There is a moment familiar to anyone who has run a marketing budget. You change the attribution setting in your analytics platform, and the channel that was underperforming last week is suddenly carrying half your pipeline. Nothing about the actual marketing changed. Only the arithmetic used to describe it.
That moment is worth sitting with, because it reveals what marketing attribution really is. It is not a measurement. It is a rule for dividing credit, and the rule is chosen rather than discovered.
What the Models Assume
Last click is the default nearly everywhere, and it awards everything to the final touchpoint before conversion. It is simple, it is defensible in a meeting, and it systematically flatters whatever sits closest to the purchase. Branded search does astonishingly well under last click, which is unsurprising, because someone typing your company name has already been persuaded by something else.
First click has the opposite bias, handing everything to whatever introduced the customer and nothing to the work that closed them. Linear splits credit evenly across every touchpoint, which is fair in the sense that a raffle is fair. Time decay weights recent interactions more heavily. Position-based models give the largest shares to the first and last touches and divide the remainder among the middle.
Data-driven attribution uses the platform's own modelling to assign credit based on observed patterns, which is genuinely better and comes with two caveats: it needs substantial volume to be stable, and it is a black box owned by a party with an interest in how the answer comes out. The academic framing of attribution in marketing has been arguing about this for years without arriving at a model that is right rather than merely less wrong.
The practical takeaway is that comparing channels across different attribution models is meaningless, and that whichever model you adopt, you should expect it to overstate something.
The Measurement Is Getting Worse, Not Better
An uncomfortable trend runs underneath all of this. The tracking that attribution depends on has degraded substantially.
Third-party cookies have been restricted or removed across major browsers. Mobile platforms require explicit consent for cross-app tracking, and most people decline. Large platforms report conversions inside their own walls using their own rules, which is why the numbers in three ad dashboards never sum to the number in your analytics. And an increasing share of genuine influence happens where no tracking exists at all: private messages, group chats, podcasts, a recommendation over coffee.
None of that shows up. So a model that already rested on assumptions is now applying those assumptions to a shrinking and unrepresentative sample of what actually happened.
Why B2B Is Harder Still
Anyone doing b2b marketing attribution faces the problem in acute form. Purchases are made by committees rather than people, cycles run for months, and the person who read your comparison article in March is frequently not the person who submits the form in September.
Lead-level attribution attributes the deal to whoever happened to fill in the form, which in enterprise sales is often a junior researcher acting on somebody else's instruction. Account-level thinking is closer to reality but far harder to instrument, and the honest conclusion is that no touchpoint model will capture a nine-month decision made by seven people, three of whom never identified themselves.
What to Use Instead, or Alongside
The most reliable answer is also the least fashionable: run experiments. Turn a channel off in some regions and not others, or hold back spend for a defined period, and measure the difference in total outcomes. Incrementality testing answers the question attribution only pretends to answer, which is whether this spend caused anything.
Media mix modelling works at a higher altitude, using aggregate spend and outcome data over time rather than individual journeys. It ignores tracking entirely, which is now an advantage, and it requires a decent history of varied spend to say anything useful.
And then there is the question that costs nothing: asking people how they heard about you, in an open text field, at the point of purchase. The data is messy and self-reported and it will surface channels your analytics has never mentioned. Used as a cross-check rather than a source of truth, it is one of the highest-value fields on any form, a point marketers in the r/marketing community make repeatedly when comparing dashboard figures against what customers actually say.
The Numbers That Survive All This
When per-channel credit is contested, the aggregate figures become more important, not less.
Work out how to calculate customer acquisition cost properly and use it as your anchor: total sales and marketing spend for a period, including salaries, tools and agency fees, divided by the number of new customers acquired in that period. Be consistent about what is included, and be honest about the lag between spending money and acquiring somebody.
Then set it against lifetime value and against payback period. A blended acquisition cost that is stable or falling while volume grows tells you the machine is working, regardless of which channel your dashboard is currently flattering. That framing also makes budget arguments less theological, which is really what the seo vs ppc debate needs, since the two are usually measured under models that structurally favour one of them.
The same caution applies when you expand internationally. Attribution across markets compares channels that carry entirely different intent, and a term that converts in one language may not exist as a search behaviour in another, which is why localization and SEO translation belong in the measurement conversation rather than after it.