Research on Consumer Portraits of Offline Fast Fashion Shoe Stores Based On IoT Smart Hardware

There is a close relationship between consumer's portrait and product's attributes. Consumer portraits are usually obtained from information provided by consumers or by using data analysis of computer vision technology when they were in store. However, there was few concerns on the product's attribute which was a critical factor affecting consumer portrait establishment. Therefore, the purpose of this study was to establish IoT based product attributes' data collection system then to use this system to portrait consumers behaviors. Firstly, we used our own developed smart hardware to collect consumers' attention data on products of fast fashion shoe stores. Then the product attention index was obtained by combining sales data, and the quantitative attributes of the products with the highest attention index were analyzed, including the age, style and price. At last, improved the TOFA model to make it suitable for the conversion analysis of product attributes to consumer portraits. The results showed that there were core hedonic middle-aged consumer groups and potential thrifty youth consumer groups in the store, and the styles of shoes tend to be fashionable and casual. The conclusion was that the new model can effectively analyze the core consumer portraits of shoe stores and provide strategies for shoe store positioning and supply.