Deep Linking With QR Codes for Fast Interaction Attribution
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Solution Overview
Problem
Existing systems struggle to efficiently track and manage interactions between multiple operators and users, particularly in contexts like athlete training and vehicle sales, where quick and accurate data retrieval is challenging.
Innovation Solution
A tracking system that generates and manages visually scannable codes, such as QR codes, to encode user and operator interactions, enabling efficient data retrieval and attribution of actions to specific operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If visually scannable codes are used to track user interactions, then data retrieval speed is improved, but system complexity increases
Solution Approach 1:
The system segments interaction tracking into discrete scannable code events, where each code represents a specific interaction type. This allows rapid data retrieval by scanning individual codes rather than processing continuous data streams, improving speed while maintaining manageable system complexity through modular event categorization.
Solution Approach 2:
Visually scannable codes serve as intermediaries between users and the tracking system. Instead of direct digital communication requiring complex protocols, the system uses simple scannable codes that can be read by various devices, simplifying the interaction layer while enabling fast data capture and retrieval.
2Ease of operation
If deep links are used to access application content, then user experience is improved, but security risks increase
Solution Approach 1:
The system pre-generates deep links with embedded expiration timestamps and validation parameters before they are used. This preliminary configuration ensures that links have built-in security constraints, allowing seamless user access while preventing unauthorized or expired link usage, thus maintaining both user experience and security.
Solution Approach 2:
The system implements real-time validation feedback for deep links by checking expiration timestamps and usage status when links are accessed. This immediate feedback mechanism allows legitimate users to access content smoothly while automatically blocking invalid or expired links, resolving the security-risk-versus-usability contradiction.
3Measurement precision
If multiple operators are tracked in interactions, then attribution accuracy is improved, but data management complexity increases
Solution Approach 1:
The system segments operator attribution into discrete, scannable code events, where each interaction is independently tagged with operator identifiers. This segmentation enables precise attribution by tracking individual operator actions separately, while the modular event structure keeps data management complexity manageable through standardized categorization.
Solution Approach 2:
The system creates standardized templates for interaction events that can be copied and reused across multiple operators and interaction types. This templating approach maintains precise attribution by preserving operator-specific identifiers in each copy, while reducing management complexity through template-based standardization rather than custom tracking for each scenario.
Data Source
AI summary
Methods and systems disclosed herein enable accessing and updating content using deep linking. The system may generate a deep link for a user that links the user to an operator that requested generation of the deep link. The deep link may be encoded into a wirelessly detectable tag or a visually scannable code, which may be stored with user action data and an operator identifier. When a visually scannable code is scanned or a wirelessly detectable tag is accessed, the deep link is decoded and all data associated with the deep link is retrieve and generated for display, when needed. By generating deep links in combination with the visually scannable codes or wirelessly detectable tags, the system is able to provide quick and efficient data retrieval and update.