Client feature library generating method and device

Abstract

The invention provides a client feature library generating method, which comprises the steps of: calculating the sum d of intra-class distances under different cluster numbers k, and drawing a k-d value curve; finding a turning point according to the k-d value curve, finding a k value corresponding to the turning point, looking for an optimal cluster number kopt within a range near the k value, and utilizing a cluster result corresponding to the optimal cluster number as a final cluster result. The invention also provides a client feature library generating device. The scheme of the invention can improve the cluster searching efficiency and precision, and the method of the invention is used for performing client analysis according to the obtained client feature library to improve the recommendation success rate and client satisfaction degree of a marketing campaign.

Description

technical field [0001] The invention relates to the technical field of information processing, in particular to a method and device for generating a customer feature library. Background technique [0002] The customer feature database, as the name suggests, is a database that classifies customer groups according to customer feature information and stores the classified customers. The customer profile library plays a vital role in the marketing execution process: [0003] For managers, customer business matching can be achieved by matching customer characteristics with their suitable businesses and channels; accurately locating target customer groups, and designing differentiated marketing rules for differentiated customers, so that users can experience fast and accurate refinement Marketing design to realize intelligent marketing management; [0004] For front-line marketers, it can help them quickly identify the business that customers are suitable for, clear the recommen...

AI Technical Summary

Problems solved by technology

However, the algorithm of this model will cluster almost every user, and the complexity of the algorithm is very high, which means that the system resource consumption is very high

[0014] Since the K-MEANS clustering algorithm is a basic and most widely used division method in cluster analysis methods, it is a method for discovering classes and class centers in non-class labeled data, which cannot meet the requirements for feature classification in marketing activities. Requirements of users for refined and differentiated analysis

If the traditional K-MEANS algorithm is directly applied to customer segmentation, considering the user scale, the resource consumption cost will be quite large

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