Attendee Engagement Prediction Using Hybrid Classification
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Solution Overview
Problem
Existing methods fail to accurately predict attendee engagement in live web-based content presentations, especially when registration is done through third-party services or for private, professionally oriented content, as they rely on click data or social network trends which are not effective in these contexts.
Innovation Solution
A system and method that integrates data extraction, preprocessing, feature extraction, and classification using a combination of decision trees and deep neural networks to predict attendee engagement levels before a presentation, considering past engagement metrics, registration methods, and content types, allowing for personalized communication and prioritization during events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If click data and time spent on page are used to predict engagement, then engagement prediction accuracy is improved for publicly shared content, but the method becomes ineffective for third-party registered events and private professional content
Solution Approach 1:
The system changes the parameters used for engagement prediction based on content type. For publicly shared content, it uses click data and time spent; for third-party registered events and private professional content, it switches to analyzing registration timing, historical engagement patterns, and professional profile data. This parameter adaptation resolves the contradiction by making the prediction method effective across different content types while maintaining accuracy for each specific type.
2Measurement precision
If social network trends are monitored to predict engagement, then prediction capability is improved for publicly shared content, but the method fails for privately oriented professional content that cannot be shared
Solution Approach 1:
The system introduces alternative intermediary data sources for private professional content where social network trends are unavailable. Instead of monitoring public social interactions, it uses registration metadata (timing, method, source), historical engagement with similar content, and professional profile information as intermediary indicators. This allows the system to predict engagement for private content without relying on public social network data.
3Ease of operation
If detailed attendee information is collected before broadcast, then presenter can prioritize attention to engaged attendees, but system complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the most critical engagement indicators from the collected attendee data, such as registration timing relative to broadcast, historical engagement scores, and key profile attributes. Instead of processing all detailed information, it isolates and processes only the essential features needed for effective prioritization, thereby reducing system complexity while maintaining presenter interaction efficiency.
4Productivity
If engagement prediction is performed for all registrants, then resource allocation can be optimized, but computational resources and processing time are consumed
Solution Approach 1:
The system performs engagement prediction selectively rather than uniformly for all registrants. It prioritizes prediction for high-value attendees (e.g., those registered close to broadcast time, with profiles indicating high engagement potential, or from important demographic segments) while using simplified heuristics for lower-priority cases. This partial action approach optimizes resource allocation efficiency while significantly reducing computational resource consumption compared to universal prediction.
Data Source
AI summary
A system and method determines engagement for a future presentation for one or more future attendees/registrants for the future presentation. The system and method may extract features from the registrant data for each registrant of the future presentation, generate a model based on the extracted features and use a classification process to generate the engagement determination for each attendee/registrant of the future presentation.