Location-Based Product Recommendation System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Commercial property owners and operators face challenges in providing personalized marketing messages to consumers within their properties, as existing methods require manual labor and are not tailored to individual preferences, leading to missed revenue opportunities due to the lack of in-store product recommendations based on consumers' physical and digital behaviors.
Innovation Solution
A location-based product recommendation system that uses beacons to collect data on consumers' proximity to products and combines this information with their profile data to generate personalized recommendations, transmitted to their mobile devices, allowing for real-time, contextually relevant product suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual marketing messages (signs, audio messages, video screens) are used in commercial properties, then marketing communication is provided to consumers, but the messages are not personalized to individual consumers and require manual labor and scheduling to change as the property layout changes
Solution Approach 1:
The patent replaces manual mechanical systems (physical signs, audio messages, video screens that require manual installation and updating) with an automated digital system. Mobile devices display marketing messages and product recommendations automatically based on consumer location data, eliminating the need for manual labor to change physical marketing materials while enabling personalized content delivery to each consumer.
Solution Approach 2:
The system enables self-service marketing communication where consumers receive personalized product recommendations and marketing messages automatically on their own mobile devices. The system autonomously tracks consumer location, retrieves relevant profile data, and displays appropriate content without requiring manual intervention from property operators, thus providing both personalization and operational efficiency.
2Productivity
If property layout is changed to keep consumer experience exciting and fresh, then consumer engagement is maintained, but the in-facility marketing messages must be physically changed which is time-consuming and not personalized
Solution Approach 1:
The patent implements a dynamic marketing message delivery system that automatically adapts to changing property layouts. Instead of physically relocating and reconfiguring static marketing materials, the system dynamically generates and delivers personalized digital messages to consumers' mobile devices based on their real-time location within the property, allowing instantaneous adaptation to layout changes without time-consuming physical modifications.
Solution Approach 2:
The system creates digital copies of marketing messages and product recommendations that are delivered to consumers' mobile devices. These digital copies can be instantly updated and redistributed without physical manipulation, allowing the property to maintain fresh, engaging content across the facility without the time and labor required to physically change traditional marketing materials.
3Ease of operation
If product recommendations based on online browsing behavior are provided on the Internet, then shoppers can find the right product and discover new products, but shoppers in physical stores do not have access to such recommendations thus losing convenience and revenue opportunities
Solution Approach 1:
The patent extends the product recommendation functionality from online-only to both online and offline (physical store) environments. The system integrates location-based services with existing consumer profile data, enabling the same recommendation engine to operate in physical stores by delivering personalized product recommendations to consumers' mobile devices within the store, thus providing universal access to recommendation capabilities across all shopping channels.
Solution Approach 2:
The patent introduces mobile devices as an intermediary that bridges the gap between physical store environments and digital recommendation systems. The mobile device receives location data from the store's beacon infrastructure, retrieves relevant consumer profile information, and displays personalized product recommendations, thereby transferring the convenience of online recommendations to the physical retail environment.
Data Source
AI summary
A location-based product recommendation system uses consumer profile data, a network of beacons that emit unique identifiers, and a recommender to determine current product-correlated location information for a space in which a consumer's mobile device is physically present. The system may use the current product-correlated location information and the consumer's profile data to generate a recommendation for a candidate product for the consumer to consider. The system also may incorporate data from other interactions with products such as purchases, clicks on a website, etc. The system also may incorporate data generated on multiple channels (e.g., a product click on a personal computer at work, a mobile device location in a physical store, a product purchase on a personal computer in the home) into a single profile for the consumer.