Attribute Table Generation for Real-Time Customer Analytics
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Solution Overview
Problem
Existing systems lack efficient methods for generating attribute tables and aggregating customer profile data in real-time to support business decisions, especially during the approval process of business plans, and for performing analytics on customer behavior across multiple channels.
Innovation Solution
The system automates the generation of attribute tables and data models using a computer-assisted retrieval engine, which creates joins and rollups in real-time based on customer input, allowing for the aggregation of customer attributes and demographic data without manual intervention, optimizing data storage and access for analytics on customer behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to generate attribute tables and aggregate customer profile data, then data accuracy and customization can be maintained, but development time and manual effort increase significantly
Solution Approach 1:
The system enables self-service by allowing business users to automatically generate attribute tables and aggregate customer profile data without requiring manual intervention from data engineers or analysts. The automated workflow responds to business plan approvals by automatically creating the necessary data structures and population processes.
Solution Approach 2:
The system performs preliminary action by pre-defining templates and structures for attribute tables that can be quickly instantiated when business plans are approved. This preparation of data structures in advance enables rapid deployment without manual configuration during execution.
2Loss of information
If comprehensive customer data from multiple sources is collected to understand 360° customer view, then analytical insights improve, but data volume and processing complexity increase
Solution Approach 1:
The system segments customer data from multiple sources (Enterprise Data, Social Data, Mobile Data, Online Data) into distinct categories while maintaining their relationships. This segmentation allows the system to manage complex multi-source data through structured attribute tables that organize information by type and source.
Solution Approach 2:
The attribute table structure serves multiple functions: it stores customer profile data, enables analytics processing, supports business plan evaluation, and facilitates 360° customer views. This universal data structure handles diverse data types from multiple sources through a unified framework.
3Speed
If real-time data aggregation is implemented to support business decisions, then decision-making speed improves, but system resource requirements and processing load increase
Solution Approach 1:
The system implements periodic action through automated workflows that trigger data aggregation at specific intervals or events (such as business plan approvals). This approach enables real-time responsiveness when needed while avoiding continuous processing that would consume excessive system resources.
Data Source
AI summary
The present disclosure extends to methods, systems, and computer program products for generating attribute tables for holding attributes and a retail data model linking customer attributes to perform analytics on customer behavior that is optimized for the Hadoop platform.