Shopify Average Order Value Analytics

Average Order Value shows the average discounted product sales value, excluding tax, across the relevant Shopify orders. It helps explain whether growth comes from more orders, larger orders, or both.

How Shopify Average Order Value is calculated

Average Order Value = Discounted product sales excluding tax ÷ Orders Count

Because the numerator leaves out tax, shipping, duties, and fees, Average Order Value describes the merchandise value of a typical order rather than the total amount the customer paid.

Why Average Order Value matters

Growing revenue has only two sources: more orders, or bigger orders. Average Order Value tells you which one you actually got, and the answer changes what you should do next.

If revenue is up because order count is up, your marketing is working and the question becomes whether you can fulfil the volume. If revenue is up because Average Order Value is up, something in your merchandising is working: bundles, free-shipping thresholds, cross-sells, or a shift toward pricier products. Those need very different follow-up.

Average Order Value also tells you:

• Which customer segments and B2B accounts place the orders worth having
• Whether a free-shipping threshold is actually lifting basket size
• Which acquisition channels bring browsers and which bring buyers
• How much room you have on shipping costs, since a $40 order and a $400 order tolerate very different shipping spend

Always read it beside Orders Count. A high average across nine orders is a different business result from the same average across nine thousand.

What Average Order Value measures

Mixtable calculates Average Order Value from eligible Shopify product sales after discounts and excluding taxes, divided by the relevant distinct order count.

It is not:

• Average product price
• Average unit price
• Total Sales divided by orders when Total Sales includes shipping, taxes, duties, and fees
• Customer lifetime spend
• Profit per order

Add Average Order Value to a Shopify data worksheet

1. Open a supported Customer, Company, or Company Location worksheet, then select an empty column or insert a new one. Empty columns have a non-green header; a green header means the column is already linked to Shopify data

2. Click the button in the column header

3. Choose Analytics

   

4. Under Which metric?, select Average Order Value

5. Under Over what time period?, choose All time, Fixed dates, Rolling period, Calendar year, or Calendar month

6. Under Limit which orders count, add any optional filters supported by the metric

7. Click Save Column

Add Orders Count and Net Sales beside the new column so the average has its volume and value context.

Compare Average Order Value with a Pivot Table worksheet

The Customer segment contribution report includes Average Order Value beside orders, sales, discounts, and refunds.

Pivot Table worksheets can use Average Order Value for supported vendor, product type, product category, product tag, collection, Market, and customer-segment reporting.

Use it to answer:

• Which customer segments place larger orders?
• Which Shopify Markets have higher basket value?
• Which vendors or product groups appear in larger orders?
• Do heavily discounted groups actually produce larger baskets?

Track Average Order Value over time

When a Time Series worksheet uses Store, Average Order Value can become a row with recent monthly or yearly columns.

For vendor, product type, Market, customer segment, or marketing channel breakdowns, Average Order Value can be the single measure tracked across time.

This helps separate two kinds of growth:

• Orders Count rises while Average Order Value stays stable, which points to more transactions
• Average Order Value rises while Orders Count stays stable, which points to larger baskets

Add Net Sales when you need to see the combined result.

Filter Average Order Value

Depending on the reporting target, filters can include:

• Customer segment or country
• Orders with a discount
• Repeat customers
• Fulfillment status
• B2B or B2C order type
• Company or company location
• Order source, app, publication, or retail location
• Parent discount campaign
• First-touch or last-touch attribution values

Matched filtered columns make useful comparisons. For example, compare Average Order Value for B2B and B2C orders, or for two Shopify customer segments, using the same timeframe.

Avoid misleading averages

Before acting on Average Order Value:

• Check Orders Count for sample size
• Compare the same timeframe and filters
• Review refunds and discounts
• Remember that a few large orders can move the average
• Compare with average gross and net quantities when basket unit count matters

For skewed customer groups, also sort individual customer Total Sales and Orders Count. A segment average can hide very different behavior among its members.

Average Order Value and Customer Lifetime Value

These two metrics answer different questions. Average Order Value describes the size of a typical order in revenue terms. Customer Lifetime Value measures profit per customer: their Net Sales excluding tax, minus the estimated cost of the goods they bought.

Reading them together is useful, because they can move in opposite directions. A promotion that bundles more items into each order lifts Average Order Value, while Customer Lifetime Value falls if the extra volume arrives at a thinner margin.

Use Average Order Value when you want average order size, Customer Lifetime Value when you want customer profit, and Net Sales or Total Sales with an All time timeframe when you want cumulative customer spend.

See Shopify Customer Analytics for customer-level reporting.