Time-decay attribution: recent touches weigh more
Time-decay attribution weights touchpoints by recency: the closer a touch is to the conversion, the more credit it earns, usually following an exponential decay with a configurable half-life. It is a compromise between last-click and linear, but its recency bias under-credits the early demand-creating touches.
What this means
Time-decay applies a decay curve so touches near the conversion earn the most credit and older touches earn progressively less. A 'half-life' parameter sets how fast credit fades — a seven-day half-life means a touch a week earlier is worth roughly half a touch at conversion.
Recency is an assumption
The model assumes recent touches are more influential. That fits some journeys (an impulse purchase) and badly distorts others (a long B2B evaluation where the first webinar created all the demand). Changing the half-life changes the credit split, so two teams using 'time-decay' can disagree sharply.
It is a useful middle ground, but the half-life is a judgement call, not a measurement.
- Credit fades with distance from conversion
- Half-life parameter controls the fade rate
- Built-in penalty on early awareness touches
How it appears in analytics and logs
Time-decay output reflects recency as much as influence. A channel that consistently appears late in paths will look strong; early discovery channels will look weak by design.
Diagnostic use case
Use time-decay when later touches plausibly matter more (short consideration cycles), while remembering it built-in penalises early awareness work.
What WebmasterID can help detect
WebmasterID keeps weighting choices explicit and confidence-labelled, so a recency-weighted view is read as one assumption among several, not as truth.
Common mistakes
- Treating the half-life as a fact rather than a chosen knob.
- Applying recency weighting to long consideration cycles.
- Comparing time-decay outputs across differing half-life settings.
Privacy and accuracy notes
Time-decay needs only the timestamps of one site's own touchpoints relative to the conversion. No cross-site identity is required.
Related pages
- Linear attribution: equal credit to every touch
Linear attribution divides a conversion's credit equally among all touchpoints in the path. It is the simplest multi-touch model: every touch matters the same. That even-handedness avoids the single-touch extremes, but it also pretends a fleeting impression and a decisive demo are worth the same — which is rarely true.
- Position-based (U-shaped) attribution
Position-based (U-shaped) attribution gives most credit to the first and last touchpoints — commonly 40% each — and shares the remaining 20% among middle touches. It tries to honour both discovery and closing while still acknowledging the middle. The specific weights are a convention, not a measured truth.
- Lookback and conversion windows explained
A lookback (or conversion) window is the period before a conversion in which earlier touchpoints are eligible for credit. Touches outside the window are ignored entirely. Because every attribution model only sees touches inside this window, its length quietly governs which channels can ever receive credit.
- Attribution analytics
Weighted paths with explicit assumptions.
Sources and verification notes
Last reviewed 2026-06-24. Facts are checked against primary/official sources where available; uncertain specifics are marked “Data not yet verified” rather than guessed.