Design And Implementation Of Data Collection And Analysis System Based On Customer Behavior

Under the impact of the Internet economy,offline retailing faces the challenge of digital transformation.How to use existing technology to collect and analyze customer behavior data in offline retail scenarios has become an urgent problem to be solved.The development of Internet of Things technology and wireless network technology provides a technical basis for solving this problem.The e-commerce platform represented by Alibaba and JD and the offline retailers represented by Suning and Gome have carried out digital upgrades to offline retail scenes.Based on the “JD Home” data business upgrade project participated in the “Big Data and Intelligent Supply Chain Division” of JD during the professional internship,this article focuses on how to effectively collect offline customer behavior data and how to use customer behavior data.The analysis model is established,and the three aspects of the generated customer behavior data model are visually presented.The data acquisition and analysis system based on customer behavior is designed and implemented.The system collects and analyzes customer behavior data based on the fact that when a customer carries a mobile device for shopping,the mobile device exchanges data frames with the site in the form of a broadcast,whether or not it establishes a connection with the WiFi access site in the store,including the mobile Basic data such as the device's MAC address,RSSI value,and data report timestamp.Through further analysis of this part of the data,this paper establishes an analysis model based on customer behavior data based on member data and commodity sales data.The model presents data on customer traffic data,location data,mobile route,duration of stay,hot spot of the store,merchandise sales,and gender and age of member customers in the offline retail scenario to assist the merchant in scientific decision-making and operation.management.The paper mainly includes the following three aspects of work:1.Designed a technical solution for customer behavior data collection in offline retail scenarios.Based on the reading of related literatures at home and abroad,three offline behavior data acquisition methods based on wireless sensing,visual detection and mobile positioning are studied.The advantages and disadvantages of the three methods are discussed in terms of data information,data accuracy,deployment difficulty,hardware cost and other factors.The customer behavior data acquisition technology based on mobile location is determined and the network topology design,hardware deployment and data acquisition test are carried out.2.For the data acquisition method based on mobile positioning,there are two shortcomings of thin data information and slightly poor data accuracy.This paper proposes a "customer behavior data analysis hybrid model" that integrates store member data and product sales data,and improves the information volume of customer behavior data collection.By constructing a location fingerprint database,the accuracy of the system for customer location data is improved.3.According to the requirements of customer analysis,passenger flow line analysis,store hot zone analysis and hardware management,the system architecture was designed.The MVC-style data acquisition and analysis system in B/S environment was written using Play framework.In the data processing part,the asynchronous nonblocking communication mechanism is adopted to improve the robustness of the system in the concurrent environment.In the data display part,the responsive development of the Web page is realized,and the environmental adaptability of the system is enhanced.