2 weeks Ago
Anton Timashev, co-founder and CEO at Wayvee Analytics – emotion recognition technology for capturing direct customer feedback in retail. Understanding customers’ needs and feedback has always been important for businesses. But it’s not just about identifying whether customers are satisfied or dissatisfied; the real value lies in understanding why they feel how they do.
For decades, customer experience has been measured through metrics such as customer satisfaction score (C-SAT), net promoter score (NPS) and customer effort score (CES). These metrics are typically gathered through surveys or feedback tools, where customers share opinions on their experiences, product availability, likelihood of recommending a business and others. While surveys provide valuable insights, only a small percentage of customers typically participate, limiting the scope of the feedback.
Processing survey data can also take weeks or even months, creating delays between the customer’s actual experience and the resulting insights. Delays and external factors can affect memory, leading to feedback that may not fully reflect the customer’s actual experience. With the rise of online shopping, it has become easier for customers to share feedback through reviews and social media right after their experience and even in the moment.
While this helps businesses gather insights quickly, many customers still choose to ignore feedback requests or dismiss pop-ups. As a result, businesses may struggle to form a comprehensive understanding of customer experience. Advancements in AI, computer vision and natural language processing (NLP) have paved the way for emotion AI, a transformative technology that provides deeper, real-time insights into customer sentiment.
By using more advanced tools instead of traditional feedback methods, businesses can better recognize, interpret and respond to customers’ emotional reactions instantly, allowing for more personalized and intuitive interactions. Emotion AI analyzes nonverbal cues —such as tone of voice, facial expressions, gestures and posture—to detect emotions like frustration, anger or excitement. More advanced systems even analyze physiological signals —like heart rate or breathing patterns—to gain a more nuanced understanding of emotional reactions, providing deeper insights into customer experience and price perception at the shelf, for example.
Why does this matter? Studies show that most purchasing decisions are driven by emotions and happen right in the store . For example, imagine walking through a store and hearing holiday music or catching a familiar scent that reminds you of home. These subtle emotional triggers can influence your decision to buy something—often without you even realizing it.
There are also direct factors, like price or shelf layout, that can either encourage or discourage a purchase. Emotion AI can detect and analyze these moments, giving businesses unbiased, actionable insights into what really drives consumer behavior. Several tools and methods have been developed to recognize and analyze customer emotions.
Depending on the technologies they are based on, there are differences in the sources they analyze and the applications in which they can be implemented: By using computer vision to analyze facial expressions, businesses can detect emotional responses to advertisements, products or in-store experiences. This approach has been widely used in advertising and gaming to understand how audiences react to specific content. However, in physical retail spaces, facial recognition raises privacy concerns, as it often involves personal data without explicit customer consent.
For example, Kroger’s attempt to integrate facial recognition with electronic shelf labels sparked public backlash due to privacy issues. Using speech signal processing, deep learning models and NLP, voice analysis can interpret emotions through tone. This method is commonly used in call centers to gauge customer satisfaction during interactions.
While voice analysis doesn’t necessarily involve personal identification, some companies, like Patagonia, faced legal challenges for implementing it without informing customers. This method analyzes written content, such as social media posts or online reviews, to classify sentiment as positive, negative or neutral. Powered by NLP and machine learning, it’s particularly effective for online channels but limited to text-based insights.
By analyzing body language and movements through computer vision, businesses can identify emotional cues. This method is commonly used in industries like healthcare, sports and automotive, where body language provides valuable context about emotions. The latest and most advanced method involves analyzing physiological signals—like breathing patterns, heart rate and micro-movements—using radio waves and AI.
This gives a deeper and more complete understanding of customers’ reactions and emotions, helping retailers see how people perceive prices, respond to new shelf layouts or react to in-store changes. One major benefit is privacy—this method doesn’t rely on visual or audio data, so it’s the most privacy-friendly option. While it’s still emerging, this technology is already being used in retail and has the potential to be applied in areas like healthcare, banking, hospitality and more.
However, while this method offers exciting potential, it’s still relatively new and needs more real-world data to fully validate its accuracy. There’s been a lot of conversation around the use of emotion AI and similar technologies, especially regarding customer privacy and the potential analysis of highly personal data. These concerns are valid, as businesses can gain insights into customer behaviors.
However, as technology evolves rapidly and privacy concerns become more critical, the latest advancements in tech should balance both: respecting customer privacy while still providing valuable insights. The real value of emotion AI lies in its ability to provide businesses with deeper context into how customers feel about their shopping or service experience, not just what they think. By analyzing things like tone of voice, sentiment and movements, businesses gain access to a more intuitive understanding of customer experience.
This allows brands to move beyond traditional feedback loops. With these insights, businesses can tailor their offerings in real time to match the customer’s mood or needs. This creates opportunities for a far more personalized experience.
Whether it’s adjusting content on a website or optimizing prices, businesses can better align with customers’ expectations, creating a shopping experience that feels relevant and uniquely suited to each individual. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?.
