Attribution

Marketing Attribution Models Compared: Which One Should You Actually Use?

Written by Abdul Kayium

September 4, 2026 10 min read

Blog Attribution Marketing Attribution Models Compared: Which One Should You Actually Use?

Abdul Kayium

Tracking & attribution consultant

September 4, 2026 10 min read

Every attribution model looks at the exact same customer data and tells a slightly different story. None of them is "correct" in an absolute sense — each one optimizes for a different question. The mistake isn't picking the wrong model; it's not knowing which question you're actually trying to answer.

Every major model, side by side

Model	How credit is split	Question it answers
First-touch	100% to the first interaction	What creates awareness / brings in new people?
Last-touch	100% to the final interaction before conversion	What's there the moment someone finally decides to buy?
Linear	Equal split across every touchpoint	A simple, defensible baseline when you don't want to argue about weighting
Time-decay	More credit to touchpoints closer to conversion	Which recent touches mattered most, when recency itself is meaningful
Position-based (U-shaped)	Large fixed share to first + last (e.g. 40/40), rest split across the middle	Balancing 'what found them' against 'what closed them'
Data-driven	Calculated per-account from actual conversion patterns	The most accurate picture, given enough data to calculate it from

First-touch and last-touch are covered in full depth, with a worked example, in first-touch vs. last-touch attribution. Linear, time-decay, and position-based are covered together, alongside data-driven, in multi-touch attribution. This article is the decision layer on top of both — how to actually pick, given your specific situation.

What Google Ads' own shift tells you

It's worth noting that Google Ads itself moved away from offering first-click, linear, time-decay, and position-based as selectable options — they're no longer supported, with accounts automatically upgraded to data-driven attribution as the default. That's a meaningful signal about where the industry has landed: fixed-rule models are a reasonable starting point, but a data-driven approach — where the weighting comes from your actual conversion patterns instead of an arbitrary formula — tends to outperform them once there's enough data to support it.

A practical way to choose, without a data science degree

If you're just starting to look past last-click reporting:

Start with position-based (U-shaped). It's the easiest model to explain to a founder, client, or CFO — "the touch that found the customer and the touch that closed them both matter most" — and it doesn't require significant conversion volume to be meaningful.

If your sales cycle is very short and mostly single-session:

Last-touch alone can be a reasonable simplification — there often isn't much journey to lose information from. This is genuinely rare outside of impulse-purchase ecommerce and some direct-response categories.

If you have meaningful, ongoing conversion volume:

Move to data-driven attribution. It adapts to your actual customers instead of applying the same fixed assumption every account gets, and it's specifically designed to handle exactly the kind of multi-channel, multi-session journeys covered in customer journey analytics.

If your real conversion happens outside your website entirely (B2B):

The model matters less than making sure the data feeding it is complete — see B2B attribution and offline conversion tracking. A sophisticated model applied to incomplete data (missing everything that happens in your CRM) will still give you a misleading answer.

The honest caveat

No model perfectly reconstructs why a person actually bought something — that decision happens in their head, not in your analytics platform. Every model here is a reasonable approximation, not a ground truth. The goal isn't finding the one "correct" model; it's picking one that's honest about what it's measuring, and being consistent about it so your reporting doesn't silently shift underneath you.

The model is secondary to the visibility

Every model on this list is trying to summarize the same underlying thing: the actual sequence of touchpoints a customer went through. If you can already see that full sequence — every channel, every campaign, every gap in between — the exact model you apply on top of it becomes a much lower-stakes decision, because you're never blind to what's actually happening. That visibility, more than any specific model, is what separates useful attribution from a report nobody trusts. If you haven't already, start with what marketing attribution actually is and work outward from there.

Frequently asked questions

What's the best attribution model?

There isn't a universally best model — each one answers a different question. Position-based is a good general starting point; data-driven attribution tends to be most accurate once you have enough conversion volume to support it.

Why did Google Ads remove some attribution models?

Google deprecated first-click, linear, time-decay, and position-based as selectable models in Google Ads, upgrading accounts to data-driven attribution by default — reflecting that a model calculated from actual account data tends to outperform fixed-rule approximations.

Can I use different attribution models for different reports?

Yes, and it's often useful — looking at the same data through both a first-touch and a last-touch lens, for example, reveals which channels are doing acquisition work versus closing work, which neither model shows on its own.

How much conversion data do I need before data-driven attribution is reliable?

This varies by platform and isn't a fixed number, but generally you need consistent, ongoing conversion volume for the model to find reliable patterns. Lower-volume accounts often get more stable, explainable results from a simpler fixed-rule model like position-based.