Ask three marketers which attribution model is the right one and you’ll get three answers and a turf war. That’s because there is no right one. An attribution model isn’t a measurement — it’s an opinion about who deserves credit for a sale, written down as math. The useful question was never “which model is correct?” It’s “which opinion helps the decision in front of me?”
What a model actually decides
A buyer touches your brand several times before they convert: an ad, a search, an email, a return visit. Attribution decides how to split the credit for that one sale across those touches. Hand it all to the first touch and you reward discovery. Hand it all to the last and you reward the close. Same journey, wildly different story — and the model is the storyteller.
The five models, at a glance
| Model | Who gets credit | Best for | Blind spot |
|---|---|---|---|
| First-touch | The first interaction | Demand gen, new markets | Ignores everything that closed the deal |
| Last-touch | The final interaction | Short, simple sales cycles | Over-credits brand and bottom-funnel |
| Linear | Every touch equally | Long, multi-touch journeys | Treats a glance like a sales demo |
| Time-decay | Recent touches more | Considered B2B purchases | Still under-credits early discovery |
| Data-driven | Modeled per touch | High-volume accounts | A black box you can’t fully audit |
The asterisk nobody mentions
Here’s what most explainers skip: a growing share of the data feeding these models is now modeled, not measured. Privacy changes broke the deterministic trail, so platforms estimate the gaps. “Data-driven attribution” often means “a vendor’s model filling holes with assumptions you’ll never see.” That’s not useless — but false precision is dangerous.
Stop hunting for the true model. Pick the lens that fits the decision — and be honest about what it can’t see.
How to actually choose
A framework that ends the religious war:
- Growing awareness or entering a new market? Lean first-touch.
- Short cycle, one or two touches? Last-touch is fine, and simpler.
- Long B2B journey you’re trying to understand? Linear or time-decay.
- High volume and a modeling layer you trust? Data-driven, with guardrails.
- Reporting to a CFO? Anchor everything to blended results and revenue.
The move that beats picking a model
The teams that escape the debate stop treating any one platform’s attribution as gospel. They unify touches into a single dataset, apply one consistent set of rules, and report one reconciled number — then use the per-model views for diagnosis, not for the headline. That’s the subject of why your numbers never match, and it’s what Maven is built to do. Getting ROAS right is the next piece — or book a demo and see your own channels reconciled on one screen.
Jamie Isabel
Founder at Maven
