Attendance Prediction Neural Network for Event Targeting
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Solution Overview
Problem
Conventional digital communication distribution systems suffer from inaccuracies, inefficiencies, inflexibility, and reactivity, leading to wasted resources and suboptimal event attendance due to broad-based targeting and inability to adapt to changing circumstances.
Innovation Solution
An attendance optimization system utilizing a neural network approach and machine learning techniques to predict attendance probabilities and generate a recommended target audience for events, improving key performance indices such as attendance and registration by identifying similar individuals and adjusting invitations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If broad-based targeting techniques are used to distribute digital communications, then the system can reach a large number of prospects, but the accuracy of targeting decreases and resources are wasted on prospects with low probability of attending
Solution Approach 1:
The system segments the prospect population into distinct groups based on their probability of attending the event. By using machine learning models to predict individual attendance likelihood, the system divides prospects into high-probability and low-probability segments, allowing targeted communication strategies for each segment rather than treating all prospects uniformly.
Solution Approach 2:
The system applies different communication strategies and levels of engagement to different segments of prospects based on their local characteristics. High-probability prospects receive personalized, high-priority communications, while low-probability prospects receive different treatment, optimizing resource allocation according to the specific needs and likelihood of each segment.
2Area of stationary object
If digital communications are distributed to many prospects using broad-based targeting, then coverage is increased, but computer resources such as computing time and power are wasted generating and distributing communications to prospects with low probability of attending
Solution Approach 1:
The system performs preliminary analysis using machine learning models to predict which prospects have a high probability of attending before distributing communications. This preliminary action filters the prospect list to identify only those worth contacting, preventing waste of computing resources on generating and sending communications to prospects unlikely to attend.
Solution Approach 2:
The system changes the parameter of prospect selection from broad-based inclusion to probability-based filtering. By adjusting the threshold for prospect inclusion based on predicted attendance probability, the system optimizes the balance between coverage and resource efficiency, communicating only with prospects above a certain probability threshold.
3Loss of information
If conventional systems wait for responses to invitations before determining attendance, then they can gather actual attendance data, but the system becomes reactive and slow to adapt to changing circumstances
Solution Approach 1:
The system performs preliminary prediction of attendance using machine learning models before the event occurs. By analyzing historical data, prospect attributes, and engagement metrics in advance, the system predicts who will attend without having to wait for explicit responses, enabling proactive decision-making and faster adaptation to changing circumstances.
Solution Approach 2:
The system uses feedback from actual attendance outcomes to continuously improve its prediction models. By comparing predicted attendance with actual attendance and using this feedback to retrain and refine the machine learning models, the system becomes increasingly accurate over time while maintaining its proactive, predictive approach rather than remaining purely reactive.
4Ease of operation
If conventional systems use fixed industry standard thresholds for targeting, then the system is simple to operate, but the system lacks flexibility to adapt to different event goals and prospect attributes
Solution Approach 1:
The system transitions from static, fixed thresholds to dynamic, adaptive targeting criteria. Machine learning models continuously learn from data and adjust prediction thresholds based on event-specific goals, prospect attributes, and performance metrics. This dynamic approach allows the system to automatically adapt to different event types and objectives without requiring manual reconfiguration, maintaining ease of operation while gaining flexibility.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a recommended target audience based on determining a predicted attendance utilizing a neural network approach. For example, the disclosed systems can utilize an approximate nearest neighbor algorithm to identify individuals that are within a similarity threshold of invitees for an event. In addition, the disclosed systems can implement an attendance prediction model to determine a probability of an invitee attending the event. The disclosed systems can further determine a predicted attendance for an event based on the individual probabilities. Based on identifying the similar individuals to, and the attendance probabilities for, the invitees, the disclosed systems can generate a recommended target audience to satisfy a target attendance for an event based on a predicted attendance for the event.