Anton Timashev, co -founder and chief executive at Wayvee analyzes – Emotion recognition technology to obtain immediate customer feedback in retail trade.
Understanding the needs and comments of customers has always been important to businesses. But it’s not just about determining if customers are satisfied or unhappy. The real value lies in understanding why they feel how they are doing.
For decades, customer experience has been measured through measurements, such as C-SAT, Net Promoter (NPS) and Customer Effort (CES) rating. These measurements are usually gathered through feedback research or tools, where customers share opinions on their experiences, product availability, the possibility of recommending a business and others.
While surveys provide valuable knowledge, they usually participate in a small percentage of customers, limiting the scope of comments. Research data for processing can also take weeks or even months, creating delays between the actual customer experience and the resulting ideas. Delays and external factors can affect memory, leading to feedback that may not fully reflect the actual customer experience.
With the rise of electronic markets, it has become easier for customers to share comments through critical and social media immediately after their experience and even at the moment. While this helps businesses quickly gather ideas, many customers are still choosing to ignore feedback requests or reject pop windows.
As a result, businesses can fight to form a comprehensive understanding of customer experience.
Emotion AI: Understanding customers beyond comments
Developments in AI, computer vision and natural language processing (NLP) have paved the way for AI emotion, a transformative technology that provides deeper knowledge in real time in customer feeling.
Using more advanced tools instead of traditional feedback methods, businesses can better recognize, interpret and respond to customer’s emotional reactions, allowing more personalized and intuitive interactions.
Emotional AI Analyzes non -verbal points– Like the tone of voice, facial expressions, gestures and attitude – to detect emotions such as frustration, anger or excitement. The most advanced systems even analyze even normal signals– Like breathing standards – to gain a more special understanding of emotional reactions, providing, for example, deeper knowledge of customer experience and prices on the shelf.
Why does this matter? Studies Show that most buying decisions are driven by feelings and to happen correctly in the shop. For example, imagine walking through a store and listening to a holiday music or catching a familiar fragrance reminiscent of a home. These subtle emotional scandals can affect your decision to buy something – often without realizing it. There are also immediate factors, such as the value or layout of the shelf, that can either encourage or discourage a market.
AI emotion can detect and analyze these moments, giving businesses to be impartial, active knowledge of what really leads to consumer behavior.
Emotion measurement: existing methods
Various tools and methods have been developed to identify and analyze customer feelings. Depending on the technologies on which they are based, there are differences in the sources they analyze and the applications to which they can be applied:
Face recognition
Using the computer’s vision to analyze facial expressions, businesses can detect emotional answers to ads, products or experiences in the store. This approach has been widely used in advertising and gambling to understand how the audiences react to specific content.
However, in natural retail areas, facial recognition raises concerns about privacy, as it often includes personal data without explicit customers. For example, Kroger’s attempt to incorporate face recognition with electronic shelf labels triggered a public reaction due to privacy issues.
Speech and voice analysis
Using speech signal processing, deep learning models and NLPs, voice resolution can interpret emotions through tone. This method is usually used in call centers to measure customers’ satisfaction during interactions.
While vocal analysis does not necessarily include personal recognition, some companies, such as Patagonia, face legal challenges for its application without informing customers.
Analysis emotion based on the text
This method analyzes written content, such as social media positions or internet revisions, to classify emotion as positive, negative or neutral. NLP feeding and mechanical learning is particularly effective for online channels, but is limited to text -based ideas.
Gesture and attitude recognition
By analyzing the language and movements of the body through computer vision, businesses can identify emotional indications. This method is commonly used in industries such as health care, sports and automotive industry, where body language provides a valuable framework for emotions.
Signal analysis
The latest and most advanced method involves the analysis of normal signals-such as breathing patterns, heart rate and micro-moving-using radio waves and AI. This gives a deeper and fuller understanding of customer reactions and feelings, helping retailers see how people perceive prices, respond to new shelves or react to changes to the store.
An important benefit is privacy-this method is not based on visual or sound, so it is the most privacy-friendly. While still emerging, this technology is already used in retail trade and has the ability to apply to areas such as health care, banking, hospitality and much more. However, while this method offers exciting capabilities, it is still relatively new and needs more real world data to fully validate its accuracy.
What AI emotion is changing for business
There has been a lot of discussion on the use of AI emotion and similar technologies, especially in terms of the privacy of customers and the possible analysis of the particular personal data. These concerns are valid as businesses can obtain information about customer behaviors.
However, as technology is evolving rapidly and concerns about privacy are becoming more critical, the latest technology developments should balance both the respect for the privacy of customers, while providing valuable knowledge.
The true value of AI emotion lies in its ability to provide businesses deeper into how customers feel about their shopping or service experience, not only what they think. By analyzing things such as the tone of voice, emotion and movements, businesses gain access to a more intuitive understanding of customers’ experience. This allows brands to go beyond traditional feedback loops.
With these ideas, businesses can adjust their real -time bids to match the customer’s mood or needs. This creates opportunities for a much more personalized experience. Whether it is to adapt content to a website or price optimization, businesses can be better aligned with customer expectations, creating a shopping experience that feels relative and unique for each person.
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