Customer age prediction in telesales through voice data analysis using advanced deep learning techniques

Abstract

Through voice data analysis, this research presents a novel deep-learning approach to predict customer age ranges in telesales. Utilizing the rich dataset from Mozilla's 'Common Voice' project, the study focuses on extracting vocal features using Librosa and building a model with TensorFlow and Keras. Based on LSTM layers, the model is trained to recognize patterns correlating vocal attributes with customer age. The research demonstrates the model's efficiency through various performance metrics, aiming to enhance customer service personalization in telesales.This research presents a novel deep-learning approach to predict customer age ranges in telesales, utilizing the rich dataset from Mozilla's 'Common Voice' project. By extracting vocal features using Librosa and building a model with TensorFlow and Keras, this study shows that LSTM layers can effectively recognize vocal attributes correlating with customer age. The results, demonstrating a validation accuracy of 54.25\%, underline the potential for enhancing personalized customer service through voice data analytics. This methodological innovation represents a significant step toward practical applications in customer relationship management with advanced machine learning techniques.