Average Order Value Calculator
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Change any scenario value below. Your original calculation stays unchanged.
Scenario calculations use the same formula and v2.5 validation rules as the main calculator. No scenario values are sent to Borkish.
Calculations and What-If scenarios run in your browser. Borkish does not require you to submit these values to calculate the result.
What the Average Order Value Calculator measures
AOV is a core ecommerce metric for pricing, upsells and promotion planning. Measure ecommerce performance, order economics, marketplace costs and store growth without spreadsheets.
The result becomes more useful when every input follows the same definition and reporting period. That keeps comparisons between campaigns, products, customers and time periods meaningful.
When to use this calculator
- Use the Average Order Value Calculator to review store economics using order, revenue and customer data.
- Compare product or marketplace performance using the same reporting period.
- Model how changes in conversion, fees or order value affect the business.
Formula
Use one currency consistently for every monetary input. The calculator changes the display symbol only; it does not perform foreign-exchange conversion.
How to use this calculator
- RevenueUse the value from the same reporting period or scenario as your other inputs.
- OrdersUse the value from the same reporting period or scenario as your other inputs.
- Complete the required inputsThe result updates automatically as the values become valid.
- Compare the resultUse a previous period, target or relevant internal benchmark before making a decision.
Worked example
Using the demonstration values — Revenue = 20000, Orders = 250 — the calculator returns $80.00. The example shows how the formula behaves; replace the demonstration data with your own before using the result for planning.
How to interpret the result
AOV is a core ecommerce metric for pricing, upsells and promotion planning. Use these calculators for store planning, product economics, checkout performance and marketplace decision-making.
Check the definition of Revenue, Orders, the attribution or accounting rules behind those inputs, and any important costs or outcomes that the formula does not include.
Common mistakes to avoid
- Using Revenue and Orders from different reporting periods or definitions.
- Ignoring marketplace, payment or fulfilment costs when they materially affect the result.
- Using order and customer counts from different periods.
Frequently asked questions
What does this calculator do?
Calculate average revenue generated per order.
Where should I get the input values?
Use your own advertising platform, ecommerce system, accounting report, CRM, analytics platform or forecast — whichever source is authoritative for the metric. Keep all inputs on the same basis and date range.
Is there one good result I should target?
Usually not. A useful target depends on your margins, acquisition model, operating costs, channel, market and business goals. Your own historical performance is often a better starting benchmark than a generic number.
Can I use this for forecasting?
Yes. Enter forecast values to model a scenario, but treat the output as an estimate based on those assumptions rather than a prediction of future performance.
Can I use a different currency?
Yes. Select a display currency and keep every monetary input in that same currency. The calculator does not convert exchange rates.
What to calculate next
AOV is a core ecommerce metric for pricing, upsells and promotion planning. A single metric rarely explains the whole decision, so compare this result with the related cost, conversion, margin or growth metrics below before acting on it.
Calculate ecommerce conversion rate from orders and sessions.
Calculate product-level profit before overhead from selling price, landed cost and fees.
Calculate the percentage of customers who purchased more than once.
Keep reporting periods and metric definitions consistent when moving between calculators. That makes the comparison more useful than treating each result as a standalone benchmark.