Linear Attribution
AnalyticsAlso: Equal Credit Attribution · Uniform Attribution
Quick definition
Linear attribution is a model that divides conversion credit equally across every marketing touchpoint in the customer journey. If a customer saw four ads before buying, each ad receives 25% of the credit. It acknowledges the full path without making any judgment about which touch mattered most.
A customer touches all four, then converts.
Credit distributed
Linear splits credit evenly across all four touches. Fair on paper, but treats a top-of-funnel impression the same as the closing search click.
How it varies across Australia
Linear attribution is rarely used as a primary reporting model in Australian businesses, but it appears often as a comparison model alongside last-click or data-driven attribution. Its value is mostly diagnostic rather than operational.
See data and tracking patterns across Australian industries →What it actually means
Linear attribution solves the attribution problem by refusing to solve it. Instead of deciding which touchpoint deserves the most credit, it declares all touchpoints equal and moves on.
In practice: a customer clicks a display ad, then an email, then a paid search ad, then arrives direct and buys. Linear attribution hands 25% of the conversion credit to each of those four touches. No winner, no loser.
That sounds fair. It is fair. The problem is that fairness and accuracy are different things. Most customer journeys are not equal-influence events. The display ad that ran six weeks ago probably did less to cause the purchase than the branded search click on the morning the customer converted. Treating both equally tells the media planner that display is performing well. It might not be.
Where linear attribution earns its place is in the comparison view. Running linear alongside last-click and time-decay attribution shows you how different models shift credit across channels. When all three models agree a channel is working, it probably is. When they disagree sharply, that disagreement is the signal worth investigating.
The attribution model that is most often confused with linear is time-decay attribution, which also spreads credit across all touches but weights recent ones more heavily. The distinction matters when you are evaluating upper-funnel channels like display and social video, which tend to look much worse under time-decay than under linear.
Linear attribution is the fairest model and possibly the least useful one. Fairness and usefulness are not the same thing.
How it shows up
Linear attribution shows up in Google Analytics 4 as a comparison model in the attribution settings, and in most paid platforms as a secondary view alongside their default model. In practice, marketers use it to audit whether their primary model is giving wildly different credit distributions. When linear and data-driven attribution show similar channel rankings, the conclusions are more defensible. When they diverge, it usually means certain channels are doing invisible work that only some models can see.
The Australian context
Australian advertisers running across a smaller number of channels than typical US counterparts sometimes find linear attribution less distorting than it would otherwise be. With three or four active channels rather than eight, the equal-split problem is smaller. That said, the same principle applies: the model does not reward closing intent differently from early awareness, and that gap matters regardless of market size.
Where people get this wrong
Related terms
Common questions
When should I use linear attribution?
Use it as a comparison model, not a primary one. It is most useful when you want to audit whether your default model is systematically under-crediting certain channels. Running linear alongside last-click or data-driven attribution surfaces those gaps quickly.
How is linear attribution different from time-decay attribution?
Both spread credit across all touchpoints, but time-decay weights recent touches more heavily than older ones. Linear treats a display ad from six weeks ago the same as the search click from this morning. Time-decay does not. For short sales cycles, time-decay is usually more realistic.
Does Google Analytics 4 support linear attribution?
Yes. Google Analytics 4 allows you to compare attribution models in the Advertising section. Linear is available as a comparison model, though data-driven attribution is the default. You can switch the reporting model without changing your conversion tracking setup.
Why do agencies sometimes prefer linear attribution?
Because it tends to credit every channel with something, which is convenient when you manage multiple channels. That is not a good reason to use it. Pick the model that most accurately reflects how your customers actually convert, not the one that makes every line item look useful.
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About New Rebellion
New Rebellion is a marketing intelligence consultancy. We build tools, score Australian businesses on how their marketing actually performs, and publish Debrief every day. This dictionary is part of how we work in the open.
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