CRM-based customer data analysis system and method

By processing multi-source data and performing multi-layered customer analysis based on a CRM system, multi-dimensional customer profiles are generated, solving the problem of incomplete traditional customer data analysis, enabling more accurate customer insights and marketing strategies, and reducing customer churn rate.

CN121032552APending Publication Date: 2025-11-28杭州佑首科技有限公司 +1
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Patent Information

Application Number
CN202511268292.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-05
Publication Date
2025-11-28

AI Technical Summary

Technical Problem

Traditional customer data analysis methods suffer from incomplete and low-accuracy results due to limited data sources and superficial analysis.

Method used

Based on the CRM system, customer master data view is generated by extracting multi-source customer data, customer segmentation feature set is constructed, cluster analysis is performed to form multi-level value customer groups, and customer basic information, purchase cycle fluctuation coefficient, service response preference, etc. are extracted through multi-layer customer analysis network to generate multi-dimensional customer profile.

Benefits of technology

It significantly improves the comprehensiveness and accuracy of customer insights, supports enterprises in developing personalized marketing strategies, reduces customer churn, increases customer value, optimizes resource allocation, and enhances market competitiveness.

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Abstract

The invention relates to the technical field of big data, and discloses a CRM-based customer data analysis system and method, and the system comprises a main data view construction module which is used for carrying out the data processing of multi-source customer data, and generating a customer main data view of a target customer; the customer value grouping module is used for constructing a customer grouping feature set of the target customers according to the customer main data view, and performing clustering analysis on the target customers to obtain a multi-level value customer group; the customer data analysis module is used for constructing a multi-layer customer analysis network of target customers, extracting basic information and enterprise characteristics of the target customers, analyzing purchase cycle fluctuation coefficients and service response preferences of the target customers, and calculating potential demand probabilities and loss risk probabilities of the target customers; the customer portrait construction module is used for generating a multi-dimensional customer portrait of the target customer; and the customer analysis report module is used for generating a customer analysis report of the target customer. According to the invention, the comprehensiveness and accuracy of customer data analysis can be improved.
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