POS Data Analytics for Customer Location Tracking
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Solution Overview
Problem
Current market tracking and reporting methods rely on indirect and often inaccurate data, providing limited and delayed information, which is undesirable for investors and stakeholders due to their reliance on interviews and speculative data rather than actual transaction data.
Innovation Solution
A system and method utilizing point-of-sale (POS) data to track and report market trends by analyzing transaction data from multiple POS devices, including merchant identifiers, account identifiers, transaction amounts, and time of day, to determine customer shopping patterns and location usage, generating reports on customer behavior and market trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If interviews and indirect techniques are used to obtain market data, then data collection is simpler, but the information provided is limited and inaccurate
Solution Approach 1:
The patent replaces manual interview-based data collection with automated electronic POS systems that electronically capture and transmit transaction data. This substitution eliminates the mechanical process of human interviews while providing precise, objective data about actual customer purchasing behavior, thereby improving measurement precision without significantly increasing operational complexity.
Solution Approach 2:
The POS systems automatically collect, store, and transmit transaction data without requiring manual intervention or interviews. The system serves itself by autonomously capturing market information through electronic transactions, eliminating the need for human operators to manually gather data while ensuring accurate and comprehensive market intelligence.
2Ease of operation
If interviews with merchants are conducted to obtain market information, then data gathering is more straightforward, but delays occur and information is not timely
Solution Approach 1:
The POS systems continuously and automatically capture transaction data in real-time as purchases occur, eliminating the intermittent nature of interview-based data collection. This continuous data stream ensures that market information is always current and immediately available for analysis, removing delays inherent in scheduled interviews while maintaining operational simplicity.
Solution Approach 2:
The system performs preliminary data capture and processing automatically at the point of sale, before any analysis or reporting is needed. Transaction data is immediately recorded, stored, and made available for analysis, eliminating the time lag between data generation and data availability that occurs when waiting for merchant interviews to be conducted and processed.
3Measurement precision
If POS data is collected and analyzed to determine customer location usage, then accurate market trends are obtained, but system complexity increases
Solution Approach 1:
The patent segments the complex data processing task into distinct functional modules: data collection at POS terminals, data transmission through communication networks, data storage in databases, and analysis by processing systems. This segmentation allows each component to perform its specific function independently, managing overall system complexity while maintaining high measurement precision through specialized processing at each stage.
Solution Approach 2:
The patent introduces intermediary components including communication networks and database systems that mediate between the POS terminals and the analysis systems. These intermediaries simplify the overall architecture by providing standardized interfaces and data formats, reducing the complexity of direct connections while enabling accurate aggregation and analysis of market trend data from multiple sources.
Data Source
AI summary
One embodiment provides a method for evaluating transaction data to determine point of location usage. This could be, for example, to determine where customers are mostly likely to shop before or after shopping at a given merchant. For instance, the method could show the percentage of customers that shop at certain types of stores during a time period right before or after shopping at the merchant's location. As another example, the method could be used to determine when a merchant's customer makes a purchase at the merchant's store, then makes a purchase at a competing merchant's store within a specified time.