Automated Price Comparison System for Retail Customer Engagement
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Solution Overview
Problem
Customers face difficulties in reliably comparing prices of goods within retail venues, leading to potential purchases of incorrect items or loss of loyalty due to unawareness of competing prices, and existing solutions fail to utilize data for both store owners and customers effectively.
Innovation Solution
A system comprising processors and memory devices that monitor customer location and proximity to goods, conduct price comparisons across retailers, and provide price comparisons to customers, utilizing machine learning to analyze purchasing behavior and adjust pricing competitively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If customers manually check prices using search engines or smart device applications, then they can compare prices, but the process is time-consuming and error-prone
Solution Approach 1:
The system enables automatic price checking where the scanning system and processors automatically compare prices without requiring manual customer intervention. The system self-services by automatically detecting goods, searching prices across retailers, and presenting comparison results, eliminating the need for customers to manually search and compare prices themselves
Solution Approach 2:
The patent replaces manual mechanical price checking (customers physically using search engines or applications) with an automated electronic scanning system. The scanning system uses optical or RFID technology to automatically detect goods and triggers automatic price searches, substituting the manual mechanical process with an automated electronic system that reduces both time and errors
2Reliability
If store owners implement automated scanning systems for price checking, then price comparison accuracy improves, but system complexity increases
Solution Approach 1:
The scanning system is designed to perform multiple functions: it scans goods to identify them, automatically searches for prices across multiple retailers, determines customer interest based on scanning patterns, and presents price comparison results. This multi-functional approach consolidates what would otherwise require separate systems into a single integrated solution, managing complexity while maintaining high accuracy
Solution Approach 2:
The system introduces processors as an intermediary layer between the scanning system and the price comparison database. The processors receive scan data, automatically search multiple retailer sources, process the pricing information, and generate comparison results. This intermediary layer manages the complexity of coordinating multiple functions and data sources through a centralized processing unit
3Adaptability or versatility
If existing solutions track customer proximity to goods and offer discounts, then customer engagement improves, but data utilization for mutual benefit is insufficient
Solution Approach 1:
The system continuously monitors customer scanning behavior and uses this feedback to determine interest levels. When a customer repeatedly scans a good or keeps it in the virtual cart, the system feeds this information back to adjust pricing offers in real-time. The feedback loop enables dynamic pricing adjustments based on actual customer interest, maximizing data utilization for mutual benefit
Solution Approach 2:
The system performs preliminary price comparisons and determines customer interest before the customer reaches the checkout stage. By proactively identifying interested goods and preparing price comparison data in advance, the system can present relevant information when the customer is most receptive, improving engagement while efficiently utilizing collected data
Data Source
AI summary
A system for determining customer interest in goods includes one or more memory devices storing instructions and one or more processors configured to execute the instructions. The processors are configured to receive customer location data from a smart device associated with a customer indicating the customer is within a retail venue of a retailer and to monitor, based on the customer location data, a current location of the customer within the retail venue. The processors are further configured to receive goods location data indicating locations of goods for sale within the retail venue and determine that the customer is interested in a particular good for sale within the retail venue based on the current customer location remaining in proximity to the location of the particular good for a predetermined period of time. The processors also conduct a search of pricing of the particular good at one or more other retailers and send a price comparison to the customer.