What Is Linear Attribution?
Linear attribution is one of the five classic multi-touch attribution models available in web analytics platforms. Unlike first-touch attribution, which credits only the initial touchpoint, or last-touch attribution, which credits only the final touchpoint, linear attribution takes a democratic approach. It records every channel a customer visited before converting and divides the credit equally among all of them.
The maths is straightforward.
If a customer first arrived from an organic search result, then returned via a Facebook ad, then came back after clicking an email newsletter link, and finally converted after clicking a Google retargeting ad, linear attribution assigns 25% credit to each of those four channels.
The model makes no judgment about which touchpoint was more important. This neutrality is both its strength and its limitation.
The practical value of linear attribution is that it forces a more complete view of the customer journey compared to single-touch models.
Marketing teams that switch from last-touch to linear attribution typically discover that their SEO programme, content marketing, and paid social awareness campaigns contribute far more to revenue than last-touch data suggested.
Channels that operate in the middle of the funnel, such as display retargeting and email newsletters, often receive appropriately sized credit under linear attribution rather than being ignored entirely or over-credited.
Linear attribution is available in Google Analytics 4 through the Attribution settings in Admin, where you can apply different models to the same conversion data and compare the results side by side. This comparison approach is more valuable than committing to any single model, because each model highlights different aspects of campaign performance that a single view would miss.
Linear Attribution In Practice
A Johannesburg-based financial services brand runs a five-touch customer journey: a LinkedIn ad introduces the brand, an organic blog post answers a key question, an email nurture sequence builds trust, a display retargeting ad reminds the prospect, and a branded Google search completes the purchase.
Under last-touch, Google Ads gets 100% of the credit. Under linear attribution, each channel receives 20%, which prompts the marketing team to evaluate each channel's actual contribution and cost per assisted conversion rather than simply optimising the Google Ads account in isolation.
For South African B2B marketers, linear attribution often reveals that LinkedIn and content marketing are dramatically under-credited in last-touch reports. These channels rarely close deals directly but frequently start journeys that Google Ads and email later complete.
Linear attribution makes those upstream contributions visible, which leads to better-informed decisions about where to allocate the rand budget across a full-funnel campaign strategy. Running linear attribution alongside the default data-driven model in GA4 is a useful sense-check before making significant channel budget shifts.
Credit per channel = 100% divided by number of touchpoints
4 touchpoints = 25% credit each | 5 touchpoints = 20% credit each
How linear attribution works
Linear attribution is an attribution model that distributes credit for a conversion equally across all the touchpoints in the customer's journey. If a customer interacted with an ad, then a search result, then an email, then a return visit before converting, linear attribution gives each of those four touchpoints an equal share of the credit, a quarter each in this case. This contrasts with single-touch models that credit only the first or last interaction, and with weighted models that give some touchpoints more credit than others. The appeal of linear attribution is that it recognises the whole journey, crediting every interaction that contributed rather than ignoring all but one, which gives a fuller, more balanced picture than last-click attribution. Its simplicity, equal credit to all, makes it easy to understand and apply, while still acknowledging that conversions usually result from several touches rather than a single decisive one.
When to use linear attribution
Linear attribution suits situations where you want to recognise every touchpoint's contribution and value the full journey, without the complexity of deciding how much more one interaction deserves than another. It is a reasonable default when you believe each touch genuinely plays a part and no stage clearly dominates, and it is a marked improvement on last-click attribution for understanding the channels that assist conversions but rarely close them, such as awareness activity, which last-click ignores. Its limitation is that equal credit is a simplification: in reality some touchpoints matter more than others, a decisive final interaction or a pivotal first introduction may deserve more credit than a minor middle touch, which linear attribution cannot reflect. More sophisticated models, such as position-based attribution, which weights the first and last touches, or data-driven attribution, which uses actual data to assign credit, address this. Linear attribution is best seen as a balanced, simple model that fairly acknowledges the whole journey, appropriate when you want that even-handed view and the extra precision of weighted models is not needed or feasible.
FAQ
When is linear attribution the right choice?
Linear attribution suits businesses with long, multi-touch sales cycles where awareness, nurture, and closing all play roughly equal roles. It is a good starting point for businesses that want to move away from single-touch models without the data requirements of data-driven attribution, and it removes the bias that comes from crediting only the first or last channel.
What is the limitation of linear attribution compared to data-driven attribution?
Linear attribution assumes every touchpoint contributes equally, which is rarely true in practice. A brand awareness display ad and a high-intent branded search click are unlikely to be equally responsible for a conversion. Data-driven attribution uses statistical analysis of actual conversion paths to assign credit more accurately, but requires larger data volumes to be reliable.
What is the limitation of linear attribution?
Equal credit is a simplification: in reality some touchpoints matter more than others, a decisive final interaction or pivotal first introduction may deserve more than a minor middle touch, which linear attribution cannot reflect. Models like position-based or data-driven attribution address this by weighting touches differently based on importance or actual data.