In-store Consumer Behavior Metadata Aggregation and Verification
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Solution Overview
Problem
Existing systems for consumer behavior analysis and targeted advertising are not effectively applicable in real-world in-store settings due to challenges in product metadata integrity and obtaining consumer behavior metadata, and they lack the ability to provide comprehensive AI insights beyond simple targeted advertising.
Innovation Solution
A system for in-store consumer behavior event metadata aggregation, data verification, and AI analysis that uses a trained AI analytics engine to interpret data and trigger actions, including product suggestions and informational notifications, by collecting data from consumer electronic devices and optimizing product metadata models to address the challenges of varying product availability and pricing across stores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If online targeted advertising analytical engines are used for in-store consumer behavior analysis, then consumer interest tracking capability is improved, but product metadata integrity deteriorates due to varying product availability and pricing across different stores
Solution Approach 1:
The system adapts the generic online advertising analytical engine to local store conditions by integrating store-specific product metadata, availability information, and pricing data. This allows the system to maintain consumer behavior tracking capabilities while adjusting to local product variations, thereby preserving metadata integrity in the in-store context.
Solution Approach 2:
The system dynamically adjusts product metadata parameters (availability, pricing, location) based on store-specific conditions and real-time data verification. By changing these parameters locally rather than using uniform online advertising data, the system maintains both tracking capability and metadata reliability across different store environments.
2Loss of information
If comprehensive AI analysis is implemented for in-store consumer behavior, then consumer behavior insights are improved, but system complexity increases due to data aggregation and verification requirements
Solution Approach 1:
The system segments the complex AI analysis task into distinct functional modules: data collection from consumer devices, data verification against product metadata, AI analytics processing, and action triggering. This modular segmentation reduces overall system complexity while maintaining comprehensive consumer behavior insights through coordinated module operations.
Solution Approach 2:
The system introduces intermediary components including data verification controllers that mediate between raw consumer behavior data and AI analysis, and action triggering controllers that mediate between AI insights and consumer device notifications. These intermediaries simplify the overall system architecture by handling data validation and action execution separately from core AI processing.
3Reliability
If real-time data verification is performed to optimize product metadata integrity, then data reliability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary data verification by pre-validating product metadata (availability, pricing, location) before it enters the main AI analysis pipeline. This preliminary action ensures high data reliability is achieved upfront, reducing the need for repeated verification during processing and thereby minimizing overall processing time.
Solution Approach 2:
The system implements feedback mechanisms where data verification results are continuously fed back into the metadata database, allowing the system to learn from verification outcomes and optimize future verification processes. This feedback loop improves processing efficiency over time while maintaining high data reliability standards.
Data Source
AI summary
There is provided a system for in-store consumer behaviour event metadata aggregation, data verification and the artificial intelligence analysis thereof for data interpretation and associated action triggering. The system may collect in-store consumer behaviour event metadata from a plurality of consumer electronic devices and then uses a trained artificial intelligence analytics engine to provide various artificial intelligence insights useful to such consumers which may further modify consumer behaviour. The trained artificial intelligence analytics engine may have a data interpretation controller configured for intelligently interpreting such aggregated in-store consumer behaviour event metadata and triggering actions accordingly which are then sent electronically to the consumer electronic devices. The data interpretation controller may have a data verification controller configured to optimise the data integrity of a consumer product metadata database representing a plurality of consumer products according to availability and other metadata.