Analytics in Casino Industry

–  By Khushbu Mehta

Image result for casino image

“A dollar won is twice as sweet as a dollar earned,” said Paul Newman in The Colour of Money. But with growing competition for the entertainment dollar, winning over customers has never been more challenging.” Casinos are being run like financial services firms and analytics professionals are moving from banking to the gaming industry. That makes some sense, since companies in both industries need to balance financial risks and returns in making decisions and placing their business bets. Hence casino companies are highly relying on analytical technologies.

Analytics were applied to general business issues mainly in marketing but recent development said it could be applied on the gaming floor. Running a casino presents a big stats problem and a correspondingly large opportunity. Casino managers know the probability of gamblers winning different games, but laying out the floor to optimize the amount of money that can be taken in is not a sure bet. All those games generate plenty of data, though. That combination makes the casino industry a prime candidate for analytical technologies and applications.

Rom Hendler, chief administrative officer at Las Vegas Sands Corp., owns nine casinos and resort facilities firmly believes in the idea that data analytics gives casino operators an edge . “Analytics technology is essential to be competitive,” he said in an interview at SAS Institute Inc.’s 2014 Premier Business Leadership Series conference in Las Vegas.

When deciding which games to offer or replace, casinos may look at historic results and reason that games which were popular in the past will continue to be so in the future. Therein lies a missed opportunity. With lot invaluable customer data available, a growing number of casinos around the world are turning to advanced analytics to assist with slot floor planning. Making sense of the data is important. SAS-based analytical technologies help to predict the impact of moving games from one area of a casino floor to another. To start, they collect data on how much money each game, whether table games or slots, currently brings in. They also collect information on how people move about the casino. When the gathered data is combined with the odds of a particular game paying out, the analytics team can model what its performance would look like in different locations to help determine where the game should be placed in order to achieve the optimal performance level. The process of positioning games brings into a broader data-driven approach to optimizing revenues. For example, Hendler said a good way to encourage gambling is to give customers free nights or discounted dinners in the hotel that houses a casino. But the casino would lose money if it did so for everyone, because some people don’t gamble much. To help highlight such offers, Sands runs customer analytics applications on data it has collected showing how often individuals gamble, how much money they tend to spend in the casino and what kinds of games they like.

The first step in the process was assessing and cleaning the available data to enable a detailed categorization of relevant information to gain insights into slot performance to date. The data needed to be reviewed and revised for consistency to allow the history of similar games to be tracked. This was a key challenge of the initiative because results of analysis can only be as good as the information that gets analysed.

Next, the team used this information to provide a best-case predictive forecast into how each game would perform in the year to come. In the process, it became possible to begin collecting insight into leading predictors of guest preference that would optimize profitability while supporting the integrity of fair and random play.

Leveraging categorization and each game vendor’s market research, it became possible to isolate a surrogate to help it analyse options for new game purchases. Moving forward, the technology will allow the industry to predict the potential impact of changes on slot performance based ‘what if ’ scenarios. This provides much greater forecasting power than the traditional approach to decision making, which was limited to reports based on one variable, looking exclusively at historical data.

Finally, advanced optimization was utilized to determine the best approach to future business, considering factors such as physical space and budget. This information will help them optimize its slot purchase options, including the analysis of which machines to replace and when to replace them, while also ensuring player experience is not hampered through down-time. In the end, the solutions offered by the test case promise a new perspective.

This solution has helped to immediately improve the understanding of customer preferences. As the databases become richer with new game and machine attributes, they’ll also have stronger predictors of long-term performance, allowing us to make better decisions in the future.






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