Generative AI Web Content A/B Testing
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Solution Overview
Problem
A/B testing for web content faces challenges such as the local maxima problem, where incremental changes may not discover superior approaches, and statistical noise and biases that can lead to unreliable results due to small sample sizes or external factors.
Innovation Solution
A system configured to receive activity records of webpages, generate metric values, and create alternative webpages using generative AI, where the alternative webpage is generated based on the original webpage's content objects and activity records, and replaced if it yields improved metric values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional A/B testing with incremental changes is used, then implementation complexity is low, but the ability to discover superior content approaches is limited due to the local maxima problem
Solution Approach 1:
The system transforms content generation from incremental parameter adjustments to holistic content transformation by using generative AI to create entirely new content variations. This allows exploration of broader content spaces beyond small modifications, resolving the local maxima problem while maintaining automated generation processes.
Solution Approach 2:
The patent replaces the mechanical A/B testing framework with a generative AI system that uses probabilistic models and neural networks to generate content. This substitution enables discovery of superior content approaches that would be inaccessible through traditional incremental testing methods.
2Use of energy by moving object
If traditional A/B testing with small sample sizes is used, then resource consumption is low, but measurement precision deteriorates due to statistical noise and biases
Solution Approach 1:
The system performs preliminary content generation and evaluation using the generative AI model before full-scale deployment. This preliminary action allows for pre-screening of content variations, reducing the need for large sample sizes in traditional A/B testing and thereby lowering resource consumption while maintaining measurement precision.
Solution Approach 2:
The generative AI model acts as an intermediary between content creation and traditional A/B testing. It generates high-quality content variations that can be evaluated with smaller sample sizes, reducing statistical noise and biases while lowering the resources needed for comprehensive testing.
3Reliability
If generative AI is used to generate alternative webpages, then content engagement quality improves, but device complexity increases
Solution Approach 1:
The system segments the content generation process into distinct modules: a generative AI component for content creation, an evaluation component for metric assessment, and a deployment component for implementation. This segmentation manages device complexity by distributing functions across separate system components while maintaining high content engagement quality.
Solution Approach 2:
The generative AI system is designed to handle multiple content types and evaluation metrics through a unified architecture. This multi-functionality reduces overall system complexity by avoiding the need for separate specialized systems for different content generation tasks.
4Adaptability or versatility
If generative AI generates multiple alternative webpages, then content creativity increases, but processing time increases
Solution Approach 1:
The system generates a limited number of high-quality content variations using the generative AI model, rather than exhaustively exploring all possible variations. This partial action approach maintains content creativity while reducing processing time by focusing computational resources on generating the most promising content options.
Solution Approach 2:
The content generation and evaluation process operates in periodic cycles, where the generative AI model periodically produces new content variations that are then evaluated and deployed. This periodic action balances content creativity with processing time by scheduling generation tasks at intervals rather than continuously.
Data Source
AI summary
A system and method for improving traffic of a webpage is presented. The method includes receiving a first plurality of activity records respective of a webpage, the webpage including a plurality of content objects, each content object including a markup language code; generating a first metric value based on the first plurality of activity records; generating an alternative webpage based on the plurality of content objects; generating a second metric value based on a second plurality of activity records respective of the alternative webpage; generating a second alternative webpage in response to determining that the second metric value is lower than the first metric value; and replacing the webpage with the alternative webpage, in response to determining that the second metric value is higher than the first metric value.