Customer Profiling System with Residual Network-Based Face Recognition

Abstract

Customer profiling is an essential aspect of customer relationship management. Knowing who your customers are, what they need, and how to reach them is crucial in creating an effective marketing strategy. However, it can be challenging for some sellers to identify and track their loyal customers. This is where a customer profiling system can be invaluable. Such a system uses data analysis and deep learning techniques to track customer behaviour and identify preferences. One approach to customer profiling is through face recognition technology. Facial recognition is an effective method for identifying people, and it can be used to track customer attendance and identify regular customers. Therefore, this work presented the development of a customer profiling system using a deep learning technique to detect customer faces in real time. Experimental results showed that the system obtained 90% accuracy in detecting customers' faces. This work conducted a user acceptance test (UAT) to evaluate the system's effectiveness. The results indicated that the system provides many benefits and advantages to customers and sellers, including improved customer loyalty and satisfaction.