Store Visit Data Aggregation via Signal Extraction
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Solution Overview
Problem
Store owners face challenges in tracking store visitors and understanding customer behavior without infringing on privacy, as existing methods require additional setup and raise privacy concerns by collecting MAC addresses of smartphones.
Innovation Solution
A method and system where user devices receive signals from store devices, generate and aggregate store visit data, and transmit it to an analytics server after reaching a threshold, ensuring privacy by anonymizing data and using random device identifiers, reducing the need for store owners to install beacons or modify WiFi routers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If WiFi routers or store devices are used to connect to and keep a record of smartphones passing through the store, then store owners can obtain visitor analytics data, but privacy issues arise as MAC addresses of smartphones are known
Solution Approach 1:
The patent extracts only the necessary identifying information (store identifier and presence indication) from the smartphone data, while deliberately excluding sensitive information such as MAC addresses. This allows analytics to be generated without compromising user privacy, as the system processes minimal data required for the analytical purpose.
Solution Approach 2:
The patent introduces an intermediary processing layer that receives signals from store devices, generates analytics data, and then removes or anonymizes identifying information before storing or sharing the data. This intermediary step ensures that privacy-sensitive information is not directly accessible while still enabling analytical functionality.
2Measurement precision
If beacons or WiFi firmware modifications are installed to track visitors, then visitor tracking capability is improved, but additional work and setup complexity is required by store owners
Solution Approach 1:
The patent enables store devices (such as existing WiFi routers) to automatically perform tracking and analytics generation without requiring store owners to manually install or configure specialized equipment. The system uses readily available store devices that can autonomously detect smartphones and generate analytics, eliminating the need for additional setup work.
Solution Approach 2:
The patent makes existing store devices serve multiple functions: they continue to provide their primary function (WiFi networking) while simultaneously performing visitor tracking and analytics generation. This eliminates the need for separate beacon installations or firmware modifications, as ordinary store devices are repurposed to handle both networking and analytics tasks.
3Measurement precision
If MAC addresses are collected for analytics, then accurate visitor identification is achieved, but privacy compliance is compromised
Solution Approach 1:
The patent extracts and uses only the minimal necessary data (store identifier and visit presence) while deliberately excluding MAC addresses and other personally identifiable information from collection, storage, and sharing. This extraction approach maintains analytical capability while ensuring privacy compliance.
Solution Approach 2:
The patent changes the data parameters from sensitive identifiers (MAC addresses) to anonymized or aggregated metrics (visit counts, presence duration). This parameter transformation maintains the utility of analytics data for business insights while removing privacy-sensitive elements to ensure compliance.
Data Source
AI summary
A method includes receiving, at a user device, a plurality of signals pushed to the user device from a plurality of store devices located in a plurality of physical storefronts. Each signal includes a store identifier identifying the associated physical storefront. Store visit data is generated for each signal, the store visit data including the store identifiers and time data representing times associated with visits to the physical storefronts. Store visit data is aggregated for the plurality of signals pushed to the user device. Aggregated store visit data is transmitted to an analytics server configured to combine aggregated store visit data from a plurality of user devices and generate analytics data from the combined aggregated store visit data. At least a portion of the time data is removed from the aggregated store visit data before transmission to the analytics server.