Customer multi-dimensional data entry and management system based on CRM

Abstract

The invention discloses a customer multi-dimensional data entry and management system based on CRM. The customer multi-dimensional data entry and management system comprises 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 is used for acquiring multi-dimensional data of clients in modes of manual input, API (Application Program Interface) butt joint, image / voice recognition and the like; the data management center cleans, verifies and stores the data and generates labels; the intelligent analysis layer analyzes data by using a customer credit scoring model and the like and outputs customer quality conditions; and the application interaction layer displays a result through data visualization and supports authority management. The customer credit scoring model comprehensively considers factors such as weight, integrity, timeliness and accuracy. The system solves the problems that a traditional CRM system is low in data management efficiency, insufficient in risk identification and the like, efficient data management, accurate risk early warning and effective business opportunity mining are achieved, and the enterprise management efficiency and competitiveness are improved.

Description

Technical Field

[0001] This article deals with the customer multi-dimensional data entry and management system based on CRM. Background Art

[0002] Traditional enterprise information management systems (CRM) face data integration challenges when processing customer data. Customer data is scattered across independent modules such as sales, finance, and logistics, lacking a unified data model. Data synchronization relies on manual input and updates, resulting in a high update lag rate, easily forming data silos and causing inefficient management.

[0003] In addition, the existing customer management system has insufficient risk identification capabilities and lacks a systematic abnormal customer identification mechanism. It relies more on the experience and judgment of business personnel and can only monitor single-dimensional risks. It is unable to identify potential risks through multi-dimensional correlation analysis such as "sudden increase in order volume + extended payment cycle + surge in complaints", which can easily lead to operational risks.

[0004] To sum up, traditional systems have weak functions in data management, risk identification and business opportunity mining, and are difficult to adapt to the new challenges of "high real-time data requirements, increased risk complexity, and fragmented distribution of business opportunities" in the digital business environment. It is urgent to build an intelligent customer management system through technological innovation. Summary of the Invention

[0005] The purpose of this invention is to provide a customer multi-dimensional data entry and management system based on CRM. Through scientific management methods, it can effectively obtain real-time information about customers through their multi-dimensional data, thereby managing customers in a targeted manner. The specific system is as follows:

[0006] A CRM-based customer multi-dimensional data entry and management system, including the following levels:

[0007] S1. Multi-dimensional data collection layer: Obtain basic customer information, transaction details, and order status through manual entry, API connection, and image recognition, and generate editable data;

[0008] S2, Data Governance Center: Cleans, verifies, stores, and labels the collected data;

[0009] Intelligent analysis layer: Analyzes customer data through data analysis algorithms and models, and outputs the customer's quality status based on the analysis results;

[0010] S3. Application interaction layer: Visually display the analysis results of customer data in the form of charts and reports.

[0011] In the intelligent analysis layer, the customer quality status is analyzed through the customer credit scoring formula. The specific formula is as follows:

[0012] Customer credit scoring formula:

[0013]

[0014] Where Q is the customer quality score (0-100). A score below 80 triggers an early warning, below 60 triggers an alarm, and below 40 triggers an automatic verification.

[0015] W i : The weight of the corresponding field, such as W 纳税信用等级 The weight of is 0.15, where the sum of the n fields is 1.

[0016] The timeliness decay factor can be set to decrease the contribution value of the data to the score every time it expires for a period of time, so as to reduce the impact of the customer's historical problems on the current status.

[0017] C i : The effective data length of the i-th data dimension, that is, the length of the content actually collected or filled in this field.

[0018] M i : Indicates the maximum allowed data length of the i-th data dimension.

[0019] The ratio is the integrity coefficient of the data, and A is the accuracy coefficient, both of which are σ-1.

[0020] For example, the "Contact Information (Mobile Phone)" field is fixed to 11 digits, so Mi = 11. When the "Contact Information (Mobile Phone)" field is fully filled, C i =11. Then the whole The ratio is 1. When C i <M i , it indicates that the data is missing or incomplete.

[0021] Secondly, for fields with a specified data length, such as mobile phone numbers, C i <M i When C i Automatically reset to zero to remind the input personnel to check the integrity of the data.

[0022] For some fields with uncertain data length, such as customer's registered address, customer's business status, etc., there is no need to set C i Automatic zeroing steps.

[0023] At the same time, for the above data that needs to be collected, in order to improve the input capability level, the multi-dimensional data collection layer includes the following input methods:

[0024] S11. Manual / batch entry: The staff manually inputs the customer's basic information, transaction details, customer contact information, and transaction details;

[0025] S12. API integration input: Acquire real-time dynamics of customers and their industries through synchronous sharing with other internal enterprise systems and external resources;

[0026] S13. Image recognition and voice recognition access: Through OCR technology and voice-to-text technology, non-digital information such as paper documents, pictures, and recordings are digitized and the corresponding information is obtained.

[0027] At the same time, to save time on manual input and reduce repetitive labor, the multi-dimensional data collection layer supports the collection and processing of unstructured data, including customer social media comments, customer service chat logs, and industry report documents. Natural language processing technology is used to extract key information and convert it into structured data. Specifically, this layer supports the collection of unstructured data such as customer social media comments, customer service chat logs, and industry reports, extracts keywords (such as "quality issues" and "cooperation intentions") through natural language processing (NLP), and converts them into structured tags (such as "high complaint risk" and "potential business opportunities").

[0028] When collecting the above data, in order to save the amount of calculation in the analysis phase, the data governance center includes data cleaning, verification, storage and label management; through cleaning and verification, the impact of interfering data on the results is reduced, thereby improving the accuracy of the calculation formula for the results.

[0029] S21. Data cleaning: De-duplication, error correction, and completion processing are performed on the collected data, duplicate data records are removed, erroneous information in the data is corrected, and missing data fields are supplemented;

[0030] S22, rule verification: Data is verified in real time using data verification rules. Verification rules include data format verification and data logic verification. Data that passes verification is allowed to enter the data storage link; data that fails verification generates a correction task to remind relevant personnel to correct the data.

[0031] S23. Data storage: Storing the cleaned and verified qualified data in the system database. At the same time, the data is classified and stored to facilitate subsequent query and analysis.

[0032] S24. Tag management: Generate corresponding tags for customers based on the different dimensions and characteristics of customer data. The tag content may include "high-risk customers", "high-quality customers", and "potential business opportunity customers". Establish an association between tags and customer data, classify and manage customers through tags, and facilitate quick retrieval and positioning of target customer groups.

[0033] Furthermore, the data governance center also includes a data traceability module, which is used to record the data collection source, entry time, modification history and operator information, form a data traceability log, and support users to trace back and query the data change process.

[0034] To grant different roles different permissions and prevent the leakage of commercial secrets caused by abuse of permissions, the application interaction layer has a permissions management module that supports the allocation of data access and operation permissions by role, including administrator, salesperson, and analyst roles. Different roles correspond to different data viewing scopes.

[0035] In order to further save storage resources and computing resources, the system sets up a data closed-loop processing process. A data buffer queue is set up between the multi-dimensional data collection layer and the data governance center to temporarily store abnormal data that fails the rule verification. It supports manual intervention and correction and then resubmission for verification, forming a "collection-verification-correction-reverification" data closed-loop processing process.

[0036] Beneficial effects:

[0037] This solution leverages a systematic architecture and intelligent models to significantly enhance customer data management efficiency. The multidimensional data collection layer connects multiple data sources, enabling the full capture and integration of customer information. The data governance center utilizes standardized cleansing, intelligent validation, dynamic tag management, and a full-process traceability mechanism to build a precise and unified data system. This addresses the data silos and update lags of traditional systems, significantly improving data acquisition and processing efficiency and laying a solid data foundation for refined enterprise management.

[0038] Finally, the credit scoring model in the intelligent analysis layer replaces manual judgment with scientific algorithms, generating customer risk scores in real time and triggering a graded early warning mechanism. Combined with a visual interactive system, it helps companies accurately capture market trends and improve the scientific nature of their decision-making in the modern competitive business environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flow chart of a customer multi-dimensional data entry and management system based on CRM. DETAILED DESCRIPTION

[0040] In order to deepen the understanding of the present invention, the present invention will be further described in detail below with reference to embodiments and drawings. The embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0041] Example 1: Risk warning scenario:

[0042] Customer A in the manufacturing industry experienced the following abnormal data in the second quarter of 2024:

[0043] S1, Behavioral risk dimension: Order volume increased by 300% compared with the previous quarter. The weight of this item is W1 = 0.3. Since the complete reason for the sudden increase in orders cannot be obtained, the integrity coefficient of this data is This data has occurred for 30 days, and the timeliness attenuation factor is e -0.001*30 ≈0.97, the accuracy has been verified by multiple parties and is judged to be basic preparation, and the value of A is 0.9.

[0044] The overall score for this dimension is

[0045] From the above calculation process, it can be seen that even if the customer's orders increase, the total score is still not high because the real reason is unknown.

[0046] S2, Financial Risk Dimension: The payment cycle is extended to 60 days, while the previous average number of days was 30 days. The final calculation result of the comprehensive weight W2 is 20 points.

[0047] S3, Industry Risk Dimension: The industry is entering a recession, with a weight of W3 = 0.2, an accuracy coefficient of A = 0.9, and data from other dimensions are normal. The final score for the Industry Risk Dimension is 18 points.

[0048] The final overall score was 53.714, triggering an early warning. This shows that even if the customer's order volume is in good condition, the lack of relevant information and the overall decline in the industry will still issue a warning to managers, indicating potential business risks.

[0049] Example 2: A manufacturing company compares and analyzes manufacturing customer B

[0050] At this time, the quality of the customer is judged from three dimensions: order stability, supply chain coordination, and financial health.

[0051] Q = order stability (fluctuation <5% for 12 consecutive months, score 90×0.3) + supply chain coordination (on-time delivery rate 95%, score 85×0.25) + financial health (asset-liability ratio 60%, score 75×0.3) = comprehensive score 81.25 points.

[0052] Based on the score, we can increase the proportion of medium- and long-term orders for Customer B. This shows that when a customer's volatility is low and their financials are strong, the risk of maintaining a partnership with this customer is low, thus ensuring a good relationship.

[0053] However, in some complex business cooperation models, it is often necessary to combine Example 1 with Example 2 to comprehensively judge the quality of a specific customer from six or even more perspectives, including behavioral risk, financial risk, industry risk, order stability, supply chain coordination, and financial health. In this case, more data is required and the final result is more accurate.

[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. CRM-based customer multi-dimensional data entry and management system, characterized by: The following levels are included: Multi-dimensional data collection layer: Through manual entry, API connection, and image recognition, it obtains basic customer information, transaction details, and order status, and generates editable data; Data Governance Center: Cleans, verifies, stores, and labels collected data; Intelligent analysis layer: Analyzes customer data through data analysis algorithms and models, and outputs the customer's quality status based on the analysis results; Application interaction layer: Visually display the analysis results of customer data in the form of charts and reports.

2. The CRM-based customer multi-dimensional data entry and management system according to claim 1, characterized in that: in, In the intelligent analysis layer, the customer quality status is analyzed through the customer credit scoring formula. The specific formula is as follows: Q is the customer quality score (0-100). A score below 80 triggers an early warning, below 60 triggers an alarm, and below 40 triggers an automatic verification. W i : The weight of the corresponding field; the sum of the weights of n fields is 1; Timeliness attenuation factor, where t is the current time and t0 is the time when the corresponding event occurs; C i : The effective data length of the i-th data dimension, that is, the actual length of the content collected or filled in this field; M i : Indicates the maximum allowed data length of the i-th data dimension; The ratio is the integrity coefficient of the data, and A is the accuracy coefficient, both of which range from 0 to 1.

3. The CRM-based customer multi-dimensional data entry and management system according to claim 1 or 2, characterized in that: The multi-dimensional data collection layer includes the following input methods: S11, manual / batch entry: through manual input by staff, the customer's basic information, transaction details, customer contact information, and transaction details are entered; S12. API integration input: Acquire real-time dynamics of customers and their industries through synchronous sharing with other internal enterprise systems and external resources; S13. Image recognition and voice recognition access: Through OCR technology and voice-to-text technology, non-digital information such as paper documents, pictures, and recordings are digitized and the corresponding information is obtained.

4. The CRM-based customer multi-dimensional data entry and management system according to claim 3, characterized in that: The data governance center includes data cleaning, verification, storage and label management; S21. Data cleaning: De-duplication, error correction, and completion processing are performed on the collected data, duplicate data records are removed, erroneous information in the data is corrected, and missing data fields are supplemented; S22, rule verification: Data is verified in real time using data verification rules. Verification rules include data format verification and data logic verification. Data that passes verification is allowed to enter the data storage link; data that fails verification generates a correction task to remind relevant personnel to correct the data. S23. Data storage: Storing the cleaned and verified qualified data in the system database. At the same time, the data is classified and stored to facilitate subsequent query and analysis. S24. Tag management: Generate corresponding tags for customers based on the different dimensions and characteristics of customer data. The tag content may include "high-risk customers", "high-quality customers", and "potential business opportunity customers". Establish an association between tags and customer data, classify and manage customers through tags, and facilitate quick retrieval and positioning of target customer groups.

5. The CRM-based customer multi-dimensional data entry and management system according to claim 3, characterized in that: The data governance center also includes a data traceability module, which is used to record the data collection source, entry time, modification history and operator information, form a data traceability log, and support users to trace back and query the data change process.

6. The CRM-based customer multi-dimensional data entry and management system according to claim 3, characterized in that: The multi-dimensional data collection layer supports the collection and processing of unstructured data, including customer social media comments, customer service chat records, and industry report documents. It extracts key information through natural language processing technology and converts it into structured data.

7. The CRM-based customer multi-dimensional data entry and management system according to claim 3, characterized in that: The application interaction layer has a permission management module that supports the allocation of data access and operation permissions by role, including administrator, salesperson, and analyst roles. Different roles correspond to different data viewing scopes.

8. The CRM-based customer multi-dimensional data entry and management system according to claim 3, characterized in that: A data buffer queue is set up between the multi-dimensional data collection layer and the data governance center to temporarily store abnormal data that fails the rule verification, and supports resubmission for verification after manual intervention and correction, forming a data closed-loop processing flow of "collection-verification-correction-reverification".

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