What is time decay attribution?
Time decay attribution splits a sale's credit across the touches before it and gives the most to the latest ones. Google's old default halved a touch's credit for every 7 days before the sale. GA4 and Google Ads no longer offer it, so you calculate it yourself.
By Joris van Huët, Founder & CEOUpdated 6 min read
Time decay attribution splits the credit for a sale across the touches before it, and gives the most to the ones closest to the purchase. Google's old default halved a touch's credit for every 7 days it sat before the sale. GA4 and Google Ads no longer offer it, so today you usually calculate it yourself.
What one store's data shows
The usual answer stops at the definition: recent touches count for more. Whether that changes anything depends on how long your journeys last. One store's export shows how much of its revenue the clock could even reach.
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.
| What the export shows | Share of revenue | Source cell |
|---|---|---|
| Journeys with 1 touch (0.5 days to buy) | 79.5% | Journeys sheet, 1 touch row |
| Journeys with 2 to 3 touches (12.5 days to buy) | 12.2% | Journeys sheet, 2-3 touches row |
| Journeys with 4 to 9 touches (16.9 days to buy) | 5.4% | Journeys sheet, 4-9 touches row |
| Journeys with 10 or more touches (16.0 days to buy) | 3.0% | Journeys sheet, 10+ touches row |
On the Journeys sheet, journeys with 1 touch hold 79.5% of revenue, and those buyers took 0.5 days to buy. A path with one touch hands that touch everything, whatever the rule. For this money, time decay, last click and first click all agree.
The clock only matters for the rest. Journeys with 2 or more touches hold 20.6% of revenue on the Journeys sheet (12.2% + 5.4% + 3.0%), about a fifth.
Those journeys are slow. On the Journeys sheet, journeys of 2 to 3 touches took 12.5 days to buy, and journeys of 4 to 9 touches took 16.9 days. That is long enough for a half-life to bite.
Google's legacy Analytics help puts it plainly: a touch 14 days before a conversion gets a quarter of the credit of a same-day touch. So on a journey of about two weeks, the opening touch gets a small slice and the closing touch the lion's share.
What the export cannot show is when each touch happened. It records how long journeys took, not where in those days each click fell. Time decay needs exactly that detail, so any version built from a file like this rests on a guess about spacing.
And none of it says the closing touch caused anything. Time decay is a rule about clocks, not about influence.
Why does "recent means important" mislead?
Because recency is a guess about influence, and the guess favours closers. The touches right before a purchase are often a brand search, a reminder email or a retargeting ad. They meet people who have mostly decided. Time decay pays them the most because they sit next to the checkout.
The early touches are where many buyers first heard of you. Universal Analytics, the old Google Analytics, pitched time decay for short consideration phases, such as one-day or two-day promotions. For campaigns built to create initial awareness, the same help page pointed to first interaction instead.
Google Ads hints at the same tension today. Its help on the Path metrics report says brand awareness campaigns may show longer time lags before conversion. Longer lags mean smaller slices under time decay, so the model quietly taxes the work that starts journeys.
The half-life is a dial, not a fact. Seven days was a default. Halve it and the closer's share grows. Stretch it far enough and every touch drifts towards an equal share, which is linear attribution by another name.
GA4's data-driven model does weigh time, but it learns how much from your data. Google says the model incorporates factors such as time from key event, device type and the order of ad exposure. Time decay fixes that weight before it has seen a single path.
Google has dropped the rule-based version. GA4 lists time decay among the models no longer available as of November 2023. Google Ads moved conversion actions that still used it to data-driven attribution, and kept last click as the alternative.
What can time decay not tell you?
It cannot tell you which touch changed a mind. A reminder email on purchase day takes the biggest slice whether or not the buyer needed reminding.
It cannot see past your lookback window. GA4's window decides how far back a touch can earn credit at all, and for purchases it defaults to 90 days. Research that started earlier gets nothing under any model.
It cannot see what never became a visit, such as a podcast, a shop window or a friend's tip.
And it cannot tell you what to cut. To learn what a channel adds, pause it for part of your audience and compare sales, as a holdout test does.
What to do this week
- Read how long your top paths take. In GA4, click Advertising, then Key event attribution paths under Key events, pick purchase and sort by Purchase revenue. Read Days to key event for the top rows. Pass: the big rows close within a few days, so time decay would land close to last click. Fail: they run past a week, so the half-life would move real money.
- Find your openers. In GA4, click Advertising and go to Attribution > Attribution models. Set one Attribution model (non-direct) column to Data-driven and the other to Paid and organic last click. Pass: both agree on your top channels, so a time-weighted view adds little. Fail: a channel gains a lot under Data-driven, so it likely works early in journeys and time decay would short it.
- Test the dial on three paths. For illustration, weight your three biggest multi-touch paths with a 7-day half-life, then with a 3-day one. Pass: the channel order holds. Fail: it flips, so the setting is picking your winners, not your customers.
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: About the default MCF attribution models (legacy) (Google); Get started with attribution (Google); About attribution models (Google); Select attribution settings (Google); About attribution reports (Google); Key events attribution paths report (Google); Key event attribution models report (Google)
Related answers
Frequently asked questions
What half-life does time decay attribution use?
Google's old Analytics default was 7 days. A touch 7 days before a conversion got half the credit of a same-day touch, and a touch 14 days before got a quarter. The half-life is a setting, not a law: change it and every share moves.How is time decay different from position-based attribution?
Position-based splits credit by place in the path; time decay splits it by the clock. Google's legacy example for position-based gave 40% each to the first and last touch and 20% to the middle. Under time decay, a first touch made on purchase day weighs as much as the last one.When does time decay attribution make sense?
When journeys are short and promotions drive them. Google's legacy help suggested it for one-day or two-day promotions, where touches from a week earlier deserve little credit. If your sales start with awareness work weeks before the purchase, it tends to short-change the start.
Go deeper: Incrementality testing, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
Keep reading
Terms in this article
- Attribution ModelAn Attribution Model defines how credit for conversions is assigned to marketing touchpoints. It dictates how marketing channels receive credit for sales.
- Attribution ReportAttribution Report shows which touchpoints or channels receive credit for a conversion. It identifies which campaigns drive desired actions.
- Google AnalyticsGoogle Analytics is a web analytics service that tracks and reports website traffic.
- Holdout TestA holdout test is an experiment where a portion of the audience does not see a campaign. This measures the campaign's true incremental impact.
- IncrementalityIncrementality measures the true causal impact of a marketing campaign. It quantifies the additional conversions or revenue directly from that activity.
- Incrementality TestingIncrementality Testing measures the additional impact of a marketing campaign. It compares exposed and control groups to determine causal effect.
- Linear AttributionLinear Attribution assigns equal credit to every marketing touchpoint in a customer's conversion path. This model distributes value uniformly across all interactions.
- Time Decay AttributionTime Decay Attribution is a multi-touch attribution model. It assigns increasing credit to marketing touchpoints closer to a conversion.