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Identifying patrons at risk of abuse

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Identifying patrons at risk of abuse

Predictive models of worth will likely take into account the factors that predict whether a guest will play on a future trip. It is also a good idea to build a separate model to identify patrons that are likely to use a future offer and not play at all. Decision trees and logistic regression are common statistical methods used to identify patron characteristics that predict the likelihood of a patron (or segment of patrons) to abuse an offer.

Some factors that are likely predictors of abuse are age (younger patrons are more likely to abuse), gender, and history of abuse. Additionally, survey data (e.g., from follow-up surveys after a patron’s visit) that is linked to individual patrons can be used to identify other predictors. If a patron thought they were treated unfairly and had a bad experience in the past, they might take an offer for a free room and/or free play as revenge for that bad experience. When they come in they take all the perks available from the offer then go elsewhere to gamble and spend money so they can later get a better offer from the other casino. By identifying the patrons at risk of abusing offers, the business can decide how to market to those risky patrons. For instance, someone might reach out to the patron to try and rectify the situation if they had a bad experience. Instead of sending them the general offer for free play, they would be sent an offer that requires them to play to a certain level or they won’t get the free play.