The Design And Application Of Precise Marketing Of Commercial Bank In The Context Of Big Data

The rapid development and application of "Internet +" has brought a new business environment for commercial banks.Banks deposit huge amounts of data while providing customers with richer products.In this context,traditional marketing strategies are faced with increasingly severe challenges due to their unidirectional and popular nature,lack of rapid and targeted strain capacity,and difficulty in achieving ideal results.However,challenges and opportunities coexist.Against the background of big data,banks can use various methods to collect data to build customer database,build customer portrait model through machine learning technology,and use personalized recommendation system to achieve precise marketing of two-way interaction with customers.In other words,commercial banks should explore customer demands while improving customer loyalty and cultivating new business and profit growth points.From the technical level,the application of big data raises new research questions for commercial bank marketing.In fact,as a new marketing paradigm,precision marketing is far from mature in its connotation,architecture,tools,etc.Although banks can obtain a large amount of unstructured data from the outside,it is not easy to transform the data into text data and apply it to customer portraits.At present,the research status in this field is as follows: first,there are few researches on personalized recommendation algorithms and relevant cases for banks.Secondly,there are not many quantitative researches to evaluate the effect of precision marketing based on customer purchase behavior.Thirdly,there are few marketing designs to enhance customer value.Fourth,the absence of successful cases based on customer portraits;Fifth,there have been successful cases based on customer portrait research in the fields of e-commerce,hospitals,insurance,exhibition and retail.This situation provides the opportunity and space for this study,which is also the value of this study.According to the above understanding,judgment and thinking,this paper defines the research questions,mainly: what is the realization path of bank precision marketing? How to choose the methods and tools to achieve accurate customer portraits for banks? How to make marketing more accurate and effective through personalized recommendation system? How to evaluate the effect of bank precision marketing and improve customer value? Backpointing is an accurate problem in the context of big data.Based on solving problems and starting from the current marketing status of commercial banks and the problems faced by precision marketing,this paper studies the design and application of precision marketing of commercial banks in the context of big data.On the basis of literature review,an analytical framework for precision marketing is constructed,with emphasis on the relationship between big data and precision marketing,as well as the process and key nodes to achieve precision marketing.It includes: establishment of precision marketing research framework in the context of big data,systematic analysis of decision support system architecture,data acquisition system architecture,customer portrait architecture and recommendation system architecture,and discussion of logicality.Big data technology is used to design customer portrait label,generate portrait by computing engine,introduce bank cases,establish neural network to build customer portrait model and conduct empirical evaluation.The collaborative filtering algorithm based on customer base attribute clustering evaluates the marketing business requirements of commercial Banks,optimizes the design of personalized recommendation algorithm,and conducts comparative experiments and model evaluation of the algorithm,thus providing a new practical idea for solving the precision marketing problems of commercial banks.At the same time,the thesis evaluates the commercial bank's marketing effect and designs its application.