Dynamic Ticket Allocation Using Actor Characterization
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Solution Overview
Problem
The conventional ticket purchasing process for events is often stressful and inefficient, with true fans facing difficulties in securing prime tickets due to the influence of ticket resellers and the lack of flexibility in ticket availability and group seating.
Innovation Solution
A ticket management system that uses a learning model to identify and favor 'good actors' by predicting desirable characteristics, such as event attendance and fan club membership, offering preferential opportunities like holding or reserving tickets, and modifying ticket assignments to facilitate group seating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ticket requests are processed immediately when tickets are on sale, then ticket availability is maximized, but true fans are deprived of prime tickets due to resellers and bots
Solution Approach 1:
The system performs preliminary characterization of actors before ticket allocation by evaluating multiple attributes (past event attendance, fan club membership duration, social media engagement) to identify true fans in advance. This preliminary assessment enables the system to prepare preferential treatment for legitimate fans before the high-pressure ticket sale moment arrives.
Solution Approach 2:
The system continuously monitors and updates actor characteristics based on observed behavior patterns, including attendance history, interaction with event content, and social network analysis. This feedback mechanism allows the characterization model to adapt and improve its identification accuracy over time, ensuring reliable distinction between true fans and resellers.
2Measurement precision
If the characterization assessment procedure is made complex to accurately identify good actors, then identification accuracy improves, but system complexity increases
Solution Approach 1:
The assessment procedure is divided into multiple independent modules, each evaluating a specific attribute (event attendance history, fan club engagement, social media behavior, purchase patterns). This segmentation allows the system to manage complexity through modular design while maintaining comprehensive evaluation coverage. Each module can be optimized independently and combined to produce the overall actor characterization.
Solution Approach 2:
The characterization model serves multiple functions simultaneously: it identifies true fans, detects resellers, predicts attendance likelihood, and determines appropriate ticket allocation priorities. By making the assessment procedure multi-functional, the system achieves high measurement precision across multiple dimensions without requiring separate complex systems for each function.
3Ease of operation
If ticket offers are made flexible to accommodate group purchases, then buyer satisfaction improves, but ticket allocation fairness deteriorates
Solution Approach 1:
The system applies different ticket allocation rules to different actors based on their characterized traits. True fans with strong community connections receive preferential treatment for group purchases, while actors with patterns indicating reseller behavior receive standard or restricted treatment. This localized differentiation maintains overall fairness while providing necessary flexibility for legitimate group attendance.
Solution Approach 2:
The ticket allocation policy is dynamic and adapts to individual actor characteristics rather than being static. The system adjusts offer parameters (such as maximum tickets allowed, priority level, and allocation timing) based on real-time evaluation of actor behavior patterns, ensuring that flexibility is granted only when it aligns with fairness principles for genuine fans.
Data Source
AI summary
Techniques herein attempt to provide actors with more flexible and satisfactory experiences regarding obtaining tickets for an event. A learning model may identify attributes indicative of whether a particular actor (e.g., attempting to purchase tickets to an event) possesses a desirable characteristic (e.g., is likely to attend the event). Each actor can then be evaluated to estimate whether she is a good actor (possesses the characteristic). If so, favored opportunities may be made available, such as the opportunity to buy high-demand tickets. An actor may further have the opportunity to hold or reserve tickets for a period time, during which other actors cannot purchase them. A fee for holding or reserving tickets (and/or maintaining the hold or reserve) can be dynamically set based on market factors. Opportunities to modify seat assignments to allow a group of friends to sit together may also be provided.