With the advent of AI, every business sector is harnessing its potential to improve customer satisfaction and optimize their business processes. The Retail industry has become highly competitive with the popularization of eCommerce platforms. All players are trying to maximize their market share through various avenues. Commoditization has increased, which means the margins are now lower than ever before. This makes it is imperative to study every type of customer interaction data to build an understanding of changes in customer preferences. Doing so will help retailers predict trends in buying behavior, and will eventually help in moving volumes. Although retail stores capture a lot of customer interaction data each day, they are not able to optimally utilize them to draw any meaningful insights. One of the ways to do that is by leveraging the customers’ activity captured by the store’s cameras. To derive real-time customer behavioral insights, we developed a computer vision and AI-powered solution that consumes the video feeds captured by cameras in the stores and provides useful notes on customer-product interactions. It enriches the retailers’ understanding of customers’ behavior by providing analytics on purchase patterns, product placements, service times, etc. This white paper elaborates on this solution which was implemented using advanced technologies like Convolutional Neural Networks, Image Processing, Object Detection, and Tracking Algorithms.