Leveraging Artificial Intelligence And Machine Learning For Advanced Customer Relationship Management In The Retail Industry

Maintaining success in the ever-changing retail sector requires careful attention to customer interactions. Understanding and satisfying client expectations are crucial for growth in the retail industry, which is becoming increasingly competitive. The level of consumer awareness that is typically required for traditional customer relationship management (CRM) solutions to function properly is frequently in excess of what is possible. The present CRM systems' incapacity to process huge, complicated information places a cap on the insights that may be obtained from these data. Specifically, this importance is brought to light by the findings of the study. This is despite the fact that AI and ML have been widely adopted over the past several years. In order to fill in this knowledge vacuum, this study investigates how Deep Support Vector Machines (SVMs) might be utilised to turn consumer data into actionable insight for improved decision-making in retail customer relationship management (CRM). This paper investigates the challenges that can arise when attempting to improve customer relationship management (CRM) in the retail industry by employing AI and ML, more specifically through the application of Deep Support Vector Machines (Deep SVM). The capability of the model to anticipate the actions and preferences of customers will be trained and validated using data collected from actual customers shopping in a number of different retail situations. One of the outcomes that is anticipated is the development of a much improved customer relationship management system that has the capacity to give more accurate customer insights and predictions.