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A high-value customer identification method, system, device and storage medium

A customer identification, high-value technology, applied in character and pattern recognition, instruments, calculations, etc., to achieve high recognition accuracy and strong pertinence

Active Publication Date: 2021-01-22
PING AN BANK CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But so far there is still no effective method that can accurately locate and identify high-value customers from a large number of customers, and realize the conversion of the value of customers from qualitative description to quantitative measurement, so that enterprises can maximize the use of high-value customer resources and carry out high-value customers. Analysis and prediction of value customer behavior, classify high-value customers, determine their stretchable asset space and business path, invest more business resources in a targeted and directional manner, develop personalized services, and improve customer satisfaction Degree, and then improve the level of operation, this is a problem that operating companies need to explore and solve on the road of operation

Method used

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  • A high-value customer identification method, system, device and storage medium
  • A high-value customer identification method, system, device and storage medium
  • A high-value customer identification method, system, device and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0056]This embodiment provides a method for identifying high-value customers, such asfigure 1 As shown, including the following steps:

[0057]S1. Collect historical high-value customer information whose contribution value exceeds the first preset threshold to form historical high-value customer group sample information. Specifically, you can select the past year's contribution income (high value) to rank in the top n1Of customers, form historical high-value customer group sample information to identify high-value groups, among which the first preset threshold n is determined according to actual business conditions1Value (may be 5%-10%);

[0058]S2. Clustering and grouping sample information of historical high-value customer groups based on preset customer characteristic information. The preset customer characteristic information includes assets, turnover characteristics, liabilities, consumption power and preferences, investment product characteristics, and basic personal characteristics...

Embodiment 2

[0111]The features of this embodiment that are the same as those of the first embodiment will not be repeated here. The features of this embodiment are different from those of the first embodiment:

[0112]Specifically, you can select the top n of contribution income (high value) in the past six months1Of customers, form historical high-value customer group sample information to identify high-value groups, among which the first preset threshold n is determined according to actual business conditions1Value (can be 5%);

[0113]S221. Discard features with a high missing rate. The cleaning features may be incomplete due to different business conditions, and the removal rate is greater than n3Characteristics of n3It can be 10%.

[0114]S23. To TOP n1Customers are clusters of samples of historical high-value customer groups to obtain k historical clustered customer groups. Take the K-means algorithm as an example, use the contour coefficient method to determine the number of clusters, that is, tr...

Embodiment 3

[0125]The features of this embodiment that are the same as those of the first embodiment will not be repeated here. The features of this embodiment are different from those of the first embodiment:

[0126]Specifically, you can select the top n of contribution income (high value) in the past year1Of customers, form historical high-value customer group sample information to identify high-value groups, among which the first preset threshold n is determined according to actual business conditions1Value (can be 10%);

[0127]S221. Discard features with a high missing rate. The cleaning features may be incomplete due to different business conditions, and the removal rate is greater than n3Characteristics of n3It can be 30%.

[0128]S23. To TOP n1Customers are clusters of samples of historical high-value customer groups to obtain k historical clustered customer groups. Take the K-means algorithm as an example, use the contour coefficient method to determine the number of clusters, that is, try to ...

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Abstract

The present invention relates to a high-value customer identification method, system, equipment and storage medium. By inputting the customer information to be identified into the similar group identification model of the benchmark group to generate the identification result matching with the corresponding historical benchmark group information, the identification accuracy is high, and it is truly Transform the value of customers from qualitative description to quantitative measurement, and by classifying high-value customers into high-value potential customers similar to the corresponding benchmark groups, determine their stretchable asset space and business path, targeted and effective Put in more operating resources in the direction, improve customer satisfaction, and then improve the operating level. It can accurately identify high-value but low-contribution customers, improve operating efficiency, provide better services for high-value but low-contribution customers, optimize customer experience, increase contribution value, and then improve operating results, and provide better services for high-net-worth customers. Reduce the churn rate of high net worth clients.

Description

Technical field[0001]The invention relates to the field of data mining, and in particular to a method, system, equipment and storage medium for identifying high-value customers.Background technique[0002]High-value customers have contributed immense value to operating companies, and at the same time they have brought rich social relationships and strong social influence. Therefore, high-value customers have become the focus of corporate market competition. However, there is still no effective method to accurately locate and identify high-value customers from a large number of customers, and realize the transformation of the value of customers from qualitative description to quantitative measurement, so that enterprises can maximize the use of high-value customer resources and develop high-value customers. Analysis and prediction of value customer behavior, classify high-value customers, determine their stretchable asset space and business paths, put more business resources in a targe...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q30/02G06K9/62
CPCG06Q30/0201G06F18/23G06F18/214
Inventor 李明王伟李双根黄丽诗
Owner PING AN BANK CO LTD
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