Abstract: Deep learning models are ideal for procure perception into the user experience in customer service reciprocity. Deep learning can predict the sentiment of the customer during customer service reciprocity which is crucial for improving the customer adventure. Automated customer service systems are great for reducing phone queues efficiently solving simple problems but when the customer is stressed with a large problem, most people want to speak to a human operator. The traditional solution to this would be to invest in a more refined automated service or invest in more human operators. With deep learning, these costs can be avoided by adeptly allocating the human operators’ time to problems where human interaction is necessary. Customer service phone calls can also be analysed using speech-to-text technology to analyze what the customer is saying, in combination with analyzing the audio directly to understand how the customer is saying it. Customer behavior analysis is an essential issue for retailers, allowing for optimized store performance, enhanced customer experience, reduced operational costs, and consequently higher profitability. Nevertheless, not much attention has been given to computer vision approaches to automatically extract relevant information from images that could be of great value to retailers. In this paper, we present a low-cost deep learning approach to estimate the number of people in retail stores in real-time and to detect and visualize hot spots. For this purpose, only an inexpensive RGB camera, such as a surveillance camera, is required. To solve the people counting problem, we employ a supervised learning approach based on a Convolutional Neural Network (CNN) regression model.

Keywords -Deep learning, CNN, Supervised learning

| DOI: 10.17148/IARJSET.2021.86104