AI solution for customer support calls analysis in educational camps
April 15, 2024
5 minutes read
SUMMARY
The client needed to analyze and understand the effectiveness of their employees' communication with customers and automate this process. It was crucial to assess how well the employees adhered to the sales script. By implementing an AI-driven solution, the client aimed to gain insights into the quality of interactions and ensure consistent delivery of the sales script, enhancing overall customer engagement and sales performance.
Additionally, the client needed to see the level of conflict in each call and the professionalism of the manager during each interaction. These two metrics were essential for the team to detect and analyze from the calls.
TECH STACK
PyTorch
Python
DELIVERY TIMELINE
1 Week
Solution Architecture Design
2 Weeks
Calls Analysis Model Development
1 Week
Deployment & Testing
TECH CHALLENGE
Client provided a repository of call recordings, these were not accompanied by assessments of interaction quality. Consequently, our team was tasked with meticulously listening to each recording to evaluate the conversations based on several parameters including tone, clarity, and conflict presence. Following this initial assessment, we collaborated closely with the client to calibrate these evaluations, aligning them with specific metrics and expectations set forth by the client. This foundational work was crucial for training our AI model to accurately reflect and enhance the camp's communication standards.
SOLUTION
The solution involved a proprietary AI model designed to analyze call quality and detect conflict levels in conversations. This model was integrated into Telegram, where staff could upload call recordings and receive instant feedback on the interaction quality. Initially, the audio recordings were converted into text using OpenAI's Whisper model, a robust and accurate speech-to-text converter. Following transcription, the text underwent detailed language processing to assess tone and context effectively. After the preprocessing steps, the core analysis was conducted using a custom-built neural network based on Long Short-Term Memory (LSTM) architecture. This network was specifically trained to identify nuances in conversation that indicate conflict levels and overall communication quality. Using advanced audio processing techniques and neural network analysis, the system provided detailed assessments that helped improve staff training and conflict resolution strategies, ensuring more effective and harmonious interactions in the camp environment.
.svg)
