What is linear attribution?

Linear attribution splits the credit for a sale equally across every recorded touch on the buyer's path. Three clicks before a purchase get a third each. GA4 and Google Ads no longer offer it, though Shopify's marketing reports still do. It is easy to explain, but it usually rewards how often a channel shows up, not what it caused.

What one store's data shows

The usual answer calls linear the fair middle ground: every touch gets its share. The catch is the size of the shares. One store's export shows how many touches its revenue had to be shared between.

One store's anonymised GA4 export, 1 January 2024 to 21 August 2026. It holds shares of revenue only: no ad spend, no order counts.

Linear divides each sale by the number of touches on its path. So the row a journey sits in decides how big each slice is.

On the Journeys sheet, journeys with 1 touch hold 79.5% of revenue, with 0.5 days to buy. One touch means one slice: the whole sale. For that money, linear says exactly what last click says.

Journeys of 2 to 3 touches hold 12.2% on the Journeys sheet. Under linear, each touch there gets a half or a third of the sale. That is most of the money linear actually splits in this one store.

The thin slices sit in the long journeys, and they carry little. Journeys of 4 to 9 touches hold 5.4% on the Journeys sheet, and journeys of 10 or more hold 3.0%. For illustration, a 10-touch journey hands each touch a tenth of the sale.

Time gets the same flat treatment. On the Journeys sheet, journeys of 2 to 3 touches took 12.5 days to buy, and journeys of 4 to 9 took 16.9. Linear pays a click from the first of those days exactly what it pays the click at checkout.

What the export cannot show is whether any slice was earned. Linear, like every credit rule, divides sales that already happened among touches that were recorded. It never asks whether a touch changed anything.

Why does equal credit mislead?

Because equal per touch is not equal per channel. Shopify's help says its Linear model gives equal credit to each click in the customer's journey that contributed to a sale, including direct visits. So a channel that brings the buyer back three times collects three slices.

For illustration, take a €90 order that followed three clicks: a Meta ad, then two reminder emails. If the order is split evenly, each click gets €30, so Email takes €60 and the ad that found the buyer takes €30. Add one more reminder before the next order and Email's take grows again, with nothing else changed.

That makes linear a frequency score. Channels built to repeat, such as email flows, retargeting and typed return visits, win by turning up often. Channels that open a journey once and step aside lose by the same rule.

Google stopped offering it. In April 2023, it announced that first click, linear, time decay and position-based models were leaving Google Ads and GA4. Its reason: rules-based models lack the flexibility to adapt to changing customer journeys. At the time, it said, less than 3% of Google Ads web conversions used them.

GA4 lost the four models in November 2023. In Google Ads, conversion actions that used them were moved to data-driven attribution, and last click is still on offer. Shopify's marketing reports still offer Linear. For the wider family of split-credit models, read what multi-touch attribution is.

What can linear attribution not tell you?

It cannot tell you which touch mattered. Every recorded touch gets the same slice, whether it changed the buyer's mind or just happened to be on the way. A branded search on the way to checkout earns as much as the ad that introduced you.

It can tell you one useful thing: where a channel tends to sit in the path. Put a channel's linear credit next to its last click credit. If linear gives it more, it often shows up before the final click. If linear gives it less, it mostly closes journeys that other channels also touched.

And it cannot tell you what to cut. Credit is not cause. To learn what a channel adds, pause it in some regions, keep it running in others and compare total sales, as a holdout test does.

What to do this week

1. Read the gap for each campaign. In your Shopify admin, go to Analytics > Reports, filter the Category to Marketing and open Performance by UTM campaign. In the Attribution menu of the configuration panel, include Linear and Last click for last month. Pass: the campaigns that gain under Linear are the ones you run to find new buyers. Fail: your email or retargeting campaigns gain most, so linear is mostly counting repeats.
2. Find the repeaters. In GA4, click Advertising, then Attribution paths under Attribution, pick purchases and read the top paths by purchase revenue. Pass: most paths name each channel once. Fail: one channel appears again and again inside mixed paths, so much of its linear credit is frequency, not influence.
3. Test the biggest gainer before you pay it more. Take the channel that gains most under Linear and pause it in some regions while it runs in the rest. Pass: total sales fall where it went quiet, so it earns at least part of its credit. Fail: sales hold, so linear was paying it for turning up.

Check the homework. Your GA4 Attribution paths export already holds the evidence. Causality Engine reads that one file and shows what each channel caused next to what last-click gave it, in 1 to 2 minutes, for €99 once (excluding VAT), refundable within 30 days. Check the homework

Sources, 1 October 2026: Marketing reports (Shopify); First click, linear, time decay, and position-based attribution models are going away (Google); Get started with attribution (Google); About attribution models (Google); Key events attribution paths report (Google)

Related answers

• All Attribution models questions
• How to run linear attribution in Shopify and GA4
• Linear attribution, defined
• Linear attribution model: why equal credit misleads
• Incrementality testing, explained