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Table of contents
- Promoting responsible gambling on the front page
- Vattnets egenskaper by Mattias Stolt on Apple Books
- Identification of high risk gambling in player data
- iTunes is the world's easiest way to organize and add to your digital media collection.
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Here, you find a collection of our activities in this area. The thesis shows that Playscan is most commonly used by players with a higher risk level, the self-test is the most used feature and in addition, the self-test had good psychometric properties. Read the thesis here. He examined the Playscan 3-data of 9 online gamblers who used the tool voluntarily and investigated if there are different subclasses of users by conducting a latent class analysis.
He observed number of visits to the site, self-tests made and advice used. The study has shown that the tool has a high initial usage and a low repeated usage. Latent class analysis yielded five distinct classes of users: Multinomial regression revealed that classes were associated with different risk levels of excessive gambling.
The self-testers and multi-function users used the tool to a higher extent and were found to have a greater risk of excessive gambling than the other classes. As long as the right ones actually use the tool, which is exactly what we found. People with a higher risk level are using Playscan more. Find the study here.
Promoting responsible gambling on the front page
The prevention of problematic gambling is a complex issue. We at Playscan know it all too well. But in order to learn about effective prevention initiatives we use the method of validated learning for acquiring new knowledge. By practising hypothesis-driven development for responsible gambling we see the development of new tools and services as a series of experiments to determine whether an expected outcome will be achieved — or not. With this we challenge the concept of having fixed requirements when we develop new features.
Instead, the process is iterated until we reach a desirable outcome.
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Let us look at an example: In interviews with users we often ask them to describe their general attitudes toward their risk assessment. We hear players ask themselves: This is where we get the chance to identify what the user is expecting from us.
Vattnets egenskaper by Mattias Stolt on Apple Books
From this it is our responsibility to design features that address the problem. We base the work on the products Impact Map, a document that help us drive our software development towards effect, meaning delivering the right responsible gambling initiative to the right player. This is measured with an online questionnaire; click through on recommendations and analysis of the gambling behavior. Best practices and research inspire us when we work on a solution. We talk it through with our experts on problematic gambling, write texts and produce real content. During the process of making the solution alive software developers, UX-designers and copywriters work closely together.
Simply because it always gives us the best result. Then we launch it. This is where we collect feedback from the player and can see if the solution delivers the use we expected. Or do we need to change anything? Here we learn and iterate and make it even better. To ensure that we are on the right course, we work in short iterations that are generally two weeks long. We build the system with small additions of user-valued functionality and evolve by adapting to user feedback.
Have we stumbled on any mines?
Identification of high risk gambling in player data
For every experiment we do we always learn something new. Even if we had a great hypothesis based on good observations or research sometimes the results are just neutral. But this is why this method is so effective: It is said that a big win, early on in a persons gambling career, could lead to false expectations of future wins.
This in turn could lead to increased gambling, which consequently increases the risk of becoming a problem gambler.
We took a closer look at this phenomenon by studying how players behave after a big win. One could have expected this number to be even greater, or at least we did — the interesting part is that instead of an even higher gambling activity, we see differences in behaviour between different families of games. SECONDLY, players who prefer games of chance tend to slightly increase their level, but do not increase their betting amounts and therefore manage to keep a big portion of their winnings.
THIRDLY, we note a low correlation between the size of the win and the change in gambling behaviour, with one exception: Poker players are more likely to increase their betting amounts and even if some of them actually manage to continue their winning streak, some lose their winnings rather quickly.
So, is a big win a risk factor for developing problems? Well, by analyzing this particular set of data, we find that a big win does have an impact on future gambling behaviour — even if the effect seems to differ depending on what family of games you prefer. Reaching the right player with the right intervention, at the right time, might be the one of biggest challenges we face in the industry, since we do not wish to disturb players without a cause.
However, with this information it is fairly safe to say that one should pay extra attention to the aftermath — and stop players from crashing after a big win. We will then study how this information impacts future behaviour — hoping to see more players keeping their scoop after winning. Stay tuned for the results. The authors, Dr Richard Wood and Dr Michael Wohl, conducted the first study of this kind to use actual behavioral data, from 1, Internet players in a real-life setting.
The research provides valuable insight into how a well-designed player-tool, such as Playscan , can be utilized to ensure players have a more responsible gambling experience. Playscan is thrilled to have been part of the study and to contribute to a better understanding of how to support responsible play.
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The ambition of the Playscan team remains the same, as the development of the tool maintains its progress. Therefore; the product will no longer be commercially available. Today, there are several variations of responsible gambling tools like Playscan on the market. Our solution has been ahead of this curve, leading the way, which is really inspiring. However, we see that many operators pursue their own solutions, making demand for an off the shelf solution low.
We are also open for new forms of collaboration with the industry and the research community. We will carry on with educating operators, promote a mutual exchange of knowledge and share best practices on how to prevent problem gambling, throughout the industry. It will be exciting to follow its future development. How do players use a responsible gambling tool — why user interface design matters — talk by Natalia Matulewicz.
Is mandatory or voluntary the way to go? This talk means to inspire and give new ideas to how to increase the players interest and usage of responsible gambling tools. The latest features from Playscan are now available to players gambling at Miljonlotteriet. The lottery has offered Playscan to their players for more than four years and by upgrading they will now supply players with a new and more detailed view of their gambling behavior. It helps us understand and evaluate the impact of our overall responsible gambling initiatives.
Every week Playscan analyzes the gambling habits of 4. Players appreciate the system, they reflect on their habits, perform self-tests and value the fact that it warns them if their gambling behavior changes into becoming more of a risky one. Which means they remain as a healthy player, as well as in control of their gambling habits. We use the Playscan Risk Analysis for two purposes.
The original purpose is to use it as a basis for interventions and communication to at-risk players — now, we equally use it together with operators and creators of RG. By looking at levels of risk and changes herein, between groups, marketing campaigns, interventions, etc. We quantify, instantaneously and at scale, our mistakes and our successes — every day and for everything we do. By bringing in Playscan at the early stage of the project, we will help you track and quantify the results, and measure the effect of your new initiative.
As an initial step, you may want to try out our metrics by looking at previous years of operation, to get a feeling both our metrics and how they benefit your operation today. When you decide to have these as recurring KPI: We will join forces between your strategies and tools, and our day-to-day experience in reaching the at-risk player. On our three-color-scale green for low risk, yellow for at-risk, and red for high-risk , we see a big difference between green and yellow players. While the latter is not a uniform group of people, they tend to share traits and behavior that relieves us from much of the fear of annoying or even accusing the low risk players.
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After all, the green majority of players are those for whom the industry should focus on for a good and exciting experience. In contrast, the yellow players are slightly different.