Research On Strategy Of RFM Data-Driven Precision Marketing On Personal Customer For Bank B

In the past,the marketing and promotion of banking and financial products were relatively rough.Due to the lack of personalized promotion channels,marketing was not targeted,and the efficiency and effectiveness were generally poor.Precision marketing differs from traditional marketing in that it focuses on targeted marketing for different customer groups.Precision marketing uses modern technology to segment customers and develop targeted marketing strategies based on their different consumption psychology,behavior,and demand characteristics to achieve strong and effective communication with different consumer groups in the target market with high input-output ratio.With the continuous deepening of data science research,new algorithms that emerge are gradually proven to be effective,and their role in modern business is increasingly valued,providing technical and methodological support for precision marketing.Therefore,research on data-driven precision marketing is of great significance for improving the marketing effectiveness of banks.B City Commercial Bank is a city commercial bank located in southern China.The bank has always followed traditional marketing methods,mechanically classifying individual customers based only on their deposits,and has been unable to accurately grasp customer needs,resulting in poor marketing results.This paper takes B City Commercial Bank as the research object,and combines theories and methods such as precision marketing and data mining to study precision marketing strategies for individual customers.The main work of the paper includes:(1)Analysis of the environment and business of B City Commercial Bank.Using methods such as PEST analysis,Porter’s Five Forces model,and micro-environmental analysis to systematically analyze the social environment,industry competition,micro-environment of the enterprise,and the business status of B City Commercial Bank,and summarize the problems in the marketing of individual customers;(2)Based on B City Commercial Bank’s RFM-driven data,the bank’s customers are segmented and profiled.First,the K-means algorithm is used to cluster the RFM data to achieve customer group segmentation.Then,the mutual information is used to calculate the correlation between each dimension and the result,and the impact of customer attributes on the segmentation of customer groups is determined.Finally,customer profiles are determined using the impact degree,describing the consumption psychology,demand orientation,channel preferences and other characteristics of each customer group;(3)Based on customer segmentation and profiling,precision marketing strategies for B City Commercial Bank’s individual customers are proposed,mainly including insight into customer needs based on customer characteristics.The research in this paper is beneficial for marketing personnel of B City Commercial Bank to quickly focus on customer group characteristics,develop customized personalized services and products,and meet the specific and common needs of different customer groups while reducing the difficulty of developing products,shortening the product development cycle,and reducing the bank’s customer maintenance costs,thereby improving marketing efficiency and effectiveness.The methods and analysis processes used in this paper are summarized and sorted,and have certain universality.They can not only be applied to B City Commercial Bank,but also have some reference value for other banks to carry out data-driven precision marketing.