Marketing Attribution Statistics 2026: Sourced Data Only

Most marketing attribution statistics pages are compilations of compilations. A figure from a vendor survey is quoted by a blog, re-quoted by a second blog with the sample size dropped, and arrives in a slide deck as a fact about "marketers". This page does the opposite. Each of the fifty-five rows below was read in the publisher's report or release, or a verbatim copy of it where the original was blocked, on November 5, 2026, and carries the publisher, the report title, the date, the sample where stated, and a label for what kind of evidence it is.

Attribution here means the family of methods marketers use to credit outcomes to the media that produced them: rule-based and data-driven multi-touch attribution, incrementality experiments that compare exposed and unexposed groups, and marketing mix models that estimate channel contribution from aggregate data. The sources below cover all three, because the most useful 2026 finding is about how they are used together.

One honest limit before the numbers. We looked for a large, published survey of attribution practice specific to B2B teams and did not find one among primary sources. Where a row is about consumer app marketers, US buy-side media decision-makers or large enterprises, the sample column says so, and the reader should not stretch it further than that.

The last column of every row carries two things: the sample, as the source states it, and an evidence type. Survey means an independent or trade-body questionnaire with a stated population. Vendor-run survey means the same instrument fielded by a company that sells measurement, analytics or attribution software; the figures are real but the respondents are often the vendor's own audience. Vendor claim means a product statement or a figure from a vendor's own research with no stated sample. Forecast means a modelled projection, not a measurement.

Where a source states no sample size, the column says "not stated" or "size not stated" rather than omitting the row. A figure without a denominator is still a published figure; it is simply weaker evidence, and a citing writer should carry the caveat with it. One worked example of why this matters: a widely repeated claim that 19% of marketing leaders run no formal attribution model turns out, in the publishing report, to rest on 50 interviews. That is nine or ten people, and it is listed in the "not used" section below rather than in the table.

Rows are grouped by theme rather than by source, so that a reader looking for adoption figures or confidence figures finds them together. Numbering runs continuously from 1 to 55 across the four tables so a row can be cited by number.

The strongest 2026 adoption data comes from the IAB's State of Data report, fielded with BWG Strategy among 430 senior planning and analytics decision-makers at US brands and agencies. Its central finding is that between 67% and 76% of that population use at least one of the three advanced methods, with incrementality testing the most common at 76%, and that only 39% use all three together despite each serving a distinct purpose. The rows that follow it show how differently smaller or vendor-run samples answer the same question.

The example that shows the sample effect: the IAB's 76% for incrementality and Supermetrics' 8% for the same method are both published figures from 2025 and 2026. The first surveyed senior decision-makers at US brands and agencies; the second surveyed just over 200 marketers globally for a marketing-data software vendor's report. The failure case is quoting either as "marketers use incrementality testing" without the population attached.

Three widely quoted confidence figures sit in this group, and they cannot be reconciled because they measure different things. Nielsen's 85% is confidence in measuring holistic ROI, from a global survey by a company that sells measurement, with no sample size stated in the report we read. Gartner's 52% is the share of 378 senior marketing leaders who said they succeeded in proving marketing's value and getting credit for it, in a 2024 survey. Branch's 18% is confidence in tying app installs to their source, among 750 leaders at companies with over 500 employees, from a mobile-attribution vendor. Each is a legitimate data point about its own question.

The CMO Survey's pair of numbers is the most careful statement of the gap. On a seven-point scale, its 308 US marketing leaders rated their companies at 4.5 for generating ROI from marketing technologies and 4.4 for demonstrating it in 2026, up from 4.2 in 2024; the report attributes the gap to persistent measurement and attribution challenges, though its own insight text pairs the 2026 4.5 with the 2024 4.2. The failure case in this group is the missing denominator: Ruler's 43.9% very confident plus 50.2% fairly confident is a real pair, but the page states no sample size or fieldwork date for any of its figures, so treat its rows as indicative rather than precise.

Adoption says little about satisfaction. The IAB asked users of each method whether it delivered the rigour, coverage, timeliness, trust and efficiency needed to assess ROI, and between 60% and 75% said it underperformed, with bases of 292 incrementality users, 315 attribution users and 287 MMM users. The same report carries the AI expectations that vendors now quote back: a 2–3× increase in measurement frequency and a $32 billion opportunity figure that is an IAB model, labelled here as a forecast rather than a measurement.

Google's incrementality rows deserve a specific caution. The 80% figure comes from a Google and BCG survey of 567 US senior marketing analytics professionals at organisations spending over $500,000 a year on advertising, published on a Google Ads Help page that announces improvements to Google's own incrementality product. The cost drop from $100,000 to $5,000 per experiment and the "up to 50% more frequently" conclusive-results claim are product statements. All three are labelled vendor on that basis.

Gartner's CMO Spend Survey is about budgets rather than attribution, and it is included for one reason: its June 2026 release ties spend allocation to measurability. Less mature organisations, in Gartner's words, "may be over-indexing on short-term optimization and channels that are easiest to measure and automate", while spending on loyalty and retention has fallen 29% to under 15% of media spend. That is an attribution finding expressed as a budget finding: what can be measured is what gets funded.

A sourced table is defined as much by what it leaves out. The following figures circulate widely and were excluded because no primary page could be read that states them, or because the primary says something different.

Also excluded: compilation figures attributed to Flint, Kantar, Swydo and others that trace back to third-party citations; a CaliberMind landing-page teaser with no method; and a widely repeated "41% cannot measure ROI" figure attributed to a CRM vendor's annual report, which the report page we fetched does not contain. None of this means those numbers are wrong. It means they could not be verified on November 5, 2026, and a reference table should not carry what its authors could not read.

For a writer citing a row, carry three things with the figure: the publisher, the date and the population. "73% of US buy-side decision-makers use attribution analysis (IAB, February 2026, n=430)" is a claim a reader can check; "73% of marketers use attribution" is not. The row numbers above exist so that a citation can point at one line of one table.

For a marketing leader using the table to argue for budget, the most defensible pairing is adoption against satisfaction: most senior US buy-side decision-makers use the advanced methods and most of those users say the methods underperform. That supports investment in the quality of measurement, not merely in more of it. Our decision matrix for media mix modelling, attribution and MTA sets out when each method is the right instrument, and our MMM versus attribution playbook covers how teams combine them. The GA4 source-group guide is the practical companion to row 15, given how many respondents name GA4 as their primary tool.

For a team deciding what to build, the IAB's 39% is the number to sit with. Fewer than four in ten senior US buy-side decision-makers run incrementality, attribution and MMM together, even though, in the IAB's words, each serves a distinct purpose. The gap between that design and that adoption is where our analytics practice spends most of its time: not choosing a method, but making three of them agree.