Why Martech Is Now Trying to Measure Emotional Metadata, Not Just Clicks

For years, marketing technology has been built on structured data. Audience segments, campaign IDs, channels, formats and conversion metrics have shaped how brands understand performance. But as AI systems take on a larger role in decision-making, a new layer of data is beginning to enter the stack. This layer is not about what content is, but how it feels.

Emotional metadata, a term still evolving in definition, refers to tagging and analysing content, interactions and customer signals based on emotional tone and perceived user state. It moves beyond traditional sentiment analysis and attempts to capture nuance such as reassurance, urgency, curiosity or frustration. In practical terms, it is about helping systems decide not just what to show, but how it should feel to the person receiving it.

The idea is gaining traction across digital asset management, customer engagement platforms and creative optimisation tools. While the concept is not entirely new, recent advances in AI have made it easier to operationalise at scale. The shift is also being driven by a growing recognition that performance differences often lie in tone rather than targeting alone.

Recent data from creative optimisation studies shows this clearly. An analysis of 1.1 million video ad creatives across gaming and non-gaming categories, covering around 2.4 billion dollars in ad spend, found that narrative styles tied to emotional contrast significantly outperformed others. “Failure to success” storytelling formats delivered 78 percent higher installs per thousand impressions while requiring 40 percent less spend compared to straightforward success narratives. In social apps, storytelling hooks accounted for just 6 percent of spend but delivered the highest seven day retention at 8.4 percent.

Adam Smart, director of product gaming at AppsFlyer, said, “We are seeing a clear shift where marketers are not just scaling what works, but scaling variation in emotional storytelling.”

This points to a broader change in how content is evaluated. Traditional metadata might identify a campaign as a product launch targeting a specific audience segment. Emotional metadata adds another dimension, asking whether that content is calming, energising or confidence-building. Two assets can be identical in targeting and message, but differ significantly in impact depending on tone.

The business case for this shift is also supported by experience data. A global study based on 65,000 customer evaluations found that 70 percent of consumers choose brands based on expected experience quality. More importantly, emotionally connected customers show significantly stronger outcomes than those who are only functionally satisfied. Net Promoter Score for emotionally connected customers stood at 67 compared to 31 for others. Retention rates were 76 percent versus 52 percent, while satisfaction scores were 71 percent compared to 43 percent.

These differences help explain why marketers are investing in systems that can detect and act on emotional signals earlier in the journey.

The infrastructure for this shift is already in place. AI adoption across martech has accelerated over the past two years, particularly in content management and analytics. A recent study found that 41 percent of organisations have either fully integrated AI into their digital asset management systems or are in the process of scaling it. However, only 33 percent reported having a clearly defined AI strategy with measurable goals.

At the same time, 90 percent of respondents said human oversight remains critical, especially for maintaining brand consistency and ensuring responsible use of data. This suggests that emotional metadata is being layered onto existing systems rather than replacing them entirely.

Murat Aykol, SVP of strategy at Bynder, said, “AI should be treated as an assistant to human creativity, not a replacement. Emotional interpretation is still something that requires human judgement.”

Customer engagement platforms are also incorporating similar capabilities. A survey of 2,300 marketing leaders across 18 countries found that 39 percent are already using AI tools to analyse customer data in more advanced ways, while 38 percent are specifically using AI to understand sentiment and preferences. Interestingly, 60 percent of brands that reported concerns about emotionally connecting with customers still exceeded their revenue targets, suggesting that attention to emotional engagement may correlate with better performance.

The same study introduced the idea of “digital body language” to describe behavioural signals such as browsing patterns, message interactions and content engagement. These signals are increasingly being used to infer context, which can then influence content delivery.

Todd Bursey, vice president of operations at Twilio, explained, “Personalisation is no longer just about knowing who the customer is. It is about understanding intent and responding to it in the moment.”

In practice, analysing emotional metadata typically follows a structured process, even if the outputs appear abstract.

The first step is content tagging. Instead of categorising assets only by topic or format, teams assign emotional labels based on intended effect. For example, a financial services brand might tag a loan explainer as reassuring, while marking a rewards campaign as aspirational. The goal is to create a usable vocabulary that can guide both content creation and distribution.

Many teams are keeping this vocabulary intentionally simple. Rather than building complex taxonomies, they focus on a small set of emotional states such as calming, energising, reassuring and motivating. This makes the system easier to scale and apply consistently across teams.

The second step is behavioural analysis. Here, emotional signals are inferred from user interactions. Metrics such as dwell time, navigation patterns, repeated searches and content sequencing can indicate different states of mind. A user repeatedly visiting a help page may signal confusion or frustration, while rapid browsing through product pages could suggest exploration or intent to purchase.

In customer support settings, transcript analysis and language cues provide additional context. Certain phrases or patterns may indicate urgency or dissatisfaction, which can then trigger different response strategies.

However, it is important to note that these signals are probabilistic rather than definitive. Research in emotion analysis has consistently shown that it is difficult to determine a person’s true emotional state from limited data. As a result, most systems treat emotional metadata as a guiding signal rather than a final judgement.

The third step is experimentation and validation. Emotional metadata becomes valuable only when it can be linked to measurable outcomes. Marketers typically test variations of content that differ only in tone while keeping other variables constant. For example, one version of a landing page may emphasise reassurance, while another focuses on urgency.

The impact is then measured across metrics such as conversion rates, bounce rates or retention. Over time, these tests help refine the emotional tagging system and improve decision-making.

The fourth step is integration into decisioning systems. Emotional metadata can influence recommendations, content sequencing and customer journeys. For instance, a system might prioritise calming content for users showing signs of hesitation, or more energetic messaging for users closer to conversion.

This integration is becoming more important as AI-driven decisioning becomes more common. A recent study found that 82 percent of business leaders believe prompt-based analytics tools are significantly reducing the time required to generate insights, often replacing processes that previously took weeks.

At the same time, consumer expectations around transparency are increasing. Around 95 percent of consumers now expect explanations for AI-driven decisions, and 63 percent say their demand for transparency has grown over the past year. This creates additional pressure for marketers to ensure that emotional metadata systems are explainable and auditable.

Despite the potential benefits, the use of emotional metadata also raises concerns.

Trust remains a key challenge. A global consumer study found that only 26 percent of people trust organisations to use AI responsibly. Comfort with AI across common activities declined by more than 10 percentage points year on year. Concerns about the lack of human interaction have also increased, with more than half of respondents expressing unease.

India stands out as an exception, with 67 percent of consumers indicating higher trust in AI use. However, even in markets with higher acceptance, the risk of overreach remains.

Another study found that only 14 percent of consumers believe they will personally benefit the most from AI advancements. This gap between perceived value and actual benefit can influence how emotional targeting is received.

Regulation is also evolving in this space. European guidelines have drawn a clear distinction between general emotional analysis and systems that attempt to infer emotions from biometric data such as facial expressions or voice patterns. The latter are subject to stricter scrutiny and, in some contexts, outright restrictions.

For martech teams, this means that not all emotional data is treated equally. Systems based on behavioural and content analysis are generally less risky than those relying on biometric inference. However, even these must comply with broader data protection and consumer rights frameworks.

Regulators have also raised concerns about speculative inference. There is growing scepticism about whether AI systems can accurately detect emotions, particularly when claims are based on limited or indirect data.

This scepticism is reflected in industry practice. Most organisations are taking a cautious approach, focusing on improving relevance rather than attempting to define emotional states with certainty.

Looking ahead, emotional metadata is likely to remain a supporting layer rather than a standalone solution. Its value lies in enhancing existing systems rather than replacing them.

The key challenge for marketers will be balancing opportunity with responsibility. Emotional metadata can improve personalisation and engagement, but it also requires careful implementation to avoid misinterpretation or intrusion.

As content volumes continue to grow and AI takes on a larger role in decision-making, the importance of tone and context is likely to increase. Topic and timing alone are no longer sufficient in a crowded digital environment.

The shift towards emotional metadata reflects a broader change in how marketing is understood. It is moving from a focus on transactions to a focus on experiences. In this context, understanding how content feels may become as important as understanding what it says.

For now, the most effective use of emotional metadata appears to be grounded in simplicity, testing and transparency. Systems that treat emotional signals as guidance rather than certainty are more likely to deliver consistent results.

As one industry executive noted, “The goal is not to read minds. It is to respond better to the signals customers are already giving.”

That distinction may define how this emerging layer of martech evolves in the coming years.