Development of an AI-Driven Customer Emotion and Conversation Analytics System for Financial Services

The client faces difficulties in assessing customer satisfaction during face-to-face interactions with bank managers. Existing processes lack real-time emotion tracking, limiting insights into customer experience and individual employee performance. This results in inefficiencies, inability to proactively address negative customer sentiments, and suboptimal service quality.

A large, state-owned banking institution operating across multiple European countries, seeking to enhance customer interactions and operational efficiency.

• Implement a real-time emotion detection system using facial recognition to monitor customer emotional states (positive, negative, neutral).
• Analyze conversation flow to evaluate the quality of customer-employee interactions.
• Provide management with actionable insights to improve service quality and customer satisfaction.
• Reduce operational inefficiencies and costs associated with poor customer experience management.
• Achieve a measurable improvement of at least 15% in customer experience and a 7% reduction in operational costs.

• Facial emotion recognition using machine learning models integrated with cameras facing customers.
• Real-time analysis of customer emotional states (positive, negative, neutral).
• Conversation flow analysis to assess interaction quality and detect potential issues.
• Dashboards or reports providing management with real-time and historical insights.
• Secure data handling to ensure privacy and compliance with applicable regulations.
• Alerts and notifications for negative emotional signals or conversation anomalies.

• Camera systems facing customer-facing staff
• Real-time data streaming for insights and alerts
• Existing employee management and scheduling systems (if applicable)

• High accuracy in emotion classification, targeting at least 88% accuracy comparable to previous benchmarks.
• Real-time processing with minimal latency to ensure timely insights.
• Scalability to accommodate multiple locations and high customer volume.
• Robust security measures to protect sensitive facial and interaction data.
• System availability of 99.9% to support continuous operations.

The implementation is expected to improve overall customer experience by at least 15%, reduce operational costs by around 7%, and enable the client to proactively address customer satisfaction issues, thereby strengthening customer loyalty and operational efficiency.