Multi-Touch Attribution

Multi-touch attribution (MTA) is an attribution approach that accounts for the value contributed by multiple touchpoints across the customer journey, instead of assigning all the credit to a single interaction. Where single-touch models like last click or first click value only one contact point, MTA distributes the conversion proportionally across all relevant touchpoints.

The goal of multi-touch attribution is a more realistic assessment of advertising impact: not only the last click before the purchase should count as effective, but also the display ad that sparked interest, the video that explained the brand, and the retargeting that brought the user back. Each touchpoint gets a fair share – based on its actual influence.

Modern customer journeys are complex. A typical Amazon buyer interacts with several touchpoints before purchasing: they see a DSP display ad, click on a Sponsored Brands ad, compare products, come back through a Google search, and finally buy via a Sponsored Products ad. In the last-click model, only the last step gets the credit – all previous interactions appear worthless.

This leads to systematic bad decisions: awareness campaigns (DSP, video, Sponsored Brands) get cut because they deliver no direct ROAS in last-click reporting. Shortly afterward, brand searches and the conversion rates of lower-funnel campaigns decline – because the supply of new prospects has dried up. Multi-touch attribution solves this problem by making the contribution of each touchpoint visible.

Multi-touch attribution covers several attribution modeling approaches that differ in how they distribute the credit. The most common ones:

Amazon uses last-click attribution in its standard reports. Anyone who wants to run multi-touch analyses has to turn to advanced tools.

In practice, most brands start with simple comparisons: last-click ROAS versus total ROAS (incl. view-through conversions). The difference already shows how much value sits in the non-final touchpoints. AMC then offers the next step toward data-driven multi-touch attribution.

• Data silos: Amazon, Google, and Meta each have their own attribution systems that don't talk to each other. Cross-channel MTA requires a shared data layer or tools like AMC.
• Data volume: Data-driven MTA needs thousands of conversions to deliver statistically reliable results. For smaller sellers with only a few hundred sales per month, rule-based models (linear, position-based) are more realistic.
• Offline touchpoints: Word-of-mouth recommendations, influencer content, or print advertising are hard to track and are missing from the MTA analysis. The results always reflect only the measurable part of the journey.
• Complexity: MTA results are harder to communicate than a simple last-click ROAS. Rolling it out requires buy-in from all stakeholders who work with the reports.