Your report says Google brought the sale. The content team knows that person showed up three weeks earlier through an article, followed along by email, and only searched your brand at the very end. Both are true: what changes is the attribution model, the rule that decides who gets credited with the goal. Picking it without thinking is how budget moves to the wrong channel while looking data-driven.
What each model rewards
An attribution model splits credit for a conversion across the touchpoints that preceded it. The ones you will find in any platform:
- Last click. All credit to the final touch. Rewards closing channels: brand search, retargeting, cart emails. It is the default almost everywhere and the easiest one to misread.
- First click. All credit to the first touch. Rewards discovery and hides whatever sustained the decision for weeks.
- Linear. Splits evenly across every touch. Honest and not very informative: it treats an impression the same as a booked demo.
- Position based. More weight to the first and last touch, the rest shared. A reasonable middle ground when the journey is short.
- Data driven. The platform splits credit with its own model. Useful, with big fine print: it only sees what happened inside that platform.
The problem no model solves
They all split credit for something that already happened. None of them answers what would have happened without that channel, which is the only question that justifies a budget. That answer comes from a different measurement, built on control groups rather than sharing rules: it is the gap between attribution and incrementality.
And there is a problem that comes before the model. If every platform counts conversions by its own criteria, the reported total beats the real sales of the business and nobody knows which number to trust. Before arguing about models, every channel has to report against the same data, offline sales included. That is what a CDP is for.
How to choose without losing your mind
- Start from the decision, not the model. Write down what you would do differently depending on the result.
- If the decision is moving money across channels, use a multi-touch model and read the series over time, not one month’s snapshot.
- Unify the source: one definition of a conversion for every platform.
- Whatever moves the needle, validate it with an incrementality test before scaling it.
A well chosen attribution model organises the argument. What ends it is measuring on your own data, which is exactly the job of a data marketing agency.