Revenue per visitor (RPV)
Revenue per visitor (RPV) is total revenue divided by the number of visitors over a period. Because it combines conversion rate and average order value, it captures trade-offs a single metric hides — a change that lifts conversions but cuts order value may leave RPV flat. It is a common overall evaluation criterion in commerce experiments. This page defines RPV and its caveats.
What RPV combines
RPV equals total revenue divided by visitors. Algebraically it is the conversion rate multiplied by the average order value, so it folds two levers into one number. That is its strength as an experiment metric: it resists the trap of optimising conversion rate in isolation while order value erodes.
- RPV = total revenue / visitors
- Equivalent to conversion rate × average order value
- Captures the conversion/order-value trade-off
Why it can be noisy
Revenue is often skewed — a few large orders dominate — so RPV has higher variance than a conversion rate, meaning experiments on RPV typically need more data to reach significance. Outlier orders can swing the average; capping or winsorising extreme values is a common, disclosed practice to stabilise the estimate.
RPV versus per-converter value
RPV is over all visitors, not just buyers. Keep it distinct from average order value, which is per purchase. A change can raise per-buyer order value while lowering RPV if it converts fewer people overall — which is exactly the kind of trade RPV is designed to expose.
How it appears in analytics and logs
RPV moving differently from conversion rate tells you order value shifted. A conversion win with flat RPV means you converted cheaper baskets, not more value.
Diagnostic use case
Use RPV as the overall success metric in commerce tests so you don't reward a change that raises conversion rate while quietly shrinking order value.
What WebmasterID can help detect
WebmasterID's first-party value events combined with visitor counts let you compute RPV per variant without third-party cookies or cross-site tracking.
Common mistakes
- Confusing RPV (per visitor) with average order value (per order).
- Reading RPV significance without accounting for revenue skew.
- Letting a few outlier orders drive an undisclosed RPV swing.
Privacy and accuracy notes
RPV is an aggregate ratio of revenue to visitor count. It needs no personal identifiers — only totals over a defined period and audience.
Related pages
- Value per visitor for non-purchases
Value per visitor generalises revenue per visitor to sites without direct sales: you assign an estimated value to each goal (a lead, a signup, a download) and divide total assigned value by visitors. It makes mixed conversion goals comparable, but the result is only as honest as the values you assign. This page explains the method and the disclosure it demands.
- Average order value (AOV)
Average order value (AOV) is total revenue divided by the number of orders. It is simple but easy to misread: a few large orders pull the mean upward, refunds and taxes change what 'revenue' means, and mixing currencies without conversion corrupts it. For skewed order sizes, the median order value is often more honest.
- Conversion rate: definition and denominators
Conversion rate is the share of some base that converted. The trap is the denominator: conversions per session, per user, and per unique visitor give different numbers and mean different things. Without stating the base, a conversion rate is ambiguous — and comparing rates with different bases is meaningless.
- North star metric
A north star metric is the one measure a team chooses to represent the core value it delivers, used to align decisions. Its value is focus: a single shared metric stops teams optimising in different directions. Its risk is tunnel vision — any single metric can be gamed, so it needs guardrail metrics around it and a clear link to real value.
Sources and verification notes
- Wikipedia — Conversion marketingRevenue and conversion relationship; no benchmarks cited.
Last reviewed 2026-06-24. Facts are checked against primary/official sources where available; uncertain specifics are marked “Data not yet verified” rather than guessed.