Hash Matching for Online-to-Offline Purchase Attribution
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Solution Overview
Problem
Current techniques for attributing online impressions to purchases made at brick-and-mortar merchants are limited in scope and accuracy, particularly in tracking and rewarding referrals effectively across different channels and environments.
Innovation Solution
The described techniques utilize a combination of user identity information, such as names and credit card details, to generate hashes that are used to match online impressions with subsequent transactions at physical merchants, enabling accurate attribution and commission payment for successful referrals, while ensuring privacy through non-reversible hash values and considering temporal proximity to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cookie-based tracking is used to attribute online impressions to e-commerce purchases, then referral attribution accuracy is improved, but the system remains limited to e-commerce environments and cannot track offline purchases
Solution Approach 1:
The patent extends referral tracking from e-commerce-only to multi-environment (online and offline) by using universal identifiers like email addresses and phone numbers that work across both digital and physical retail channels, enabling the same tracking system to function in diverse contexts
Solution Approach 2:
The system introduces an intermediary data matching process that connects online impression data with offline transaction data through shared customer identifiers, enabling attribution across environments that would otherwise be inaccessible to traditional cookie-based tracking
2Adaptability or versatility
If customer identifying information is collected to enable offline purchase tracking, then referral attribution capability is improved, but customer privacy protection is worsened
Solution Approach 1:
The system uses disposable, single-use tokens or one-time codes that are generated for specific tracking purposes and then discarded, minimizing the retention of sensitive customer information while maintaining tracking capability
Solution Approach 2:
The patent extracts only the minimum necessary identifying information (such as email or phone number) required for attribution, separating this from other potentially sensitive customer data, and uses it solely for referral tracking purposes
3Measurement precision
If multiple data sources are integrated to improve matching accuracy, then referral attribution precision is improved, but system complexity is worsened
Solution Approach 1:
The patent segments the data matching process into distinct stages: collecting identifying information from impressions, storing it in a structured database, receiving transaction data, and performing matching operations, allowing each segment to be optimized independently
Solution Approach 2:
The system creates standardized data formats and templates for storing and processing customer information across different sources, enabling consistent matching operations without requiring complex custom integration logic for each data source
Data Source
AI summary
Customers receive advertisements or "impressions" related to brick and mortar merchants while accessing online content. The merchants or other entities track which impressions correlate with customers coming to a physical store and conducting a transaction (e.g., making a purchase) by comparing transaction information with information about the customer that is provided by the source of online impressions. In one implementation, the merchant creates a hash from the customer's name and account number on a payment card. This hash is compared with a hash from the impression provider that is generated using the same technique. When a match is found, it is inferred that exposure to the online impression caused the customer to make a purchase at the physical store. Merchants may pay the impression providers an advertising or referral fee based on the matches.