Study On Customer Classification Of Mobile E-commerce Platform Based On Loyalty

With the rapid development of the Internet and mobile communications in China,the market share of smart phones is also gradually increasing.The access of Mobile Internet has become an important cornerstone of modern people's lifestyle.People are gradually using mobile smart phones and other mobile terminal devices for online payment,information services,online mobile banking,online shopping,etc..This kind of mobile data terminal equipment combined with traditional business operation of the new industry-mobile e-commerce is rapidly emerging.For companies which use mobile e-commerce as their main business,it has become a problem companies need to solve urgently that how to effectively use reserve big data to efficiently and accurately recognize the characteristics of their own customers and to personalize their accurate marketing in the face of a fiercely competitive market environment.In the thesis,the following researches are conducted on the classification of mobile ecommerce platform customers.Firstly,based on the collation of domestic and foreign research results on relevant issues,the definition and characteristics of the mobile e-commerce platform are clarified.The opportunities and challenges faced by the mobile e-commerce platform under the data-driven background are analyzed in detail.Before classifying them,the thesis define the connotation,measurement method,and classification basis of the customer classification of mobile e-commerce platform,and point out the differences from the traditional customer classification.Secondly,through the consolidation of factors affecting loyalty and the combination of expert meeting method,the thesis constructs the evaluation index system of customer loyalty for mobile e-commerce platform,determines the weight of evaluation system indicators using principal component analysis,and establishes an evaluation model for customer loyalty.Next,a comprehensive review of data mining clustering methods was conducted.Specific clustering methods were selected based on the enterprise management objectives and actual application effects.The improved RFM model was combined with K-means and Bayesian discriminant analysis methods on the mobile e-commerce platform.The customer classification method is used to model which is empirically analyzed using real enterprise customer data.Finally,based on the results of customer classification,combined with the characteristics of mobile e-commerce,it proposes countermeasures and suggestions for customer precise marketing,and provides effective methods and basis for enterprises to identify customers and improve customer loyalty through effective marketing strategies.