Customer Relationship Graph System
I. Multi-Source Data Integration and Governance: Addressing “Information Silos”
To tackle the widespread issue of fragmented data, the system integrates data from multiple channels and applies intelligent governance to build a unified customer data asset repository:
· Multi-Channel Data Access: Supports seamless integration of internal enterprise systems (CRM, ERP, etc.) and external sources (social media, news reports, etc.).
· Automated Data Governance: Utilizes data cleansing and merging technologies to eliminate redundancy and conflicts, ensuring accuracy and consistency.
· Flexible Data Management: Supports bulk imports (via standardized Excel templates) and real-time data subscriptions, enabling centralized management of tens of thousands of customer records.
II. Fine-Grained Customer Information Management: Solving “Data Fragmentation and Inefficient Management”
Focusing on the pain point of dispersed data and inefficiency, the system provides dual-dimension (individual and corporate) customer management:
· Comprehensive Data Coverage: Includes personal data (ID, occupation, income), corporate data (registration, legal representatives, shareholder structure), transaction records, and interaction history.
· Convenient Operations: Supports querying, adding, modifying, deleting, and batch processing of customer data, greatly improving management efficiency.
· Standardized Storage and Display: Ensures structured storage and presentation of data, eliminating information fragmentation.
III. Customer Relationship Graph Construction and Visualization: Addressing “Insufficient Customer Insight”
Leveraging graph database technology (Neo4j), the system builds a customer relationship network and presents it in a visualized format to enhance insights:
· Multi-Type Relationship Identification: Recognizes direct relationships (transactions, partnerships), indirect relationships (affiliated enterprises, community memberships), and influence-based connections.
· Multi-View Visualization: Offers multiple visualization modes such as organizational charts and social network graphs for intuitive relationship mapping.
· Interactive Operations: Supports zooming, dragging, and search functions to help users quickly locate key customer nodes and potential links.
IV. Intelligent Analysis and Risk Forecasting: Improving “Decision Efficiency and Reducing Customer Churn”
The system integrates intelligent analytics powered by machine learning, enabling data-driven decision-making and early risk warnings:
· Core Value Mining: Analyzes customer behavior, relationship stability, and transaction patterns to identify key clients and uncover new opportunities (e.g., linked demand among related customers).
· Churn Risk Alerts: Provides early warnings of potential customer attrition, helping enterprises craft effective retention strategies.
· Risk Scoring: Delivers customer risk-value scoring to support proactive risk assessment and improve decision accuracy.
V. Permission Control and Collaborative Operations: Balancing “Inefficient Collaboration and Data Security”
With fine-grained permission settings and real-time collaboration features, the system ensures both secure data usage and efficient teamwork:
· Granular Permission Management: Allows creation, modification, and deletion of roles, along with menu-level permission settings. Ensures that users at different levels (administrators, relationship managers, etc.) only access authorized data.
· Real-Time Collaboration: Supports multi-user sharing of customer information and analytical results in real time, breaking down collaboration barriers.
· Audit Logging: Provides complete operation logs of all access and modification activities, ensuring both efficiency and security.
