Harness the power of emotion recognition to elevate customer experience, strengthen employee performance, and increase bottom line.
Our solution uses the mix of cutting-edge visual intelligence technology and deep learning techniques to provide valuable insights from every customer’s interaction whether it is for one individual in a controlled environment or for a group of individuals at a live event.
Simplify in-store consumer experience with emotionally-aware sentiments, and create more effective advertising campaigns
Focus on making cars safer and more personal to drive
Measure customers' satisfaction by recognizing their facial expressions of emotion, and use to improve customer service protocols
Carry out an in-depth analysis of customers’ emotions and adopt the results in their customer experience strategy to increase customer loyalty
Help teachers or tutors by providing quantitative feedback on the effectiveness of their teaching, as well as monitor the behavior of their students
Enhance healthcare provider’s ability to get to the heart of mental or behavioral health issues using advanced video analytics and machine learning based facial analysis
Through conventional video camera, we capture the customer’s response in an image content format with the client consent.
The images are uploaded to our cloud servers, where they’re securely stored and processed - facial detection and expressions are analyzed, and the results are aggregated and reported on our online dashboard in a real-time manner.
Systems Setup: Setup a standard high definition camera to observe and capture in-store customer engagement activities
Image Capture: System detects up to 10 (or as defined) random faces and takes a snapshot at defined interval
Emotional Analysis: Analyze each image taken, and extract facial expressions, such as level of happiness, anger, and surprises
Intelligence Reports: Using facial expressions, an Interest Graph (IG) reports will be generated, which includes relevant graphs and visual showing engagement and consumer interest level.
The industry is in a dynamic feedback loop state where a continuing stream of successful proof-of-concept projects drive more and more enterprises to consider AI solutions.
Sentiment Analysis is already widely used by different companies to gauge consumer mood towards their product or brand in the digital world. However, in offline world users are also interacting with the brands and products in retail stores, showrooms, etc. and solutions to measure user’s reaction automatically under such settings has remained a challenging task. Emotion Detection from facial expressions using Deep Learning algorithms can be a viable add-on to automatically measure consumer engagement with their content and brands.
Problem Statement: Our team worked with one of the largest retail coffee chain in the U.S. with the aim to improve understanding of the in-store customer experience by analyzing customer sentiment and emotions.
Approach: Using captemo™ cloud-based AI solution, we executed the project to detect, measure and analyze the most transitory of facial expressions, such as happiness, anger, and surprises. Additionally, we have assimilated emotions data with other key internal and external data sources such as sales, employee, weather to provide valuable business insights.
Outcome: Business is able to develop and monitor new key performance indicators (KPIs) which is based on customer emotional sentiments such as “Customer Happiness Index” by day, time of the day, employee shift time, etc. and evaluate strategies to consistently promote customer experience and excellence program.
Problem Statement: Our team engaged by one of the big 4 consulting firm to evaluate the effectiveness of the trainings conducted by vendors for their employees.
Approach: Using captemo™ cloud-based AI solution, we have examined whether facial expression of the participants is a tool for the trainer or facilitator to interpret comprehension level of participants in a physical classroom environment, and also to identify the impact of facial expressions during the training session and the level of comprehension shown by these facial expressions.
Outcome: The analysis was done through face detection and recognition techniques, which includes the recording of participant’s behaviors at defined interval through the training session. The result shows that facial expression is the most frequently used nonverbal communication mode by the participants, and it is significantly correlated to their emotions which helps to recognize their comprehension towards the session and evaluate the effectiveness score of the overall training program.
Problem Statement: Car Manufacturers around the world are increasingly focusing on making cars safer and more personal for us to drive. In their pursuit to build more smart car features, it makes sense for makers to use AI to help them understand the human emotions. Using facial emotion detection smart cars can alert the driver when he is feeling drowsy.
Approach: Facial emotion detection can find subtle changes in facial micro-expressions that precedes drowsiness and send personalized alerts to the driver asking him to stop for a coffee break, change music or temperature.
Additional value-added insights: Facial expressions can help the sales associates to assess the customer experience while providing demo of features, during the test drive, and use to improve on customer service protocols.
Headquarter: Chicago, Illinois
+1 312 898 2671 | [email protected]
AI Lab: Hyderabad, India
+91 89780 66911
Australia (Sales): Sydney, New South Wales
+61 4 0690 2693
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