Real-Time Web Content Personalization for Anonymous Users
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Solution Overview
Problem
Current solutions for personalizing business website content are limited to identified users and rely on rule-based personalization, failing to effectively engage anonymous users by mapping relevant content based on business relevance and sales cycle stages, especially in B2B contexts where real-time predictive analytics with big data processing are not utilized.
Innovation Solution
A method that tracks user behavior on websites to identify parameters, applies statistical algorithms for user classification, and dynamically adjusts webpage content in real-time based on user behavior analysis, creating anonymous profiles and engaging users with personalized content relevant to their industry and location, using clustering, probability, and collaborative filtering algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If rule-based personalization is used for identified users, then content personalization is achieved, but anonymous users cannot be engaged and real-time predictive analytics are not utilized
Solution Approach 1:
The system automatically detects anonymous users, creates profiles, and personalizes content without manual intervention or predefined user identification. The predictive analytics model autonomously processes big data to real-time content adjustment, enabling the system to serve itself in profiling and engaging users without requiring identified user accounts.
Solution Approach 2:
The invention transitions from static rule-based personalization to dynamic real-time personalization by changing the temporal parameter from pre-defined rules to live behavior-based adjustments. The system continuously monitors user behavior parameters and updates content delivery in real-time based on predicted user needs, transforming the personalization approach from static to dynamic.
2Ease of manufacture
If simple click stream data is used, then implementation is simple, but big data processing and predictive analytics are not enabled
Solution Approach 1:
The system creates a universal data processing framework that handles multiple data types (click stream data, navigation paths, content usage patterns, user profiles) within a single integrated platform. The predictive analytics model serves multiple functions: profiling anonymous users, predicting behavior, and guiding real-time content personalization, replacing the need for separate simple implementations with a comprehensive multi-functional system.
Solution Approach 2:
The invention introduces predictive analytics as an intermediary layer between raw big data and content personalization decisions. This intermediary process transforms complex behavioral data into actionable predictions, enabling accurate user behavior analysis while maintaining system scalability. The predictive model acts as a mediator that processes big data and outputs personalized content recommendations.
3Productivity
If real-time content adjustment is implemented, then user engagement is improved, but system complexity increases
Solution Approach 1:
The system segments the content personalization process into distinct modular components: user behavior monitoring module, predictive analytics module, content selection module, and content delivery module. Each component handles a specific function independently, making the complex real-time personalization system manageable through segmentation. The segmentation allows parallel processing and reduces the complexity burden on any single component.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing user behavior data, content metadata, and predictive models in advance. The predictive analytics model is trained and ready before actual content delivery occurs. This preliminary preparation enables rapid real-time responses without requiring complex computations at the moment of content delivery, reducing system complexity during execution.
Data Source
AI summary
A method and system for providing adjusted content in a webpage are described. The system monitors traffic to a website and tracks users that are visiting the website to identify one or more parameters relating to relating to the user, including parameters associated with an identity of the user, navigation behavior for the user within the website, and usage of content by the user within the website. The system analyzes the parameters and selects at least one statistical algorithm for a type of the parameter, and based on the analysis, identifies an organization to which the user belongs. The system selects and presents content for the website to be presented to the user based on the analysis.