The Charts page is an advanced feature in the TIXnGO system (currently work on demand on Production only), that shows organizers a graphical representation of the life cycle of their tickets, per event. This feature makes use of our Machine Learning algorithm to give trust scores to each user and detect fraudulent behavior.
How to generate the chart?
Go to Chart > Click on the Event
What is the Spectators Behavior Analysis?
TIXnGO works every day to improve security and reduce fraud in your events. The spectator behavior analysis is a Machine Learning tool developed by TIXnGO that classifies the actions of spectators during an event. Its purpose is to provide organizers with a deeper understanding of tickets' life within their events and to help detect users that might need monitoring.
How does it work?
This tool uses an ensemble of Machine Learning models and classifies regularly each spectator's behavior based on his transfer activity in every event where tickets have been injected. The Charts tab allows you to see an almost real-time representation of your events. This functionality coupled with our Blockchain technology grants you the insurance to see who's in possession of your tickets at all times. This representation comes with a classification of spectators to help you identify users that might act in an unwanted way.
Disclaimer
Please bear in mind that this tool is an ongoing project that will improve upon time and data. Furthermore, the model used in the classification runs under the assumption that some kind of undesirable behavior will exist in the event. This implies that the resulting classification might not be relevant if every user acts with respect to the terms of use. The same applies to events with a very small amount of spectators.
How to read it?
The color-coded nodes represent users with tickets. The label on the left side explains the behavior for each color.
Green nodes are spectators with Adequate Behavior, yellow ones are more dubious, oranges behave in a way we would prefer to avoid and red nodes are clearly bad behavior.
The size of nodes represents the inverse of the trust score.