Linear attribution is a model that shares the credit for a conversion equally between every touchpoint in the customer’s journey. If someone clicked a Facebook ad, then an email, then a Google ad before buying, each of the three receives a third of the sale.
How linear attribution works
The tool lists every recorded touchpoint within its lookback period and divides the conversion by the number of them. A £600 order with four touchpoints gives each £150; an enquiry with two gives each half an enquiry. Because it is a fixed rule, anyone can recalculate it in a spreadsheet and get the same answer, which is a large part of its appeal.
The result depends heavily on what counts as a touchpoint. Some tools count only clicks; others include ad impressions or email opens. Repeat visits from the same channel usually count separately, so three organic visits and one paid social click give organic search three quarters of the credit.
At the time of writing (October 2026), you cannot choose linear attribution in Google Ads or GA4. Google removed it, along with first click, time decay and position-based, from both products during 2023, leaving only last-click attribution and data-driven. You will still find it in some CRMs and dedicated attribution tools, and you can rebuild it yourself from journey data, for example from GA4’s BigQuery export.
Why it matters
Linear sits between the extremes of first click and last click. For a UK business with a long, many-step buying process, such as a B2B supplier whose buyers attend a webinar, read case studies, search for comparisons and respond to an email before asking for a quote, it shows which channels play a part at all. Channels that last click scores at zero suddenly appear.
It is also easy to explain to a finance director, which matters when a marketing budget has to be defended.
But equal credit is an assumption, not a finding. A glance at a banner gets the same share as the demo request that settled the decision. And channels that touch people often, such as newsletters and remarketing, collect a large share simply through frequency. Linear can make a busy channel look important without showing that it changed anyone’s mind.
Common mistakes
- Reading equal credit as evidence that every channel matters equally.
- Counting impressions and email opens as touchpoints, so the most frequent channels dominate the results.
- Comparing linear figures from a CRM with last-click figures from Google Ads as if they measured the same thing.
- Expecting ad platforms to bid on linear figures. Google Ads and Meta optimise using their own models, whatever your report says.
- Forgetting the touchpoints no tool can see: word of mouth, offline adverts and visits from people who declined cookies.
How to act on it
Use linear as a diagnostic rather than a verdict. Run it alongside last click and first click: a channel that scores well under linear but poorly under last click is probably contributing early or mid-journey, and deserves a test before any cut. Compare it with data-driven attribution too, which tries to estimate each touchpoint’s real effect rather than assuming it.
Decide what counts as a touchpoint before you start (clicks only is the safest default), keep one model in your regular reports and note the date if you ever change it. For decisions involving serious money, confirm what the model suggests with a holdout or regional test.
Judging every channel against one shared target, rather than against each platform’s own claims, is how I approach performance marketing.