Linear attribution model
Distributes revenue credit equally across every tracked touchpoint in the buyer journey. The most balanced single-model view, though it treats a blog read and a demo request as equally valuable.
What is the linear attribution model?
The linear attribution model splits the total conversion credit evenly across every marketing touchpoint recorded in a buyer's journey. If a deal closed for $10,000 and the journey contained five tracked interactions, each interaction receives $2,000 of revenue credit, regardless of whether it was a first blog visit, a webinar registration, or the final sales call.
Linear attribution is often chosen as a "neutral" starting point because it avoids the extreme bias of single-touch models like first-touch and last-touch. It acknowledges that a buyer's decision involves multiple interactions rather than just one, which makes it a more accurate reflection of how most B2B purchases actually work.
The model's neutrality is also its principal limitation. Real journeys are not linear: some touchpoints genuinely move the deal forward and others are passive observations. Assigning identical weight to all of them obscures which interactions actually matter. Linear is most useful as a comparative baseline when running alongside weighted models.
How credit is distributed
Each touchpoint receives a weight of 1 / N, where N is the total number of touchpoints in the journey. A four-touch journey assigns 25% to each interaction. A ten-touch journey assigns 10% to each.
| Touchpoint | Position | Credit weight |
|---|---|---|
| Organic blog visit | 1st | 25% |
| LinkedIn ad click | 2nd | 25% |
| Webinar attendance | 3rd | 25% |
| Demo request | 4th (conversion) | 25% |
Compare with time-decay attribution, which applies an exponential weighting that increases as you approach the conversion date, or U-shaped attribution, which places structured milestones at 40/20/40 rather than equal weights throughout.
When to use linear attribution
Linear attribution is most appropriate when:
- You need a neutral baseline to compare against more opinionated models without introducing single-touch bias
- Your marketing team operates many channels that each contribute to the journey, and you want to stop internal arguments about which channel "owns" the deal
- You are early in your attribution maturity and want a model that does not require assumptions about which milestones are most important
Linear attribution works best as one panel in a multi-model view. On its own it produces very flat channel reports that are hard to act on. Comparing linear to W-shaped attribution immediately reveals which channels punch above or below their equal-weight allocation at key pipeline milestones.
Pros and cons
Pros
- Treats every touchpoint as a contributor, removing single-point bias
- Easy to explain: revenue credit is divided equally across all recorded interactions
- Works well for long journeys where many channels contribute meaningfully
- Reduces internal channel wars because no single team can claim all the credit
Cons
- ✕Assumes every touchpoint is equally valuable, which is rarely true
- ✕Can dilute the signal from high-impact interactions like demos or trials
- ✕Penalises campaigns with few touches even if those touches were decisive
- ✕Difficult to use for budget optimisation because it flattens meaningful differences
How AttriByte handles linear attribution
AttriByte computes linear attribution across the full identity-resolved event stream in your warehouse. Because identity stitching connects anonymous sessions to known accounts across devices, the linear model operates on a complete journey rather than the fragmented view you would get from session-based analytics tools.
The linear model in AttriByte sits side-by-side with all five others in every channel and campaign report. Because all models share the same underlying data, you can immediately see where linear diverges from time-decay or W-shaped and investigate why specific channels perform differently under different weighting assumptions.
Atlas AI, AttriByte's built-in analyst, can point directly to the channels where the linear-to-W-shaped gap is largest, which is typically where the most interesting budget reallocation opportunities live. Explore the full feature set on the product page.
Explore other models
Compare linear to five other attribution models
AttriByte runs all six models simultaneously on your warehouse data, no model lock-in required.