Proactive Web Content Recommendations for Page Effectiveness
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Solution Overview
Problem
Conventional methods for creating effective webpages rely on iterative processes involving trial and error, requiring deployment and analysis of webpages to gather data on visitor interactions, which is time-consuming and costly, and often results in ineffective webpages that fail to fulfill their intended purpose.
Innovation Solution
A proactive webpage content recommendation system that analyzes analytics data to suggest edits in real-time during webpage construction, predicting attribute changes that will increase effectiveness, such as positioning, sizing, and color schemes, based on comparisons with similar content from deployed webpages in the same category or on the same website.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional iterative methods are used to create webpages by deploying and analyzing data, then analytics data can be gathered to evaluate effectiveness, but the process is time-consuming and costly
Solution Approach 1:
The system performs preliminary analysis of analytics data from similar webpages before the current webpage is deployed. By pre-processing and analyzing data from comparable pages in advance, the system generates effectiveness predictions and content recommendations before deployment, eliminating the need for iterative trial-and-error testing and significantly reducing the time required to create effective webpages.
2Loss of information
If analytics data is collected from deployed webpages to determine effectiveness, then insight about visitor interactions can be gained, but the webpage remains ineffective for prolonged periods during the iterative process
Solution Approach 1:
The system implements a feedback mechanism that uses analytics data from similar deployed webpages to generate predictions and recommendations for the current webpage during construction. This feedback loop allows the system to leverage insights from actual visitor interactions with comparable pages to improve the current webpage's effectiveness before deployment, rather than waiting for iterative testing.
Solution Approach 2:
The system identifies and analyzes similar webpages from the same category or website as templates. By copying and adapting proven effective content attributes and structural patterns from these similar pages, the system can create an effective webpage more quickly, reducing the period of ineffectiveness during iterative development.
3Reliability
If trial and error methods are used with adjustments based on gathered analytics data, then webpage effectiveness can be improved, but substantial time and effort are required
Solution Approach 1:
The system performs preliminary analysis of analytics data from similar webpages before the current webpage is deployed. By pre-processing and analyzing data from comparable pages in advance, the system generates effectiveness predictions and content recommendations before deployment, eliminating the need for iterative trial-and-error testing and significantly reducing the time required to create effective webpages.
Solution Approach 2:
The system analyzes various content attributes (text, images, layout, calls-to-action) from similar webpages and identifies parameter changes that correlate with higher effectiveness. By applying these identified parameter changes directly to the current webpage during construction, the system improves effectiveness without requiring multiple iterative cycles of testing and adjustment.
Data Source
AI summary
Methods and systems are provided for providing suggested edits for content placed into a webpage under construction using a webpage content recommendation system. Attributes associated with placed content added to a webpage under construction can be compared with predictive content to determine if any edits are predicted to increase effectives of the webpage. Predictive content can be content that is the same, or similar to, the placed content added to the webpage. Predictive content can be taken from a webpage in the same category as the webpage (e.g., a category can be vertical sporting goods stores or healthcare providers) or from deployed pages on the same website as the webpage. In this way, a suggested edit can be determined based on the predictive content and provided to a user.