“ The industry is acutely aware of the value proposition , but implementation of personalisation at scale is not an easy undertaking and there are many existing priorities that take precedence ”
Jason Angelides , Epoxy
teams can predict how likely a player is to suffer an injury , for example , or to start to show signs of decline .
Kornilov believes this kind of technology can also yield great results in the gambling industry , but says it ’ s still early days . Nevertheless , certain uses of predictive analytics can prove highly effective for operators .
According to the Betegy CEO , behavioural models can help identify a player who is likely to churn several weeks before they do so , giving CRM teams time to nurture and reactivate that customer relationship in a tailored way .
“ All of this is very hard to segment by a human brain ,” he explains . “ You cannot create micro segments . That ’ s why you need to have certain tools and big data analytics and AI to make sure everything works in the best possible way .”
The two Epoxy co-founders believe predictive analytics and AI models should play a strong role in CRM in the gaming industry , giving operators an overview of the individual traits and behavioural patterns of a player over time .
“ That allows an operator to much more effectively market to that user ,” says Reynolds .
TACKLING HARM WITH AI
However , it ’ s not just in the realm of marketing and increasing retention where predictive models can come into play : they are also driving innovative and personalised solutions for reducing gambling-related harm .
One of the best examples of this use of AI is Mindway , the supplier of safer gambling software GameScanner , which leverages AI and cutting-edge neuroscience to identify players with a high potential to become problem gamblers in the future .
Mindway ’ s algorithms are trained using brain scans and the combined expertise of numerous psychologists , clinical psychiatrists and neuroscientists to create a scalable tool that can act as an early detection system .
According to testing and certification company Gaming Laboratories International ( GLI ), 99 % percent of players who are manually identified as potential problem gamblers are also classed by GameScanner as either at-risk or potential problem gamblers , while at least 88.5 % are classified as potential problem gamblers .
Not only does the tool give operators a wider overview of their pool of customers , but it also enables much more personalised interventions : for example , younger players could be reached more effectively via a push notification or text , while others may respond best to email .
“ We have come a long way already when it comes to detection and monitoring of the gambling behaviour ,
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