Glossary
MTA
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Multi-touch attribution (MTA) is a method that divides credit for a conversion among the ad interactions a person had before converting, instead of giving all the credit to the last click.
The IAB Tech Lab's definition is close to this (IAB Tech Lab, Data Clean Rooms Guidance, v1.0). It credits the touch points, such as channels where a customer saw or engaged with an ad, that led to an action like a purchase. Models range from fixed rules, such as first click or linear, to data-driven models that estimate each touch's contribution from past paths.
The rule-based models are fading. Google Ads no longer supports first click, linear, time decay or position-based models; it moved those conversion actions to data-driven attribution and still offers last click (Google Ads Help, accessed 9 November 2026). MTA needs user-level paths, so cookie loss and closed platforms leave gaps, and clean-room protocols such as ADMaP try to fill them. MTA also measures association, not cause; incrementality tests show whether the ads changed behaviour.
Example: A shopper sees a display ad, clicks a social ad, clicks a search ad and buys from an email link. Last click gives the email 100% of the credit. A linear model gives each touch 25%. A data-driven model might give search 40% if paths with search convert far more often.
Related terms: MMM, attribution window, ADMaP, incrementality, clean room, geo holdout.