Measurement · also called MTA
Multi-touch attribution (MTA)
Multi-touch attribution (MTA) splits credit for a conversion across several ads and touchpoints a customer encountered, using rules or statistical models, instead of giving it all to one.
MTA tries to reflect reality: people usually see several ads before they buy. It tracks a customer's path of exposures and clicks, then shares the credit among them.
Rule-based versions include linear (equal credit), time-decay (more credit to recent touches) and position-based (more credit to first and last touches). Data-driven or algorithmic MTA uses statistical models to compare paths that converted with those that did not, estimating each touchpoint's contribution.
MTA depends on user-level tracking across sites, apps and devices, which third-party cookies, mobile ad IDs and identity graphs used to provide. App Tracking Transparency, browser restrictions and privacy laws have eroded that data, and walled gardens rarely share user-level exposure logs, so many MTA models now see incomplete paths. Even with good data, MTA measures correlation, not causation. That is why many marketers combine it with marketing mix modeling and incrementality tests.
Think of it like this
MTA is like sharing the credit for a group project among everyone who contributed, rather than just the person who handed it in.
An example
A travel booking site in Spain uses a time-decay MTA model: of a €600 booking, €300 is credited to a retargeting click the day before, €180 to a search ad a week earlier and €120 to a display ad two weeks earlier.
Related terms
Attribution
Attribution is the process of assigning credit for a conversion, such as a sale, sign-up or install, to the ads, channels or touchpoints that a customer encountered beforehand.
Last-click attribution
Last-click attribution gives 100% of the credit for a conversion to the final ad a customer clicked before converting, ignoring every earlier ad or touchpoint.
Marketing mix modeling (MMM)
Marketing mix modeling (MMM) is a statistical method that estimates how much each marketing channel, plus factors like price and seasonality, contributed to sales, using aggregate historical data.
Incrementality
Incrementality is the extra result, such as sales, installs or visits, caused by advertising that would not have happened without it, usually measured by comparing exposed and unexposed groups.
Signal loss
Signal loss is the reduction in data available to target, measure and optimise advertising, caused by browser cookie blocking, mobile ID restrictions, privacy laws, consent choices and ad blocking.
Sources: IAB guidelines