What is multi-touch attribution?
Multi-touch attribution (MTA) is an approach that spreads credit for a conversion across several touchpoints in a customer's journey, instead of giving it all to one. Credit can be split by rules or by statistical models.
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How multi-touch attribution works
MTA collects the sequence of tracked touchpoints for each customer, such as an ad click, an email open, and a site visit. It then assigns each one a share of the conversion.
Splits can be even, weighted toward the start and end, weighted by recency as in time-decay attribution, or learned from data as in data-driven attribution.
Limits of multi-touch attribution
MTA only sees touchpoints it can track and link to the same person. Privacy rules, cookie limits, and cross-device behavior leave gaps, and offline channels are usually missing.
It also doesn't prove cause on its own. Pair it with experiments or marketing mix modeling for a sturdier read.
Common questions
What is the difference between single-touch and multi-touch attribution?
Single-touch models, such as first-click and last-click, credit one touchpoint. Multi-touch models share credit among several.
What are examples of multi-touch models?
Common examples are linear, which splits credit evenly, time-decay, which favors recent touches, position-based, which favors the first and last, and data-driven models. Google Ads and Google Analytics 4 no longer offer the linear, time-decay, or position-based models.
Why is MTA harder now?
Privacy regulations, browser limits on cookies, and consent choices reduce how much of each journey can be tracked, which leaves gaps in the paths.
Does MTA include offline channels?
Generally not, because it relies on user-level tracking. Marketing mix modeling is better suited to offline channels.
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