Content Selection Based on Feature Acceptance
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Solution Overview
Problem
Existing methods for providing content items to online users lack automation in selecting content based on user technology adoption levels, relying on manual determination of early adopters and not efficiently targeting specific user segments.
Innovation Solution
A system and method that identifies user technology adoption levels to serve content items by sending messages to client devices indicating feature availability, receiving acceptance, and selecting content items from a database based on these parameters, allowing for targeted content delivery based on adoption scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual determination of early adopters is used to select content items, then content relevance to specific user segments can be achieved, but automation and efficiency are reduced
Solution Approach 1:
The system enables self-service by automatically tracking user acceptance of new features and using this data to autonomously select and deliver relevant content items. The server computer monitors feature adoption parameters and automatically matches users with appropriate content without manual intervention, allowing the system to serve itself in identifying early adopters and delivering targeted content.
Solution Approach 2:
The system utilizes changes in user behavior parameters (acceptance of new features) to dynamically determine content selection. By monitoring whether users accept or reject new features, the system changes its content delivery parameters automatically, selecting content items based on these observed parameter changes rather than static manual categorization.
2Reliability
If content items are selected based on user technology adoption levels, then relevance and engagement are improved, but system complexity increases
Solution Approach 1:
The server computer performs multiple functions using a unified approach: it delivers the online product, tracks feature acceptance, monitors user behavior parameters, selects content items, and delivers targeted content all through a single integrated system. This multi-functionality reduces overall system complexity by consolidating what could be separate complex subsystems into one cohesive platform that handles both product delivery and personalized content selection.
Solution Approach 2:
The system implements feedback loops where user responses to feature availability messages (acceptance or rejection) are continuously monitored and fed back into the content selection process. This feedback mechanism allows the system to automatically adjust content selection based on observed user behavior, improving relevance without requiring complex manual analysis, as the feedback naturally drives the selection algorithm.
Data Source
AI summary
Serving a content item based on the acceptance of a new feature of an online product, includes sending a message to the client device indicating availability of a feature for the online product and requesting acceptance of the feature. An indication of acceptance of the feature is received and stored in a parameter. When a request for a content item is received, a content item is selected from a content item database based, at least in part, on the parameter.