Linear attribution is a type of marketing attribution model that gives equal credit to each touchpoint across the customer journey.
In this guide, we’ll break down how linear attribution works, where it’s most useful, its tradeoffs, and how to apply the strategy so you can make smarter marketing decisions moving forward.
Linear attribution is a multi-touch attribution (MTA) model that helps brands understand how different marketing channels contribute to a conversion.
In this approach, every touchpoint in the customer journey receives equal credit. Because each interaction is weighted the same, linear attribution is also known as an even-weighted model.
Think of the path to purchase like a relay race: each touchpoint passes the baton forward until the customer reaches the finish line — AKA the sale. Linear attribution doesn’t try to guess which runner mattered most; it assumes every handoff played an equally important role.
Touchpoints can include anything from a social ad click to an email open to a direct website visit. Each one is treated as a meaningful step that helped move the customer closer to conversion.
Triple Whale provides two versions of linear attribution.
Elijah is shopping for a new pair of running shoes.
This total purchase order is $100.
In this scenario, each touch point earns equal credit for the sale – which is 15% of the value, or $15 each.
Linear attribution can be expressed through a simple formula:
Conversion value ÷ Number of touchpoints = Credit per touchpoint
Using the example above:
$100 ÷ 5 = $20 per touchpoint
Here, the conversion value could be the revenue generated from a purchase or another relevant metric you’re measuring (like a lead or subscription).
Linear attribution is a great middle ground model. It’s not as fixated as single-attribution, and not as complicated to run as other weighted or causal approaches. Here are the main benefits to consider.
Remember that viral video you saw shared by a friend that eventually led you to a purchase? Linear attribution recognizes the significance of such seemingly indirect interactions in the sales funnel and those marketing channels.
It acknowledges that every touchpoint, regardless of its timing or proximity to the conversion to sales, plays a role in influencing the customer's decision-making process.
Because linear attribution gives equal credit to every touchpoint, it avoids the bias of single-touch models like first-click or last-click.
That makes it especially useful in a multi-channel world where awareness, consideration, and conversion often happen across different platforms and over time.
The result is a more balanced view of which channels are consistently supporting conversions — not just which one happened to show up first or last.
Linear attribution is also one of the easiest multi-touch models to operationalize. There’s no complex weighting system or advanced modeling required — credit is simply distributed evenly across touchpoints.
That simplicity makes the model approachable for teams with limited technical resources, while still providing meaningful insight into how customers move through your funnel.
As we celebrate the advantages we still have to acknowledge the limitations of this marketing attribution model. Below are a few examples.
Linear assumes each touchpoint influences the conversion equally, no matter when it happens.
But in many journeys, touches closer to conversion (like retargeting or branded search) may have more impact. Linear doesn’t reflect that.
Customer journeys aren’t uniform. Someone might read blog posts early on, compare products later, then convert through a high-intent ad.
Linear doesn’t capture those differences, so it can over-credit lighter touches and under-credit decisive ones.
If customers hit 20+ touchpoints before converting, linear spreads credit so thin that interpretation becomes messy.
In long, complex journeys, a weighted or data-driven model may offer clearer insight.
There are many different types of attribution models out there.
They mostly fall into two buckets: single-touch models that credit one moment, and multi-touch models that distribute credit across the journey.
Single-touch models intentionally simplify things:
Linear is a multi-touch model, meaning it distributes credit across multiple steps. Other multi-touch models apply uneven weighting:
You can also use data-driven attribution, which uses your historical data to estimate influence and assign credit unevenly based on real patterns. Linear is rule-based, so it won’t adapt by channel performance.
Finally, there are incrementality tests and marketing mix modeling (MMM). These aim to measure causal lift (what actually created new conversions). Linear measures contribution within observed paths, not causation. It’s simpler, but can also work well alongside causal methods.
There are a few key moments when a linear attribution model makes the most sense. You should consider using linear attribution when:
As mentioned, we offer two types of linear attribution: Linear (All) and Linear (Paid).
Implementing linear attribution is pretty straightforward, but the quality of your results depends heavily on the tools you use and the data you feed them. Here’s a clean way to approach it.
GA4 includes several free attribution models, including linear, but it often falls short in accuracy. Cookie consent opt-outs, cross-device gaps, and data sampling can leave you with an incomplete view of the customer journey.
Attribution is only as good as the data behind it. If your tracking is partial or inconsistent, even the “right” model won’t give you trustworthy insights.
Triple Whale Pixel is an advanced tracking technology that captures more customer behavior data than standard tracking solutions, giving you a more accurate view of your marketing performance and ROI.
Before you analyze anything, define what a “conversion” actually means for your business. That could be purchases, subscriptions, lead form completions, or even micro-conversions like add-to-carts or product view depth.
Clear goals make linear attribution far more useful because you know exactly what outcome each touchpoint is sharing credit for.
Now you’re ready to look at what linear attribution is telling you. Because every touchpoint shares credit, this view is especially helpful for spotting:
Attribution only matters if it changes what you do next. Use your linear insights to:
While this is a pretty straightforward method of attribution, there are some best practices you should consider following.
As mentioned before, linear attribution only works if you actually see most of the journey. Missing touchpoints leads to bad “equal credit” math.
That said, aim to capture:
The lookback window changes the story. Too short and you under-credit upper funnel. Too long and you over-credit noise.
In general, the best practice is to evaluate performance over enough volume (such as weeks, not days). Try the following:
Linear is democratic, which is useful, but can fall too flat. Ideally, you run different models. You can compare linear to:
For example, if linear and time decay tell the same story, your funnel is stable. If they diverge a lot, it’s a clue about where influence is really concentrated.
Different cohorts often have totally different path structures, and linear helps you see that without biasing to first touch and last touch.
Try segments such as:
Linear attribution provides a valuable lens through which your business can move beyond simplistic single-touch models and gain a deeper understanding of the journey customers take before conversion.
Triple Whale gives you linear attribution and a lot more—like AI-powered insights, agentic decisioning, and a complete ecommerce intelligence layer that helps you act on what attribution reveals. If you want a clearer view of performance and a smarter way to scale, Triple Whale is here. Book a demo today.
This model distributes credit evenly along all touchpoints in the path to conversion.
It can be calculated as: Conversion value ÷ Number of touchpoints = Credit per touchpoint.
A marketing attribution model determines how credit for a conversion is distributed across touchpoints in a customer journey. In the case of linear attribution, the credits are equally weighted.
Linear attribution is a type of marketing attribution model that gives equal credit to each touchpoint across the customer journey.
In this guide, we’ll break down how linear attribution works, where it’s most useful, its tradeoffs, and how to apply the strategy so you can make smarter marketing decisions moving forward.
Linear attribution is a multi-touch attribution (MTA) model that helps brands understand how different marketing channels contribute to a conversion.
In this approach, every touchpoint in the customer journey receives equal credit. Because each interaction is weighted the same, linear attribution is also known as an even-weighted model.
Think of the path to purchase like a relay race: each touchpoint passes the baton forward until the customer reaches the finish line — AKA the sale. Linear attribution doesn’t try to guess which runner mattered most; it assumes every handoff played an equally important role.
Touchpoints can include anything from a social ad click to an email open to a direct website visit. Each one is treated as a meaningful step that helped move the customer closer to conversion.
Triple Whale provides two versions of linear attribution.
Elijah is shopping for a new pair of running shoes.
This total purchase order is $100.
In this scenario, each touch point earns equal credit for the sale – which is 15% of the value, or $15 each.
Linear attribution can be expressed through a simple formula:
Conversion value ÷ Number of touchpoints = Credit per touchpoint
Using the example above:
$100 ÷ 5 = $20 per touchpoint
Here, the conversion value could be the revenue generated from a purchase or another relevant metric you’re measuring (like a lead or subscription).
Linear attribution is a great middle ground model. It’s not as fixated as single-attribution, and not as complicated to run as other weighted or causal approaches. Here are the main benefits to consider.
Remember that viral video you saw shared by a friend that eventually led you to a purchase? Linear attribution recognizes the significance of such seemingly indirect interactions in the sales funnel and those marketing channels.
It acknowledges that every touchpoint, regardless of its timing or proximity to the conversion to sales, plays a role in influencing the customer's decision-making process.
Because linear attribution gives equal credit to every touchpoint, it avoids the bias of single-touch models like first-click or last-click.
That makes it especially useful in a multi-channel world where awareness, consideration, and conversion often happen across different platforms and over time.
The result is a more balanced view of which channels are consistently supporting conversions — not just which one happened to show up first or last.
Linear attribution is also one of the easiest multi-touch models to operationalize. There’s no complex weighting system or advanced modeling required — credit is simply distributed evenly across touchpoints.
That simplicity makes the model approachable for teams with limited technical resources, while still providing meaningful insight into how customers move through your funnel.
As we celebrate the advantages we still have to acknowledge the limitations of this marketing attribution model. Below are a few examples.
Linear assumes each touchpoint influences the conversion equally, no matter when it happens.
But in many journeys, touches closer to conversion (like retargeting or branded search) may have more impact. Linear doesn’t reflect that.
Customer journeys aren’t uniform. Someone might read blog posts early on, compare products later, then convert through a high-intent ad.
Linear doesn’t capture those differences, so it can over-credit lighter touches and under-credit decisive ones.
If customers hit 20+ touchpoints before converting, linear spreads credit so thin that interpretation becomes messy.
In long, complex journeys, a weighted or data-driven model may offer clearer insight.
There are many different types of attribution models out there.
They mostly fall into two buckets: single-touch models that credit one moment, and multi-touch models that distribute credit across the journey.
Single-touch models intentionally simplify things:
Linear is a multi-touch model, meaning it distributes credit across multiple steps. Other multi-touch models apply uneven weighting:
You can also use data-driven attribution, which uses your historical data to estimate influence and assign credit unevenly based on real patterns. Linear is rule-based, so it won’t adapt by channel performance.
Finally, there are incrementality tests and marketing mix modeling (MMM). These aim to measure causal lift (what actually created new conversions). Linear measures contribution within observed paths, not causation. It’s simpler, but can also work well alongside causal methods.
There are a few key moments when a linear attribution model makes the most sense. You should consider using linear attribution when:
As mentioned, we offer two types of linear attribution: Linear (All) and Linear (Paid).
Implementing linear attribution is pretty straightforward, but the quality of your results depends heavily on the tools you use and the data you feed them. Here’s a clean way to approach it.
GA4 includes several free attribution models, including linear, but it often falls short in accuracy. Cookie consent opt-outs, cross-device gaps, and data sampling can leave you with an incomplete view of the customer journey.
Attribution is only as good as the data behind it. If your tracking is partial or inconsistent, even the “right” model won’t give you trustworthy insights.
Triple Whale Pixel is an advanced tracking technology that captures more customer behavior data than standard tracking solutions, giving you a more accurate view of your marketing performance and ROI.
Before you analyze anything, define what a “conversion” actually means for your business. That could be purchases, subscriptions, lead form completions, or even micro-conversions like add-to-carts or product view depth.
Clear goals make linear attribution far more useful because you know exactly what outcome each touchpoint is sharing credit for.
Now you’re ready to look at what linear attribution is telling you. Because every touchpoint shares credit, this view is especially helpful for spotting:
Attribution only matters if it changes what you do next. Use your linear insights to:
While this is a pretty straightforward method of attribution, there are some best practices you should consider following.
As mentioned before, linear attribution only works if you actually see most of the journey. Missing touchpoints leads to bad “equal credit” math.
That said, aim to capture:
The lookback window changes the story. Too short and you under-credit upper funnel. Too long and you over-credit noise.
In general, the best practice is to evaluate performance over enough volume (such as weeks, not days). Try the following:
Linear is democratic, which is useful, but can fall too flat. Ideally, you run different models. You can compare linear to:
For example, if linear and time decay tell the same story, your funnel is stable. If they diverge a lot, it’s a clue about where influence is really concentrated.
Different cohorts often have totally different path structures, and linear helps you see that without biasing to first touch and last touch.
Try segments such as:
Linear attribution provides a valuable lens through which your business can move beyond simplistic single-touch models and gain a deeper understanding of the journey customers take before conversion.
Triple Whale gives you linear attribution and a lot more—like AI-powered insights, agentic decisioning, and a complete ecommerce intelligence layer that helps you act on what attribution reveals. If you want a clearer view of performance and a smarter way to scale, Triple Whale is here. Book a demo today.
This model distributes credit evenly along all touchpoints in the path to conversion.
It can be calculated as: Conversion value ÷ Number of touchpoints = Credit per touchpoint.
A marketing attribution model determines how credit for a conversion is distributed across touchpoints in a customer journey. In the case of linear attribution, the credits are equally weighted.
Body Copy: The following benchmarks compare advertising metrics from April 1-17 to the previous period. Considering President Trump first unveiled his tariffs on April 2, the timing corresponds with potential changes in advertising behavior among ecommerce brands (though it isn’t necessarily correlated).
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