Average order value (AOV)

In retail, AOV is often used synonymously with "average basket value". Strictly speaking, order value refers to completed orders, whereas basket value can also include open baskets that were never submitted. In day-to-day practice, both terms almost always mean the same figure: revenue per order. AOV is one of three adjustment screws that make up shop revenue (traffic, conversion rate and order value), and the only one you can turn without paying for every additional visitor.

Calculation and a worked example

The formula is deliberately simple:

AOV = total revenue / number of orders

If a shop generates €250,000 in revenue from 1,000 orders in a month, the average order value is €250. It is important that numerator and denominator use the same basis: anyone who calculates revenue net must also consider the corresponding orders net. In B2B in particular, this gross/net question is not a minor matter, because prices there are usually shown net. If you compare an unadjusted B2B net AOV with a B2C gross AOV, you are talking past reality. You should also define whether shipping costs, vouchers and returns are included in revenue, because these definitional questions alone shift the figure noticeably.

The real appeal of the metric becomes apparent as soon as you hold it against the other revenue drivers. The following example keeps traffic and conversion rate constant and varies only the order value.

An order value that is 20 per cent higher feeds through one to one into revenue here: €18,000 more per month without having bought a single additional click. It is precisely this leverage that makes AOV so interesting for mid-sized companies, where advertising budgets are finite and every new visitor via paid channels costs money.

The three revenue drivers behave multiplicatively, not additively: revenue is traffic times conversion rate times AOV. Anyone who improves all three values by ten per cent each increases revenue not by thirty but by around thirty-three per cent, because the effects reinforce each other. This explains why working on order value pays off even when traffic and conversion rate are already well optimised. AOV is rarely the most spectacular lever, but often the cheapest, because unlike reach it does not require a recurring media budget.

Why average order value matters

More revenue without more traffic is the core argument. Additional visitors via Google Ads or other paid channels incur ongoing costs with every click, and click prices rise year after year in many industries. A higher order value, on the other hand, is mainly conceptual work: you set up recommendations, sets or a free shipping threshold once, and the effect then carries through every order without the marginal cost per order rising significantly. This makes AOV one of the few levers that scales well.

Effect on contribution margin

Part of the costs per order is largely fixed: packaging, picking, the base amount of the payment fee, handling a possible return. These items are similar whether a customer orders for €60 or for €120. If the order value rises, the fixed processing costs are spread over more revenue, and the contribution margin per order grows disproportionately to revenue. Anyone who raises AOV therefore improves not only the top line of the profit and loss account but usually also the margin per order. This is why experienced retailers rarely look at AOV in isolation, but always with an eye on contribution margin.

Recouping customer acquisition costs

Customer acquisition cost (CAC) indicates what it costs to win a new customer. A shop that spends €40 per new customer and calculates with a 50 per cent gross margin just about covers this expense with a first order value of €80. If the order value is €130, the acquisition has already been paid for with the first order, and every further purchase by the same customer contributes to profit. AOV thus indirectly helps determine the point from which paid traffic becomes profitable at all and how aggressively you can invest in acquiring new customers. For shops with a long repurchase cycle, this calculation shifts to customer lifetime value, but the first order remains the most important first step.

Levers for increasing order value

You do not increase AOV with a single trick but with a bundle of coordinated measures. The following levers have proven themselves in retail and can be implemented in most shop systems without much effort:

• Cross-selling: offer complementary products to the current selection, such as matching accessories or consumables. The classic is the "Frequently bought together" block on the product or basket page.
• Upselling: recommend the higher-value variant of the same product, for example the larger pack, the professional version or the model with a longer warranty. Works best when the added value is named specifically.
• Bundles and sets: bundle several items as a package with a small price advantage. This increases order value and at the same time reduces pick-and-pack costs per item sold.
• Free shipping threshold: grant free shipping above a defined order value. The progress bar in the basket ("Only €12 to go until free shipping") is a strong incentive to fill up the basket.
• Volume and tiered pricing: discounts from certain purchase quantities that motivate customers to buy larger units. In B2B, this is often the expected pricing logic anyway.
• B2B price lists and customer-specific prices: customer groups, framework agreements and individual terms ensure that business customers purchase larger volumes per order in a plannable way. Shopware maps this natively via customer groups and dynamic price lists.

A real-world example of the power of good recommendations comes from Amazon and its recommendation engine. A frequently quoted estimate by McKinsey from 2013 states that 35 per cent of what customers buy on Amazon comes from algorithmic product recommendations. This figure often circulates in shortened form as "35 per cent of Amazon's revenue", which the original source does not say: it refers to the share of purchases, not of revenue. The value is also old, has never been officially confirmed by Amazon and is better suited to illustrating the order of magnitude than as a reliable benchmark. Nevertheless, it is meaningful as evidence that recommendation logic moves order value.

The tension between AOV and conversion rate

AOV cannot be optimised without regard to the conversion rate, because the two metrics can work against each other. Anyone who inserts too many expensive alternatives into the checkout or overloads customers with intrusive upsell banners risks undecided customers abandoning the purchase altogether. A rising order value with a simultaneously falling conversion rate can even reduce revenue on balance. It is therefore usually wiser not to maximise AOV in isolation but to keep an eye on revenue per visitor, which combines conversion rate and order value in one figure. As a rule of thumb: recommendations may help, they may not push. An A/B test shows more reliably than any assumption whether an AOV measure brings in net revenue or merely flatters the order value while buyers are lost in the background.

Measuring AOV in Shopware and analytics

In Shopware 6, average order value is one of the standard metrics. Alongside total revenue, orders and customer numbers, the Shopware Analytics module explicitly shows "average order value" and allows the data to be filtered by period, sales channel, country and customer group and exported as CSV. Filtering by customer group is particularly valuable in B2B, because it separates the often considerably higher order value of business customers from B2C business and thus avoids mixing averages.

Anyone who wants to segment more deeply adds a web analytics tool such as Google Analytics 4, in which order value can be broken down via the e-commerce events by campaign, device or new versus existing customers. It is worth looking at two metrics instead of one: the arithmetic mean reacts sensitively to individual large orders, while the median (the value below which one half and above which the other half of orders lie) reflects typical purchasing behaviour more robustly. If the mean and median differ greatly, this indicates a few very large orders pulling the average up. Together, both values give a more honest picture than either figure on its own.

Frequently asked questions about average order value

What is a good average order value?

There is no universal target value, because AOV depends heavily on industry, range and price point. A shop for industrial supplies is naturally many times higher than a shop for cosmetic samples. A comparison with your own history is meaningful: an AOV that rises over the months is a better signal than an absolute value without context.

How do AOV and average basket value differ?

Average order value refers to orders actually completed; basket value can also include open baskets that were never paid for. In everyday language, the two are usually equated; what matters is that you define a clear definition internally and use the same one consistently.

How often should AOV be evaluated?

For ongoing controlling, a monthly look is enough, supplemented by an analysis by seasonal phases and larger campaigns. After every AOV measure, such as a new free shipping threshold, a shorter check over a few weeks is worthwhile, ideally as an A/B test against the previous state.

Why is the median sometimes more meaningful than the mean?

Because individual very large orders can severely distort the arithmetic mean. The median shows the order value of the "middle" order and is therefore more robust against outliers. Especially in B2B, where a few large orders dominate the average, you should read both values side by side.

Does a higher AOV automatically increase profit?

Not necessarily. A higher order value bought through deep discounts or a lowered free shipping threshold can eat up the margin. Only together with the contribution margin per order and the conversion rate does it become clear whether an AOV measure is really profitable on balance.