Web Server Engine Mining First-Time Visitor Sessions
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Solution Overview
Problem
Current web usage mining techniques lack effective methods to characterize and engage first-time website visitors, hindering the conversion of these visitors into loyal users, which is crucial for enhancing business opportunities and improving user experience.
Innovation Solution
A method and system that collect user session data to identify first-time visitors, determine their features, create rules for content recommendations, and update these rules based on user actions, utilizing a web server and web server engine to monitor and personalize content suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If web usage mining techniques are used to track browsing activities, then user access patterns can be determined, but effective methods to characterize and engage first-time visitors are lacking
Solution Approach 1:
The patent segments users into different categories (first-time visitors vs. returning users) and applies different mining and engagement strategies to each segment. The system specifically identifies first-time visitors through segmentation of user session data and applies targeted content recommendations tailored to this specific user segment, thereby resolving the contradiction between general pattern detection and specific visitor engagement.
Solution Approach 2:
The patent implements preliminary actions by analyzing user session data before a user becomes a loyal customer. The system proactively identifies first-time visitors and applies content recommendations in advance, based on predicted patterns, to convert them into loyal users. This preliminary engagement strategy addresses the lack of effective methods for first-time visitor characterization and engagement.
2Productivity
If content recommendations are made to first-time visitors, then user engagement increases, but the system complexity increases
Solution Approach 1:
The patent makes the web server engine multi-functional by enabling it to perform both traditional web serving functions and web usage mining operations. The same server engine that handles HTTP requests also collects session data, identifies first-time visitors, determines features, creates rules, monitors actions, and updates recommendations. This universal approach increases productivity in converting visitors to loyal users while avoiding the need for separate complex mining systems.
Solution Approach 2:
The system implements self-service by having the web server engine automatically perform mining operations and content recommendations without external intervention. The engine self-collects session data, self-identifies first-time visitors, self-determines features and rules, self-monitors user actions, and self-updates recommendations. This self-service mechanism increases conversion effectiveness while minimizing the complexity that would otherwise require manual configuration and external processing systems.
Data Source
AI summary
One website mining embodiment is for characterizing first time users of a website, collecting user session data of the users visiting the website and identifying first time visitors, determining features of the first time visitors utilizing the user session data, determining rules utilizing the features of the first time visitors, monitoring actions of the first time visitors on the website, updating the rules utilizing the monitored actions of the first time visitors and recommending web content utilizing the rules to the first time visitor.