What is multi-touch attribution?
Multi-touch attribution (MTA) is a way of measuring marketing performance that gives credit for a conversion to every tracked touchpoint that led to it, not just the last one. If a customer saw a video ad, clicked a display ad and then searched for the brand before buying, MTA assigns each of those steps a share of the sale.
The goal is to understand which channels, campaigns and creatives actually move people toward a purchase, so budget can be shifted to match.
How multi-touch attribution works
MTA tools collect user-level data on ad impressions, clicks and site visits, link them to the same person or device, and then apply a model to split the credit. Common models include:
Linear: every touchpoint gets equal credit.
Time decay: touchpoints closer to the conversion get more credit.
Position-based (U-shaped): the first and last touchpoints get most of the credit, often 40% each, with the remaining 20% shared across the middle.
Data-driven: an algorithm compares converting and non-converting paths to estimate each touchpoint's contribution.
For example, take a $100 sale with four touchpoints. A linear model gives each one $25. A position-based model gives $40 to the first, $40 to the last and $10 to each of the two in the middle.
Data-driven models are now the default in many analytics and ad platforms, because they adapt to your actual conversion paths instead of fixed rules. They need enough conversion volume to work well, so smaller accounts often fall back to simpler rule-based models.
Why multi-touch attribution matters
Last-click reporting overvalues the final step, usually search or retargeting, and undervalues the display, video and CTV that created demand earlier. MTA gives a fuller picture of the path, compares channels on a common basis and helps improve ROAS by moving spend to what actually contributes.
It has real limits. MTA depends on tracking individuals across sites and devices, which has become harder with cookie restrictions, mobile ID opt-ins and walled gardens that do not share user-level data. It also shows correlation, not cause: a touchpoint on the path did not necessarily change the outcome. That is why many advertisers pair MTA with incrementality tests. Marketing mix modeling, which works with aggregated spend and sales data instead of user paths, is another common complement.
Multi-touch attribution vs last-click attribution
Last-click attribution gives 100% of the credit to the final click before a conversion. It is easy to set up and explain, but it ignores every ad that came earlier, including views that never got a click. Multi-touch attribution spreads credit across the path. It is more complex and needs better data, but it gives a more balanced view of how channels work together.
Multi-touch attribution with AdGeeks
DV360, Amazon DSP and Adform each report on click and view based conversions, and our traders combine that data with your analytics to judge which tactics contribute. Where attribution alone cannot answer the question, we recommend a lift test. To talk through your measurement setup, request access.
