CN120631969A - Customer multi-dimensional data entry and management system based on CRM - Google Patents
Customer multi-dimensional data entry and management system based on CRMInfo
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- CN120631969A CN120631969A CN202510756479.XA CN202510756479A CN120631969A CN 120631969 A CN120631969 A CN 120631969A CN 202510756479 A CN202510756479 A CN 202510756479A CN 120631969 A CN120631969 A CN 120631969A
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Abstract
本发明公开了一种基于CRM的客户多维数据录入与管理系统，包括多维数据采集层、数据治理中心、智能分析层和应用交互层。多维数据采集层通过手动录入、API对接、图像/语音识别等方式采集客户多维度数据；数据治理中心对数据清洗、校验、存储并生成标签；智能分析层利用客户信用评分模型等分析数据，输出客户质量状况；应用交互层通过数据可视化展示结果并支持权限管理。客户信用评分模型综合考虑权重、完整性、时效性和准确性等因素。本系统解决了传统CRM系统数据管理低效、风险识别不足等问题，实现高效数据管理、精准风险预警和有效商机挖掘，提升企业管理效率与竞争力。
The present invention discloses a customer multi-dimensional data entry and management system based on CRM, including a multi-dimensional data acquisition layer, a data governance center, an intelligent analysis layer and an application interaction layer. The multi-dimensional data acquisition layer collects customer multi-dimensional data through manual entry, API docking, image/voice recognition and other methods; the data governance center cleans, verifies, stores and generates labels for the data; the intelligent analysis layer analyzes the data using customer credit scoring models and other methods, outputs customer quality status; the application interaction layer displays the results through data visualization and supports authority management. The customer credit scoring model comprehensively considers factors such as weight, integrity, timeliness and accuracy. This system solves the problems of inefficient data management and insufficient risk identification in traditional CRM systems, realizes efficient data management, accurate risk warning and effective business opportunity mining, and improves enterprise management efficiency and competitiveness.
Description
Technical Field
This document relates to a CRM-based customer multidimensional data entry and management system.
Background
The traditional enterprise information management system (CRM) is faced with the difficult problem of data integration in the process of customer data, the customer data are distributed in independent modules such as sales, finance, logistics and the like, a unified data model is lacking, the data synchronization is updated by means of manual input, the updating hysteresis rate is high, a data island is easy to form, and the management efficiency is low.
The existing customer management system is insufficient in risk identification capability, lacks a systematic abnormal customer identification mechanism, relies on experience judgment of service personnel, can monitor single-dimension risks, cannot identify potential risks through multidimensional association analysis such as 'order quantity sudden increase + payment period extension + complaint sudden increase', and the like, and is easy to cause potential management hazards.
In summary, the conventional system has weak functions in terms of data management, risk identification and business mining, and is difficult to adapt to the new challenges of high data real-time requirement, high risk complexity and business fragmentation distribution in the digital business environment, and an intelligent customer management system needs to be constructed through technical innovation.
Disclosure of Invention
The invention aims to provide a customer multidimensional data input and management system based on CRM, which can effectively acquire real-time information of a customer through multidimensional data of the customer in a scientific management mode, thereby managing the customer in a targeted manner, and the specific system comprises the following steps:
A CRM-based customer multidimensional data entry and management system comprising the following hierarchy:
S1, a multidimensional data acquisition layer acquires basic information, transaction details and order conditions of a client through manual input, API (application program interface) docking and image recognition modes, and generates editable data;
s2, a data management center is used for cleaning, checking, storing and managing labels of the acquired data;
The intelligent analysis layer is used for analyzing the client data through a data analysis algorithm and a model and outputting the quality condition of the client according to the analysis result;
And S3, an interaction layer is applied, wherein the analysis result of the client data is visually displayed in the form of a chart and a report.
In the intelligent analysis layer, the client quality condition is analyzed through a client credit scoring formula, and the specific formula is as follows:
Customer credit scoring formula:
Wherein Q is a customer quality score (0-100), which is lower than 80 time-sharing triggering early warning, lower than 60 time-sharing triggering alarm and lower than 40 time-sharing triggering automatic verification;
W i the weight of the corresponding field, e.g., W Tax credit rating may be set to 0.15, where the sum of the n fields is 1.
The timeliness attenuation factor can set that the contribution value of the data to the score correspondingly drops every time the data expires, so as to reduce the influence of the client history problem on the current state.
And C i, the effective data length of the ith data dimension, namely the length of the content which is actually acquired or filled in the field.
M i represents the maximum allowed data length for the ith data dimension.
The ratio of (2) is the integrity coefficient of the data, the A value is the accuracy coefficient, and the values are sigma-1.
Such as "contact (handset)", his field is fixed to 11 bits, so mi=11. When the "contact (handset)" field is filled in completely, C i =11. Then as a wholeThe ratio of (2) is 1. When C i＜Mi, the data is reflected in a missing state or incomplete state.
Next, C i＜Mi may be set for a field of a rated data length such as a mobile phone number, and C i may be automatically zeroed. Thereby prompting the input person to check the integrity of the data.
For some fields with uncertain data length, such as the registration address of the client, the business state of the client, etc., the step of setting the auto-zero of C i is not required.
Meanwhile, for the data to be acquired, in order to improve the input capacity level, the multidimensional data acquisition layer comprises the following input modes:
s11, inputting basic information, transaction details, customer contact ways and transaction detail information of a customer through manual input of staff;
S12, API docking input, namely acquiring real-time dynamics of a client and real-time dynamics of industries in which the client is located through synchronous sharing with other systems in an enterprise and with external resources;
S13, image recognition and voice recognition access, namely performing digital processing on non-digital information such as paper documents, pictures, sound recordings and the like through an OCR technology and a voice-to-text technology, and acquiring corresponding information.
Meanwhile, in order to save the time of manual input and reduce the labor of repeatability, the multidimensional data acquisition layer supports the acquisition and processing of unstructured data, including customer social media comments, customer service chat records and industry report documents, and key information is extracted through a natural language processing technology and converted into structured data. The method is characterized by supporting the collection of unstructured data such as customer social media comments, customer service chat records, industry reports and the like, extracting keywords (such as 'quality problems', 'intention of collaboration') through a Natural Language Processing (NLP) technology, and converting the keywords into structured labels (such as 'high complaint risks', 'potential business opportunities').
When the data are collected, in order to save the calculated amount in the analysis stage, the data management center comprises the steps of cleaning, checking, storing and label management of the data, and the influence of interference data on the result is reduced through cleaning and checking, so that the accuracy of a calculation formula on the result is improved.
S21, data cleaning, namely performing de-duplication, error correction and complementation on the acquired data, removing repeated data records, correcting error information in the data and supplementing missing data fields;
s22, rule checking, namely checking the data in real time through a data checking rule. The verification rule comprises data format verification and data logic verification, wherein the verification rule allows the data passing through the verification to enter a data storage link;
S23, storing the cleaned and checked qualified data into a database of the system, and storing the data in a classified manner so as to facilitate subsequent inquiry and analysis;
And S24, label management, namely generating corresponding labels for clients according to different dimensions and characteristics of client data, wherein label content comprises high-risk clients, high-quality clients and potential business clients, establishing association relations between the labels and the client data, classifying and managing the clients through the labels, and conveniently and quickly searching and positioning target client groups.
Further, the data management center further comprises a data tracing module for recording the acquisition source, the input time, the modification history and the operator information of the data to form a data tracing log and supporting the tracing inquiry of the user on the data change process.
In order to give different roles different rights, the problem of leakage of business secrets caused by rights abuse is prevented. The application interaction layer is provided with a permission management module which supports the allocation of data access and operation permissions according to roles, wherein the permission management module comprises roles of an administrator, a salesman and an analyst, and different roles correspond to different data viewing ranges.
In order to further save storage resources and operation resources, the system is provided with a data closed-loop processing flow, a data buffer queue is arranged between the multidimensional data acquisition layer and the data treatment center and is used for temporarily storing abnormal data which does not pass the rule check, and the system supports the resubmitting check after the manual intervention correction to form the data closed-loop processing flow of acquisition, check, correction and rechecking.
The beneficial effects are that:
The scheme relies on a systematic architecture and an intelligent model, and the client data management efficiency is remarkably improved. The data management center constructs a precise and unified data system through standardized cleaning, intelligent checking, dynamic label management and a full-flow tracing mechanism, improves the problems of data island and update hysteresis of the traditional system, greatly improves the data acquisition and processing efficiency, and builds a firm data root for enterprise fine management.
And finally, the credit scoring model carried by the intelligent analysis layer replaces manual experience judgment by a scientific algorithm, so that a customer risk score can be generated in real time and a hierarchical early warning mechanism can be triggered. By combining a visual interaction system, the power-assisted enterprises accurately capture market dynamics, and the decision scientificity of the enterprises in a modern competitive business environment is improved.
Drawings
FIG. 1 is a flow diagram of a CRM-based customer multidimensional data entry and management system.
Detailed Description
The present invention will be further described in detail with reference to the following examples and drawings for the purpose of enhancing the understanding of the present invention, which examples are provided for the purpose of illustrating the present invention only and are not to be construed as limiting the scope of the present invention.
Embodiment 1 risk early warning scenario:
some manufacturing industry customer a developed the following anomaly data in the second quarter of 2024:
S1, a behavior risk dimension, wherein the order quantity is increased by 300% in comparison with the previous quarter, the weight W 1 = 0.3 of the item, and the integrity coefficient of the item data is obtained because the integral reason of the order increase cannot be obtained The time-dependent attenuation factor e -0.001*30 is approximately 0.97, the accuracy is verified in multiple directions, the data is judged to be basically ready, and the value of A is 0.9.
The overall score for this dimension is
As can be seen from the above calculation process, even if the order of the customer increases, the total score caused by the fact is still not high due to the fact that the real reason is unknown.
S2, financial risk dimension, wherein the payment period is prolonged to 60 days, the average day before is 30 days, and the final calculation result of the comprehensive weight W 2 is 20 minutes.
S3, industry risk dimension, namely enabling the industry to enter a decay period, wherein the weight W 3 =0.2, the accuracy coefficient A=0.9 and other dimension data are normal. The final score for the industry risk dimension was 18 points.
And finally, the overall score is 53.714 minutes, and early warning is triggered. It follows that even if the customer's order volume is in good condition, due to the lack of relevant information and the slipping down of the industry as a whole, a warning will still be given to the manager at this time, suggesting a potential business risk.
Example 2 comparative analysis manufacturing customer B for a manufacturing company
At this time, the quality of the customer is judged from three dimensions of order stability, supply chain coordination, and financial health.
Q=order stability (continuous 12 month fluctuation <5%, score 90×0.3) +supply chain synergy (on-time delivery rate 95%, score 85×0.25) +financial health (liability rate 60%, score 75×0.3) =composite score 81.25 points.
Based on the scoring, mid-term order proportion may be added to customer B. It follows that when the customer's fluctuations are small and the finance is good. By doing the calculations in these dimensions, the risk of such clients maintaining cooperation is low, so that good cooperation can be maintained.
In some complex business cooperation modes, it is often necessary to combine the embodiment 1 with the embodiment 2, and comprehensively determine the quality of a specific customer from six or more angles of behavioral risk, financial risk, industry risk, order stability, supply chain coordination degree, financial health degree, and the like, and at this time, more data are required, so that the obtained final result is more accurate.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered by the scope of the claims of the present invention.
Claims (8)
1. A CRM-based customer multidimensional data entry and management system comprising the following hierarchy:
The multidimensional data acquisition layer acquires basic information, transaction details and order conditions of clients through manual input, API docking and image recognition modes and generates editable data;
the data management center is used for cleaning, checking, storing and label management of the acquired data;
The intelligent analysis layer is used for analyzing the client data through a data analysis algorithm and a model and outputting the quality condition of the client according to the analysis result;
And (3) applying an interaction layer, namely visually displaying the analysis result of the client data in a chart form and a report form.
2. The CRM-based customer multidimensional data entry and management system of claim 1, wherein in the intelligent analysis layer, the customer quality status is analyzed by a customer credit scoring formula, the specific formula is as follows:
Q is a customer quality score (0-100), which is lower than 80 time-sharing triggering early warning, lower than 60 time-sharing triggering alarm and lower than 40 time-sharing triggering automatic verification;
W i, the weight of the corresponding field, wherein the total weight of n fields is 1;
a time-dependent decay factor, wherein t is the current time and t 0 is the occurrence time of the corresponding event;
C i, the effective data length of the ith data dimension, namely the length of the content actually collected or filled in the field;
m i represents the maximum allowed data length for the ith data dimension;
the ratio of (2) is the integrity coefficient of the data, the A value is the accuracy coefficient, and the values are all 0-1.
3. The CRM-based customer multidimensional data entry and management system as recited in claim 1 or 2, wherein the multidimensional data acquisition layer comprises an input mode of S11, manual/batch entry, wherein basic information, transaction details, customer contact information and transaction detail information of a customer are input through manual input of a staff member;
S12, API docking input, namely acquiring real-time dynamics of a client and real-time dynamics of industries in which the client is located through synchronous sharing with other systems in an enterprise and with external resources;
S13, image recognition and voice recognition access, namely performing digital processing on non-digital information such as paper documents, pictures, sound recordings and the like through an OCR technology and a voice-to-text technology, and acquiring corresponding information.
4. A CRM-based customer multidimensional data entry and management system as claimed in claim 3, wherein the data management centre comprises cleaning, verification, storage and tag management of the data;
S21, data cleaning, namely performing de-duplication, error correction and complementation on the acquired data, removing repeated data records, correcting error information in the data and supplementing missing data fields;
s22, rule checking, namely checking the data in real time through a data checking rule. The verification rule comprises data format verification and data logic verification, wherein the verification rule allows the data passing through the verification to enter a data storage link;
S23, storing the cleaned and checked qualified data into a database of the system, and storing the data in a classified manner so as to facilitate subsequent inquiry and analysis;
And S24, label management, namely generating corresponding labels for clients according to different dimensions and characteristics of client data, wherein label content comprises high-risk clients, high-quality clients and potential business clients, establishing association relations between the labels and the client data, classifying and managing the clients through the labels, and conveniently and quickly searching and positioning target client groups.
5. The CRM-based customer multidimensional data entry and management system according to claim 3, wherein the data governance center further comprises a data tracing module for recording the acquisition source, entry time, modification history and operator information of the data, forming a data tracing log, and supporting the tracing inquiry of the user on the data change process.
6. The CRM-based customer multidimensional data entry and management system of claim 3, wherein the multidimensional data collection layer supports collection and processing of unstructured data, including customer social media comments, customer service chat records, industry report documents, and wherein the key information is extracted by natural language processing techniques and converted into structured data.
7. A CRM-based customer multidimensional data entry and management system as claimed in claim 3, wherein the application interaction layer is provided with a rights management module supporting role-by-role allocation of data access and operation rights, including administrator, salesman, analyst roles, different roles corresponding to different data viewing ranges.
8. The CRM-based customer multidimensional data entry and management system as recited in claim 3, wherein a data buffer queue is provided between the multidimensional data acquisition layer and the data management center for temporarily storing abnormal data which fails to pass the rule check, and the data buffer queue supports the resubmission check after the correction by manual intervention, thereby forming a data closed loop processing flow of acquisition-check-correction-re-check.
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