User Funnel Stage Score Determination via Vector Analysis
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Solution Overview
Problem
Current content delivery systems fail to accurately select and transmit relevant content to users based on their funnel stage within the purchase funnel process, leading to inefficient content presentation and user engagement.
Innovation Solution
A method involving user profile analysis to generate vector representations, determining user funnel stage scores, and selecting content items for transmission based on these scores, utilizing machine learning models to identify user interests and conversion probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content delivery systems transmit content to all users regardless of their funnel stage, then content coverage is maximized, but content relevance and user engagement deteriorate
Solution Approach 1:
The patent segments users into different funnel stages (awareness, consideration, conversion, retention) and delivers customized content to each segment. This segmentation enables precise content relevance by matching content type to user stage, while avoiding irrelevant content transmission to users who don't need it, thereby maintaining high engagement without unnecessary information loss.
Solution Approach 2:
The system performs preliminary classification of users into funnel stages before content delivery. By pre-determining which users are at which stage and what content they need, the system ensures content relevance is maximized from the start, avoiding the transmission of irrelevant content that would waste user attention and reduce engagement.
2Measurement precision
If vector representations and machine learning models are used to determine funnel stage scores, then content selection precision is improved, but system complexity increases
Solution Approach 1:
The patent uses vector representations as simplified copies of complex user behavior patterns. Instead of analyzing raw user data directly, the system creates compact vector representations that capture essential funnel stage characteristics. This copying approach maintains high scoring accuracy while reducing computational complexity by working with condensed data structures rather than raw data.
3Productivity
If all content items are presented to users, then content availability is maximized, but bandwidth utilization and user experience deteriorate
Solution Approach 1:
The system extracts and transmits only the specific content items needed for each user based on their funnel stage, rather than transmitting all available content. This extraction approach maximizes content delivery efficiency by sending only relevant content, while simultaneously reducing bandwidth consumption by eliminating unnecessary data transmission.
Data Source
AI summary
One or more computing devices, systems, and/or methods are provided. A user profile database may be analyzed to identify a first set of user profiles associated with conversion events associated with a first entity and/or a second set of user profiles that are not associated with conversion events associated with the first entity. A first set of vector representations may be generated based upon the first set of user profiles. A second set of vector representations may be generated based upon the second set of user profiles. A request for content associated with a client device may be received. A first vector representation may be generated based upon a first user profile associated with the client device. A user funnel stage score associated with the first entity may be generated based upon the first vector representation, the first set of vector representations and/or the second set of vector representations.