Research On Customer Segmentation Based On Wi-Fi Detecting Customer Flow Data

In recent years,with the rise and popularization of the Internet,many enterprises are facing the status quo of transformation to the Internet.Especially the development momentum of e-commerce is extremely fierce.Traditional offline consumers are shifting to the online one after another.The customers in offline business scenarios are severely diverted.The development of real economy is facing unprecedented severe challenges.On the one hand,due to the characteristics of the real economy,such as the difficulty of data collection and analysis and utilization of existing data,offline business scenarios have the problem of low efficiency of resource allocation;on the other hand,the huge impact of online business scenarios makes offline business scenarios gradually lose market vitality and competitiveness.The dual challenges brought by these two aspects make the development of offline business scenarios extremely difficult.Many offline business scenarios are also seeking transformation opportunities,relying on Internet technology to develop their own online business,but ignoring the redesign of offline business scenarios.Firstly,this paper combines the current situation of offline business scenarios,takes Wi-Fi detection of customer flow data as the research object,and studies the customer segmentation method suitable for offline business scenarios.Secondly,the traditional RFM model is improved with customer length,customer proximity,number of arrivals,length of stay and average arrival cycle as subdividing indicators.The customers in offline business scenarios are classified into five categories: important retention,important value,less important value,general development and low value by using K-means clustering in data mining process.Then we use the entropy method to calculate the weight of each index,establish a customer value scoring model,and calculate the value of these five types of customers.We can get the value scores of different types of customers and explain that according to the characteristics of different sub-groups' behavior to shop,we put forward some suggestions to maintain and develop users in offline business scenarios.Finally,in the application of marketing strategies for offline business scenarios put forward the idea of service upgrading optimization.The result of segmentation shows that this method has strong applicability to offline business scenarios.Customer segmentation basically follows Pareto's principle,which makes it possible for enterprises to allocate limited resources reasonably and effectively.