Revenue Attribution

Attributed Revenue · Revenue Credit

Definition

Revenue attribution is the method of assigning revenue to the initiatives or channels that led to a purchase; the basis for computing the return of individual features, widgets and campaigns.

The problem with simple attribution

The most common way to measure recommendation performance is to count the revenue from shoppers who
clicked the widget. It is convenient and wrong: among those shoppers were people who would have
bought anyway. The real added value is only the incremental part.

Important: attributed revenue with no control group is an upper bound on the effect, not the
effect. The gap can be substantial — particularly in popular categories, where the shopper would
have found the product regardless.

Attribution methods in e-commerce personalization

Click-based attribution

The simplest: credit all revenue from shoppers who clicked a recommendation within the attribution
window. It overstates the effect. Useful for operational monitoring, not for strategic decisions.

A/B attribution

The correct method: compare two groups, with and without the recommendation, and treat the revenue
difference as the incremental effect. It requires a configured test, and it gives a precise answer.

Group A (no recommendation): RPV = 4.80
Group B (with recommendation): RPV = 5.28
Incremental effect: +0.48 per visitor (+10%)

Holdout attribution

The long-run variant: a group of users is excluded from personalization for three to six months. The
difference in total revenue shows the cumulative effect of the whole programme rather than of one
widget.

Attribution window: how many days?

Initiative type	Recommended window
Onsite recommendations	7–14 days
Popup or banner	1–7 days
Email with recommendations	7–14 days
Push notification	1–3 days

The window choice moves the final number: 30 days inflates attributed revenue relative to 7. What
matters most is using the same window when comparing strategies.

Cannibalisation: the hidden threat

Cannibalisation is when a recommendation redistributes purchases inside a session without raising
total revenue. The shopper intended to buy product A, saw a recommendation for B and bought B
instead. The click is credited, and total revenue did not move.

An A/B test at the level of group revenue — revenue per visitor rather than revenue from clicks —
neutralises this automatically, because what was bought does not matter, only the total per group.

Frequently asked questions

Why is revenue after a recommendation click not attribution?

Because some of those purchases would have happened anyway — the shopper would have found the product regardless. Correct attribution measures the incremental effect: how much revenue the recommendation added beyond the counterfactual. That requires a control group.

What is an attribution window and how do you choose one?

The attribution window is the period during which a purchase is credited to an initiative. For e-commerce recommendations, 7–14 days after a click is the standard. Too short a window understates the effect; too long overstates it.

How does an A/B test solve attribution?

An A/B test compares two groups with equivalent traffic. The difference in total revenue between them is the incremental effect of the change, with no need to track individual clicks and orders. A holdout group extends the same principle to the whole personalization programme.

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