The true cost of trust in marketing measurement

In an era of better tools and richer data, measurement confidence should be climbing. It isn't. EMARKETER and TransUnion's July 2025 survey of marketers found confidence in performance metrics has stalled at 54.1% — down from 61.7% — even as budgets and reputations increasingly ride on those same numbers being believed.

What's actually blocking precision

The top barriers aren't new problems — they're old ones that never got fixed: siloed or incomplete data (49.5%), cross-channel deduplication challenges (48.0%) and walled-garden reporting limitations (40.8%). Tools that don't talk to each other make accuracy structurally impossible, regardless of how good any single platform's dashboard looks.

Confidence is lowest of all in influencer marketing (44.4%), in-store or offline activity (38.3%) and social platforms (35.2%) — precisely the channels most brands are increasing investment in.

When the numbers aren't trusted, the budget follows

60.2% of marketers say internal stakeholders question their metrics at least sometimes, and 20.4% aren't even sure whether their reports are trusted. That doubt has a direct cost: over a quarter of marketers (28.6%) had 11–20% of budget put at risk or reallocated in the past year specifically because of measurement doubts.

The relationship runs both ways — marketers with high stakeholder trust are meaningfully more likely to see zero budget at risk, and far more likely to receive a 10%+ budget increase. Internal trust is turning out to be as commercially important as customer trust.

AI is being asked to close the gap — belief is ahead of deployment

Half of marketers have adopted or plan to adopt AI/ML specifically to automate reporting, and 40% already use AI to analyze data and build reports. But only 9% currently use AI for marketing performance analytics — a figure expected to roughly triple to 29% as adoption catches up to intent. The gap between what executives believe AI can do and what's actually been deployed is one of the more actionable findings in the whole study: early movers on that gap gain a real, if temporary, advantage.

Four pillars of future-proof measurement

Make measurement cross-functional — unite marketing, analytics, IT and finance around one source of truth, ideally through a standing "measurement council" rather than ad hoc reconciliation.

Treat AI as an efficiency engine, not a black box — automate the repetitive dashboard work and use it for attribution scenarios and anomaly detection, but keep humans validating the output (37% of marketers say this is necessary, and we'd agree).

Invest in experimentation — incrementality testing, lift studies and geo-holdouts, funded at 5–10% of media budget, build the evidence library that makes future reporting defensible.

Prioritize transparency — share data sources, assumptions and limitations, and attach a methodology brief to every report. It's a small habit that does most of the work of rebuilding stakeholder trust.