Digital Messaging Content Selection Using Link Click Data
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Solution Overview
Problem
Existing digital messaging systems fail to effectively identify and present content that aligns with user interests based on their interaction activities, leading to inefficient content targeting and user engagement.
Innovation Solution
A system and method that detect user link selections in digital messages, collect associated data and metadata, and use interest taxonomies to determine user interests, enabling personalized content selection and presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital messaging systems present generic content to all users, then system complexity is low, but user engagement and content relevance are poor
Solution Approach 1:
The system pre-collects user interaction data (link selections, message responses) and pre-builds interest profiles before content delivery. This preliminary action enables personalized content selection without adding complexity to the real-time content delivery process, as the personalization logic is already prepared in advance
Solution Approach 2:
The patent introduces an intermediary component (content selection system) that sits between the messaging system and users. This intermediary analyzes user data, determines interests using taxonomies, and selects appropriate content, thereby isolating the complexity from both the messaging system and the end users while enabling personalized content delivery
2Measurement precision
If the system collects and analyzes user interaction data to identify interests, then content relevance improves, but data processing complexity and time increase
Solution Approach 1:
The patent segments the user interest identification process into distinct components: data collection from messaging interactions, data processing and analysis, interest determination using predefined taxonomies, and content selection. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by breaking down the complex task of user profiling into manageable stages
Solution Approach 2:
The system transforms raw user interaction data into structured interest profiles by changing the parameters from unstructured click data to categorized interest tags. This parameter transformation enables precise user interest identification while simplifying subsequent content matching, as the interest taxonomy provides a standardized framework for interpretation
3Productivity
If personalized content is presented based on user interests, then user engagement increases, but content selection complexity increases
Solution Approach 1:
The patent changes the parameters of content selection from generic audience targeting to personalized user interest matching. By transforming user interaction data into interest profiles and using interest taxonomies, the system enables precise content matching that increases engagement while managing complexity through structured data transformation and predefined classification frameworks
Data Source
AI summary
Information is collected about a user, e.g., the user's interests, from the user's interaction with digital messaging content. Information collected about the user can be used to identify an interest of the user. The identified interest(s) can be used to select content to be presented to the user. By way of a non-limiting example, information collected in response to the user clicking on a link in an electronic mail, email, message can be used to identify one or more content items to be presented to the user. By way of yet another non-limiting example, the identified content item(s) can comprise advertising content, news articles, etc.