What RPV is and how to compute it
Revenue per visitor is the average revenue from one unique visitor over a period:
RPV = revenue / unique visitors
If a store took 3,000,000 in revenue from 100,000 unique visitors in a week:
RPV = 3,000,000 / 100,000 = 30 per visitor
RPV is not what each visitor pays. It is an average across everyone: most buy nothing, a minority
produce the entire revenue.
RPV as the product of CR and AOV
RPV decomposes into two components:
RPV = CR × AOV
At a 2% conversion rate and an order value of 1,500: RPV = 0.02 × 1,500 = 30.
That matters for reading A/B tests: RPV can grow through conversion, through order value, or
through both. Watching one component while ignoring the other risks the wrong conclusion.
Why RPV is the best A/B test metric
E-commerce tests routinely face the question of which primary metric to use — conversion, order
value or revenue. RPV settles it:
| Scenario | CR | AOV | RPV | The right conclusion |
|---|---|---|---|---|
| Conversion up, order value flat | ↑ | → | ↑ | A win |
| Conversion down, order value up | ↓ | ↑ | ↑ | A win, invisible in CR |
| Conversion up, order value down | ↑ | ↓ | → or ↓ | Ambiguous |
| Nothing moves | → | → | → | A draw |
Tip: use RPV as the primary metric and conversion and order value as secondary ones that
explain the mechanism. If RPV rose only through order value while conversion fell, check whether
upper-funnel behaviour degraded.
RPV by segment
An aggregate RPV hides differences between segments. When RPV rises in a test, it is worth checking:
- Did it rise equally on mobile and desktop?
- Does the effect differ between new and returning shoppers?
- Are there categories where the change did the opposite?
Breaking RPV down by segment is a standard step of post-test analysis.
RPV and personalization
Personalized recommendations move both components of RPV:
- Conversion rises because the shopper finds a relevant product faster
- Order value rises through cross-sell and upsell on relevant items
The combined effect on RPV is multiplicative, which is why well-designed personalization experiments
tend to show a strong return.