Multi-Touch Attribution
Last updated September 2026
Multi-touch attribution assigns conversion credit across every marketing touchpoint a customer interacted with before converting, rather than crediting only the first or last click. It gives a more accurate picture of which channels actually contribute to a sale, especially for longer buying journeys that cross multiple sessions and channels.
Most buying journeys are not one click and done. A person sees a display ad, searches the brand a week later, clicks an organic result, then converts a month after that from an email link. Last-click attribution gives all the credit to the email. First-click gives it all to the display ad. Multi-touch attribution splits credit across all of it, based on a model for how much weight each touchpoint deserves.
01
Common attribution models
Linear attribution splits credit evenly across every touchpoint. Time-decay weights recent touchpoints more heavily than early ones. Position-based models give extra weight to the first and last touch while splitting the rest. Data-driven attribution, the model GA4 uses by default, lets an algorithm assign weight based on actual patterns in the account's own conversion paths, rather than a fixed rule.
02
Why this matters for channel decisions
A channel that never appears as the last click before a conversion, but consistently shows up early in a customer's path, will look worthless under last-click attribution and get cut, even if it is actually driving the awareness that makes every other channel more effective. Multi-touch attribution is the correction for that blind spot.
- 01
Linear: equal credit to every touchpoint in the path.
- 02
Time-decay
more credit to touchpoints closer to the conversion.
- 03
Position-based
extra weight on first and last touch, less on the middle.
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Data-driven
algorithmic weighting based on the account's actual conversion patterns, GA4's default.
03
Where this shows up in Conduit's reporting
Conduit configures GA4's attribution reporting as part of the GPS setup, so agencies can see the full path to conversion, not just the last click, when deciding where to shift budget across paid, SEO, and content. Cutting a channel based on last-click data alone is a common, avoidable mistake, the kind of gap a KPI vs KPA review is built to catch.
Agencies should be plain with clients about the limits of MTA models too: they estimate influence, they do not measure it directly the way a holdout test does, and the estimate is only as good as the tracking feeding it.
