What is average order value (M) in a target group profile?

‘This audience spends €500 on average’ becomes useful only when the amount’s basis is clear. Per order, customer, year or project? Average order value (M) describes the amount per relevant order. You also record which orders count, how amounts are composed and what you know when purchase data is missing.

Average order value (M) is, in the target group profile, the total amount of a defined set of relevant orders, under an explicit monetary definition, divided by the number of those same orders.

This is Neurofactor’s practical profile definition. State the period, product and supplier scope, currency, VAT treatment and data source. The formula describes observed orders; it does not yield a budget, maximum willingness to pay or profit.

M in RFM stands for monetary value. That label alone does not specify whether total spending or an average per transaction is used. This profile field explicitly uses average order value. Never copy an M value from another system without checking its definition.

A project contract, order, invoice and payment need not correspond one to one. One order may be paid in instalments. Counting those instalments as separate orders answers a different question. Define the object before combining amounts.

The arithmetic mean per order is a descriptive measure, not a separate psychological theory with one established inventor. Scientific publications do address the role of M in customer analysis and its distinction from willingness to pay.

In their RFM model, Fader, Hardie and Lee distinguish observed average transaction value from its underlying expected value. A historical average is therefore not automatically the best forecast. Fader, Hardie & Lee, 2005.

Wertenbroch and Skiera describe willingness to pay as a maximum price for a given quantity. An observed purchase price does not directly reveal that upper limit. Wertenbroch & Skiera, 2002.

Our practical application is to state what an amount measures and which claims the data supports. The documentation rules below are our methodological guidance, not a validated scale or a measured audience price profile.

For this business profile, use an amount excluding VAT when appropriate to the data and research question; always state the choice. The examples below use euros, excluding VAT.

For orders from a defined period, you can allocate refunds to those original orders up to a stated cut-off date. Also specify whether a fully returned order remains in the denominator. If it does, its adjusted amount can be zero. Do not mix this with a cash-flow report that records a refund on its payment date.

1. Define the analytical question. Choose audience, product context, supplier scope and period.
2. Define order and amount. Specify counting rule, VAT, discounts, returns, charges and currency.
3. Check the selection. Remove duplicate records and handle cancellations under the chosen rule. Check whether orders from some channels are missing.
4. Link amounts to orders. Use the order amount once, even when the export contains several product lines.
5. Mark missing amounts. An order with an unknown amount is not a zero-euro order. Report the limitation and do not invent a value.
6. Divide the same set. Average order value = sum of selected order amounts ÷ number of those orders.
7. Show the distribution. Where useful, add median, range and order count. Specify which measures concern orders and which concern customers.
8. Check comparability. Differences in product mix, quantity, discount or period can produce a different average. Investigate the explanation before attributing a change to motivation.

With no orders, average value per order cannot be calculated. That differs from a known order with an amount of zero. Where some amounts are missing, you can show the average for the complete subset, but label it accordingly and report the missing orders.

For a segment average per order, combine the segment’s amounts and orders. Do not take an unweighted mean of customer averages when the question concerns orders. An unweighted customer average may be a legitimate different measure, but gives each customer equal weight rather than each order.

Ask, for example: ‘Which specific project or order does this amount concern?’, ‘Does it include or exclude VAT?’ and ‘Is this an allocated budget, a quotation or an order actually paid for?’ Preserve the original precision of the answer.

Without a defined product or offer, one order value for a job-role profile generally cannot be established responsibly. Describe available budget context or an order of magnitude separately and leave measured average order value unknown.

Fictional worked example — no customer data, benchmark or pricing advice. We examine one product category from one supplier for January through March 2026. All known amounts are after discounts, excluding VAT and shipping; these example orders have no returns. We count unique paid, non-cancelled orders.

For the four orders with known amounts from A and B:

(€100 + €100 + €100 + €900) ÷ 4 = €300 per order.

The unweighted mean of the two customer averages is (€100 + €900) ÷ 2 = €500. That answers the question about the mean of customer averages, not the average across all four orders. The median of the four known order amounts is €100. The single large order makes the arithmetic mean higher than that median.

C does not add a zero-euro order. D does have a relevant order, but its amount is missing. A complete average for all five orders therefore cannot yet be calculated. The €300 result applies only to the four complete orders.

An appropriate profile sentence reads: ‘In the fictional quarter, four complete orders average €300 excluding VAT, with a median of €100; one other relevant order has a missing amount.’

An associative target group is a bounded group of people whose similar associations guide decisions within a specific choice context.

Order value describes a monetary amount attached to behaviour; it does not explain the association pattern. Two identical order amounts may concern different quantities, terms or needs. A high order value alone does not indicate that someone seeks prestige or has little price sensitivity.

Use amounts as context for questions about a specific choice: which associations with price, certainty, quality or risk played a part? These are research directions, not established group characteristics. Also investigate the offer and alternatives available to the participant at the time.

Do not infer BIS, BAS or delay-discounting scores from an order average. Amount, motivation, willingness to pay and time preference remain separate questions.

• Copying M without its definition. Check whether the source field contains total spending, average transaction value or a score.
• Selecting numerator and denominator differently. Use amounts and counts from the same orders.
• Averaging customer means without weighting for an order question. State whether orders or customers determine the weighting.
• Storing budgets or quotations as purchase data. Preserve the amount’s status.
• Treating missing as zero. Report missing amounts and the result’s coverage.
• Removing genuine large orders because they raise the average. Investigate errors and report the distribution; justify exclusions.
• Presenting higher order value as higher profit or willingness to pay. Those conclusions need their own data and analysis.

With order data:

‘Within [category, supplier and period], [number] complete orders average [amount and currency], under [VAT and adjustment rule], with [distribution measure] and [coverage limitation].’

Without order data:

‘Average order value is [unknown/not calculable]; available information is [reported amount, budget or range] for [context], from [source], without treating it as a measured order average.’

Retain source, order selection, monetary definition, cut-off date and any M-score version. Dutch, English and German versions may share the same euro example; translation does not change amounts or currency. For actual international data, document the conversion rule and exchange-rate date rather than silently adding different currencies.

Average order value becomes useful when the included orders, amounts and adjustments are clear. Keep the order average separate from customer averages, total spending and budget. This adds monetary context to the profile without presenting a historical amount as an explanation or a maximum for future choices.