What Linear Attribution Is
Linear attribution is the simplest multi-touch model. A user hits your site four times — via a LinkedIn Ad, a Google search, a newsletter link, and a direct visit — then converts. Linear gives each of those four touchpoints 25% of the conversion credit.
Visually:
LinkedIn Ad → Google → Newsletter → Direct → Conversion worth $1,000
Each touchpoint gets $250 of attributed revenue.
Compare to:
- First-touch: LinkedIn Ad gets $1,000. Everything else gets $0.
- Last-touch: Direct gets $1,000. Everything else gets $0.
- Time decay: Direct gets 40%, Newsletter 30%, Google 20%, LinkedIn 10%.
- U-shaped: LinkedIn gets 40%, Direct gets 40%, middle touches get 10% each.
Linear s appeal is its honesty about uncertainty. It does not claim to know which channel really converted the user. It just says: they all played a role, so they all get equal credit.
When Linear Is the Right Model
Linear works well when three conditions are roughly true:
- Your funnel is short. 2–5 touches total over days or weeks, not months.
- Touchpoints are comparable in cost. You are not mixing $50 LinkedIn clicks with $0.25 Facebook clicks.
- You genuinely do not know which touchpoint matters most. You have no strong prior based on customer interviews or past data.
Concrete scenarios where linear is the honest choice:
- A mid-ticket info product (course, membership) sold through a 7–14 day funnel across email + paid + organic social.
- A services business where most deals follow a similar pattern of research, referral, call, close.
- An early-stage SaaS with too few deals to derive data-driven attribution responsibly.
When Linear Is Dangerously Wrong
Long B2B cycles with dominant touchpoints
A user visits your site 28 times over 4 months before booking a demo. The first visit came from LinkedIn Ad A. The final two visits were direct (typing in URL). Linear attribution gives LinkedIn Ad A the same 1/30th credit as each of those direct visits. But directionally, the LinkedIn Ad actually created the user; the direct visits are just the same user coming back. Linear systematically under-credits demand creation and over-credits demand capture.
When you have lots of touches from the same channel
If a user visits via 10 organic search sessions and 1 paid LinkedIn Ad before converting, linear gives organic search 10/11 of the credit. That might look right. But if the LinkedIn Ad is what caused them to start Googling your brand in the first place, you are robbing paid of the credit it deserves. This is extremely common in branded search — paid demand gen creates brand searches that then get credited to organic or direct.
When some touchpoints are much cheaper than others
Retargeting ads cost pennies; cold LinkedIn Ads cost $50+ per click. If a user has 10 retargeting touches + 1 cold click, linear gives 10x more credit to the cheap touches — and you will conclude cold is underperforming. You will cut cold spend. Pipeline will dry up 60 days later. This is one of the most common and painful attribution mistakes B2B SaaS makes.
When cold audiences dominate your pipeline
If 80% of your deals come from people who had never heard of you before a specific ad, linear hides that truth by dispersing credit across subsequent touches (all of which would not exist without the first cold touch).
Alternatives to Consider
| Your Funnel | Better Model | Why |
|---|---|---|
| Long B2B, cold matters most | U-shaped (40/20/40) or First-Touch-heavy | Rewards both demand creation and closing touch. |
| Long B2B, sales does the work | Time decay | Weights later touches higher; matches sales-led motion. |
| Short, simple funnels | Linear is fine | Low complexity = low risk of systematic bias. |
| High data volume (1000+ deals/month) | Data-driven (GA4 or HockeyStack) | Algorithmic weighting based on your actual data. |
| Mixed media mix, enterprise scale | Media Mix Modeling (Northbeam, LiftEdge) | Moves beyond single-user attribution to holistic channel incrementality. |
See my full ranking of 7 attribution models for when each one actually works.
How to Implement Linear in GA4
GA4 supports multiple attribution models natively. To see linear-attributed data:
- Open GA4 → Advertising → Attribution → Model comparison.
- Select Linear as one of the models in the comparison.
- Compare it to Data-Driven (GA4 default) and Last Click.
- If linear and data-driven produce very different results, you have a funnel shape that is not a good fit for linear — use the insight to pick a better model.
The most valuable thing you can do with linear attribution is not adopt it as your primary model, but use it as a sanity check. Compare what your primary model says to what linear says. When they diverge dramatically, investigate why — that is where the real attribution learning happens.