The motivation for developing computer vision is the human vision system which is the richest sense that we have. To us, vision seems an easy task ofjust seeing objects in daily life and identifying them, but in reality, our eyes along with the brain are processing information of around 50 images everysecond with millions of pixels in each image. Most of these images obtained are currently just looked at by people. The challenging task is to processimages from all these cameras and allow automation of tasks never before considered. Neural networks help us in making cameras intelligent enoughto understand the images it captures. Convolutional neural networks (CNN) are trained to give image classification results of good accuracy, with thechallenge to improve utilization of computing resources. Google Net is in its essence a deep CNN that uses inception architecture to attain leadingedge results for classification and detection problems. In this paper, a study was made on applications of computer vision techniques in retail andcustomer strategic projects. Further, it was analyzed that if cameras trained with CNN can work well enough to be deployed in retail market scenariosto automate sales and stock supervision.