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Today, the analytics space is more crowded than ever. Standard ETL-solution providers are adding analytics (at least descriptive analytics) to their multitude of offerings. Many of these new players in the Master Data Management (MDM) field have BI platforms that combine integration, preparation, analytics and visualization with governance and security features. Such standard analytics processes as column dependencies, clustering, decision trees, and a recommendation engine are all included in many of these software offerings. Instead of forcing clients to purchase modules on top of modules, new software companies are creating packages that contain many built-in analytical functions. Open source products like R, Python, and the WEKA collection can easily be added to many of these software solutions, thereby reducing the need for expensive analytics layers. 

Unsupervised learning, or Pattern Discovery can be described as trying to find hidden structures in unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning.

Predictive analytics (or Supervised Learning) is the use of statistics, machine learning, data mining, and modeling to analyze current and historical facts to make predictions about future events. Said another way, it gives mere mortals the ability to predict the future like Nostradamus. In recent years, data-mining has become one of the most valuable tools for extracting and manipulating data and for establishing patterns in order to produce useful information for decision-making. Whether you love it or hate it, predictive analytics has already helped elect presidents, discover new energy sources, score consumer credit, assess health risks, detect fraud and target prospective buyers. It is here to stay, and technology advances ranging from faster hardware to software that analyzes increasingly vast quantities of data are making the use of predictive analytics more creative and efficient than ever before. Forecasting can be looked upon as an extension of supervised learning where there is a time dependency involved in the outcome of interest

It could be argued that the most valuable use of predictive analytics is in the marketing and sales department. Imagine the ability to accurately predict not only who your best leads and prospects will be, but when and how will be the most effective ways to reach them and then to engage. This ability alone will empower marketers and salespeople in the coming seasons to be radically more productive and profitable than they are today. Used properly, it can transform the science of sales forecasting from a dart-throwing exercise to a precision instrument.

The concept of sales and marketing automation has already produced some of the highest-flying success in high-tech. Companies like have been wildly successful in automating the sales process for salespeople and managers. Organizations like Marketo have enriched the marketing discipline with automation and tools for lead generation, lead nurturing and lead scoring.

For a sports book, analytics could be used to spot problem gamblers, risk management, increase share of wallet of other gamblers, incentivize people to spread out their gambling spend as well as a whole host of other things.


Descriptive analytics

For a casino company, descriptive analytics could include pattern discovery methods such as customer segmentation, i.e., culling through a patron database to understand a patron’s preferred game of choice. Simple cluster segmentation models could divide customers into their preferred choice of games and this information can be given to the marketing department to create lists of baccarat players for a baccarat tournament. Market basket analysis, which utilizes association rules, would also be considered a descriptive analytics procedure. Casinos should use market basket analysis to bundle and offer promotions as well as gain insight into gaming, shopping and purchasing behavior. I will go into full detail on this in chapter three.


Diagnostic analytics is a form of advance analytics that examines data or content to answer the question, “Why did it happen?” Diagnostic analytics attempts to understand causation and behaviors by utilizing such techniques as drill-down, data discovery, data mining and correlations. Building a decision tree atop a web user’s clickstream behavior pattern could be considered a form of diagnostic analytics.

Edge Analytics

The concept of “Edge Analytics” – i.e., the processing of analytics at the point or very close to the point where the data is being collected, which can exponentially increase the predictive analytics use cases for a casino. In short, edge analytics brings analytics to the data rather than vice-versa, which, understandably, can reduce cost as the data it analyzed close to where it is needed. This also reduces latency, which could be the difference between useful and useless analytics. As Bernard Marr (2016) argues in his article Will ‘Analytics on The Edge’ Be The Future Of Big Data?, “Rather than designing centralized systems where all the data is sent back to your data warehouse in a raw state, where it has to be cleaned and analyzed before being of any value, why not do everything at the ‘edge’ of the system?”

Marr (2016) uses the example of a massive scale CCTV security system that is capturing real-time video feeds from tens of thousands of cameras. “It’s likely that 99.9% of the footage captured by the cameras will be of no use for the job it’s supposed to be doing – e.g. detecting intruders. Hours and hours of still footage is likely to be captured for every second of useful video. So what’s the point of all of that data being streamed in real-time across your network, generating expense as well as possible compliance burdens?” The solution to this problem, Marr (2016) argues is for the images themselves to be analyzed within the cameras at the moment video is captured. Anything found to be out-of-the-ordinary will trigger alerts, while everything deemed unimportant would either discarded or marked as low priority, thereby freeing up centralized resources to work on data of actual value (Marr, 2016).

Besides the obvious use by a casino security team, for both patron and, potentially, perimeter security, edge analytics could be used to spot high rollers venturing onto a property, or uncovering problem gamblers, [EDIT]. “Large retailers could analyze point of sales data as it is captured, and enable cross selling or up-selling on-the-fly, while reducing bandwidth overheads of sending all sales data to a centralized analytics server in real time” (Marr, 2016). As today’s integrated resorts are also, in many cases, huge retail malls retail edge analytics could, potentially, become part of a package the casino company makes available to its retail clients. 

Predictive Analytics

Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavioral patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown, whether that is in the past, the present or the future.

Predictive analytics uses many techniques from data mining to analyze current data to make predictions about the future, including statistics, modeling, machine learning, and artificial intelligence. For example, logistic regression can be used to turn a market basket analysis into a predictor so that a casino can understand what items are usually purchased together. Of course, the old beer and diapers story market basket wouldn’t fit for a casino, but gleaning data from the casino floor could reveal second favorite games that patrons like to play. This could be useful information when a patron is having a run of bad luck on his or her favorite game. Perhaps a marketing offer for a game he or she sometimes plays would be appreciated rather than an offer on his or her favorite game, as that might not be seen in such a positive light while the patron is in the midst of a losing run.  


Prescriptive analytics tries to optimize a key metric, such as profit, by not only anticipating what will happen, but also when it will happen and why it happens. “Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options” (Wikipedia).

Prescriptive analytics can ingest a mixture of structured, unstructured, and semi-structured data, and utilize business rules that can predict what lies ahead, as well as advise how to exploit this predicted future without compromising other priorities. Stream processing can add an entirely new component to prescriptive analytics.

How can analytical marketing increase gaming revenue?