Last-click attribution: simple, and what it hides
Last-click attribution assigns 100% of a conversion's credit to the last touchpoint before it. It is simple, deterministic, and the historical default — which is exactly why it misleads: it ignores every earlier touch that created demand, systematically overrating bottom-funnel channels and underrating discovery.
What this means
In last-click, the final touchpoint before a conversion gets all the credit. If a visitor discovered you via a blog, returned via search, and converted from a direct visit, direct gets 100%. It is easy to compute and easy to explain.
What it hides
By definition it ignores everything before the last touch. Awareness channels (content, social, referral) that start journeys are systematically undervalued, and bottom-funnel channels (branded search, direct) are over-credited. That is fine for a quick baseline but dangerous as the only lens for budget decisions.
- All credit to the final touchpoint
- Under-credits discovery and assist channels
- Good as a baseline, weak for budget allocation
How it appears in analytics and logs
Last-click tells you which channel closed the conversion, not which created the demand. A channel that looks weak in last-click may be doing heavy top-of-funnel work.
Diagnostic use case
Use last-click for a simple, stable baseline, while knowing it under-credits awareness and assist channels — and never treat it as the whole truth.
What WebmasterID can help detect
WebmasterID presents directional attribution with per-row confidence instead of a single hard model, so you are not misled by last-click's blind spots.
Common mistakes
- Cutting budget to channels that look weak only in last-click.
- Treating last-click as the true contribution of each channel.
- Ignoring assist/awareness touches entirely.
Privacy and accuracy notes
Attribution joins touchpoints for one site's own visitors; it does not require cross-site identity. WebmasterID keeps attribution directional and first-party, with confidence labels rather than false precision.
Related pages
- Data-driven attribution: promise and caveats
Data-driven attribution (DDA) assigns credit using a model trained on a site's own conversion paths rather than a fixed rule like last-click. Done well it credits assist touches more fairly. Its caveats are real: it needs enough conversion volume, it is a model not a measurement, and it cannot see touches that were never tracked.
- Source / medium: the core traffic-origin dimension
Source/medium is the dimension that records where a visit came from (the source, e.g. google) and how it arrived (the medium, e.g. organic). It is derived from the referrer and UTM parameters, with rules that vary by tool. The big caveat: when neither is available, the visit lands in 'direct / (none)', which is a catch-all, not a channel.
- Conversion rate: definition and denominators
Conversion rate is the share of some base that converted. The trap is the denominator: conversions per session, per user, and per unique visitor give different numbers and mean different things. Without stating the base, a conversion rate is ambiguous — and comparing rates with different bases is meaningless.
- Attribution analytics
Directional attribution with confidence labels.
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.