Beacon-Based Shopping Assistance System
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Solution Overview
Problem
Existing customer shopping systems at physical merchant locations are limited in their ability to provide personalized shopping assistance, as they can only analyze data from transactions within a specific merchant's domain, failing to offer recommendations based on broader customer behavior and preferences across multiple merchants.
Innovation Solution
A customer shopping help system utilizing beacon devices to track customer location within a merchant location, analyzing this data to recommend products from areas the customer has not visited, including those on sale, regularly purchased, or on their shopping list, through a system provider device that integrates merchant and customer information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system only analyzes transaction data from a single merchant, then the data collection is simple and limited to that merchant's domain, but the customer shopping assistance becomes narrow and lacks broader behavioral insights
Solution Approach 1:
The system integrates multiple data sources including transaction data from various merchants, customer location data from beacons, and shopping list information to provide comprehensive shopping assistance. The platform serves multiple functions: analyzing purchase patterns across different merchants, tracking customer movement through physical stores, and generating personalized recommendations that combine both online and offline behavior data.
2Measurement precision
If the system integrates multiple data sources including location data and shopping lists, then personalized recommendations improve, but the system complexity increases
Solution Approach 1:
The system uses a centralized processing platform that acts as an intermediary to collect, normalize, and analyze data from multiple sources including beacons, transaction records, and shopping lists. This intermediary layer coordinates the complex data integration and generates unified recommendations, shielding the user from the underlying system complexity while delivering precise personalized assistance.
3Productivity
If real-time location tracking is implemented using beacons, then customer engagement and personalized assistance improve, but the technology infrastructure becomes more complex
Solution Approach 1:
The beacon system operates autonomously to track customer locations and trigger personalized recommendations without requiring active participation from customers. The beacons automatically detect customer presence, analyze their movement patterns, and initiate relevant shopping assistance actions, reducing the need for complex user interaction while maintaining high effectiveness.
Data Source
AI summary
Systems and methods for providing customer shopping help include a system provider device that receives first customer location information from a plurality of beacon devices at a merchant physical location. The first customer location information is collected during a shopping session from a first customer device that is associated with a first customer. The system provider device analyzes the first customer location information to determine a merchant physical location area that is included in the merchant physical location and in which the first customer has not been located during the shopping session. The system provider device then selects a first product, from a plurality of products that are associated with the merchant physical location in a database, which is located in the merchant physical location area. The system provider device then provides a product recommendation for the first product over the network for display on the first customer device.