Attribution & Conversion Counting

Data-Driven Attribution vs Time-Decay Attribution

In short: Both try to account for the whole conversion path rather than crediting one touch, but data-driven attribution (DDA) derives its weighting from your account's actual converting and non-converting paths, while time-decay imposes a fixed recency-based rule with no learning involved. DDA is Google Ads and GA4's current default and needs sufficient conversion volume to model well; time-decay was dropped from both platforms in 2023 and survives only in outside tools. The practical divide is evidence versus assumption: DDA says this is what correlated with conversions in your data, time-decay says recent touches probably mattered more, and applies a fixed half-life to prove it. If you have the conversion volume, trust DDA; if you do not, or need a transparent recency-weighted alternative, time-decay is the older tool for that job.

By the AdFlint research team · Fact-checked against current Google and Meta platform behavior · Last reviewed July 2026

Data-Driven Attribution

Uses a platform's own modeling of converting and non-converting paths to distribute fractional credit based on observed contribution rather than fixed rules.

The platform compares paths that converted with paths that did not, then assigns each touchpoint a fractional share reflecting its measured contribution. It is the default in Google Ads and GA4 and needs enough conversion volume before the model has data to learn from. Two things get misread: the credit is modeled rather than observed, and it remains in-platform, so it cannot see channels outside that platform's data.

Full definition

Time-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 definition

Side by side.

The differences that actually change what happens in your account.

 Data-Driven AttributionTime-Decay Attribution
Basis for the credit splitComparison of converting versus non-converting paths in your account, modeled by the platform.A fixed half-life formula applied to how many days separate a touch from the conversion.
Does it adapt to your businessYes, the model is trained on your data and can weight channels differently than it would for another advertiser.No, the half-life default, commonly seven days, applies the same shape to every account unless you manually change it.
Minimum conversion volumeNeeds enough conversions for the model to find reliable patterns; too few and it cannot build a confident model.None, runs the same formula regardless of volume.
Where it lives todayDefault in Google Ads and GA4.Removed from Google Ads and GA4 reporting in 2023; found in third-party or custom-built models.
How a touch with no timing pattern is handledJudged on whether it correlates with conversion outcomes, not on when it happened.Judged purely on recency; a touch's actual contribution is irrelevant if it is outside the decay window.
ExplainabilityHarder to explain, you see the output split, not a formula you can hand-recompute.Fully explainable, anyone can recompute the decay curve from the half-life and touch dates.
What it costs you when wrongA low-volume account gets a model trained on too little signal, which can look precise while being unreliable.A business with a short or unusual sales cycle inherits a default half-life that does not match how their customers actually behave.

What actually separates them.

01

DDA's weighting comes from statistical comparison of paths that did and did not convert; time-decay's weighting comes from a fixed exponential formula keyed only to elapsed time.

02

DDA can assign a touchpoint near-zero credit even if it happens right before conversion, if that position does not correlate with conversion in the data; time-decay guarantees the most-recent touch a large share regardless of whether it is actually predictive.

03

Time-decay's output is fully reproducible by hand from the half-life and touch timestamps; DDA's output cannot be recomputed without access to the platform's model.

04

DDA requires a conversion volume threshold to function reliably; time-decay has no such requirement and runs the same on ten conversions or ten thousand.

05

Only DDA remains selectable inside Google Ads and GA4 as of 2026; time-decay must be reconstructed in a third-party tool or spreadsheet from exported path data.

Which one should you use?

Use Data-Driven Attribution when

  • You are inside Google Ads or GA4 with enough monthly conversion volume to trust a modeled result.
  • You want credit assignment based on what actually correlates with conversions in your account, not an assumption about recency.
  • You are feeding conversion data into Smart Bidding and want it reflecting measured contribution rather than a fixed formula.
  • You have noticed time-decay results do not match what your sales team observes about which touches actually move deals forward.

Use Time-Decay Attribution when

  • Your conversion volume is too thin for a modeled approach, or you are working entirely outside Google's reporting.
  • You want a transparent, hand-checkable formula you can explain and defend to a client.
  • Your sales cycle is long enough that you genuinely believe recency signals intent, and you can set a half-life that matches it.
  • You need attribution logic that will not shift on its own as new data comes in, for consistent period comparisons.

Common questions.

Does data-driven attribution use time decay internally?

No. DDA's methodology compares converting and non-converting paths to estimate each touchpoint's contribution; it does not apply a fixed recency formula the way time-decay does, even though DDA's output sometimes ends up looking similar for paths where recent touches genuinely correlate with conversion.

Why did switching from time-decay to data-driven change my top channel?

Time-decay always favors whichever touch is closest to conversion by construction, so a channel that is frequently the last thing before checkout, like remarketing or branded search, tends to look strong under it. DDA does not automatically reward recency; if that same channel does not actually correlate with conversion outcomes in the model, it can lose ground once you switch.

Can I still use time-decay attribution to sanity-check my data-driven numbers?

Not inside Google Ads or GA4, since time-decay was removed from both in 2023. You can approximate it by exporting conversion path data and calculating the decay formula yourself, or by using a third-party attribution tool that still offers it as a preset.

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