September 20, 2022

Call to Action – Gamalyze Pilot Article

Back in January 2022 Call to Action made the decision to enhance their Responsible Gambling focus on their webpage by offering the neuroscience-based gambling self-test Gamalyze developed by Mindway AI. In addition, Call to Action have been promoting Responsible Gambling by publishing articles on their webpage to raise awareness and educate their players by providing them with knowledge and exercises aimed at reducing their risk of suffering gambling related harms. As an example, a reflection exercise was shared which encouraged users to reflect on their gambling behaviour to better understand their experiences with gambling.

During the pilot, 1576 users have taken the self-test and viewed their results, which includes a detailed description of relevant decision-making characteristics, such as sensitivity to losses, and an overall risk level of the user’s decision making in relation to gambling. Moreover, players receive advice on how to limit the risks specific to their gambling behaviour characteristics. A player with low sensitivity to losses will be prompted to actively monitoring their spendings and to decide in advance how much they are willing to lose. This empowers players to take actions to mitigate the risks associated with their decision-making style and thereby reduce their risk of suffering harmful gambling behaviour now and in the future.

The pilot also revealed some key insights into the risk characteristics of the users engaging with the test. While the majority of people taking the test appeared to have a fairly good representation of their risk level, it was also found that 22 % of users underestimated their risk level. These players have received feedback on this observation as well as concrete advice which can further help them reflect on their own risk level. This knowledge is important for players, as knowing your own risk level can help you make more informed choices about how you want to gamble, and which responsible gambling tools might be beneficial for you to use.

Another insight from the pilot related to the overall risk tolerance of the users. It was found that 24 % of users completing the test exhibited risky decision making on the test, meaning that they showed less sensitivity to long-term outcomes and where willing to take larger risks to maximize their winnings. They have been given advice on strategies they can use to reduce their risk of experiencing gambling related harms. This includes advice on planning their gambling consumption, so that they avoid overconsumption of gambling, which can lead to gambling related harms such as spending money on gambling that should have been used for something else.

It is often observed that most people who gamble do not interact with responsible gambling tools on their own initiative, but that entities within the gambling industry needs to actively promote responsible gambling resources to engage users. From the pilot results it is clear that Call to Action have managed to create an interest for responsible gambling and make users interact with the self-test Gamalyze. This demonstrates that affiliates and other entities within the gambling industry also have the opportunity to take on responsibility by promoting sustainable gambling. Based on the pilot results, Call to Action will continue to offer Gamalyze to their customers and share knowledge and resources to their users in collaboration with Mindway AI.

The two plots display overall results from users engaging with the Gamalyze self-test during the pilot period. The values on the y-axis indicate the number of players which fall within a certain category. The graph to the left shows the distribution of the overall risk score (The higher the score, the higher the risk). In general, players exhibit risk levels across the entire risk spectrum with a concentration of players exhibiting average risk levels (around 50 on the x-axis).

The graph to the right shows the difference between the risk score estimated by the test and the users’ own perception of their risk level on the x-axis. It shows that while many players had a good representation of their risk level, there were also some who underestimated their risk (higher values on the x-axis).

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