B2B marketing and communications budgets increasingly move on the strength of a dashboard, but new research finds that fewer than half of the people building those dashboards fully trust what is in them. Only 49% of respondents are very confident in the accuracy and completeness of their own data, according to a 400-person study from 10Fold, even as that same data is cited to justify strategy or budget decisions by 88% of respondents in paid social, 87% in paid media and digital, and 85% in owned content.
The gap matters because measurement has become the currency marketing spends to defend its budget. When the currency itself is suspect, every downstream argument about what worked and what to fund next inherits that doubt.
The Confidence Gap by the Numbers
The study, “The Communications ROI Reset: What B2B Leaders Measure, Trust And Act On,” was commissioned by 10Fold and conducted by Sapio Research among 400 marketing and communications leaders at companies in the United States, United Kingdom, France, and Germany. Respondents ranged from C-level executives to directors, department heads, and managers, all responsible for measurement in some form.
The headline finding is not that teams lack data. It is that they cannot connect the data they have. Sixty-seven percent use website analytics, another 67% use social analytics, 63% pull from CRM systems, and 58% draw on marketing automation platforms. Only 35% of respondents say that reporting is fully integrated across earned media, paid social, content, and digital channels. Another 19% call their reporting partially integrated, and 18% say it is integrated but the attribution behind it is unclear or inconsistent. Thirty-seven percent still stitch reports together manually in spreadsheets.
That fragmentation is why a marketer can be swimming in dashboards and still not trust the number that lands on a CFO’s desk. Each system tells a partial, internally consistent story; nothing forces the stories to agree.
Executives Trust Outcomes, Not Activity
The research also ranks what the C-suite actually believes. Revenue impact is the single most trusted metric among CEOs and boards, at 34%. Pipeline influence and media coverage volume tie for second at 16% each, and share of voice trails at 11%. That ordering is a warning to any team still leading budget conversations with reach or impressions: the audience making the funding decision is already discounting activity metrics in favor of business outcomes.
Some of that shift downstream is already underway. Forty-eight percent of respondents now track leads or conversions influenced by earned media, edging out the 45% who still track placement counts or mentions. Once someone engages, 51% follow social ad click-through rates and 41% track form fills or inquiries. Multi-touch attribution remains the most common method for tying communications to outcomes, used by 43% of respondents; another 25% rely on correlation or directional analysis instead of a defined attribution model, a gap that helps explain the underlying confidence problem. It is the same structural weakness MarTech has covered in ad measurement, where the industry is only beginning to grade its own bias rather than assume its numbers are neutral.
AI Visibility Just Added a New Column
Teams are not waiting for existing measurement gaps to close before taking on a new one. Fifty-four percent of respondents now measure AI search visibility or brand citations in AI-generated content, and 46% already include AI visibility or AI optimization in C-suite reporting. That is a fast climb for a metric that barely existed in board decks two years ago, and it is arriving before most organizations have solved integration for the channels they have measured for a decade.
Piling a new, unproven metric on top of an already fragmented stack is how confidence gaps compound. A team that cannot yet reconcile CRM data with social analytics is unlikely to reconcile either of those with an AI-citation count pulled from a third tool with its own definitions and its own blind spots. The instinct to report on AI visibility is not the problem. Reporting it in isolation, the way earned media and paid media were reported in isolation for years, repeats the mistake the rest of the study describes.
Where the Data Sits Matters as Much as What It Says
The study’s regional and revenue-tier breakdowns point to the same root cause everywhere: ownership. Respondents across the U.S., U.K., France, and Germany described the same pattern of systems that were bought by different teams, at different times, to answer different questions, then never rationalized into one source of truth. No single vendor swap fixes that, because the fragmentation is organizational before it is technical. A CRM, a social listening tool, and a marketing automation platform can each be functioning exactly as designed and still produce three numbers that will not add up on the same slide.
What It Means for the Marketing Leader
“Marketing leaders are not short on metrics. They are short on connected metrics,” said Susan Thomas, CEO of 10Fold. “The C-suite does not need a longer dashboard. It needs a credible story that shows how visibility creates trust, how trust creates action and how action contributes to business growth. And, ideally, the communications metrics are aligned with other marketing programs, to support a fair attribution model. That is the communications ROI reset.”
Thomas’s point cuts against the instinct to respond to a trust problem by adding another tool or another chart. The study’s own data argues for the opposite: fewer, connected sources beat more, disconnected ones. Teams already running platforms that are shifting from dashboards toward analysis have a head start, because the underlying shift, from reporting activity to explaining outcomes, is the same one 10Fold’s data describes at the budget-planning level.
For a marketing leader building next year’s plan, the research suggests three concrete moves. First, audit which of the systems feeding the budget conversation are still manual, since the 37% still using spreadsheets are the likeliest source of the numbers nobody fully trusts. Second, pick one attribution method, whether multi-touch or a simpler directional model, and apply it consistently rather than letting each channel report on its own terms. Third, treat AI visibility as a metric that needs the same integration discipline from day one, rather than bolting it on as a separate report that never reconciles with the rest of the funnel.
None of that requires new headcount or a platform migration. It requires treating connection, not collection, as the actual measurement problem, which is the argument the data itself is making.
Source: 10Fold
