Dynamic Request Redirection System for Web Revenue Optimization
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Solution Overview
Problem
Online venues face challenges in directing users to the most revenue-generating pages on their websites, as these pages can change over time and vary among individuals, making it difficult to effectively redirect users for increased sales conversions.
Innovation Solution
A dynamic control system that performs production-time experiments to determine the optimal landing page for incoming requests by randomly distributing landing pages to users and using triggering criteria to redirect requests based on user landing contexts, with results analyzed to adjust the redirection strategies automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static redirection rules are used to direct users to revenue-generating pages, then implementation is simple, but the system cannot adapt to changing user preferences and page performance over time
Solution Approach 1:
The patent implements dynamic redirection by continuously monitoring user behavior metrics (clicks, conversions, time on page) and automatically adjusting redirection rules in real-time. The system transitions from static pre-defined rules to dynamic adaptive rules that evolve based on observed user preferences and page performance, resolving the contradiction between adaptability and complexity through automated feedback loops.
Solution Approach 2:
The system incorporates feedback mechanisms that track user interactions with redirected pages and use this data to refine future redirection decisions. By implementing closed-loop feedback where user behavior data feeds back into rule adjustment, the system achieves adaptability while managing complexity through data-driven automation rather than manual rule creation.
2Measurement precision
If multiple landing pages are tested to determine optimal destinations, then user directing accuracy improves, but the time and resources required for experimentation increase
Solution Approach 1:
The patent applies partial action by testing a limited subset of landing pages simultaneously rather than exhaustively testing all possible destinations. The system uses statistical sampling and confidence interval methods to determine when sufficient data has been collected to make accurate redirection decisions, reducing experimentation time while maintaining directing accuracy through controlled partial testing.
Solution Approach 2:
The system dynamically adjusts experimental parameters such as sample size, confidence levels, and testing duration based on initial performance data. By changing parameters adaptively during the experimentation process, the system achieves accurate user directing with optimized time investment, stopping experiments when statistical significance is reached rather than using fixed time budgets.
3Productivity
If redirection rules are manually created and updated, then control over user experience is maintained, but the system cannot respond quickly to changing user behavior patterns
Solution Approach 1:
The patent implements self-service automation where the system automatically monitors user behavior, analyzes performance data, and updates redirection rules without manual intervention. The automated system serves itself by detecting performance degradation or opportunity and autonomously adjusting rules to capture revenue opportunities, achieving rapid response to user behavior changes while maintaining strategic control through configured objectives and constraints.
Data Source
AI summary
Requests for content are received, for example, an HTTP request for a web page is received. Each request has a landing context, which may include information about the user sending the request, the request itself, the referrer, or another subject. This information can be found in the requests or external to the requests. The information of the landing context is compared to appropriate triggering criteria. For requests that satisfy the appropriate triggering criteria, a destination is provided based on one or more weighted treatments. The request is directed to the provided destination. The results of the requests are subsequently analyzed and used to adjust the treatments in an automated fashion.