Linear Attribution

Linear attribution splits the credit for a conversion equally among every touchpoint in the path. A customer who clicks a search ad, a social ad, an email, and a retargeting ad before buying gives each one 25 percent of the sale. It is the plain-arithmetic model: no touch matters more than any other, and no touch is ignored. Analysts use it to see which channels take part in journeys, especially long ones, without claiming that any step was decisive. In a report, a linear view often makes mid-funnel channels such as email and content look bigger than they do under last click. You will meet it in many marketing analytics tools and spreadsheets, even though the big ad platforms have dropped it from their own selectable models.

The calculation is simple. Count the qualifying touchpoints in the lookback window, divide the conversion value by that number, and assign each touch its share. A path with two touches gives 50 percent each, and a path with ten gives 10 percent each. It makes no use of timing, order, or the kind of channel. That is a strength, because it needs no assumptions about what matters, and a weakness, because it also learns nothing. A reminder click minutes before purchase gets the same share as the ad that first introduced the brand. It does not care about order, so it cannot tell you whether a channel introduces or closes. It only tells you how often a channel shows up in paths that convert.

Check availability first. Google announced that, starting in June 2023, it would stop letting advertisers pick linear for new conversion actions and, starting in September 2023, would switch conversion actions still using it to data-driven attribution, with last click as the alternative. GA4 stopped offering linear, first click, time decay, and position-based in November 2023. So if you want linear credit today, you need another analytics tool, a customer data platform, or a spreadsheet built from exported path data. Platform ad reports will not give it to you, and Meta's reporting uses its own click and view windows rather than any path-splitting model. Where you do have path data, sort the report by number of touches. Long paths are where linear and last click disagree most, and where the choice of model changes budget decisions.

The common mistake is reading equal credit as equal influence. Linear gives each touch the same share because it has no evidence to do otherwise, not because the touches did the same amount of work. Another is using it in accounts with very short paths, where it collapses into last click anyway. A third is counting repeated touches from the same channel: if a retargeting campaign fires five times, linear gives it five shares, which can inflate whichever channel shows the most frequent ads. Paths in which one channel repeats a lot, like retargeting, will push that channel's share up under linear. Look at unique touches per channel if you want a fairer comparison. A good use is as a tiebreaker.

Use linear as a neutral reference point. If a channel gets far more credit under linear than under last click, it takes part in many paths without closing them, and cutting it deserves a test before a decision. If the opposite happens, it closes more than it opens. This week, if you have path data, export your twenty most common conversion paths, count touches per channel, and see which appears most often in paths longer than three steps. Those channels are your assist players, and they deserve a holdout test. Treat the result as a question to test, not an answer: a channel with high linear credit and low last-click credit is a candidate for a holdout, not an automatic budget increase.