Multi-touch attribution
Updated 2026-11-02
Definition
Multi-touch attribution (MTA) splits the credit for a sale across more than one visit. Single-touch models give everything to one moment. Multi-touch models recognize that most purchases follow several visits from several channels.
The common rule-based versions are linear, which splits credit equally, and U-shaped, which favors the first and last visits. Time decay, which favors recent visits, is another. Data-driven models try to learn the weights from your data.
Example
This example uses made-up numbers. A buyer pays $800 after visits from organic search, email, and paid search, in that order.
| Model | Organic search | Paid search | |
|---|---|---|---|
| First-touch | $800 | $0 | $0 |
| Last-touch | $0 | $0 | $800 |
| Linear | $266.67 | $266.67 | $266.66 |
| U-shaped | $320 | $160 | $320 |
The multi-touch rows show email's contribution. Under either single-touch model, email looks like it did nothing.
Strengths and limits
Multi-touch gives a fuller picture of how channels work together. It still can't see what isn't tracked. Word of mouth, podcasts and private messages are missing, and it can't track a person across devices until they identify themselves.
Attribution also shows correlation. A channel earning credit doesn't prove the sale wouldn't have happened without it. Use it to compare channels, and use tests to confirm big decisions.
How Headstream measures it
Headstream offers two multi-touch models, linear and U-shaped, next to first-touch and last-touch. Linear is the default. Every model runs hourly across all of your payments, so you can compare them on the same data.
Headstream links visits across devices once the same person is identified by email. Attribution relies on Stripe, and connecting Stripe is in beta. To see any payment's split, open its payment trail. The marketing attribution guide goes further.