A Customer Profiling’ Machine Learning Approach, for In-store Sales in Fast Fashion

Abstract

In the last few decades, fast fashion retailers competitiveness is highly increasing, to get the market shares. With few historical data, the data analysis is a real challenge in this field. From the other hand, the customer service focus is a must, since the expectations of consumers are extremely selective. In this context, data science and machine learning tools are the latest trend in big data analysis, with short calculation time, that attract leader from many sectors to test their abilities in problem solving. This paper is a contribution to a machine learning based procedure, for customer profiling in fast fashion retail. It helps to build linking rules, of customers and their choices. Results will be a support in customer assistance, for increasing the in store sales basket size.