A customer classification method and system

Abstract

The invention discloses a customer classification method and system, and relates to the field of customer classification, and the method comprises the steps: obtaining historical data; generating a corresponding feature dimension data table according to the historical data and a preset feature dimension index, the feature dimension data table comprising feature dimension data of a plurality of customers; establishing an analysis sample pool according to the feature dimension data table; carrying out dimension reduction on the feature dimension data in the analysis sample pool by adopting a factor analysis algorithm to obtain a corresponding factor score data table; and according to the factor score data table, classifying the customers by adopting a clustering algorithm. The dimensionalityreduction effect of the factor analysis algorithm and the clustering algorithm are adopted for classification at the same time, the limitation that the number of feature dimensionalities can be considered in a conventional customer classification method is broken through, and the two algorithms are combined for use, so that the precision and the accuracy of the classification result are improved.

Description

technical field [0001] The invention relates to the field of customer classification, in particular to a customer classification method and system. Background technique [0002] Customer segmentation is commonly used by retail enterprises to classify customers according to certain standards. In the existing customer segmentation methods of retail enterprises, business personnel usually select segmentation dimensions and standards subjectively according to business needs, through business experience or business intuition, and classify customers. [0003] A simple example is as follows: 1. Business requirements: establish a customer segment to distinguish customer value; 2. Select the segmentation dimension: the customer's annual consumption amount; 3. Determine the segmentation standard: the annual consumption amount is greater than 1,000 yuan It is high value, less than 200 yuan is low value. 4. Classify customers: Customer A, whose annual consumption is 1,200 yuan, is a h...

AI Technical Summary

Problems solved by technology

[0005] 1. The selection of segmentation dimensions and standards is relatively subjective and depends entirely on the business experience and intuition of the business personnel; 2. The subjective condition segmentation has a relatively large limit on the number of dimensions that can be considered. Existing retailer customer classification usually only considers A single dimension or a cross of 2-3 dimensions; 3. Different categories of customer segmentation need to be defined in advance based on business experience or business intuition

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