What is time decay attribution for an electronics store?
Time decay gives most of a sale's credit to the touches nearest the purchase. If you sell electronics, that usually means the deal email, the price check or the brand search on buying day. The reviews that sold the model weeks earlier get a sliver.
By Joris van Huët, Founder & CEOUpdated 5 min read
Time decay gives most of a sale's credit to the touches nearest the purchase. If you sell electronics, that usually means the deal email, the price check or the brand search on buying day. The review video or comparison that sold the model weeks earlier gets a sliver, so read time decay as a closer's view.
If you sell consumer electronics
If you sell headphones, laptops, cameras or smart home kit, your buyers often take their time. They read reviews, watch unboxings, compare specs and wait for a price they like. Some hold off for a launch or a sale before they commit.
That stretches the gap between the first touch and the purchase. Google's old 7-day default shows the effect: a touch 14 days before a conversion gets a quarter of a same-day touch's credit. A touch a month out keeps a small fraction.
Your catalogue also mixes long and short journeys. A television might take weeks of research, while a cable or a case is often bought in one visit. Time decay runs both through the same clock. It barely touches the quick orders and reshuffles the slow ones.
Here is one store's export for comparison. It is one store, and nothing in it says it sells electronics.
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 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 |
In this one store, journeys with 1 touch hold 79.5% of revenue on the Journeys sheet, with 0.5 days to buy. Time decay cannot move any of that: one touch, one owner.
The long journeys look like research. On the Journeys sheet, journeys of 4 to 9 touches took 16.9 days to buy and hold 5.4% of revenue. On the same Journeys sheet, journeys of 10 or more touches took 16.0 days and hold 3.0%.
Those are the rows where time decay does its reshuffling, and in that one store they carry little of the revenue. If you sell electronics, your own long rows may carry more. The more revenue sits there, the more the half-life decides your report.
What changes for an electronics store?
Sales and launches squeeze the ending. Some buyers research for weeks and wait for a sale day. The sale email and the deal search sit next to their purchase and take the biggest slices. Google's legacy help suggested time decay for one-day or two-day promotions, which fits the sale day but not the research before it.
Your lookback window can cut the research off. GA4's window decides how far back a touch can earn credit at all. It defaults to 90 days for purchases, and you can shorten it to 30 or 60. If research starts before the window opens, those touches get nothing under any model.
First click is the counterweight. Shopify's marketing reports offer first click next to last click, though not time decay. Shopify says first click can help you see which channels generate top-of-funnel interest. Read the two side by side and you see the openers and closers that time decay blends together.
What to do this week
- Find your slow paths. In GA4, click Advertising, then Key event attribution paths under Key events. Under Path length, choose greater than or equal to, enter 4 touchpoints and click Apply. Pass: the slow paths carry little purchase revenue, so time decay changes little. Fail: they carry a big share, so the half-life will decide your report.
- Check that the window covers your research. In GA4, go to Admin > Data display > Events > Attribution settings and read the Key event lookback window. Pass: it sits on the longest option, so research from about three months back can still earn credit. Fail: it is shorter, so early research touches drop out of every model.
- Put first click next to last click for your last sale. In Shopify admin, go to Analytics > Reports, filter the Category to Marketing and open Performance by referring channel. In the Attribution menu, include First click and Last click for the sale weeks. Pass: the same channels lead both, so openers and closers agree. Fail: different channels lead, so time decay would favour the closers; keep the openers out of any cut.
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); Select attribution settings (Google); Key events attribution paths report (Google); Marketing reports (Shopify)
Related answers
Frequently asked questions
Does time decay undervalue product review videos?
Often, if the review comes weeks before the purchase. Under Google's old 7-day default, a touch 14 days out kept a quarter of a same-day touch's credit. A review watched without a click leaves no touch in GA4 at all, so no model can credit it.Do sale events skew time decay credit?
Usually, yes. If shoppers research for weeks and buy on a sale day, the sale email and the deal search sit closest to the purchase. They take the biggest shares. Compare first click with last click for the same weeks to see who opened those journeys.Should accessories and big-ticket items use the same attribution model?
They can share one, but read them apart. A cable bought in one visit gives every model the same answer. A laptop bought after weeks of research is where models disagree, so filter your paths by length before you judge a channel on time decay.
Go deeper: Incrementality testing, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- AnalyticsAnalytics is the systematic computational analysis of data. It reveals customer behavior and measures campaign performance.
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- Attribution ModelAn Attribution Model defines how credit for conversions is assigned to marketing touchpoints. It dictates how marketing channels receive credit for sales.
- Black FridayBlack Friday is the day after Thanksgiving in the United States. It marks the start of the Christmas shopping season and is a major sales event for retailers.
- CausalityCausality is the relationship where one event directly causes another, essential for identifying specific actions that drive desired outcomes in marketing.
- ConversionConversion is a specific, desired action a user takes in response to a marketing message, such as a purchase or a sign-up.
- Time Decay AttributionTime Decay Attribution is a multi-touch attribution model. It assigns increasing credit to marketing touchpoints closer to a conversion.
- TouchpointTouchpoint is any interaction a customer has with a brand throughout their journey. In marketing attribution, each touchpoint is a data signal to understand marketing impact.