Linear Attribution: every touchpoint gets equal credit.

What linear attribution is

Linear attribution divides conversion credit equally across every touchpoint in the customer journey. Five touches before a conversion means each touch gets 20 percent of the credit. Three touches means 33 percent each. The model is the simplest multi-touch attribution rule and the most agnostic — it makes no assumption about which stage of the journey matters more.

The model exists as a counterweight to first-touch and last-touch, both of which assign 100 percent to one endpoint. Linear says: every touch matters, weighted equally. The trade-off is that the assumption is rarely true in practice — some touches matter much more than others — but the model produces a more balanced view than the single-endpoint alternatives.

How it works

Track every touchpoint in the customer journey within the lookback window. Count the touches. Divide conversion value by the count. Assign that share to each touch's channel. Sum across the period to get channel performance.

The output is a channel-by-channel attribution number that gives every channel a share proportional to how often it appears in conversion paths. Top-of-funnel channels with many appearances get more credit than they would under last-touch. Closing channels get less than they would under last-touch but more than under first-touch.

When linear attribution fits

Linear is the right model when you have no strong opinion about which funnel stage matters more, and you want a balanced view across all channels. It is also useful as a sanity check against first-touch and last-touch — if linear and last-touch agree, the journey is short. If linear and last-touch disagree, the multi-touch stages matter.

Linear is the wrong model when you have a clear differentiation between awareness and closing stages, because it under-weights both endpoints. It is also wrong for direct-response campaigns where the closing channel really does deserve disproportionate credit.

Common failure modes

Two failures appear most often. Treating linear as the right answer when it is just an unopinionated default. Comparing linear attribution to last-touch without recognizing they answer different questions.

The unopinionated default. Teams pick linear because it does not commit to a position about which stage matters. The result is a model that is no one's right answer but everyone's reasonable default. Use it as a sanity check, not as the primary decision lens.

Comparing without context. A channel with high linear credit and low last-touch credit appears in many journeys but does not close them. That can be valuable (high-funnel awareness) or wasteful (the channel shows up everywhere but does no work). The interpretation depends on the channel type and the brand stage.

Quick answers

What is linear attribution?

Divide conversion credit equally across every touchpoint in the journey. Five touches = 20 percent credit each.

Why use linear?

It is the most agnostic multi-touch model. Useful as a balanced counterweight to first-touch and last-touch.

When is linear wrong?

When some stages of the journey really do matter more than others. Linear assumes equal importance, which is rarely true.

Should I use linear as my only model?

No. Use it alongside first-touch, last-touch, and data-driven to triangulate.

How is linear different from data-driven?

Linear uses a fixed equal-credit rule. Data-driven uses observed user paths to estimate each channel's contribution probabilistically.

Does linear survive iOS ATT?

Same as other multi-touch models. Aggregate-level linear works with first-party data; browser-cookie linear is degraded.

Frequently asked

What is linear attribution?

An attribution model that divides conversion credit equally across every marketing touchpoint in the customer journey. Five touches = 20 percent each; three touches = 33 percent each. The simplest multi-touch model.

How is linear different from last-touch?

Last-touch gives 100 percent to the final touch; linear divides credit equally across all touches. Linear over-credits middle-of-funnel channels and under-credits closing channels relative to last-touch.

When is linear the right model?

When you have no strong opinion about which stage matters more and want a balanced view. Useful as a sanity check against first-touch and last-touch — disagreement between them and linear is signal.

When is linear the wrong model?

When there is meaningful differentiation between awareness and closing stages of the funnel. Linear under-weights both endpoints. Also wrong for direct-response campaigns where the closing channel deserves more credit.

What is the lookback window for linear?

Same as other multi-touch models. 30 days for digital, 90 days for B2B with longer consideration. Pick the window that matches real customer journey length.

Is linear better than position-based?

Position-based (40-20-40) is generally preferred over pure linear because it acknowledges that endpoints often matter more than middles. Linear remains useful as a balanced reference.

Can I use linear with iOS ATT?

Aggregate-level linear works fine with first-party data. The browser-cookie implementation is degraded since 2021 because cross-site tracking restrictions limit visibility of multi-touch paths.

How is linear different from data-driven attribution?

Linear uses a fixed rule (equal credit). Data-driven uses observed paths and probabilistic modeling to estimate each channel's incremental contribution. Data-driven is closer to causal but requires more data.