profit falls. Your real target, therefore, is contribution margin per order, not revenue.

When does the average mislead you?

An average reacts strongly to outliers. A handful of wholesale buyers can pull the monthly figure up, and the report shows growth while typical baskets actually shrink.

That is why I look at three more things next to AOV:

1. Median order value: the order right in the middle describes your typical customer better.
2. Order value distribution: split orders into bands such as $0 to $30, $30 to $60 and $60 to $120, then track the share of each band.
3. Segments: new versus returning customers, mobile versus desktop and each channel often show very different AOV.

For instance, if mobile baskets run clearly smaller than desktop baskets, the issue may sit in the mobile experience rather than in your product range. The distribution also shows you where to set a threshold offer later. Put simply, the average tells you how much; the distribution gets you closer to why.

How do product bundles grow the basket?

Bundling means offering complementary products as a single package. The decision gets easier for the shopper, and the basket gets bigger for you. A good bundle lets customers buy, in one click, the items they would have bought together anyway.

There are three basic bundle types:

• Fixed bundle: you choose the items, for example a starter kit.
• Mix and match bundle: the customer picks items from a defined group.
• Multi pack: two or three units of the same product, which suits consumables well.

When you price a bundle, calculate margin for the whole package, not item by item. Pairing a high margin accessory with a lower margin core product, for instance, protects total margin. Also, pick bundle contents from order data: find which items already appear together often, then build bundles around those pairs. Bundles built on guesswork tend to gather dust.

A discount on the second item is another bundle mechanic, but it hits margin in a very different way. I covered that math in my article on second item discounts, so I will not repeat it here.

How should you set up cross selling?

Cross selling means suggesting a product that complements the one the customer already chose. A screen protector for someone buying a phone case, or filters for someone buying a coffee maker, are classic examples. The goal is not to show random items but to anticipate the next need.

In practice, a good cross sell passes three tests. First, do people really use the suggested item with the main product? Second, is its price small compared with the main item? Suggestions between a quarter and a half of the main price tend to land more easily; treat that as a starting point to test, not a rule. Third, is it in stock, and can it ship in the same parcel?

Once again, lean on your order data when you choose suggestions. Pull the product pairs that appeared together most often over the last six months. That way your "frequently bought together" block reflects real behaviour. In a store with a small catalogue, building this list by hand works fine; what counts is that each suggestion makes sense.

What is the difference between upselling and cross selling?

Upselling moves the customer toward a better or bigger version of the item they picked. Cross selling adds a second item next to it. Both lift average order value, but they work at different moments and carry different risks.

In practice, the healthiest order is simple. Help shoppers pick the right version on the product page, then suggest a complementary item in the cart. Showing a premium version and three add ons on the same screen makes the decision harder. Some shoppers then postpone the order altogether.

How do you build a threshold offer?

A threshold offer rewards the customer once the basket passes a certain amount. Free shipping is the best known example. Still, it is not the only option; a free gift, a tiered discount or an extra service can also serve as the reward.

Set the threshold from your order distribution, not from the average. If many orders cluster just below a level, shoppers can cross it with one small add on. Set it too high and nobody tries; set it too low and you hand a free reward to people who already spend that much.

I explained how to calculate a free shipping threshold and how to show it on the site in my free shipping threshold guide. Here, let us look at the other threshold types:

• Gift threshold: add a sample or small product above a set amount; choose items with high perceived value and low cost.
• Tiered discount: for example 5 percent off above $100 and 10 percent above $160; cap the tiers with a margin check.
• Service threshold: gift wrapping, priority dispatch or a longer return window.

To work out the net effect of each tier, you can use the discount calculator.

Which products make basket growth easier?

Not every category responds to basket growth in the same way. Where a complementary item feels natural, shoppers accept the suggestion easily. Someone buying a camera needs a memory card; someone buying a plant needs a pot. Such pairings feel like a reminder, not a sales pitch.

With consumables, multi packs and subscriptions take the lead. Cosmetics, supplements, pet food and filters run out on a schedule, so the customer will buy again anyway; you simply offer to bundle that purchase into one order. On the other hand, one time, high ticket items such as furniture respond better to upsells and service add ons like assembly, extended warranty or protection products.

Sorting your catalogue into these three groups clarifies which lever to test where. Then, instead of one generic recommendation box across the whole site, you choose the right method for each category.

Where should recommendations appear on the site?

The right suggestion in the wrong place does nothing. So each page needs a recommendation slot that matches the question in the shopper's head.

On the product page, the shopper is still deciding, so version choice and bundle offers belong there. On the cart page, the decision is made; suggest only small, complementary items that people can add in one click. At checkout, I advise against recommendations altogether, because anything that distracts at this stage raises the risk of abandonment.

The post purchase page is valuable space too. A coupon or a reminder of a complementary item on the thank you page speeds up the second order. For the overall structure of a product page, see my ecommerce product page guide. In short, growing the basket without putting the order at risk means keeping both goals in view at once.

How does price architecture shape the basket?

The way you price your products shapes the basket directly. Even with the same range, changing your price steps can lift average order value.

The first tool is good, better, best tiering. When you offer three versions, a large share of shoppers pick the middle one, which means a higher basket than the cheapest option. The second tool is quantity pricing: offering 1, 3 and 6 units with a gradually lower unit price grows baskets, especially for consumables.

The third tool is subscription or repeat delivery. Even if each order stays modest, regular orders raise revenue per customer. However, this method moves customer lifetime value more than AOV. So decide up front which metric you want to improve; otherwise you may judge a successful subscription as a failure because "AOV did not move".

Do email and loyalty flows increase basket size?

Yes, but indirectly. Email and loyalty programmes mainly raise purchase frequency. Set up well, they lift basket size too.

These are the flows I have seen work:

1. Complementary product email: a few days after purchase, it suggests items that fit what the customer bought.
2. Points threshold: multiplying loyalty points above a certain basket size nudges customers toward it.
3. Replenishment reminder: for consumables, a message sent near the run out date, combined with a multi pack offer, grows the basket.

Watch consent and frequency here. You need proper permission from everyone you email for marketing. Moreover, a discount email every week trains customers never to buy at full price. That is why I prefer flows that focus on usefulness and product tips rather than on discounts.

How do you target high value customers with ads?

If your ad account optimises only for the number of conversions, the system may favour small, easy baskets. In that case conversions go up while average order value goes down.

To prevent this, send the real order value with each purchase conversion. With value data in place, bid strategies that aim at conversion value can tell large baskets apart. In addition, turning on conversions with cart data shows you, product by product, which ads bring the bigger baskets.

Simple splits in campaign structure help as well. For example, grouping high price product lines into their own campaign lets you steer budget toward them on purpose. One of the first things my team and I check in an ad account is whether conversion value flows correctly, because no bid strategy can decide well on wrong values. If you want support on this side, our ecommerce consulting page explains how we work.

Why do mobile baskets often stay smaller?

In many stores, mobile brings most of the traffic, yet mobile baskets often stay smaller than desktop ones. Still, there is no single reason. Comparing products on a small screen is hard, recommendation blocks get lost at the bottom of the page, and people often shop on mobile to meet a quick need.

To grow mobile baskets, show fewer suggestions but make them visible. Two complementary items in the cart drawer, one tap add buttons and a progress bar toward the threshold are usually enough. Moving bundle offers right below the product image also keeps them from disappearing while people scroll. Finally, report mobile and desktop AOV separately; merging both into one average hides the problem.

How do you test a basket growth idea?

Never make a change permanent without measuring it. Basket size swings naturally with season, promotions and traffic source, so a simple before and after comparison often misleads.

For a reliable test, follow these steps. First, pick one hypothesis, such as "a complementary suggestion in the cart raises AOV". Next, split visitors into two groups and show the new setup to only one of them. Finally, track AOV together with conversion rate and gross profit per order.

Watching all three is therefore essential. If a change lifts AOV but lowers conversion rate, revenue per visitor may not have moved at all. To check whether the groups really differ, use the A/B test calculator. I recommend running each test for at least two full weeks, so both weekday and weekend behaviour shows up in the result.

Which mistakes grow the basket but shrink profit?

In practice, the mistakes I meet most often look alike. They all turn AOV into a goal on its own.

• Discounting everything: a discount that buys a bigger basket can wipe out the margin on the added item.
• Irrelevant suggestions: labelling slow stock as "recommended" damages trust.
• A crowded cart page: too many suggestions hide the checkout button.
• Tracking one metric: an AOV lift without watching conversion and return rates misleads you.
• Forgetting shipping cost: large, heavy add ons can raise shipping cost faster than basket value.

There is also a timing mistake. During big promotions, baskets change on their own, and it becomes almost impossible to isolate the effect of a new setup. So test new mechanics in calm periods and enter peak season with proven versions.

Is there a good average order value for your industry?

The short answer: no universal good number exists. Comparing a furniture store with a cosmetics shop makes little sense. Even within one industry, price position, range and audience shape baskets very differently.

Industry averages circulate widely in reports; however, many of them mix countries, currencies and definitions. For your business, the most meaningful benchmark is your own past. Compare this month's AOV with the same month last year.

Next to that, set an internal floor: what is the smallest basket that covers your fixed costs per order, meaning shipping, packaging, payment fees and ad cost? That value is your lower limit for profit. Knowing what share of orders falls below it is far more useful than any industry figure.

What should you do in the first 30 days?

Basket growth does not have to be a big project. The sequence below is a simplified version of how we approach small and mid sized stores.

1. First week, definition and tracking: write down your AOV definition and check that order value reaches GA4 and your ad platforms correctly.
2. Second week, analysis: pull the order distribution, the median and the product pairs that sell together.
3. Third week, first setup: choose one change, for example a cart cross sell or a bundle offer.
4. Final week, test and decide: finish the A/B test and decide based on AOV, conversion rate and profit.

Repeat this loop every month and small gains add up. A 3 to 5 percent step in average order value looks modest on its own; still, the extra profit it builds over a year from the same traffic tends to outlast most ad optimisations.

What should you keep in mind while raising average order value?

AOV is not a goal; it is one signal of profitable growth. Growing the basket starts with showing customers the next product that truly helps them. When you suggest the right item at the right moment, shoppers read it as help rather than pressure.

To sum up, fix measurement first, then understand your order distribution, and after that test bundles, cross sells and threshold offers one at a time. At every step, watch conversion rate and margin together. That way average order value rises while your profit grows for real.

You can run this process with your own team. Still, when tracking setup, basket analysis and test design all land at once, an outside view speeds things up. We usually start by validating measurement, then focus on the single setup with the highest expected impact. Small, measured steps beat big, vague projects every time.