Linear attribution: equal credit to every touch
Linear attribution divides a conversion's credit equally among all touchpoints in the path. It is the simplest multi-touch model: every touch matters the same. That even-handedness avoids the single-touch extremes, but it also pretends a fleeting impression and a decisive demo are worth the same — which is rarely true.
What this means
Linear takes the full conversion credit and divides it by the number of touchpoints. A four-touch path gives each touch 25%. Unlike first- or last-click, no single touch dominates, so assist channels finally appear.
The flaw inside the fairness
Equal credit is a strong, usually wrong, assumption. Real journeys have decisive moments and incidental ones; linear treats them identically. It also rewards channels that simply appear often — a retargeting impression on every path inflates its linear credit without proving influence.
Linear is a reasonable neutral default for showing multi-touch reality, but it should not be read as a measurement of each channel's true contribution.
- Credit = total divided by number of touches
- Surfaces assist channels single-touch models hide
- Assumes every touch is equally important
How it appears in analytics and logs
Linear output reflects how often a channel appears in paths, not how decisive it was. A channel present in many journeys accumulates credit even if it rarely changed the outcome.
Diagnostic use case
Use linear when you want every touchpoint represented without taking a position on which mattered most, while knowing equal weighting is an assumption, not a finding.
What WebmasterID can help detect
WebmasterID can show even-weighted paths alongside other lenses, with confidence labels, so linear's equal-credit assumption stays visible rather than hidden.
Common mistakes
- Reading equal credit as proof of equal influence.
- Letting frequently-appearing channels look strong by repetition.
- Using linear as the final word on channel value.
Privacy and accuracy notes
Linear attribution only needs the ordered touchpoints of one site's own visitors. No cross-site identity is required to split credit evenly.
Related pages
- Time-decay attribution: recent touches weigh more
Time-decay attribution weights touchpoints by recency: the closer a touch is to the conversion, the more credit it earns, usually following an exponential decay with a configurable half-life. It is a compromise between last-click and linear, but its recency bias under-credits the early demand-creating touches.
- Position-based (U-shaped) attribution
Position-based (U-shaped) attribution gives most credit to the first and last touchpoints — commonly 40% each — and shares the remaining 20% among middle touches. It tries to honour both discovery and closing while still acknowledging the middle. The specific weights are a convention, not a measured truth.
- Data-driven attribution: promise and caveats
Data-driven attribution (DDA) assigns credit using a model trained on a site's own conversion paths rather than a fixed rule like last-click. Done well it credits assist touches more fairly. Its caveats are real: it needs enough conversion volume, it is a model not a measurement, and it cannot see touches that were never tracked.
- Attribution analytics
Multi-touch paths, honestly labelled.
Sources and verification notes
Last reviewed 2026-06-24. Facts are checked against primary/official sources where available; uncertain specifics are marked “Data not yet verified” rather than guessed.