Analytic hierarchy process and RFM fused commercial bank customer rating method and device

Abstract

The invention relates to the technical field of informatization, provides an analytic hierarchy process and RFM fused commercial bank customer rating method and device, and aims to solve the problem of poor model rating effect caused by collinearity, single index and mismatching of actual business of RFM model variables in deposit business. The method mainly comprises the following implementation steps: determining a target layer and a criterion layer, wherein the target layer is used for grading a user, and the criterion layer is divided into three aspects of user activeness, user loyalty and user value; collecting user data sets in three aspects of user activeness, user loyalty and user value; carrying out data cleaning on the related data; respectively constructing scheme layer indexes of three criteria of user activity, user value and user loyalty by utilizing an RFM model; determining a scheme layer index weight based on an analytic hierarchy process, outputting a criterion layer index result, determining a criterion layer index weight, and outputting a score rating of a target layer customer; and based on the score rating of the target layer customer, finishing the formulation and popularization of the differentiated marketing strategy or activity.

Description

technical field [0001] The invention relates to the field of information technology, and provides a commercial bank customer rating method and device that integrates analytic hierarchy process and RFM. Background technique [0002] With the development of social economy and the wide application of mobile Internet, the scale of commercial bank user behavior data has increased. How to discover user value from the huge user behavior data has become a difficult problem; on the other hand, the strengthening of financial supervision, commercial banks The homogenization of inter-market products and the intensification of industry competition have put forward higher requirements for the level of commercial bank customer operations and the control of marketing costs. Therefore, based on user behavior data, it is particularly important to classify commercial bank customers and carry out accurate, personalized, and low-cost marketing activities. [0003] The traditional user stratific...

AI Technical Summary

Problems solved by technology

First of all, the traditional RFM model distinguishes the user’s critical value is subjective, and the RFM using k-means clustering can avoid subjectivity, but the classification automatically generated based on the sample distance is often easily affected by the data distribution, and the category is sometimes difficult explanation; secondly, the traditional RFM model uses too single measures of user loss possibility, user loyalty, and user value indicators, which often cannot fully and correctly measure users, and the division of users is relatively rough; thirdly, commercial banks The deposit and loan business is different from the retail consumer industry, and the traditional RFM indicators are difficult to apply

For example, the user transaction frequency index to measure user loyalty. In the deposit business, the high frequency of customer deposits may only be due to the short deposit cycle or the repeated operation of the same fund. In the index to measure customer value, The higher the user's transaction frequency, the greater the total transaction amount, and the higher the displayed user value, but the actual situation may be just the opposite; in addition, the RFM model of deposits has carried out equal weight processing on three different indicators, while the actual business scenario In this case, the importance of each variable may be different, and there is often collinearity between variables, which will also affect the model effect

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