Parallel data mining method for identifying a mass of mobile client bases

Abstract

The invention discloses a parallel data mining method for identifying a mass of mobile client bases. The parallel data mining method includes the steps of building a client value model and a client behavior model, classifying clients according to the client value model and the client behavior model, popularizing assigned preference services to the clients with the high purchasing power and the high potential purchasing inclination, and then achieving accurate marketing. According to the parallel data mining method, the mass of mobile client bases can be identified, the aspects such as Internet surfing time preferences, Internet surfing place preferences and browsed website preferences of the clients can be identified, and the social group classes of the clients can be accurately judged. Clustering and classifying can be rapidly carried out through the adopted parallel clustering algorithm and the adopted parallel classifying algorithm. By means of the parallel data mining method, different strategies can be formulated for the different client bases by an enterprise, and the important guiding function for profit maximization of the enterprise is achieved.

Description

technical field [0001] The invention relates to the technical fields of economics and computer science, in particular to a parallel data mining method for identifying mobile massive customer groups. Background technique [0002] With the further expansion of the era of economic globalization, the homogeneity of products in more and more industries is accelerating, and the market competition is increasingly intensified. The competition among enterprises is not only the competition of energy talents and technologies, but also the competition of customer resources has never stopped. However, any enterprise hopes that its customers are loyal, so that it can bring higher profits to the enterprise. However, things are often counterproductive. Many customers often behave extremely disloyal in the face of numerous merchants and products, and the characteristics of diversified and personalized customer needs are becoming more and more obvious. Therefore, for enterprises, how to ident...

AI Technical Summary

Problems solved by technology

Customer identification methods based on customer statistical characteristics (age, gender, income, occupation, region, etc.) are familiar to everyone. Although the customer statistical identification method is simple and easy to implement, it lacks effectiveness and is difficult to reflect customer needs, customer value and customer relationship. stage, it is difficult to guide enterprises on how to attract customers and keep customers, and it is difficult to adapt to the needs of core customer relationship management; the ABC analysis method in customer identification based on customer transaction behavior was pioneered by Italian economist Pareto, the core of this analysis method The idea is to distinguish the primary from the secondary among the many factors that determine a thing, and to identify a few key factors that play a decisive role in things and a large number of secondary factors that have less influence on things. This type of method is easier in practice It has been implemented, and the data is easy to obtain, but its disadvantage is that the identification of customers based on historical transactions is relatively subjective; because the relationship between customers and enterprises is constantly evolving and changing over time, customers at different stages of the relationship have different characteristics and needs

Therefore, customer life cycle management is an important content of customer relationship management, and customer identification based on the customer life cycle becomes an important identification method, but it is difficult to identify differences between customers in the same life cycle stage, and customers in the same formative period , there are differences in customer value, which cannot be identified and if the average force is used, it will be difficult to avoid bad customers

Images