The House prefers hard facts while the Players mostly rely on speculation or intuition! The bottom line is, they are both all about prediction. For some time now, Online Gaming Companies & Casinos have been analyzing their customers’ behavior. Gaming companies have access to a wealth of big data created by every click or visit of a customer. Sophisticated machine learning can be the key to extracting valuable predictive insights from this data wealth.

Casino layouts have been studied deeply and altered to better retain players while online gaming sites are using data to better anticipate players’ needs and figure out what makes them play, quit, or change games the most.

Why machine learning?

Machine learning is the ability to learn relationships and extract patterns within data without being specially programmed. Its prerequisite is large data sets and it requires meticulous planning. Each company’s priorities and goals behind developing machine learning algorithms may vary greatly. One company may want to utilize player data to inform and improve game design or detect what elements make certain games more popular than others, while another company may want to maximize revenue and identify players that are most likely to spend money.

The capability of machine learning algorithms to learn patterns and correlations from vast historical data sets of past player behavior to predict future outcomes makes them a viable solution.

Machine learning algorithms can be great for spotting addictive behavior. A gaming company can build a profile of what constitutes normal behavior for each player and machine learning algorithms will identify deviations from the normal behavioral patterns. Also, real time analytics can be used to predict the probability of a player having an unhealthy addiction when there are disruptions to the normal behavioral patterns. This can be used to alert a gaming company or a casino when a player exhibits addictive behavior so that the company can potentially intervene and take corrective action. It is important for gaming and casino companies to take a proactive approach to identifying problematic behavior.

Machine learning models can be categorized into three types; clustering, classification or regression. In a classification model, the algorithm identifies which class a data observation belongs to out of a set of pre-defined classes.  Regression models, on the other hand, find relationships between two or more variables and predict a numeric value, such as how many players will be online at a given time or how much a player is likely to spend in their lifetime.

Lastly, clustering models identify similar instances and group them into clusters.  This is often useful for recommendation algorithms, where it is possible to recommend relevant information to a player based on the similar preferences of those in their cluster. It is also a useful tool for data exploration as it automatically highlights commonalities within certain groups of players. It detects extreme or fraudulent behavior where the observation is anomalous and falls outside the cluster groups.

Like much of artificial intelligence, figuring out how to apply this wealth of data is still a new periphery. But for those with an aptitude for data analytics, opportunities in this field are infinite. Machine learning algorithms can be used for a purpose much greater than maximizing profits.

Machine learning can give online gaming companies and casinos a major boost commercially and help them to act responsibly and compliantly by predicting problem behavior before it causes too much damage. Although it requires a significant investment of time and resources, machine learning is a safe option for those that get it right.

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