What is revenue attribution?

Revenue attribution is connecting the revenue from paying customers to the channels, pages or campaigns that touched them before they bought, using a model that decides how the credit is split between those touches.

In plain terms

It needs a chain you can follow: visit, signup, account, payment. Analytics knows the visit. Your product knows the signup. Billing knows the payment. Attribution starts once those three are joined.

Then a model splits the credit. The same revenue can land on different pages depending on the model you pick. HubSpot's content revenue report, for example, has a model dropdown that changes how revenue is calculated.

The assumptions behind each model

Model	Assumes	Tends to favor
Last click	The final touch before buying did the work	Pricing pages, brand searches, comparison pages
First touch	Discovery did the work	Blog posts and other early reading
Rule-based multi-touch	Every touch shares credit by a fixed rule	Whatever the rule weights
Data-driven	Patterns in converting and non-converting paths reveal each touch's contribution	Touches the model estimates contributed, within the available data

Google Analytics now offers three models in its attribution reports: data-driven, paid and organic last click, and Google paid channels last click. First click, linear, time decay and position-based were removed in November 2023. Its models also give direct visits no credit unless the whole path was direct.

Every model shares one limit: it only sees tracked touches. An AI answer read without a click, or a colleague's recommendation, never enters the path. That's where self-reported attribution helps.

Attributed is not incremental

Attributed revenue is revenue a channel touched. Incremental revenue is revenue that would not have happened without it. The gap can be large.

In large field experiments at eBay, Blake, Nosko and Tadelis found that returns from paid search were a fraction of conventional non-experimental estimates, and that brand-keyword ads had no measurable short-term benefit. Their explanation: search clicks and purchases are correlated, so many people who clicked an ad would have bought anyway.

Why it matters for a SaaS founder

The decision it informs is where next month's effort goes. Use attribution to compare pages and channels against each other, not to claim an exact return. If you close a few dozen customers a month, a spreadsheet that joins signup source to billing is enough. I show that report in is SEO bringing paying customers.

Example

Ledgerline is an invented 35-person billing API. Last click gives most new revenue to its pricing page. First touch gives most of it to one blog post about usage-based billing. Both are correct under their own assumption. Neither shows how many of those customers would have found Ledgerline anyway.

Frequently asked questions

Which attribution model should a small SaaS use?

Pick one, write down what it assumes, and keep it stable so months compare. Look at first touch next to last click when you judge early-reading content.

What is the difference between attributed and incremental revenue?

Attributed revenue had a tracked touch from the channel. Incremental revenue exists only because of the channel. Measuring the second needs an experiment or a holdout, not a report.

Can I attribute revenue to ChatGPT?

Partly. Visits with a recognized ChatGPT referrer appear in GA4's AI Assistant channel and can be joined to signups and billing. A buyer who read an answer and arrived later another way may report that discovery if you ask; the visit alone won't identify it.