Marketers trust their results less because every platform grades its own homework: each dashboard shows a number going up, but none shows which spend actually moved revenue. Kantar's Media Reactions study, published in October 2026, found that only half of advertisers feel they have the right balance between brand building and performance, and just 51 per cent say they extract actionable intelligence from their data. The teams closing this confidence gap choose fewer, causal metrics, predict before they pay and read the whole category.
Key takeaways
- Audiences are warming to advertising, yet only half of advertisers feel their brand and performance balance is right, down from 60 per cent a year earlier, and confidence in the media mix fell from 64 to 57 per cent.
- Platform metrics grade their own homework: Kantar found platform engagement and genuine brand-building potential align only about a third of the time.
- A net 75 per cent of marketers plan to raise investment in AI assistants in 2027, while only 23 per cent of consumers trust what those assistants recommend.
- Regional teams in Southeast Asia feel the gap first, because they plan across many markets with dashboards often built for one.
- Three habits close the gap: choose fewer, causal metrics, predict before you pay and read the whole category.
Where does the confidence gap show up?
It is mid-October, and a regional head of marketing in Singapore is three weeks from locking next year's budget. Her quarterly review looks excellent. Paid social return on ad spend is up, engagement on the creator programme has doubled, and cost per click fell in every market. Then the CFO asks the one question that matters: which of these moved revenue, and which can we cut?
The room goes quiet. Every channel can point to a number that rose, but none can show the sales would not have happened anyway. The budget rolls forward with a few per cent shaved off everywhere, the same plan as last year, defended with the same slides.
That scene is playing out in marketing teams everywhere, and the data now confirms it.
What does the data say about marketer confidence?
Kantar's Media Reactions study, published on 2 October and drawing on more than 800 senior marketers and 23,000 consumers, captures a strange tension. Audiences are warming to advertising, yet the people who buy it are less certain it works.
- Only half of advertisers feel they have the right balance between brand building and performance, down from 60 per cent a year ago.
- Confidence in the media mix slipped from 64 per cent to 57 per cent.
- Just 51 per cent say they extract actionable intelligence from their data.
Kantar's own conclusion is that the answer is sharper intelligence, not a larger pile of data. I think of the underlying problem as a confidence gap: the distance between how much a team can measure and how sure it is about what to do next.
Why do green dashboards mislead?
The gap persists because each number looks healthy on its own. Every platform reports its own engagement, attributed return and reach, and each one, read in isolation, says the money is working. These metrics grade their own homework, and they frequently point the wrong way.
Kantar's creator research earlier this year found that platform engagement and genuine brand-building potential align only about a third of the time. A team choosing creators on engagement alone is misled two times out of three. Multiply that across search, social, retail media and video, and the result is a dashboard that is busy, green and quietly uninformative.
Part of the problem is perception rather than performance. Kantar's effectiveness data shows that channels working together drove 43 per cent of campaign performance between 2020 and 2025, up from 18 per cent before 2014. Marketers are getting better at making a fragmented mix pay, but they cannot see that clearly enough to defend it.
Why will AI assistants widen the gap?
A new channel is arriving before the ruler does. A net 75 per cent of marketers plan to raise investment in AI assistants in 2027, and 62 per cent expect generative AI to play a pivotal role in brand recommendations. Yet only 32 per cent of consumers use these assistants to research brands, and just 23 per cent trust what they recommend. Spending is running ahead of evidence.
The buy side already feels the strain. In the IAB's outlook last month, 86 per cent of US buyers said they are changing how they measure media because of conversational AI and agents. Another 45 per cent named comparing AI-driven and traditional customer journeys as their hardest measurement problem. Being found, cited and recommended inside AI answers is becoming a goal in its own right, with no agreed method for counting it.
Why does Southeast Asia feel it first?
The confidence gap runs deeper in this region for structural reasons. A regional team plans across half a dozen markets, several languages and a platform mix that changes at every border, from marketplaces to super apps to messaging-led commerce. Global brands often hand local teams a direction from headquarters and a dashboard built for a single market, then ask them to defend spend line by line.
Agencies carry the same load with fewer people. An independent shop pitching against a network rarely loses on ideas. It loses on the depth of the read it brings into the room. The insight stacks agencies build now matter as much as their creative, because whoever explains the category, the audience and the competitive moves fastest earns the right to say what the numbers mean.
Which habits close the confidence gap?
The teams narrowing the gap are not adding dashboards. They are changing what they examine before they spend, and three habits stand out.
- Choose fewer, causal metrics. Select the handful of numbers that track revenue or margin, and validate them with holdout tests rather than trusting each platform's attribution.
- Predict before you pay. Score creative and audiences before the budget goes out, so the post-campaign report confirms a bet instead of explaining one away.
- Read the whole category. Study what competitors and audiences are doing across the market, not only inside your own funnel. A number means something only when set beside the market it sits in.
What does this look like in practice?
As the founder of SOMIN, I see this pattern across brands and agencies: read the category first, find the few signals that predict outcomes, and let those signals set the plan. One case from our own work illustrates the approach. It is shared as an example of a method, not as proof that any product works.
At Mothercare Singapore, the local team tracked more than 4,000 category ads a year by hand and still guessed which content would resonate with Singaporean parents. Benchmarking the category by engagement per post rather than by volume revealed that content about the real challenges of parenthood earned roughly 39 engagements per post, against about seven for festive gifting. With that read in hand, the team reported an 80 per cent drop in time spent on research and ideation, and audience segmentation that became three times more refined.
Which three numbers would you keep?
None of this demands a bigger martech stack. It demands the discipline to retire metrics that flatter and keep the few that predict, and the confidence to explain that choice aloud to the CFO, to clients and to the market. Leaders who do so publicly, with a steady voice and the data behind it, will set the terms everyone else is measured by.
So before the 2027 plan is locked, consider this: if you had to cut your dashboard to three numbers tomorrow, which three would you keep, and would your CFO choose the same ones?
Where does SOMIN fit?
The confidence gap closes when teams can see their category clearly, understand their audience deeply and test their ideas before money moves, and that is the connected job SOMIN is built to do. SOMONITOR gives brands and agencies a live read of what competitors and the wider category are doing, through Brand Tracker, Content Library, Perspective Studies and SoInspire, while our partnership with GWI grounds those reads in real audience understanding rather than platform self-reporting. SODA adds the layer that matters most when budgets are scrutinised, helping teams anchor decisions in a small set of predictive signals they can explain to a CFO. As AI assistants emerge as a channel nobody can yet measure, SOMIN gives regional teams the category context to decide where to place their bets, and the evidence to defend them.
FAQ
What is the marketing confidence gap?
It is the distance between how much a team can measure and how sure it is about what to do next. Kantar's 2026 data shows it widening even as audiences warm to advertising: confidence in the media mix fell from 64 to 57 per cent in a year.
Why can't platform metrics be trusted on their own?
Each platform reports its own engagement, attributed return and reach, so each one says the money is working. Kantar's creator research found that platform engagement and genuine brand-building potential align only about a third of the time.
Which marketing metrics should you keep?
Keep the handful of numbers that track revenue or margin, and validate them with holdout tests instead of each platform's attribution. A useful test is whether you and your CFO would pick the same three.
How will AI assistants change marketing measurement?
In the IAB's outlook, 86 per cent of US buyers said they are changing how they measure media because of conversational AI and agents, and 45 per cent named comparing AI-driven and traditional journeys as their hardest problem. There is no agreed method yet for counting visibility inside AI answers.
Related reading
- Why do marketers distrust AI insights, and how do you make them verifiable?
- What is creative clickability, and can you predict it before spend?
- Why is explainable AI becoming a B2B sales advantage?
- How can brands close the AI trust gap with consumers?
- How should junior marketing roles change when AI writes the first draft?
