In the field of retail industry and marketing, identifying customer segments is one of the most important tasks. A meaningful segmentation is able to help the managers to enhance the quality of products and services for the targeting segments. Most of traditional methods used POS data to classify the customer loyalty as 'heavy' segment while others are belonging to 'light' segment. Based on the previous studies, this paper presents three improvements. Firstly, in addition to customer purchasing behavior, we also include RFID (Radio Frequency IDentification) data, which can accurately represent the consumers' in-store behavior. Secondly, this paper uses broad learning system (BLS) to analyze the consumer segmentation. BLS is one of the most state-of-the-art machine learning techniques, and quite efficient and effective for classification tasks. Thirdly, the customer behavior data used in this paper are collected from a real-world supermarket in Japan. We also consider the customer segmentation as a multi-label classification problem based on both of POS data and RFID data. In the experiment, the results were compared with other popular classification models, such as neural network and support vector machine, and it was found that BLS greatly reduced training time while guaranteeing accuracy.