Average order value looks like one of the simplest ecommerce metrics. Take the value generated by orders. Divide it by the number of orders. You have AOV. Then you open two analytics systems and find two different numbers.
Your ecommerce platform says $82. Google Analytics shows something different. Your finance spreadsheet calculates $77. Another dashboard reports $85. Now the apparently simple metric is not so simple.
The problem is usually not the division. It is deciding what goes into the numerator, which orders belong in the denominator, how discounts and refunds are treated, and which period or customer group you are actually measuring.
Understanding average order value in ecommerce therefore requires more than memorizing a formula. You need to understand what the number represents before using it to make pricing, merchandising, advertising or growth decisions.
What Is Average Order Value in Ecommerce?
Average order value, usually abbreviated as AOV, tells you the average monetary value of an order during a defined period. At its simplest: Average Order Value = Order Revenue ÷ Number of Orders
Suppose your store generates $50,000 from 500 orders during a month. Your AOV is: $50,000 ÷ 500 = $100 That means each order generated an average of $100 according to the revenue definition used in your calculation.
BigCommerce describes AOV similarly as the average total of orders placed during a defined period and calculates it by dividing revenue by total orders. But that simple definition comes with an important warning. Not every ecommerce platform defines the revenue portion of AOV in exactly the same way.
The AOV Formula Is Simple. The Revenue Definition Is Not.
Suppose two analytics systems both use: Revenue ÷ Orders. That does not guarantee they will produce the same AOV. Why? Because one might use gross product sales. Another might use sales after discounts. Another might also subtract refunds.
Some may exclude taxes and shipping. Others may handle post-purchase order adjustments differently. This is why the formula alone is not enough. Before comparing AOV between systems, ask: What exactly does this platform mean by order value?
How Shopify Calculates Average Order Value
Shopify currently defines Average Order Value as the average value of orders placed while excluding post-order adjustments. Its documented calculation is: (Gross sales minus discounts) ÷ Orders
Shopify specifically notes that post-order adjustments such as later edits, exchanges and returns are excluded from its AOV calculation. That means Shopify AOV is not simply the same as net sales divided by orders.
Consider a customer who originally places a $100 order but later returns $40 of merchandise. The store's ultimate retained value from that transaction changed.
But Shopify's AOV methodology treats later adjustments differently from metrics designed to measure net sales or final revenue. This matters when you compare AOV with refund-adjusted revenue.
How WooCommerce Calculates AOV
WooCommerce Analytics uses a different approach. Its current documentation defines: Average Order Value = Net Sales ÷ Orders. WooCommerce defines net sales as: Gross Sales minus Returns minus Coupons.
Taxes and shipping are excluded from net sales. So WooCommerce AOV can incorporate return activity differently from Shopify's documented AOV calculation.
WooCommerce also records refunds on the date the return occurred rather than the date on which the original purchase was made. That can affect period comparisons.
Imagine an order was placed in August and refunded in September. The refund may influence September's WooCommerce reporting even though the original transaction belongs to August.
This is one reason two stores with apparently similar transactions can display different AOV patterns depending on the platform and reporting methodology.
So What Is the "Correct" AOV Formula?
There is no single platform-independent formula that will perfectly reproduce every ecommerce system's built-in AOV. For your own analysis, the useful approach is to define the metric clearly and stay consistent.
For example, you might deliberately define management AOV as: Net merchandise revenue after discounts and refunds ÷ Completed orders. That can be perfectly useful. But label it clearly.
Do not call one calculation "AOV" this month and switch to another calculation next month because a different dashboard became convenient. Consistency is what allows trends to mean something.
A Basic AOV Example
Suppose your store generated the following during June:
- Gross product sales: $120,000
- Discounts: $10,000
- Orders: 1,000
Using a simple sales-after-discount calculation: ($120,000 - $10,000) ÷ 1,000 = $110 AOV
Now suppose July produces:
- Gross product sales: $135,000
- Discounts: $15,000
- Orders: 1,200
AOV becomes: ($135,000 - $15,000) ÷ 1,200 = $100
Revenue activity increased. Orders increased. But average order value fell from $110 to $100. That does not automatically mean July was worse. It tells you the average value created per transaction changed.
Understanding why belongs in a different layer of analysis. Our guide on customers spending less per order covers that diagnostic process in detail.
Should Shipping Be Included in AOV?
It depends on the metric definition you have chosen. Shopify's documented AOV calculation is based on gross sales minus discounts, not total sales. Shopify separately defines total sales as net sales plus elements including shipping, duties, additional fees, and taxes.
WooCommerce also excludes shipping from net sales, and because its AOV uses net sales, shipping is not included in that AOV calculation. If you manually calculate AOV using total customer payments including shipping, you may produce a different result.
Neither calculation is automatically useless. They answer different questions. For merchandising and basket-value analysis, excluding shipping often provides a cleaner picture of how much merchandise customers purchase. For cash collection analysis, total customer payment may matter more. Just do not confuse the two.
Should Taxes Be Included?
Again, know your definition. Shopify's built-in AOV formula does not use total sales, so taxes are not part of the documented AOV numerator. WooCommerce's net sales also exclude taxes. This is important for businesses operating across countries with different tax rates.
If taxes were included, changes in geographic sales mix could move the average even when customer product spending stayed similar. For analyzing customer basket behavior, merchandise-focused values are often more informative.
Do Refunds Affect AOV?
This is where you need to be especially careful. They can, depending on the reporting system. WooCommerce AOV uses net sales, and net sales deduct returns. Its refunds are recorded in Analytics on the date the return takes place.
Shopify's AOV calculation specifically excludes post-order adjustments, including later returns. Google Analytics provides yet another perspective. Its Purchase Revenue metric subtracts refund events from purchase revenue, provided those refund events are correctly sent to GA4.
This means you should not expect an AOV calculation based on GA4 purchase revenue to automatically match Shopify's built-in AOV. The difference may be methodological rather than an error.
Do Canceled Orders Count?
That also depends on the reporting system and metric. Shopify notes within its analytics definitions that order and sales treatment can include statuses that merchants may not intuitively expect.
Its gross sales documentation, for example, notes that pending, canceled, and unpaid orders can be included in certain sales reporting contexts, while filters can be used to adjust analyses.
When manually calculating AOV, decide which order statuses belong in your denominator. If your goal is to understand completed commercial transactions, you may choose to include only completed or valid orders. Whatever rule you choose, use it consistently.
AOV Is Not the Same as Revenue per Customer
This distinction causes frequent confusion. AOV measures value per order. Customer spending measures value per customer.
- Suppose Sarah places four orders during the year: $40, $60, $75, $125
- Her total spending is: $300
- Her average order value is: $75
Those numbers answer different questions.
- AOV asks: How large is a typical transaction on average?
- Customer value asks: How much has this customer generated across their relationship with us?
Shopify treats amount spent per customer and average order value as separate analytics fields for exactly this reason.
AOV Is Also Not Customer Lifetime Value
Customer lifetime value looks beyond one transaction. A customer could have a relatively low AOV but purchase frequently enough to become highly valuable. Consider two customers.
- Customer A: AOV = $200, Orders per year = 1
- Customer B: AOV = $55, Orders per year = 8
Customer A looks more valuable when you look only at transaction size. Customer B generates more annual revenue.
This is why stores with replenishment, subscription or repeat-purchase models should be careful about making AOV the primary definition of customer quality. AOV describes transactions. Retention and lifetime value describe relationships.
AOV Is Not the Same as Items per Order
Two stores can have identical AOVs while customer baskets look completely different.
- Store A: AOV = $100, Average units per order = 1
- Store B: AOV = $100, Average units per order = 4
Store A may primarily sell products priced around $100. Store B may sell several products averaging $25 each. The same AOV hides completely different merchandising behavior. Shopify provides a separate Units per transaction metric, calculated as net quantity divided by total orders.
Reviewing AOV alongside units per transaction can help distinguish between: Customers buying more products and Customers buying more expensive products. That relationship is often more informative than AOV alone.
Why You Should Not Average Two AOVs Directly
This is one of the most useful calculations for multi-store businesses. Suppose: Store A has 10 orders with an AOV of $100. Store B has 100 orders with an AOV of $50. It is tempting to calculate: ($100 + $50) ÷ 2 = $75
That is wrong for the combined business because the two stores have very different order volumes. Store A revenue: 10 × $100 = $1,000 Store B revenue: 100 × $50 = $5,000 Combined revenue: $6,000 Combined orders: 110
The correct combined AOV is: $6,000 ÷ 110 = $54.55 Not $75. This is called a weighted calculation. Whenever you combine AOV across stores, channels, customer groups or time periods, calculate it from the underlying revenue and order counts. Do not simply average the averages.
The Same Problem Happens When Combining Months
Imagine: January AOV: $150 from 100 orders, February AOV: $75 from 1,000 orders. The simple average of those AOVs is $112.50, but the combined two-month AOV is not $112.50.
- January revenue: 100 × $150 = $15,000
- February revenue: 1,000 × $75 = $75,000
- Combined: $90,000 ÷ 1,100 = $81.82
February carries much more weight because it contains ten times as many orders. This matters whenever you create quarterly, annual or multi-store dashboards.
Why One Large Order Can Distort AOV
Average order value is an arithmetic mean. That means unusually large orders can pull it upward. Suppose nine customers spend $50 each. One customer spends $550.
- Total revenue: $1,000
- Orders: 10
- AOV: $100
But nine out of ten customers spent only $50. Calling $100 the "typical order" would be misleading. This is why AOV and median order value can tell different stories.
The median looks at the middle of the order distribution rather than dividing all revenue equally across transactions. In this example, the median is much closer to $50.
For businesses with occasional wholesale, corporate or unusually high-value orders, median order value can provide useful additional context.
AOV and Median Order Value Answer Different Questions
AOV is excellent for revenue mathematics. If you know: Orders × AOV, you can approximately connect transaction volume with sales value when the metrics use compatible definitions.
Median order value is better for answering: What does the middle transaction actually look like?
Imagine AOV rises 30%, but median order value remains unchanged. That may mean ordinary customers did not change much. A handful of unusually large transactions pulled the average upward.
Now imagine both AOV and median rise. That provides stronger evidence that order values increased more broadly. You do not necessarily need median order value on your everyday dashboard. But it is useful when AOV moves sharply and the reason is unclear.
Segment AOV Before Drawing Conclusions
A store-wide AOV can hide important differences. Instead of treating one blended average as the entire story, compare AOV across groups that matter to your business. You might compare:
- New versus returning customers.
- Direct store versus marketplaces.
- Countries or regions.
- Marketing channels.
- Product categories.
- Mobile versus desktop.
- First order versus later orders.
WooCommerce's customer reporting, for example, includes customer-level average order value and allows customers to be filtered by AOV alongside information such as order count and total spending.
Shopify also surfaces AOV in cohort and customer-related analytics contexts. Segmentation tells you whether a store-wide movement reflects changing behavior or simply a changing mix of customers.
A Blended AOV Can Fall Even When Nobody Changed Their Behavior
Consider two customer groups.
- New customers: AOV = $50
- Returning customers: AOV = $100
Last month most orders came from returning customers. This month, a successful acquisition campaign causes new customers to become the majority. The overall AOV falls. But neither new nor returning customers changed how much they normally spend.
Only the mix of orders changed. This is an important analytical concept because merchants can waste time trying to "fix" an AOV decline that simply reflects successful customer acquisition. Always ask whether the underlying segments changed before assuming individual customer behavior changed.
What Is a Good Average Order Value?
There is no universal answer. BigCommerce recommends treating AOV in the context of your own historical performance and relevant business conditions rather than relying solely on a broad benchmark.
A grocery business can have a very different natural AOV from a luxury furniture retailer. A refill business may naturally generate smaller repeat orders. A high-ticket B2B ecommerce store may receive relatively few orders but very large values.
Your most useful baseline is usually your own store under comparable conditions. Compare:
- Similar seasons.
- Similar customer groups.
- Similar product ranges.
- Similar promotions.
- Similar channels.
- Similar markets.
Ask whether AOV is moving in a commercially meaningful direction for your business model.
Higher AOV Is Not Automatically Better
Imagine you redesign your store and AOV rises from $80 to $110. Excellent? Maybe. But suppose conversion simultaneously falls from 4% to 2%. Customers who still purchase are spending more.
Far fewer customers are purchasing. Total revenue may decline. Or suppose AOV increases because customers now purchase expensive low-margin products instead of highly profitable lower-priced items.
Again, a larger AOV does not automatically mean a better result. BigCommerce explicitly recommends considering AOV alongside other ecommerce measures rather than treating it as a standalone indicator.
The useful question is: What happened to the business when AOV changed?
Which Metrics Should You Check Beside AOV?
AOV becomes much more informative when read with the metrics surrounding it. Revenue tells you the total commercial outcome. Orders show transaction volume. Units per transaction help distinguish basket quantity from product value.
Conversion shows whether efforts to increase basket size are making purchasing harder. Discounts show whether customers are spending more because of genuine basket growth or promotional incentives.
New and returning customer data reveal whether customer mix changed. Gross profit and margin help determine whether higher-value orders actually generate stronger economics. This is why AOV should be part of an analytical system rather than a standalone target. For a wider metric framework, see Ecommerce Metrics That Matter.
How Often Should Ecommerce Stores Review AOV?
Daily AOV can be noisy, especially for stores with relatively few orders. One unusually large purchase can make a quiet Tuesday look exceptional.
Weekly and monthly comparisons usually provide more useful context for management, although larger stores can review shorter intervals because more transactions reduce the effect of individual orders.
Shopify's current analytics environment includes AOV among the sales trends it evaluates for generated dashboard insights. The right review frequency depends on order volume.
A business processing ten transactions per week should not react to each day's AOV movement the same way as a business processing ten thousand.
Compare Equivalent Periods
Suppose your AOV was:
- $70 during a normal week.
- $110 during Black Friday.
- $78 the following week.
Technically, AOV fell nearly 29% from Black Friday. That does not necessarily signal a problem. The promotional period created an unusual baseline. Compare periods with enough context around:
- Promotions.
- Product launches.
- Seasonality.
- Stock availability.
- Market expansion.
- Customer acquisition campaigns.
- Large orders.
For a more detailed period-comparison framework, see How to Compare Store Performance Between Two Periods.
AOV Can Help You Evaluate Free-Shipping Thresholds
One practical application of AOV is shipping strategy. Shopify specifically identifies AOV as a metric that can help merchants set minimum order thresholds for free shipping and promotions. Suppose normal AOV is around $64.
A $65 free-shipping threshold would require very little additional spending from the average order. A $90 threshold requires a much larger basket increase. But do not simply take current AOV and add an arbitrary amount.
Look at your order-value distribution. If many orders already fall between $70 and $80, a threshold around that area may affect behavior differently from a threshold far above most transactions. Shipping cost and gross margin also need to be included in the decision. A higher basket is not useful if the promotion destroys profitability.
AOV Can Help Evaluate Product Bundles
Suppose a new bundle increases AOV from $75 to $90. That looks promising. Now check:
- Did conversion remain stable?
- Did total revenue increase?
- Did units per transaction increase?
- Were discounts required?
- Did gross margin remain healthy?
- Did customers buy products they genuinely needed, or did returns increase later?
The point of AOV analysis is not to maximize the number mechanically. It is to understand whether merchandising changes create healthier transactions.
AOV Is Useful for Comparing Acquisition Channels
Different channels can attract customers with very different buying behavior. Imagine:
- Organic search AOV: $110
- Paid social AOV: $60
- Email AOV: $125
That information can help explain how different audiences shop. But AOV alone cannot tell you which channel is best. Paid social might have a lower AOV but acquire customers much more cheaply.
Email may have the highest AOV because it primarily reaches existing customers. Organic search might attract shoppers researching higher-value products.
Look at AOV beside acquisition cost, conversion, retention, and customer value before reallocating marketing budgets.
AOV Can Also Help With Inventory Planning
Order value and inventory are more connected than they initially appear. Suppose premium products contribute heavily to store AOV.
Those products begin running out of stock. Customers switch toward lower-priced alternatives. Order count remains healthy. AOV falls. Inventory availability has changed your transaction mix.
Understanding which products normally support higher-value baskets can therefore inform replenishment priorities. That is another reason to connect order analytics with product and inventory information rather than studying AOV in isolation.
How Does AOV Work Across Multiple Ecommerce Platforms?
The underlying concept applies across Shopify, WooCommerce, BigCommerce, Adobe Commerce, Wix, Squarespace, Ecwid, PrestaShop, Shopware, Amazon, Etsy, eBay and other or custom ecommerce stores.
But do not assume every platform's built-in AOV is directly comparable. The revenue definition can differ. Refund treatment can differ. Order-status rules can differ. Marketplace fees can create another layer of analysis. Currencies can differ.
Customer information can also be more limited on some marketplace platforms. For multi-platform reporting, the best approach is to define a standardized management AOV if you need a combined business view. Then calculate every channel using the same compatible numerator and denominator wherever the available data allows it.
Never Average Platform AOVs Without Their Order Counts
This is especially important for Statty AI's multi-platform use case. Suppose:
- Shopify AOV = $90 from 1,000 orders
- Amazon AOV = $50 from 4,000 orders
- Etsy AOV = $70 from 200 orders
A simple average of: $90, $50, and $70 would produce $70, but that would ignore the dramatically different order volumes.
To calculate a meaningful combined AOV, first calculate or retrieve the comparable revenue from each platform, add that revenue together, and divide by the combined comparable order count.
That creates a weighted business-level value. This principle is essential when building cross-store dashboards.
A Practical Monthly AOV Review
A useful monthly AOV process does not need to become complicated. Use this sequence:
- Confirm the definition. Know which sales value and order types your AOV includes.
- Compare equivalent periods. Avoid partial-period and promotional-baseline mistakes.
- Check the change in dollars and percentage. Understand the commercial size of the movement.
- Review order count and revenue beside AOV. See how transaction value affected the final result.
- Check units per transaction. Separate basket quantity from price and product-mix effects.
- Segment the average. Compare major customer groups, channels, stores or markets.
- Look for outliers. Determine whether a few large orders distorted the average.
- Check profitability and conversion before acting. A higher AOV is useful only when the wider economics remain healthy.
That turns AOV from a dashboard number into something you can actually use.
Where Statty AI Fits
Average order value becomes more useful when you can see what is happening around it.
Statty AI is designed to connect ecommerce information across Shopify, WooCommerce, BigCommerce, Adobe Commerce, Wix, Squarespace, Ecwid, PrestaShop, Shopware, Amazon, Etsy, eBay and other or custom store environments.
Instead of treating AOV as an isolated number, connected ecommerce analytics can help merchants examine it alongside sales, order volume, customers, products, inventory and other store-performance signals.
That becomes particularly important for multi-store businesses where simply averaging each platform's reported AOV would produce a misleading combined result.
You can explore the Statty AI analytics features or visit Statty AI to learn more about connected ecommerce intelligence.
If your AOV has already started moving downward and you need to understand why, see Customers Are Spending Less Per Order: What Should You Check?.
Final Thoughts
Average order value in ecommerce is easy to calculate only when you have clearly defined what goes into the calculation. At its core, AOV measures the average monetary value generated by an order. But meaningful analysis requires you to know:
Which revenue definition is being used. Which orders are included. How discounts are treated. Whether refunds affect the calculation. Whether taxes and shipping are included. Whether a few unusually large transactions are distorting the average. Whether customer, product, channel, or market mix has changed.
And whether you are combining several stores correctly. Most importantly, remember what AOV does not tell you. It does not tell you whether customers are profitable. It does not tell you whether they will return. It does not tell you whether conversion is healthy.
And a higher AOV does not automatically mean the store improved. Use AOV for what it does well: understanding the average value created by each transaction and how that transaction value changes across time, customers, products and channels.
Once you understand the calculation correctly, the number becomes much more than another KPI card. It becomes a useful lens for understanding how customers are actually spending when they place an order.
