Customized User Interface Generation via Engagement Data Analysis
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Solution Overview
Problem
Current online digital web interfaces provided by organizations are stateless and not tailored to individual users, leading to user dissatisfaction and early session termination without selecting products or services.
Innovation Solution
A system utilizing a processor, communication interface, and memory to generate a customized interface by analyzing user engagement data from multiple data sources, determining rank scores for services, and transmitting optimized client-side instructions to display relevant services on a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a standardized online digital web interface is provided to all users, then the system complexity is reduced and ease of operation is improved, but the interface cannot be tailored to individual user preferences leading to user dissatisfaction
Solution Approach 1:
The system performs preliminary actions by collecting user engagement data from multiple sources (clickstream data, transaction data, demographic data) before generating the customized interface. This advance data collection and analysis enables the system to pre-determine user preferences and behaviors, allowing personalized interfaces to be generated quickly without adding significant complexity during the actual interface delivery phase.
Solution Approach 2:
The system creates simplified copies or representations of user behavior patterns through engagement data analysis. Instead of managing complex individualized interface configurations for each user, the system analyzes engagement data to create representative profiles that capture user preferences, then uses these profiles to generate customized interfaces. This copying approach reduces the complexity of managing full customization while still achieving personalization.
2Measurement precision
If user engagement data from multiple data sources is collected and analyzed, then the accuracy of service ranking is improved, but the time and computational resources required are increased
Solution Approach 1:
The system extracts only the most relevant features and signals from the multi-source user engagement data that are necessary for accurate service ranking. Instead of processing all available data in full detail, the system identifies and extracts key engagement metrics and patterns that drive personalization accuracy. This selective extraction maintains high measurement precision while significantly reducing the time and computational resources required for data processing.
3Quantity of substance
If the user interface displays all available products and services, then the completeness of information is improved, but the user becomes overwhelmed and terminates the session without selecting any service
Solution Approach 1:
The system applies local quality by customizing the interface content and presentation based on individual user characteristics derived from engagement data analysis. Instead of displaying all products and services uniformly to all users, the system tailors the displayed content to match each user's demonstrated preferences, behaviors, and demographics. This ensures that the most relevant information is prominently displayed to each user, maintaining information completeness while improving ease of operation by reducing cognitive overload.
Data Source
AI summary
Systems and computer-readable media are disclosed for utilizing one or more data sources to generate a customized user interface. A first set of services and operations may be generated. Each service and operation in the first set of services and operations may be ranked based on an analysis of user engagement data. A second set of services and operations may be generated based on the ranking of each service and operation in the first set. Client-side instructions to render the second set of services and operations may be transmitted to a user device.