Customerizer.AI

Customerizer.ai

Auto-filling customer satisfaction forms using a AI Voice Recognition Model

What is it?

In the era of customer-centric business models, ensuring high levels of customer satisfaction is paramount. To achieve this goal, this project presents a novel approach to assess customer satisfaction by analyzing the vocal cues and characteristics of both customers and company representatives during call center interactions. We propose the development of an AI model that can effectively evaluate the satisfaction levels of customers in real-time based on their voice patterns and sentiments expressed during phone conversations.

The introduction to this system can enhance the quality of customer service in call centers by providing instant feedback to companies on the satisfaction levels of their clients.And once the model is well-trained, it will provide accurate results, thereby mitigating the occurrence of fraudulent or deceptive reviews. Additionally to these use-cases, it can also save the time of the user by eliminating the need to follow the process of clicking a link -> choosing options / writing descriptions -> submitting.

Core Components of our System

Voice Analysis: Utilizing state-of-the-art Natural Language Processing (NLP) and Speech Processing techniques to transcribe and analyze the content of customer-service representative conversations.

Sentiment Analysis: Employing machine learning algorithms to detect the emotional tone and sentiment of both customers and representatives, enabling the system to gauge overall satisfaction.

Machine Learning Models: Developing machine learning models, potentially employing deep learning techniques, to predict customer satisfaction scores based on voice features and sentiment analysis.

Real-time Feedback: Integrating Django to create a web-based dashboard for call center supervisors and managers, providing real-time feedback on customer satisfaction trends and individual call assessments.