Visitor Group Identification for Personalized Content Delivery
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Solution Overview
Problem
Brick-and-mortar stores lack the ability to determine if customers are visiting with groups, such as family or friends, which prevents them from personalizing shopping experiences and tailoring content effectively, as existing techniques cannot accurately observe and analyze offline interactions and behaviors in real-world venues.
Innovation Solution
Systems and methods that use IoT devices to track physical actions and profiles to identify visitors in real-world venues, determining if they are with groups, allowing for personalized content delivery based on their current companions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If brick-and-mortar stores use traditional monitoring methods, then they can track individual visitor behavior, but they cannot determine if visitors are shopping with groups
Solution Approach 1:
The patent combines multiple tracking data sources (mobile device locations, purchase histories, behavioral patterns) into a unified analysis system. By merging these diverse data streams, the system can infer group shopping behavior without requiring complex dedicated group-detection hardware, thus resolving the contradiction between information completeness and system complexity.
Solution Approach 2:
The patent introduces an intermediary processing layer that analyzes mobile device data and behavioral patterns to infer group relationships. This intermediary system translates raw data from various sources into meaningful group identification, avoiding the need for direct complex observation of social interactions while still capturing the desired information.
2Measurement precision
If stores collect detailed visitor data to personalize experiences, then they can tailor content effectively, but they cannot accurately observe offline interactions in real-world venues
Solution Approach 1:
The patent replaces direct mechanical observation of offline interactions with digital tracking methods. By substituting physical observation with mobile device location tracking, purchase data analysis, and behavioral pattern recognition, the system achieves precise measurement of group shopping behavior without the difficulties of direct human observation in real-world venues.
Solution Approach 2:
The patent creates digital copies of visitor behavior data through mobile device tracking and purchase history records. These digital replicas of offline interactions allow for precise analysis and personalization without requiring direct observation of the actual physical shopping experience, thus resolving the measurement difficulty.
3Ease of manufacture
If digital marketing profiles are based only on online activities, then they are easy to create, but they omit substantial portions of visitor activity and are less accurate
Solution Approach 1:
The patent creates a universal profiling system that handles both online and offline visitor activities through a single integrated framework. The system processes diverse data types (online browsing, mobile device locations, offline purchases, behavioral patterns) using unified algorithms, making it equally effective for both digital and physical shopping contexts while maintaining ease of profile creation and high accuracy.
Data Source
AI summary
Methods select content to be delivered to a visitor to a real-world venue. One method identifies a visitor to a real-world venue based on tracked physical actions and a profile including previously collected visitor information associated with the venue. The method determines that members of a group are present at the venue with the visitor, where the determining is based at least in part on the previously collected visitor information. Based on determining that the visitor is at the venue with members of the group, content is selected for the visitor. Another method identifies a visitor to a venue as belonging to a group by identifying the visitor based on tracked physical actions and collected visitor information. The tracked actions and visitor information are used to determine whether the visitor is in the venue with members of the group. Tailored content is sent to the visitor based on the determination.