Attribution Models Explained: First-Touch, Last-Touch, Linear, and Data-Driven

Attribution models are the rules that decide which ad gets credit for a sale. The model you use changes your reported ROAS, your channel mix decisions, and ultimately where your budget goes.

Most marketers pick the default model their platform uses and never question it. That's a $50,000/month mistake waiting to happen.

Here's what every model actually measures — and what it hides.

The Customer Journey Attribution Models Try to Solve

A typical customer journey looks something like this:

1. Sees your Facebook ad while scrolling (ignores it)
2. Googles your category term and clicks your Google Search ad
3. Visits your site, doesn't convert
4. Gets retargeted on Instagram
5. Returns directly and buys

Five touchpoints. One sale. Which channel gets credit?

Every attribution model gives a different answer.

First-Touch Attribution

What it does: Gives 100% credit to the first touchpoint — the Facebook ad in the example above.

Strength: Shows you which channels bring new customers into your funnel. Good for top-of-funnel budget decisions.

Weakness: Completely ignores everything that actually closed the sale. You'd cut Google retargeting because it "doesn't get credit," then watch conversions fall.

Best for: Brand awareness tracking, mapping where discovery happens.

Last-Touch Attribution

What it does: Gives 100% credit to the final touchpoint before conversion — the direct visit in the example.

Strength: Simple, deterministic, easy to explain.

Weakness: Over-credits bottom-funnel channels (branded search, email, retargeting) and under-credits everything that created demand. You'll pour budget into retargeting forever and starve the top of funnel.

Google Analytics uses last-touch by default. So does every ad platform's native dashboard.

Best for: Literally nothing — it's the worst model for decision-making, and still the most widely used.

Linear Attribution

What it does: Splits credit equally across all touchpoints.

Strength: Acknowledges that multiple channels contributed. Better than last-touch for understanding the full journey.

Weakness: Equal credit is rarely accurate — a view of an ad and a click that drove a site visit are not equally valuable.

Best for: A reasonable default if you can't run data-driven attribution yet.

Time-Decay Attribution

What it does: Gives more credit to touchpoints closer to the conversion, less to early ones.

Strength: Intuitively reasonable — the last few touches likely had more influence.

Weakness: Still ignores which touchpoints actually caused conversions vs. coincidentally preceded them.

Best for: Short purchase cycles where recency genuinely predicts influence.

Position-Based (U-Shaped) Attribution

What it does: Gives 40% credit to first touch, 40% to last touch, and splits the remaining 20% across middle touchpoints.

Strength: Values discovery and conversion equally — the insight that opened the relationship and the one that closed it.

Weakness: The 40/40/20 split is arbitrary. Why not 35/35/30?

Best for: Businesses where both first brand exposure and final conversion touch are meaningful.

Data-Driven Attribution

What it does: Uses machine learning to assign credit based on which touchpoints statistically correlate with conversion — not rules.

Strength: Most accurate in theory. Looks at actual conversion paths vs. non-conversion paths and infers causal weight.

Weakness: Requires volume (Google requires at least 3,000 conversions/month). Black-box — you can't audit why a touchpoint got the credit it did. Still operates within a single platform's data.

Best for: High-volume advertisers running within a single platform (Google Ads, Meta).

The Problem None of Them Solve

Every attribution model above has a fatal limitation: they only see what their platform can see.

Google's data-driven model is data-driven within Google. It can't see that the customer also clicked a Meta ad. Meta's model can't see your Google touchpoints.

True cross-channel attribution requires pulling data from all platforms into one place, deduplicating conversions using your actual revenue source, and applying attribution across the full customer journey — not just within one platform's walled garden.

That's exactly what AdBliss does.

Which Model Should You Use?

The honest answer: no single model inside a single platform gives you accurate cross-channel attribution. You need a source of truth that sits outside the platforms.

Connect all your channels and get accurate attribution →