Cross-Channel Customer Identity Linkage via Hashed Transaction Identifiers
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Solution Overview
Problem
Current systems fail to effectively synchronize a customer's shopping experience across multiple channels, such as physical stores, online websites, and mobile applications, leading to fragmented customer data and missed opportunities for personalized marketing and reordering.
Innovation Solution
A system that generates transaction records from various channels, extracts and associates customer identities, and uses linkage rules to determine related identities, enabling seamless synchronization of shopping experiences across channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple matching methods (name and zip code) are used to link customer identities across channels, then the system complexity is reduced, but the matching accuracy and reliability deteriorate
Solution Approach 1:
The patent transforms the matching process from using simple parameters (name, zip code) to using hashed transaction identifiers derived from transaction data. This parameter transformation enables more accurate matching across channels while maintaining system scalability through standardized hashing operations.
Solution Approach 2:
The patent introduces hashed transaction identifiers as an intermediary element that bridges different shopping channels. Instead of directly comparing customer names and addresses across channels, the system uses these hashed identifiers as a common reference point to reliably link customer identities across online, mobile, and in-store channels.
2Device complexity
If no synchronization system is implemented, then the system complexity remains low, but the ability to provide personalized marketing and reordering services deteriorates
Solution Approach 1:
The patent segments customer identity information into distinct hashed identifiers for different channels (online, mobile, in-store) while maintaining a unified view through the customer profile database. This segmentation allows the system to handle channel-specific data separately while enabling cross-channel personalization through centralized profile aggregation.
Solution Approach 2:
The customer profile database serves multiple functions: storing transaction history, maintaining customer preferences, enabling cross-channel recognition, and supporting personalized marketing. This multi-functional design allows a single system component to deliver diverse services including reordering, personalization, and analytics without requiring separate systems for each function.
3Loss of information
If comprehensive customer data is collected across all channels, then the quality of data analysis improves, but the difficulty of detecting and measuring customer identities worsens
Solution Approach 1:
The patent extracts identifying information from diverse transaction records across different channels and transforms it into standardized hashed identifiers. This extraction process separates the essential identity-linking information from channel-specific formatting and noise, making it easier to detect and measure customer identities consistently across all data sources.
Solution Approach 2:
The patent creates hashed copies of customer identifying information that preserve the ability to link transactions while removing sensitive personal details. These hashed identifiers serve as surrogates that maintain data completeness for matching purposes while simplifying the detection and measurement process by providing a consistent format across all channels.
Data Source
AI summary
Systems and methods for synchronizing a customer's shopping experience over multiple channels are disclosed. A transaction record is generated in response to a purchase of goods, and transaction data is extracted from the transaction record and stored in a transaction database. Customer identities are extracted from the transaction record and stored in a customer identity database, wherein the transaction data extracted from the transaction record is associated with the customer identities. A plurality of customer identities are identified that are associated with a user and those identified customer identities that are applicable to the use case of the system are determined. Use case specific information is generated based, at least in part, on transaction data associated with each of the determined customer identities.