Content Management System Activity Scoring for User Engagement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current content management systems lack an efficient method to serve relevant content to users based on their expected activity levels, leading to suboptimal engagement and revenue generation from advertisements.
Innovation Solution
A content management system that determines an expected activity score for users by analyzing their interaction history and context, allowing content sponsors to target high-activity users with action-soliciting content and low-activity users with informative content, using a threshold-based approach to select and serve appropriate content items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If content is served uniformly to all users regardless of activity level, then system complexity is reduced, but user engagement and interaction rates decrease
Solution Approach 1:
The patent segments users into different groups based on their activity levels (e.g., high-activity users, low-activity users, new users, returning users). This segmentation allows the system to apply different content serving strategies to different user groups, thereby improving engagement rates without significantly increasing system complexity. The segmentation is achieved through analyzing user interaction history and computing activity scores.
Solution Approach 2:
The patent changes the parameter of content selection by introducing activity-based scoring and thresholding. Instead of uniform content serving, the system computes activity scores for users and uses these scores to dynamically select content types (e.g., action-soliciting content for high-activity users, informative content for low-activity users). This parameter change enables differentiated content delivery that improves engagement while maintaining manageable system complexity through algorithmic automation.
2Productivity
If activity-based content selection is implemented, then user engagement and interaction rates improve, but system complexity increases
Solution Approach 1:
The system implements self-service by automatically computing user activity scores based on interaction history and autonomously selecting appropriate content types without requiring manual intervention. The activity-based content selection process is automated through algorithms that analyze user behavior patterns, compute scores, and make content delivery decisions. This automation reduces the operational burden on content managers while maintaining the complexity benefits of personalized content delivery.
Solution Approach 2:
The patent incorporates feedback mechanisms where user interactions with served content are continuously monitored and fed back into the activity scoring system. This feedback loop allows the system to refine activity scores over time and adjust content selection strategies accordingly. The feedback mechanism enables the system to adapt to changing user behaviors while maintaining a structured approach to content management, balancing improved engagement with controlled system complexity.
3Productivity
If action-soliciting content is served to high-activity users, then revenue generation increases, but risk of user fatigue or disengagement increases
Solution Approach 1:
The patent applies local quality by tailoring content characteristics to specific user segments. High-activity users receive action-soliciting content with higher engagement potential, while low-activity users receive informative content that is less intrusive. This localized content quality optimization ensures that each user group receives content appropriate to their engagement level, maximizing revenue opportunities from active users while preserving user experience and retention for less active users.
Solution Approach 2:
The system implements dynamics by making content selection flexible and adaptive rather than static. Users can transition between activity levels over time, and the system dynamically adjusts content types based on current activity scores. This dynamic approach allows users who were previously high-activity to receive less aggressive content if their activity decreases, and vice versa. The dynamic content selection helps prevent user fatigue by adapting to changing user states, thereby maintaining both revenue generation and user retention stability.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer-readable storage medium, for serving content to a user. A method includes: receiving a request for content from a user, the content to be displayed in a slot associated with an online resource; determining an expected activity score for the user based at least in part on one or more criteria associated with a context of presenting the online resource to the user; comparing the expected activity score for the user to a threshold; selecting one or more content items to serve to the user based at least in part on the comparison; and serving the selected content items to the user responsive to the request.