Top 10 Emotional AI Examples & Use Cases in 2024

ByPaul Christiano Last Update on

Emotion detection and recognition technologies enable businesses to understand customer sentiments, enhance user experiences, and improve products and services. This article will provide an overview of 10 impactful real-world examples and use cases where companies are applying emotional artificial intelligence – from marketing and customer service to healthcare, gaming, and beyond. Let‘s dive in!

1. Optimizing Marketing Campaigns with Emotion AI

Emotion AI can help optimize marketing campaigns by detecting audience engagement and sentiment second-by-second. For example, Realeyes used its computer vision AI to analyze 130 automotive ads. The study found high performing ads elicited more intense emotions. Specifically, a humorous Volkswagen ad outperformed a product-focused Ford ad in social media engagement by over 65% according to Realeyes.

"By leveraging emotion AI, we help auto brands move beyond explicit survey data and gain an unfiltered, moment-by-moment view into how audiences truly feel when engaging with their campaigns," explains Andrew Dobbie, VP of Research at Realeyes. "This enables smarter creative testing and optimization."

Travel site Skyscanner has also deployed emotion recognition to suggest destinations based on users‘ facial expressions. If the camera detects a "sad" face, Skyscanner might recommend upbeat locations to lift the mood. This creates a fun, personalized experience that feels like a friend giving you travel tips.

2. Matching Customers with Agents Based on Emotions

Call centers can leverage emotion AI to route calls to agents best suited for that customer‘s current sentiment. For example, one European bank used software by Behavioral Signals to analyze past conversations as well as real-time speech patterns to detect emotions. The bank matched angry customers to empathetic agents with a track record of resolving issues for frustrated callers. This approach improved call resolution rates by 11% and sped up transitions from a negative to positive emotional state.

MetLife uses Cogito‘s real-time coaching app to provide agents with conversation tips based on sentiment analysis and emotional intelligence. Since implementing Cogito‘s software, MetLife reported:

  • 14 point increase in NetPromoter Score
  • 5% improvement in “Perfect Call” scores
  • 6.3% improvement in issue resolution
  • 17% reduction in call handling time

"By giving agents actionable insights into customer moods, they can have more positive conversations. This translates directly into better experiences and brand loyalty," explained Cogito CEO Josh Feast.

3. Evaluating and Improving Learning Experiences

Emotion detection offers edtech platforms an unbiased way to evaluate and enhance their teaching methods. For example, Indian tutoring startup Vedantu used facial analysis software by Entropik Tech to gauge student engagement, attention, boredom, and more during pre-recorded lessons. The API generated sentiment metrics that were 92% correlated with student feedback surveys, but provided a continuous stream of data.

"Our platform delivers insights that human observers would miss. This allows teachers to iterate and improve their curriculum based on actual student responses," said Dr. Anantha Sayana, CEO of Entropik. Results helped Vedantu optimize their e-learning content to boost engagement among students.

4. Assessing Mental Health Through Emotion AI

Emotion AI shows promise for advancing mental healthcare and detecting conditions earlier. The American Heart Association developed an app that estimates blood pressure from facial blood flow patterns extracted via video. Their deep learning model predicted blood pressure with 95% accuracy – demonstrating potential as a clinical decision support tool.

Other startups are applying speech pattern analysis to detect conditions like anxiety, depression, and PTSD. While still early stage, such tools could help clinicians more accurately evaluate patients and monitor their recovery beyond relying on subjective self-reports. Emotion AI promises more objective assessments.

"By combining facial, voice, linguistic, and other biomarkers of emotional state, we envision AI that can assist doctors in diagnosing, stratifying, and treating mental health disorders," shared Dr. Michal Kosinski, Associate Professor of Organizational Behavior at Stanford University.

5. Enhancing Customer Service with Emotion-Aware Chatbots

Chatbots like Amelia and Clara use NLP and sentiment analysis to hold more natural, contextually-aware conversations. They can respond appropriately to customer feelings – showing empathy for frustration and apologizing for difficulties. With emotion recognition, bots become more cooperative, relationship-oriented, and human-like.

For example, OCBC Bank found that 30% of customer inquiries are handled successfully by their AI chat assistant Emma. Swedbank‘s chatbot Nova can interpret expressions, ask clarifying questions, and also hand-off to a human if conversations get too emotionally charged. AI with emotional intelligence reduces call volumes and boosts efficiency.

6. Personalizing Gameplay Using Emotion AI

Emotion AI can enable more immersive, dynamically generated gaming experiences. The biofeedback horror thriller "Nevermind" uses Affectiva technology to detect players‘ emotions through webcam and microphone input. The game adjusts in real-time to be more intense when the player is calm, and more soothing if overly frightened.

As described by studio head Mark Haverstock, "By integrating emotion recognition, Nevermind creates a custom experience for each player that responds to their feelings and expectations."

Other gaming builders like Aimotive and Emteq also provide developers with SDKs to access facial and voice based emotion data and integrate it into gameplay mechanics and narratives. Expect more emotion-aware games that tailor experiences to player sentiment.

7. Quantifying Ad Performance with Emotional Analytics

Emotion AI provides granular data on ad effectiveness by monitoring viewers’ subconscious reactions. For example, Dentsu‘s Neurologic service uses webcam input to track facial expressions and microexpressions of digital ad viewers to extract metrics like enjoyment, engagement, and memory encoding.

As Prasad Narasimhan, Global CEO of Dentsu Tracking, explains, "Neurometric analysis helps us optimize campaigns and creative in a way that explicit surveys don‘t. We move from self-reported data to observed emotional data for clearer insights." Expect wider adoption across digital marketing.

8. Recommending Music Playlists Based on Listeners‘ Moods

Music apps can detect listeners‘ moods through biometrics and suggest appropriate playlists. For instance, the RebMee app proposes real-time music recommendations based on users‘ facial expressions and head movements captured via smartphone cameras.

Spotify has also patented a system that identifies songs evoking similar emotional responses through lyrical and voice analysis. The system yields personalized music recommendations tailored to match listeners‘ current moods.

"By applying emotion AI, we can deliver more customized music experiences and deepen engagement among our users," noted Spotify‘s Chief R&D Officer, Gustav Söderström.

9. Improving Eyewitness Identification with Emotion Analysis

Startups like Crime Science are applying computer vision trained on FACS (Facial Action Coding System) to analyze the emotions of eyewitnesses while reviewing footage to identify suspects. Law enforcement can use this data to more objectively evaluate witness credibility and confidence. Emotion AI aids investigators while reducing biases.

As Crime Science CEO Bethany Downer notes, "Subtle face cues captured by our algorithms reveal how confident witnesses truly feel about their recollection of events and identification of perpetrators." This technology has been tested by police departments with positive results.

10. Enhancing Market Research with Emotion Tracking

Emotion AI and facial coding gives market researchers an unfiltered view of subjects‘ instinctive reactions to products and messaging. For example, sensory measurement firm iMotions builds tech that tracks facial expressions, eye movements and more during research sessions. Their software detects confusion, disgust and other nuanced sentiments that subjects may not self-report.

"By combining emotion AI with biometrics, we help brands uncover ground truth insights that drive design, innovation and communication," explains iMotions CMO Ross Tropea. This delivers actionable data to optimize products and campaigns.

So in summary, these are 10 impactful real-world examples of companies leveraging emotion detection and recognition technology to enhance marketing, customer engagement, entertainment experiences, and more. The applications are expanding rapidly. As emotion AI improves, expect it to become a standard part of the enterprise tech stack giving companies an emotional pulse on their users and customers.

I am Paul Christiano, a fervent explorer at the intersection of artificial intelligence, machine learning, and their broader implications for society. Renowned as a leading figure in AI safety research, my passion lies in ensuring that the exponential powers of AI are harnessed for the greater good. Throughout my career, I've grappled with the challenges of aligning machine learning systems with human ethics and values. My work is driven by a belief that as AI becomes an even more integral part of our world, it's imperative to build systems that are transparent, trustworthy, and beneficial. I'm honored to be a part of the global effort to guide AI towards a future that prioritizes safety and the betterment of humanity.

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