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How We Built an AI System to Track Customer Emotions in Real Time

Explore how Trigma developed a real-time AI/ML-based emotion detection system for a retail brand. This innovative solution tracked customer expressions from store entry to exitu2014turning facial cues into actionable insights to improve service and engagement.

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How We Built an AI System to Track Customer Emotions in Real Time

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  1. HOW WE BUILT AN AI HOW WE BUILT AN AI SYSTEM TO TRACK SYSTEM TO TRACK CUSTOMER EMOTIONS IN CUSTOMER EMOTIONS IN REAL TIME REAL TIME LEADING THE FUTURE OF INNOVATION WWW.TRIGMA.COM

  2. We partnered with a retail client to We partnered with a retail client to develop an develop an AI-powered solution AI-powered solution that could detect customer that could detect customer emotions from entry to exit. The emotions from entry to exit. The goal was to capture real-time goal was to capture real-time facial expressions and turn them facial expressions and turn them into insights that could improve into insights that could improve service quality and customer service quality and customer engagement. engagement.

  3. THE BUSINESS CHALLENGE THE BUSINESS CHALLENGE Traditional CCTV and reports Traditional CCTV and reports weren’t capturing how customers weren’t capturing how customers truly felt during their visit. The truly felt during their visit. The client needed a smarter way to client needed a smarter way to monitor emotional changes monitor emotional changes across the shopping journey— across the shopping journey— without disrupting the without disrupting the experience. experience.

  4. THE AI/ML SOLUTION THE AI/ML SOLUTION We created a custom AI model We created a custom AI model trained to detect facial trained to detect facial expressions across diverse expressions across diverse demographics. Using real-time demographics. Using real-time CCTV footage, our system CCTV footage, our system analyzed emotions at entry and analyzed emotions at entry and exit, identifying shifts in exit, identifying shifts in sentiment with high accuracy. sentiment with high accuracy.

  5. HOW IT WORKED HOW IT WORKED Our pipeline handled face Our pipeline handled face detection, expression analysis, detection, expression analysis, and emotion classification. and emotion classification. Results were stored in the Results were stored in the cloud and visualized through cloud and visualized through a dashboard, giving the client a dashboard, giving the client a clear view of emotional a clear view of emotional trends across visits. trends across visits.

  6. RESULTS & IMPACT RESULTS & IMPACT The client gained data-driven The client gained data-driven insights into customer insights into customer sentiment, helping improve sentiment, helping improve service strategies and service strategies and engagement. The AI solution led engagement. The AI solution led to better in-store experiences, to better in-store experiences, stronger retention, and more stronger retention, and more targeted campaigns. targeted campaigns.

  7. THANK THANK YOU! YOU! Questions and Discussion Questions and Discussion taira@trigma.com taira@trigma.com www.trigma.com www.trigma.com

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