Though gambling remains a controversial and highly-regulated industry across the United States, it is also an immensely lucrative one that continues to grow at an astounding rate. All sectors of the industry combine to inject a jaw-dropping $138 billion (US) annually into the US economy while also combining to employ close to 750,000 Americans. Globally, annual gross returns from gambling are expected to top half-a-trillion dollars by the end of 2019. Americans alone spend more money on gaming (which besides casino visits includes everything from lottery tickets to dog racing) than they do watching movies by a ratio of 10 to 1. The average American loses more money during an evening of gambling than he or she spends at the local shopping mall.
Like so many other industries struggling to maximize profits by leveraging the reach of the Internet and the power of modern computing, the US casinos and gaming industry is already intimately familiar with the power of database marketing. They know it works, and they invest heavily in collecting marketing data. But they do it haphazardly. Machine learning, or Artificial Intelligence (AI), is poised to bring order to the chaos of current data collection and analysis methods. Relying on highly-complex algorithms and statistical models, AI not only follows instructions, it goes beyond instructions, learning reflexively and dedicating itself to doing better the next time.
The challenge, for the gambling and gaming industry and the newer AI systems, is that gambling's consumers are unique in ways that patrons of other entertainment sectors like cruise lines, restaurants, and movie theaters are not. Gamblers are terribly unpredictable in terms of how much they contribute to a company's bottom line. Customers across other industries have a relatively predictable floor and ceiling in terms of their real and potential spending. Netflix might not be sure exactly how many subscribers are planning to stay and how many are planning to leave, but it does know how much those who stay are likely to pay over the course of a year. This gives the company a greater sense of security. It can plan its future with more confidence.
May 20, 2017 Machine learning raises many ethical questions of the nature of the human mind and of the ethics of enabling a machine to think “like a human” when it would lack the two most fundamental things that make us human: our ageing bodies, and our emotions, especially since the first affects the second. Computers are becoming smarter, as artificial intelligence and machine learning, a subset of AI, make tremendous strides in simulating human thinking. Creating computer systems that automatically improve with experience has many applications including robotic control, data mining, autonomous navigation, and bioinformatics. & Data Will Change Online Gambling Forever. This demonstration of how machine learning can take into account both mathematical probabilities and their opponent’s behavior could.
Casinos and gaming operate in much more of a “Wild West” environment. Most players will lose money—that is well-known. But while some might lose $40 over the course of an evening, others can and will lose tens of thousands of dollars (or much, much more) in just a few minutes. While a few players might come out ahead and an even smaller number (of professional blackjack players for example) might enjoy a true “windfall,” even extremely healthy returns can be unsettling for gaming operators if they are wildly unpredictable. An almost infinite number of other variables, everything from weather and time of year to regulatory changes, affect casino gambling and gaming in ways that other product and service providers are better-placed to withstand.
This is where machine learning, or artificial intelligence (AI), can make a real difference.
What industry figures want are cutting-edge analytical tools that understand the truly unique patterns at the heart of their industry. They see these cutting-edge tools as integral to boosting profits and facilitating greater stability and improved planning. And indeed, nothing today collects and interprets data as effectively and insightfully as machine learning platforms. These powerful, highly-dynamic technologies have the ability to bring together individual gambling histories, demographic data, social media content, virtual personal online identities, and a range of other content, to produce “smarter” insights now beginning to drive marketing outreach. The “smarter” the insights and recommendations, the more likely it is that highly-effective marketing campaigns can be designed using these data as their foundation.
With these larger goals in mind, industry leaders are beginning to see machine learning as a way to identify their most valuable and profitable customers (their MVPs) much more accurately, and to appeal to these customers more effectively through a complex web of highly-individualized marketing efforts. In the gambling and gaming industry, however, there are different types of MVPs. The casinos always want to know which of their regular customers are likely to lose the most money and when. But they also want to know, with as much specificity as possible, which are likely to “beat the house.” Their reasons are not always nefarious. After all, customers who win, and win big, become “living advertising.” Growing the customer base depends on a certain number of players winning and sharing that information with others. And yet appealing to both big-time “winners” and “losers” often demands radically-different models of database marketing. And the same is true when it comes to appealing to everyone else: the tens of millions of US gamblers and gamers whose attitudinal and behavioral profiles fall somewhere between these two extremes, each subtly different from the rest. Operators need to know who a current or potential MVP is. Who, they want to know, has the potential to generate the biggest returns over time? And it is here that the power of machine learning promises unprecedented levels of insight.
The research community has also become increasingly interested in the potential applications of machine learning to casino gambling and gaming. Raghuram Iyengar and Jehoshua Eliashberg of the Wharton School and Sam Hui of New York University’s Stern School of Business are three of a quickly-growing body of researchers who have applied the power of machine learning to the casino floor. One of their cutting-edge mathematical models integrates the number of times a gambler visits a casino, the total amount her or she wagers per visit, and the way he or she divides table betting and the slots. Interestingly, women seem more drawn to slots while men favor the tables. Cross-referenced data suggest men demonstrate more skill at table games like blackjack and poker, most likely as a result of their experience with some of these games outside of the casino environment. Other models compare individual amounts spent or gambled to theoretical ideal amounts determined by a player's skill level (which is itself distilled from many variables). Metrics such as Average Daily Theoretical Loss and Average Trip Theoretical Loss, as well as intensive data mining (which players place the highest bets, etc.), have proven themselves useful. In short, machine learning has the capacity to absorb all of these inputs and pinpoint for the casino which of its gamblers are most valuable to the business.
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It is clear that the US casino gambling and gaming industry is well-positioned to take advantage of the burgeoning power of machine learning. By turning AI loose on the casino floor and in the virtual environment of fantasy sports and applying it to everything from horse racing to state lotteries, operators are accessing a weapon that offers unprecedented insight into their most valuable and profitable customers. This, in turn, promises much more efficient and effective database marketing efforts, which ultimately promises even greater profit margins with simultaneous increases in the stability and predictability of annual revenues. The oddity, in many respects, is that an industry so attuned to its bottom line has not been quicker to adopt cutting-edge tools that offer it such dramatic competitive advantages.
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