Alex Ovcharov is the founder and CEO of Wayvee Analytics, a real-time customer satisfaction and engagement monitoring solution for retail, and the co-founder of Sensemitter. He has extensive experience in research, product development, and customer behavior evaluation, gained through his roles as Product Director at Shazam Eastern Europe and thru his entrepreneurial ventures.
His skilled journey includes pioneering successful augmented reality (AR) campaigns at Shazam, and co-founding Sensemitter, a gaming experience analytics company. Inspired by a discovery in WiFi sensing, Alex and his team of developers and former CERN physicists introduced AI algorithms for emotional evaluation, resulting in Wayvee Analytics’s founding in May 2023. This innovation in Emotion AI is about to rework how retailers gain actionable insights on customer satisfaction and engagement, delivered in real-time without using cameras or surveys, all while respecting users’ privacy.
What inspired the founding of Wayvee Analytics, and the way did your background with Shazam and Sensemitter contribute to this journey?
My experiences have shaped what we do at Wayvee, specializing in emotion recognition. During my time at Shazam Eastern Europe, I launched the region’s first Augmented Reality (AR) campaign and saw how facial expressions revealed emotional patterns. Leading a research project using facial coding, I spotted many industries like retail were inquisitive about such technology, though privacy and tech limitations were major challenges.
Combining my background in neuroscience and product development, I saw the necessity for higher customer understanding in offline environments, where existing tools were either slow in feedback collection or privacy invasive. This led us, together with ex-CERN physicists, to develop Wayvee’s Emotion AI, overcoming these challenges with a technology that operates with radio waves, ensuring 100% customer privacy and delivering insights in real time.
Could you share more concerning the discovery in WiFi sensing that sparked the creation of Wayvee?
In May 2023, I got here across an article that actually piqued my interest – it was about Wi-Fi sensing for tracking human movement. It described how Wi-Fi-based devices could capture data on how people move, and the way radio waves are extremely sensitive to those position changes. That got me considering — if radio waves can detect movement, why couldn’t in addition they capture heart rate and respiratory? These are key indicators for understanding emotional states.
Along with Viacheslav Matiunin, Wayvee’s CTO and a physicist who led data evaluation for the LHCb experiment at CERN, and a bunch of researchers and neuroscientists, we built a prototype using a daily Wi-Fi router to check the thought. The team engineered an algorithm that might detect respiratory and micro-movements using just Wi-Fi signals, and we patented the technology. This marked the start of developing our MVP and eventually our own hardware device – Wayvee sensor.
Wayvee was launched out of stealth in 2024. Are you able to talk concerning the initial goals of the corporate and the way you envision transforming the retail analytics landscape?
As a deep tech company focused on Emotion AI for the physical world, we see various potential applications for this technology, from healthcare to smart homes. Nonetheless, my experience in customer-facing markets quickly showed that retail had the best potential for impact. Retailers are continually in search of ways to extend customer satisfaction and higher understand their audience, yet they often depend on outdated methods that don’t provide real-time insights or face privacy concerns with personal data collection.
Through our pilot stage, it’s change into clear that retailers need actionable insights, not only data. It’s not enough to easily discover unhappy customers — we help explain why and offer recommendations for immediate improvement, keeping customers satisfied within the moment.
Wayvee uses a privacy-preserving sensor with no cameras. How does your technology manage to capture physiological signals like respiratory and heart rate using radio frequency (RF) waves?
For us, privacy is an enormous deal, and that’s why we don’t depend on cameras. Сameras obviously can track where someone is and what they’re doing, but interpreting emotions might be tricky, especially if the person’s position or angle throws it off. Are you able to imagine what number of cameras you must install to have the opportunity to see an individual from different angles?
As an alternative, we use radio waves. The Wayvee sensor, installed on shelves or other key locations, emits radio signals and captures them once they bounce back, carrying a spread of information — from respiratory and heart rate to subtle shifts like posture, walking speed, and gestures. Our AI algorithms then process this data and convert it into emotional insights, recognizing if an individual is offended, comfortable, neutral, etc.
Are you able to explain how the AI algorithm processes these physiological signals and translates them into actionable insights for retailers?
Wayvee devices capture radio wave signals, allowing our algorithms to discover objects and locate people. Our AI then analyzes their responses using a trained neural network based on the arousal-valence model, which assesses emotional intensity and positivity.
We concentrate on real-time emotional shifts quite than overall states, leveraging our extensive dataset to determine baselines for identifying emotions like happiness, sadness or frustration. This data is shipped to a server that powers Wayvee, providing retailers with real-time analytics, including Customer Satisfaction (C-SAT), engagement metrics, and other insights. Retailers can generate custom reports and receive alerts for customer dissatisfaction, enabling immediate motion.
What makes your approach to emotion AI, which relies on physiological signals like HRV and body gestures, simpler than traditional methods like surveys or video surveillance?
We bring every little thing together in a single solution! Traditional surveys are slow, only capturing feedback from about 0.1% of consumers, often leading to biased responses. Our approach focuses on subconscious reactions, that are more accurate because they’re involuntary. This permits us to cover 100% of consumers interacting with a shelf and deliver real-time insights inside about two minutes through our dashboard.
Relating to video-based methods, they depend on cameras, which naturally raise privacy concerns, even when measures like face blurring are applied. We desired to create a privacy-first solution that doesn’t make people feel like they’re being watched, which is why we’ve taken a special approach entirely — one which’s respectful of customer privacy while still delivering the insights retailers need.
How does Wayvee’s RF technology ensure customer privacy while still providing deep emotional insights?
It’s pretty easy — we don’t see people’s faces or discover their figures in an area. All the information we receive is fully anonymized. Unlike other solutions that blur faces or create 3D models to cope with privacy issues, we don’t must do any of that because the way in which we gather information is completely different. We’re not working with visuals; it’s all done through signals, so privacy concerns just don’t come into play the identical way.
Wayvee offers quick feedback on metrics like customer satisfaction (C-SAT) and engagement. How do these insights impact a retailer’s ability to make swift and effective operational changes?
At our core, we concentrate on delivering actionable insights for improvement. We transcend metrics like dwell time and average speed, which might be relative but don’t tell the complete story. The true value lies in combining these metrics with deeper insights that specify the outcomes. With our data, retailers can optimize store layouts through A/B testing, experimenting with shelf arrangements, displays, and retail media to boost customer satisfaction.
We also assist with workload planning by recommending resource allocation based on customer flow and engagement. For instance, during a pilot project with a sneaker store, we discovered that faster customer movement correlated with higher purchases. Staff involvement was actually slowing down the method, so we suggested reducing staff during peak times, which increased sales. It’s amazing how small changes can have such a major impact!
As more retailers adopt privacy-driven solutions, where do you see the long run of in-store analytics heading? How do you intend to expand Wayvee’s technology and reach in the approaching years?
I feel the long run of in-store analytics will certainly lean toward being more customer-focused. It’s not nearly making their shopping experience smoother and more enjoyable, but in addition about respecting their privacy. With Wayvee, we have now big plans ahead. Beyond what we’re already doing, there are such a lot of potential use cases for our tech — whether it’s measuring the effectiveness of retail media or understanding how various kinds of content impact customers. We’re even looking into things like price prediction based on purchase intent. There’s a lot opportunity to assist retailers evolve while keeping their customers at the middle of the shopping experience.
When it comes to scalability, how easy is it for retailers to integrate Wayvee’s solution into their existing store infrastructure?
Our device is straightforward to put in and requires minimal technical know-how, needing no ongoing maintenance. Retailers can set it up in only 10 to half-hour by attaching it to a shelf and setting the monitoring zone. Unlike camera systems, there’s no need for a big upfront installation. Retailers can start with a number of sensors during a test period and expand as needed. Each device covers a 3.5-meter range, and once they send us their store layout, we’ll upload it to the dashboard for accurate data collection. All device data is centralized in a single dashboard for straightforward monitoring and comparison.
