Linear Attribution vs Time-Decay Attribution
In short: Both are rule-based multi-touch models that split conversion credit across every touchpoint in a path, unlike single-touch models that give it all to one interaction. Linear treats every touch as equally responsible; time-decay assumes touches closer to the conversion mattered more and discounts older ones on a fixed half-life. Neither is available inside Google Ads or GA4 reporting anymore, both were retired in 2023, so you will only find them in third-party attribution tools or custom modeling on exported path data. Pick linear when you want an unbiased read on path length and channel involvement; pick time-decay when your sales cycle is long and recent activity plausibly signals intent. If you cannot defend why an early touch deserves less credit than a late one, use linear; if your gut says the last two weeks mattered more, use time-decay.
By the AdFlint research team · Fact-checked against current Google and Meta platform behavior · Last reviewed July 2026
Linear Attribution
Splits conversion credit evenly across every touchpoint in the path, so a five-touch journey gives each interaction twenty percent.
Every recorded interaction receives an identical share of the conversion, which makes long paths visible without asserting that any one step mattered more. Analysts use it as a neutral starting point when comparing channel involvement rather than channel impact. Google Ads and GA4 removed it from their reporting in 2023, though it remains standard in other analytics tools. The trap is treating equal credit as evidence of equal influence.
Full definitionTime-Decay Attribution
Weights touchpoints by recency, so interactions closer to the conversion receive exponentially more credit than ones earlier in the path.
Credit decays on a half-life, commonly seven days, so a touch a week before the conversion carries half the weight of one on the closing day. It suits long consideration cycles where recent activity signals intent. Google Ads and GA4 dropped it from reporting in 2023. The misunderstanding is assuming the half-life is tuned to your business; it is a fixed default, not something the model learns from your data.
Full definitionSide by side.
The differences that actually change what happens in your account.
| Linear Attribution | Time-Decay Attribution | |
|---|---|---|
| How credit splits | Equal share to every touchpoint recorded in the path, no matter its position. | Exponentially larger share to touches near the conversion, shrinking the further back you go. |
| Where you can still select it | Not in Google Ads or GA4; available in third-party MTA tools or custom modeling on exported path data. | Same story, dropped from Google Ads and GA4 reporting in 2023, survives in outside tools and custom builds. |
| What a five-touch path looks like | Each of the five touches gets 20 percent, regardless of order. | The closing touch gets the largest slice and the oldest touch the smallest, following the chosen half-life. |
| Tunable parameters | None, it is a fixed even split with nothing to configure. | A half-life setting, commonly seven days, that controls how fast credit decays; most tools let you change it, though the default is rarely revisited. |
| Best-fit sales cycle | Short-to-medium paths where you want a neutral view of which channels show up, not which one closed it. | Longer consideration cycles where a late remarketing touch or branded search plausibly nudged the decision. |
| How it reads early-funnel channels | Just as valuable as the closing touch, which can overstate a channel that only ever appears first. | Nearly worthless if it is the only touch, understating channels that do the discovery work but never close. |
| Failure mode | Flattens genuinely different roles into one number, making a five-blog-post browse look identical to a five-ad remarketing burst. | A half-life picked for the wrong sales cycle quietly buries upper-funnel channels that actually mattered. |
What actually separates them.
Linear assigns the same fractional credit no matter how many days separate a touch from the conversion; time-decay recalculates every touch's share based on its distance from the conversion date.
Time-decay needs a half-life value to run; linear has no parameter to set or misconfigure.
Adding a touchpoint to a path lowers every existing touch's share under linear by the same amount, while under time-decay it barely dents the credit of touches near the end.
Linear gives identical answers regardless of how you sort or filter by recency; time-decay's output changes if you change the decay window, so results are not stable across tool configurations.
Because neither lives in Google's own reporting anymore, both require exporting conversion path data to a third-party or custom model before you can run either calculation.
Which one should you use?
Use Linear Attribution when
- You want a first, unbiased look at which channels appear anywhere in the path before you argue about which one really closed it.
- Your sales cycle is short enough that recency and initial discovery carry similar weight.
- You are building a channel-involvement report for a client who wants to see contribution, not attribution arguments.
- You have no strong hypothesis about which stage of the funnel matters more and do not want the model to impose one.
Use Time-Decay Attribution when
- Your typical path stretches over weeks and a late touch, like remarketing or branded search, usually precedes the sale.
- You are skeptical of first-touch or linear credit going to a channel someone saw once, months before converting.
- You can justify a specific half-life for your business, not just accept the tool's default.
- You are comparing channels in a business where warm, about-to-buy behavior is a distinct, later-stage signal worth rewarding.
Common questions.
Can I still choose linear or time-decay attribution in Google Ads?
No. Google removed all rule-based multi-touch models, including linear and time-decay, from Google Ads and GA4 reporting in 2023, leaving data-driven attribution and last-click as the only options inside Google's own tools. You can still find both models in some third-party marketing analytics platforms, or approximate them yourself from exported conversion path data.
Why does time-decay ignore my first-touch channel almost completely?
The decay is exponential, not linear, so a touch several half-lives back from the conversion date can shrink to a sliver of a percent even though it happened. That is the model doing what it is designed to do; if your business needs early-funnel channels to get real credit, linear or position-based will represent them more fairly.
Which one should I trust more for budget decisions?
Neither is more correct, they encode different assumptions about how influence works, and both are rule-based guesses rather than measured effects. Data-driven attribution, where available, replaces the guess with a model trained on your actual converting and non-converting paths, which is why Google now defaults to it over either of these.
Or stop choosing between them.
AdFlint picks the setting, writes the ads, and keeps optimizing inside the Google and Meta accounts you already own.
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