Thanks to affective computing, also known as emotion AI, computers start to recognize human emotions based on facial expressions, body language, or voice tone. While it might bring controversial surveillance issues, the insights gained from this technology can offer critical improvements for businesses. Companies can understand their customers better and provide more satisfactory services, while they can also monitor their employees’ emotional states to adjust their workload to keep them motivated.
As emotion AI technology can be introduced to many different industries with a variety of applications, tech giants and startups have started to invest in either computer vision or voice analysis to recognize human emotions. As a result, the technology has grown rapidly in the past two years and expanded into various new areas and industries to help businesses offer better customer experience and achieve real cost savings. Gartner predicts that 10% of personal devices will have emotion AI capabilities, either on-device or via cloud services by 2022. This number was less than 1% in 2018.
Below, we have collected 24 applications of affective computing technology from different resources and categorized under a wide range of business functions and industries. We have examined affective computing comprehensively before, feel free to read our affective computing guide as an intro to the topic.
Every marketer at some stage hears from some marketing guru that marketing should appeal to emotions. Until now, that was a vague, hard-to-measure concept. Now marketers have the ability to put numbers on perceived emotions as well:
- 1- Marketing communications: Businesses can analyze what makes their customers engaged and organize their communication strategies accordingly. For example, they can measure customer reactions to their campaigns, products, and services to optimize their marketing strategies.
- 2- Market research: Emotion AI can measure consumer reactions to new products and help companies understand what other products do well and what they should do to satisfy customers when they enter a new market.
- 3- Content optimization: Affective computing can also help businesses generate contents that resonate well with their customers.
- 4- Intelligent call routing: Businesses can detect angry customers from the beginning of the call, and such calls can be routed to more experienced and well-trained call agents.
- 5- Recommendations during calls: Emotion AI can also provide suggestions about handling customer calls based on similar speech patterns during the conversation.
- 6- Continuous improvement: Reviews are time-consuming and completed by only a small share of customers. Amazon sellers share that only around 3-5% of their buyers leave product reviews. Like analyzing written reviews, emotion AI can also measure how effective the calls are and if the customer is satisfied at the end of the call by leveraging voice analysis. This data can be used to improve customer services even in cases where customers do not leave reviews.
- 7- Recruitment: Businesses can observe how stressful candidates are and how they communicate emotions during interviews to make better recruitment decisions. Unilever is one of the companies that is currently using emotion AI during job interviews. However, this requires interviewee approval for recording the interview, and HR teams shouldn’t rely too much on the accuracy of affective computing as people can express themselves in different ways.
- 8- Employee training: Affective computing can be used for training employees who will interact directly with customers. Employees work with intelligent customer interaction simulations that evolve based on the employees responses and emotions, helping them improve their empathy and customer service skills.
- 9- Tracking employee satisfaction: HR teams can track employees’ stress and anxiety levels during the job and observe if they are satisfied with their current tasks and workload. However, it also brings an ethical issue of monitoring all employees during work hours and might require their consent to monitor their emotions continuously.
- 10- Patient care: A bot can be used not only for reminding patients to take their medications but also to monitor their physical and emotional well-being every day to observe if there are any problematic issues.
- 11- Medical diagnosis: Affective computing can leverage voice analysis to help doctors diagnose diseases like depression and dementia.
- 12- Counseling: Emotion AI can be used in counseling sessions to track and understand mental states better and help doctors support counselee more effectively.
- 13- Fraud detection: 27-29% of insurers have admitted to lying to their health and car insurance company to gain coverage in the US. To prevent such issues, insurance companies can leverage voice analysis to understand if a customer is lying while submitting a claim.
- 14- In-store shopping experience: Emotion AI technology can monitor their customers’ satisfaction levels and reactions while shopping in the store. With the insights gained, retailers can take more effective actions for customer satisfaction.
Autonomous driving / Driver assistance
- 15- Safety: Automotive companies can leverage computer vision to track the driver’s emotional state while driving. If the driver is too tired, stressed, or angry/sad, it can provide alerts for unsafe driving.
- 16- Driving performance: Affective computing can also be used for measuring the driving performance of autonomous cars. With cameras and microphones embedded in the vehicle, the technology can monitor the passengers’ emotional state and observe if they seem stressed or satisfied with the driving experience.
- 17- Measuring effectiveness: Sensors like video cameras or microphones can be used for students’ emotional states during lessons. Emotion AI can assess how satisfied or frustrated students are with the lessons because a task is too challenging or too simple. As a result, teachers can adapt themselves to tailor class load accordingly. A similar approach can also be used while testing learning software prototypes for online learning.
- 18- Supporting autistic children: Another use case in education is to help autistic children recognize other people’s emotions in the school environment.
- 19- Testing: Before releasing their games to the market, gaming companies can use affective computing for testing their games. Emotion AI can monitor players’ satisfaction levels, and businesses can make further improvements to increase player satisfaction.
- 20- Adaptive games: Affective computing can leverage computer vision to detect the player’s facial expressions, and the game can adapt to that mental state.
- 21- Understanding the general mood of the population: The rise of emotion AI also created new partnerships between technology vendors and surveillance camera providers. The Ministry of Happiness in the United Arabic Emirates has started an initiative to understand the general mood of the population using video analysis cameras in public places.
- 22- Tracking/estimating citizen reactions: Governments or political candidates can monitor social media to measure their population’s response to policy proposals and announcements. Political campaigns can also personalize their messages using psychometric models to optimize the emotional reaction by voters. Emotional AI book shares that emotion AI was used as a tool of sentiment analysis by Cambridge Analytica in the 2016 US presidential elections.
- 23- Integration with IoT: Emotion AI can be integrated into IoT and other smart devices so that these devices can act based on users’ emotional states detected via voice and face analysis. For example, if the customer seems too sweaty, a smart air conditioner might turn on automatically.
- 24- Workplace design: Businesses can track their employees in the workplace and conduct sentiment analysis in internal social networks and forum messages to improve physical workspace design and comfort.
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