Marketing attribution models, compared

Every marketer eventually hits the same wall: a contact saw a blog post, clicked a LinkedIn ad, opened two emails, and then converted off a branded search. Which one gets the credit? That is the entire job of an attribution model — and the reason marketing attribution AI exists is that no single model answers it honestly. This guide compares the five standard models, shows where each one quietly lies to you, and explains how dolv’s funnel intelligence runs all of them on the same data so you stop arguing about which channel “really” worked.

What an attribution model actually decides

An attribution model is a rule for splitting credit for a conversion across the touchpoints that led to it. That is the whole definition — and if the term is new to you, the marketing attribution glossary entry lays out the baseline. The disagreement is never about the data; everyone sees the same journey. It is about which interaction deserves the win. Change the rule and the same campaign can look like a hero or a waste, which is exactly why the model you pick quietly decides where next quarter’s budget goes.

Two big families exist. Single-touch attribution hands 100% of the credit to one interaction — the first or the last. Multi-touch attribution distributes credit across every touch in the path. Single-touch is simple and wrong in opposite directions; multi-touch is closer to reality and harder to read at a glance. The practical move is not to crown one model but to read several, which is what we will do below.

The five attribution models, side by side

Imagine one buyer with four touches before they convert: a blog post (TOFU), a LinkedIn ad (TOFU), a nurture email (MOFU), and a demo request (BOFU). Each model splits 100% of the credit across those four touches differently — same journey, five very different stories. Watch the LinkedIn ad as you read: under both single-touch models it earns exactly zero, and under all three multi-touch models it earns real credit. That gap is a budget decision in disguise.

First-touch attribution

All credit to the first interaction. First-touch attribution answers “what created this demand?” It flatters top-of-funnel channels — SEO, brand content, paid social — and is useful when your problem is pipeline volume. Its blind spot is conversion: it cannot tell you what actually closed the deal, because it stopped watching after the first click.

Last-touch attribution

All credit to the final interaction before conversion. Last-touch attribution answers “what closed this?” It is the default in most ad platforms because it is easy and self-serving — the platform takes credit for the last click. It systematically underrates everything that built awareness, which is why branded search and retargeting always look like geniuses while your real demand creation looks like a cost center.

Linear attribution

Credit split evenly across every touch. Linear attribution is the simplest multi-touch model and a fair baseline — it refuses to play favorites. Its weakness is that it treats a throwaway impression and a sales call as equal, which is rarely true. Use it as the neutral reference point you compare the weighted models against when a budget fight needs a tiebreaker.

Time-decay attribution

More credit to touches closer to the conversion. Time-decay attribution assumes recency matters — the email two days before signup mattered more than the blog post from three months ago. It is a strong fit for long, considered B2B cycles where late nurture genuinely moves the needle, and a poor fit when one early piece of content did all the persuading.

Position-based (U-shaped) attribution

40% to the first touch, 40% to the last, 20% spread across the middle. Position-based attribution — also called U-shaped — is the pragmatic favorite because it credits both the channel that created the lead and the one that closed it, while still acknowledging the middle existed. If you only had time to read one multi-touch model, this is usually the most balanced.

Why single-touch attribution quietly misleads you

Look back at the comparison. Under first-touch and last-touch, the LinkedIn ad earned exactly zero credit — yet under all three multi-touch models it earned real credit. If you run last-touch only, you would conclude that LinkedIn does nothing and cut it, when in reality it was a load-bearing part of the journey. That is not a rounding error; it is a wrong decision baked into the model.

This is the core argument for multi-touch attribution: real buying is a path, not a click. The goal is not to find the one true model — it is to see the same conversions through several lenses and pay attention to where they disagree. Agreement is confidence; disagreement is your next experiment. For the broader picture of reading the whole journey rather than a single metric, see how AI prepares and measures real campaign work.

Where marketing attribution AI changes the game

A spreadsheet can hold five attribution models. What it cannot do is keep them live, tie them to funnel health, and turn the result into action. dolv is a grounded AI command center — 25+ tools that execute real work, not a chatbot that returns text — and attribution is one input among many. The same conversion data that drives the five models also feeds a unified TOFU/MOFU/BOFU funnel with one weighted composite health score, so attribution and funnel diagnosis read from a single source of truth instead of two arguing dashboards. If you are weighing a command center against a marketing cloud, how dolv approaches AI marketing automation shows where the philosophies diverge.

Read the command center top to bottom and you see the whole point of marketing attribution AI. The five models disagree about LinkedIn, so the system flags it instead of hiding it. A cross-metric correlation engine adds context the attribution split alone cannot — for example, that MOFU email opens move with demo requests at r .71, separating signal from coincidence. And the funnel panel weights stages TOFU .25 / MOFU .40 / BOFU .35 against a rolling 30-day baseline, so “where do we spend?” is answered with weighted evidence, not a hunch.

From attribution to a decision you can ship

Attribution is only worth the effort if it changes what you do next. In dolv, an agent can read the split, notice an underrated channel, and prepare the work — a budget shift, a new campaign, an ICE-ranked experiment validated with a real z-test. But preparing is not shipping. Anything that touches the outside world — a change to Google, Meta, or LinkedIn Ads — lands in the Approvals inbox and waits for a human. The lifecycle is fixed: prepared → approved → executing → done.

That gate is what separates an agent from a runaway automation. Attribution can suggest where to move money; it never moves it silently. Each agent carries a role and a $250/mo budget cap with full run history, so you can see exactly what was proposed, what it would cost, and what is waiting on your sign-off. When the Director orchestrates a multi-agent campaign, adding agents means more prepared work waiting for you — not more unsupervised spend in the wild. The difference between reasoning agents and rule-based triggers is worth its own read — see AI agents vs marketing automation.

Grounding keeps attribution honest, not generic

There is a subtler reason attribution lives inside a grounded command center rather than a standalone tool. The moment an AI starts explaining the numbers — writing the budget rationale, drafting the experiment brief, summarizing the funnel for a stakeholder — it has to know your business, not just generic marketing. dolv grounds every model call by default with your company profile, playbooks, and knowledge base, so the analysis reads like your team wrote it. We go deep on why that matters in how to keep AI on-brand.

Grounding also depends on connection. Attribution is only as good as the touches it can see, so the 20 read + write integrations matter — Gmail, GA4, Search Console, the CRM, and the ad platforms. With the full journey connected, intent-signal lead scoring and the unified funnel draw from the same live data the five models read, which is what keeps a model’s “LinkedIn is underrated” claim grounded in reality rather than a partial export.

A quick rule of thumb for picking a lens

Stop hunting for the one true model and instead match the lens to the question in front of you:

• Optimizing top of funnel? Lead with first-touch and position-based to credit demand creation fairly.
• Optimizing the close? Lean on last-touch and time-decay to see what actually converts late-stage intent.
• Settling a budget fight? Use linear as the neutral baseline, then show where the weighted models disagree.
• Diagnosing a leak? Pair attribution with the weighted TOFU/MOFU/BOFU funnel to find the weakest stage before you move spend.

The takeaway: read several, decide once

Stop hunting for the one true attribution model — it does not exist, and the search wastes the data you already have. First-touch and last-touch each tell half a truth; linear, time-decay, and position-based fill in the middle from different angles. The honest practice is to read all five on the same journey, treat agreement as confidence and disagreement as a prompt to experiment, and connect the result to a funnel that can act. That is the execute-and-measure loop, and it is what marketing attribution AI in dolv is built to run — five models, one funnel, a human on the gate. dolv it.