Context-Aware In-Store Personalization via Online Interaction Classification
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Solution Overview
Problem
Commercial websites fail to leverage user interaction data to personalize the in-store experience, missing an opportunity to influence customer behavior during physical visits based on temporal relationships and location-specific inventory.
Innovation Solution
A system and method that utilize a customer location beacon, mobile device, database, and computing device to classify online interactions and select push content based on retailer information, transmitting personalized recommendations to the customer's mobile device, enhancing the in-store experience by aligning it with online behavior and store inventory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If online interaction data is collected and used for web recommendations, then web traffic and sales are improved, but the same data is not leveraged for in-store personalization
Solution Approach 1:
The patent merges online interaction data with in-store location data to create a unified customer profile. The system combines web browsing history, product views, and cart contents with real-time store location information to provide personalized recommendations that work both online and in-store, eliminating the loss of information by fully utilizing available data across channels.
Solution Approach 2:
The system creates a universal personalization engine that serves multiple functions: it provides web recommendations, in-store recommendations, and cross-channel coordination. The same online interaction data is reused for both web and physical store personalization, making the data utilization multi-functional and maximizing its value across different customer touchpoints.
2Productivity
If generic marketing is used, then coverage is broad, but influence on customer behavior is limited
Solution Approach 1:
The patent applies local quality by tailoring recommendations to specific store locations, product categories, and individual customer preferences. Instead of uniform generic marketing, the system provides localized recommendations based on real-time store inventory, location-specific promotions, and individual customer browsing history, thereby increasing sales influence while maintaining broad coverage through automated multi-channel delivery.
Solution Approach 2:
The system dynamically adjusts recommendations based on real-time factors including current store location, moment-in-time context, and evolving customer behavior patterns. The personalization adapts to changing conditions such as seasonal variations, store-specific inventory levels, and real-time customer interactions, making the marketing approach dynamic rather than static.
3Ease of operation
If real-time location data is collected and processed, then in-store personalization is improved, but system complexity increases
Solution Approach 1:
The patent introduces a centralized data processing intermediary that handles the complexity of real-time location data collection, processing, and recommendation generation. This intermediary layer sits between the customer mobile devices, store systems, and recommendation engines, abstracting the complexity from individual components while maintaining ease of operation through automated data fusion and real-time processing.
Data Source
AI summary
The disclosed subject matter relates to a system and method for personalizing customer experience at a retailer's physical location in order to increase sales and customer satisfaction. The personalization is based upon classification of customer's online interaction with the retailer. Upon detecting the customer's presence at the retailer's physical location, data of the customer's online interactions is retrieved and classified based on the type of online interactions and temporal characteristics. Push content is transmitted to the customer, the push content being based upon at least the classification and data associated with retailer's physical location.