Data Science And Problem Gambling
Technological evolutions have revolutionized the gambling industry. With the rise of gambling apps and live streams, people can now gamble anytime, anywhere with the press of a button.
Demographic gambling rates have since risen exponentially, with more women and young people taking part in the activity than ever before. In total, around 15 percent of the U.S. adult population has gambled in the past week alone. As more apps and fantasy sports teams gain popularity, the online gambling model appears to expect years of robust growth.
Prevention of harms related to gambling requires investment in population based approaches, say Heather Wardle and colleagues ### Key messages In 2017 the gambling regulator for Great Britain, the Gambling Commission, described problem gambling as a public health concern (box 1)3 and emphasised the need to increase protection from harm.4 In 2018 the Faculty of Public Health released a position. Problem gambling is not just about losing money. Gambling problems can affect a person’s whole life. Gambling is a problem when it: gets in the way of work, school or other activities harms the person’s mental or physical health hurts the person financially damages the person’s reputation causes problems with family or friends. Gambling participation and problem gambling Gambling behaviour is increasingly a subject of public health and policy interest. We regularly collect data on gambling both in terms of information. Introduction to Sports Gambling 1.1. Similarities and Differences Compared to Traditional Gambling Sports gambling is a form of betting similar to traditional probability games such as roulette, dice, or cards. The result of a sports bet is settled based on the.
With this unprecedented rise in gambling comes an increase in gambling addiction. Problem gambling affects around four to eight million Americans and disrupts a person's financial status, social life, job and related aspects. Another two million -- one percent of the population -- meet the full criteria for pathological gambling. For people such as these, the thrill of the activity transforms into an addictive craving.
Many online casinos and physical locations are taking new steps to help people overcome this addition. And big data may be the answer. These casinos can incorporate the advanced analytics they already use for marketing strategies to detect and help players who may be at risk of gambling addiction.
Identifying Problem Gambling
Retroactive approaches in detecting gambling addiction have proven reliable. Businesses can look at a customer's historical datasets to find patterns indicating a problem. But these approaches fail to solve the main issue surrounding compulsive gambling -- detecting worrisome trends before it's already too late.
Instead, software companies such as U.K.-based BetBuddy have created a more proactive, targeted approach.
Data Science And Problem Gambling Addiction
BetBuddy developed an application called PowerCrunch, which uses machine learning and data mining to calculate a risk score for each player.
The program runs based on a three-tier model that analyzes exhibited behaviors, looks at declared behaviors as the result of self-assessments and deduces inferred behaviors using insights from these first two. It uses all three behavior types to create a predictive model of the consumer's activities. The model may be able to predict anomalous actions in real-time.
Another example is Veikkaus, an organization that creates customer profiles based on their gaming transactions. The company analyzes patterns of people who have sought professional help for their gambling behaviors. They can then use this research to pinpoint areas likely to cause compulsive gambling, as well as who might be a problem gambler.
As companies such as these examine current research, social media, customer responses and even weather patterns, they've developed an accurate method of detecting changes in players before their behaviors reach harmful levels.
Fighting Against Problem Gambling
However, merely identifying a problem isn't enough. Once casinos or online gaming companies recognize addictive behaviors, they have the chance to intervene before the addiction causes irreparable harm.
One remedial action many companies employ involves their marketing techniques. As they can use big data to determine who to market to, gambling companies can also pinpoint people to whom they should not advertise.
Once a betting firm identifies a problem gambler using software like PowerCrunch, they can halt all marketing activity toward that person. Instead, they can use advertising banners to steer the customer away from the site toward a healthier alternative.
Companies may also choose to send a notification to the player at risk. But to do so, tact is critical. Instead of telling the consumer they have a problem, gambling sites can gently ask if the person would like to set up session limits or loss limits using pop-up notifications.
In extreme cases, the company can also shut the user's account down altogether.
The Future of Big Data in Battling Gambling Addictions
Big data use in fighting gambling addictions is still a relatively new concept. As more research emerges and more companies implement the software, its effects in the real world will become more transparent. But many gambling organizations are already enjoying its success.
For example, the Ontario Lottery and Gaming Corporation tried out PowerCrunch and reported nine out of ten consumers found the software useful in controlling their gambling practices.
As the gambling industry booms, both online and physical casinos have a duty to keep their players safe.
Data Science And Problem Gambling Sites
By identifying and remediating problem gambling using big data, gambling companies can help combat addictions without overly restricting the vast majority of players from enjoying the activity every once and a while.
Kayla Matthews is a senior writer at MakeUseOf and a freelance writer for Digital Trends. To read more from Kayla, visit her website productivitybytes.com.