Research On Precision Marketing Strategy Of Company A Based On Customer Portrait

With the development of big data and the improvement of algorithm model technology,the application of precision marketing in all walks of life has been further promoted.The change of consumer market structure and the diversity of value preferences,brand preferences and purchase behavior preferences of the post-00 s,post-90 s and post-80 s promote the evolution of consumer demand in the direction of diversification.The life and consumption circle of consumers are also constantly migrating,and the consumption behavior shows the trend of leisure,socialization,experience and convenience.This series of changes put forward higher requirements for enterprise marketing.Relying on the big data obtained from multiple channels on the network platform and mature big data technology,it can help enterprises accurately identify target markets and users,provide accurate decision support for enterprise product design,product pricing,advertising and channel selection,and help enterprises effectively obtain higher return on investment.This mode of combining big data with enterprise marketing is being accepted and recognized by more and more enterprises.In recent years,China’s smart consumer electronics has developed rapidly in various fields such as work,life and learning,and has accumulated a large amount of data.With the development of Internet technology,how enterprises use big data and analysis models to analyze users’ characteristics,needs and preferences,deeply combine their analysis results with precision marketing,and better help enterprises carry out new product R &D,market promotion,user research,channel strategy and other work is of great significance to intelligent electronic products enterprises.Taking a company Technology Co.,Ltd.as an example,this paper studies the case of the combination of precision marketing of intelligent electronic products and the use of big data.Through a period of application result tracking,this paper tests the research results and provides information support for future improvement and optimization.In the research process of this paper,firstly,the research ideas and research models of user portrait at home and abroad are described in detail;And the concept and theoretical knowledge of precision marketing,understand the application of precision marketing in enterprises and the problems that can help enterprises solve.Then it analyzes the user portrait of company A.Sort out the data,analyze the main cost of labels,integrate the fields with the same or similar attributes,sort out the database that can be directly used for analysis,analyze the data by using the statistical method of cluster analysis,and sort out the user portrait characteristics and subdivided group portrait characteristics.Then,we use the Vals model to classify users and summarize the characteristics of different ethnic groups.Then,combined with China-Vals model,the user segmentation group is analyzed and described.Finally,integrate the data from four aspects of consumption habits,consumption preferences,consumption psychology and lifestyle to conduct a comprehensive analysis of users,and obtain the user portrait results,so as to help the technology company of company a clearly understand the characteristics and consumption behavior of users.On the basis of user portrait,combined with the description of subdivided user characteristics,and combined with relevant marketing theories,this paper will help company a put forward accurate marketing strategies.There are different marketing strategies for different user characteristics.This chapter will study the different marketing strategies and common marketing strategies of various users.Finally,the implementation guarantee and result verification of company’s precision marketing strategy.Test from the results of precision marketing implementation,understand the differences in user behavior evaluation before and after use,so as to verify the feasibility and possible problems of precision marketing strategy,and put forward further optimization plans.Among them,the main conclusions of this paper are as follows: first,as an emerging technology,there are few enterprises that can have a large amount of user data.They need to integrate into their own relatively complete database with the help of external big data resources,which is also a very important factor restricting the accurate marketing application of big data;Second,some Internet companies have huge amounts of data,or they have solved the source problem of big data through external channels,but they lack professional talents or practical application ideas,which leads to the inability to use big data to guide precision marketing in a real sense.Therefore,starting with the case of company a,this paper expounds in detail the integration,cleaning and analysis of big data inside and outside the enterprise,the formation of user portraits and their consumption characteristics,and finally realizes the formulation of precision marketing strategy.Third,after understanding the value and realization ideas of big data precision marketing,e-commerce enterprises also analyze how to ensure the implementation of precision marketing from all aspects,and put forward a set of perfect guarantee system from improving the staff’s understanding of big data and precision marketing,introducing data analysis talents,strengthening user data management and strengthening community operation management,so as to ensure the real implementation of precision marketing,After the implementation,it has been tested by the market,and then repeatedly optimized the closed-loop operation to help company a realize big data precision marketing.The research of this paper will help company a to have a clearer understanding of its users’ consumption behavior and consumption demand,more accurately locate the target customer group and deeply understand its consumption characteristics,more scientifically provide differentiated products and services for all kinds of subdivided user groups,and then continuously improve users’ experience in the shopping process and customer satisfaction.