Artificial intelligence in enhancing CRM in the apparel retail

Abstract

The apparel industry is one of the major industries where the Artificial Intelligence tools are now being rigorously studied and implemented. It is plagued by major issues such as the “Lost sales” and deciding the “Optimal product mix” which would change the landscape of the industry. While many such tools are deployed in the online space, there is still a dearth of the application in the offline retail space. According to BCG report, approximately 65% of consumers still prefer buying apparel offline in retail stores, whereas 26% prefer online purchases. As AI continues to evolve, retail brands can leverage the technology to improve their customer experience through the CRM techniques. Bringing a synergy among AI-related technology and human emotional element, will help retailers in maintaining loyalty of customer and survive at the top in the industry. AI is being used in Retail sector and the primary goal is to develop new disruptive products, services, and processes via predictive analysis and micro-targeting. Retailers adopted things like analytics, robotics & deep-learning to optimize supply chain as fast and efficient as possible. The goal is to minimise shelf life. Artificial intelligence also enhance in-store infra and, help in increasing customer footfalls. Through this study, we wanted to get the primary insights from the sales force personnel and the managers of the apparel retail chains, mainly targeting the premium stores. The outlets which we covered were Peter England, Levis, Van Heusen, Black berry and shoppers stop. On the basis of the discussions, we observed & addressed three main pain points i.e., Minimizing the lost sales, Personalizing the loyalty programs to the consumer level, analyzing and enhancing the customer Brand perception. For the purpose, we have developed an Artificial Intelligence framework which can cater individually to the above-mentioned pain points. Once the framework was developed, we have formulated an exhaustive set of the input data points that must be fed into the Machine Learning Algorithm so that it gets trained continuously by varying the weights given to the problem parameters and improves itself in predicting the outcomes accurately. We have also tried to represent the same using the Power BI visual dashboard which can give a broad overview on the working of the system. Finally, this model can be used in the premium apparel stores where the decisions like deciding the optimal product mix of the existing and new stores, enhancing the customer loyalty by tailoring the programs to suit the specific customer, improving the overall customer experience by predictive modelling of the collected Customer feedback.