Why Emotional Analytics Are The Next Frontier In Brand Strategy

Forbes

30-07-2025

Jessica Hawthorne-Castro is CEO of Hawthorne, an analytics and technology-driven accountable advertising agency.
For years, marketers have measured the success of their campaigns by reviewing traditional performance metrics such as clicks, impressions, click-through rates and bounce rates. As data and content, from social media posts and video streams to consumer behavior insights and AI-generated assets reach an all-time high, brands are realizing that conventional metrics alone are no longer sufficient.
Now, emotional analytics are moving to the forefront of strategic planning thanks to advancements in AI and machine learning. For brands aiming to build deeper customer relationships, responding to emotional feedback in real time is becoming a necessity.
The Limitations Of Traditional Metrics
Although quantitative metrics offer valuable insights, they often overlook the emotional drivers behind consumer behavior. Clicks, views and shares can signal engagement, but they don't necessarily show whether a message genuinely resonated with an audience. Sometimes, the attention a campaign attracts stems more from controversy than an authentic connection.
Consider Coca-Cola's 'Share a Coke' initiative, which swapped the brand's iconic logo on bottles for popular first names—encouraging people to hunt for bottles with their own names or those of friends and loved ones, and 'share a Coke' together. The goal was to transform a mass-produced product into something personal, sparking joy and a sense of belonging.
The answer lies in the emotional connection it created—something that can't be captured by numbers alone.
What Are Emotional Analytics?
Emotional analytics involves uncovering how consumers truly feel. By capturing and translating emotional signals—like facial expressions, tone of voice, text sentiment and behavioral patterns—into quantifiable data, organizations can make decisions that are more human-centered and empathetic.
AI makes it possible to do this at both scale and speed. Machine learning models can analyze thousands of social media mentions in seconds, identifying not only whether consumer sentiment is positive or negative, but also whether they feel hopeful, frustrated, inspired or confused. With the help of tools such as natural language processing, sentiment analysis and facial recognition, businesses can detect emotional cues within customer interactions. These insights can be drawn from diverse sources, including support calls, social media conversations and product review videos.
Armed with this information, brands can adjust their strategies. For instance, streaming platforms can fine-tune recommendations based on viewer reactions, while customer support teams can detect frustration during a support call and intervene before a complaint escalates. Real-time reaction to emotion enables companies to move from observation to meaningful action.
Applying Emotional Analytics To Creative Strategy
The rise of AI-powered emotional analytics is redefining how marketers evaluate creative assets. With the ability to analyze thousands of ads simultaneously—both owned and competitive—brands can now identify which tones, visual cues and narrative techniques are resonating on an emotional level. This isn't just about aesthetics or execution; it's about uncovering the emotional patterns that drive real human connection.
Even more transformative is the ability to cross-reference emotional insights with real-time performance data, including promotional offers and incentives. By doing so, marketers can better understand what types of messaging fuel deeper consumer engagement and consideration. In an increasingly crowded marketplace, this kind of emotional intelligence isn't just a differentiator—it's becoming essential for breakthrough creative strategy.
From Sentiment To Strategy
When brands understand how consumers feel, they can:
• Personalize With Empathy: Craft experiences that connect on an emotional level, not just based on demographics or behavior.
• Enhance Product Development: Incorporate emotional feedback to refine features and messaging.
• Strengthen Customer Support: Spot dissatisfaction early and respond proactively.
Today, AI is taking this a step further—moving from simply analyzing sentiment or assessing the emotional tone of competitors' campaigns to enabling scalable strategy. Breakthroughs in generative technology now allow brands to produce short-form video and display creative at speeds and costs unimaginable just a few years ago. What once demanded large budgets and long timelines can be executed swiftly and efficiently. Ambitious or previously cost-prohibitive ideas are suddenly accessible, powered by tools that dramatically shorten production cycles without sacrificing quality. The result is more agile, emotionally engaging campaigns that can evolve in real time to meet cultural trends and rising consumer expectations.
However, emotional analytics come with ethical challenges, including the need for clear consent to protect privacy, the risk of cultural bias in interpreting emotions and the potential loss of trust if tracking feels intrusive. Additionally, as AI transforms creative work, it raises thorny questions about copyright—specifically, whether credit belongs to the original artists whose content trained the models or to the algorithms generating new material—underscoring the importance of grounding any emotional insights strategy in strong ethical principles.
How To Get Started With Emotional Analytics
Before diving into emotional analytics, marketers should define what they want to learn. Are they aiming to measure brand sentiment, identify emotional responses to a campaign or optimize customer experiences? Clear objectives will guide their tool selection and implementation strategy.
When evaluating tools, look for:
• Natural language processing (NLP) capabilities to detect tone, emotion and sentiment from text.
• Multimodal analysis, if also interested in analyzing voice, video or facial expression.
• Real-time analysis, for tracking emotional shifts during live events or product launches.
• Customizable taxonomies, allowing brands to tailor emotional labels (e.g., trust, surprise frustration) to brand context.
• Transparency, in how models are trained and how results are derived. This is key to building trust internally.
Even the best AI tools can misread sarcasm, cultural nuance or context. Pair AI output with human review, especially in high-stakes campaigns or when analyzing sensitive subjects.
The Road Ahead
As AI progresses, emotional analytics will help brands stay ahead by monitoring sentiment trends, tailoring content with greater precision and creating customer experiences that feel seamless and genuine. While performance metrics remain important, it's the emotional connection that ultimately builds lasting loyalty. A thoughtful, ethical and strategic approach to emotional data will help you build the kind of customer relationships that last far beyond a single campaign.
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