What Is Linear Attribution?
Linear attribution is a rule-based marketing attribution model that divides conversion credit equally among all eligible, recorded touchpoints in a customer’s conversion path. It is a form of multi-touch attribution that gives earlier and middle interactions credit alongside the final interaction.
Equal credit is a modeling assumption. It does not mean every interaction had the same influence on the customer’s decision.
How Does Linear Attribution Work?
The model identifies the touchpoints eligible for credit within the attribution window, then divides one conversion equally among them. If revenue is being attributed, the same proportions apply to the conversion’s value.
Credit per touchpoint = 1 ÷ number of eligible touchpoints
For example, suppose a customer completes four eligible interactions before making a $1,000 purchase:
| Recorded touchpoint | Conversion credit | Attributed revenue |
| Paid social ad click | 25% | $250 |
| Organic search visit | 25% | $250 |
| Email click | 25% | $250 |
| Paid search ad click | 25% | $250 |
Each touchpoint receives 0.25 of the conversion, and total credit remains one conversion. The model distributes the recorded purchase value; it does not create additional revenue.
Eligibility rules matter because tools may handle repeated interactions, direct traffic, and different interaction types differently. In a model that counts individual touchpoints, a channel appearing twice can receive two shares, so equal touchpoint credit does not necessarily mean equal channel credit.
When Is Linear Attribution Useful, and What Are Its Limits?
Linear attribution provides a transparent baseline for reviewing journeys with multiple interactions, such as ecommerce purchases or longer B2B buying cycles. It helps marketers see activity that would receive no credit under a model focused only on the final touchpoint.
Its main limitation is that it treats all eligible interactions equally, regardless of timing, engagement, or role. A brief return visit can receive the same share as an interaction that introduced the brand or resolved a purchase concern.
Results also depend on tracking coverage and the attribution window. Missing cross-device, offline, or consent-restricted interactions can leave an incomplete path, while a shorter window can exclude earlier activity and redistribute credit among the remaining touchpoints.
A common mistake is to treat attributed revenue as proof of incremental impact or as an automatic budget recommendation. Linear attribution describes how credit is allocated across recorded interactions; it cannot establish which conversions would have happened without the marketing activity.