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Karan Bhalla

"Analytics:A "Moneyball" For Gaming-Part 1 "

Karan Bhalla
Director - Analytical Solutions
IQR Consulting
 
The authors are Karan Bhalla, Director - Analytical Solutions & Prachi Shah, Marketing Consultant at IQR Consulting, a data analytics company.


This Article is also co-authored by Prachi Shah, Marketing Consultant at IQR Consulting.

A "state of business analytics" report was issued last year by Bloomberg Businessweek Research Services. In its survey of corporate executives across the globe, business analytics was identified as having “gone mainstream” with more than 97 percent of the businesses reporting some form of use. 

Yet just as interesting, this same survey showed that the vast majority of companies still rely on older performance indicators and simply do not take advantage of what analytics can do now. According to the report, for many companies, spreadsheets are still the number one tool used. As a result, their findings reveal that analytics is still in the “emerging stage.” 

This article takes a look at all the ways analytics today is a “Moneyball” and an added advantage for the companies that use it effectively. In particular, it presents examples of how casinos are using it and in what way operators can move beyond the “emerging stage” to what more can be done.  It’s a “who, what, how and why” – although not in that order.

 The “What” Of Analytics

There is no universally agreed-upon definition of analytics. In practice, it is how an entity arrives at an optimal or realistic decision based on existing data. Business managers may choose to make decisions based on past experiences or rules of thumb, or there might be other qualitative aspects to decision making. Unless data is considered, it cannot be considered a calculated analytical decision.

Taken as a whole, analytics has been used in business since time management exercises were initiated by Frederick Winslow Taylor in the late 19th century. Henry Ford measured pacing of the assembly line, thus revolutionizing manufacturing. However analytics began to command more attention in the late 1960s when computers were used in decision support systems. Since then, analytics has evolved with the development of enterprise resource planning (ERP) systems, data warehouses, and a wide variety of other hardware and software tools and applications.

In 2011, the film "Moneyball" retold the real-life story of Billy Beane, general manager of Oakland Athletics, whose baseball team created an American League record by winning 20 consecutive games. The players on that team were picked by a computer based on an analysis of baseball statistics rather than just the collective wisdom of coaches, scouts and managers, the traditional method. Not only did analysis create a winning team but at a third of the budget when compared to the baseball super club New York Yankees that spent $125 million dollars to go on the same unbeaten run.

Analytics has rewritten the rules of competition for a variety of industries and sectors including gaming. It is being used across a range of business objectives, whether it is for higher revenues, increased profits, new product launches or to better understand consumer behavior.  

Harrahs Casino may have been the first to dig deeply and systemically into its data. In doing so, the operator identified something that was contrary to conventional gaming wisdom. Harrahs identified that its most profitable customers were unique to its operations and not what other casinos identified as their players. Harrahs players were the ones playing slots on a regular basis vs. table game high rollers who wagered much but not as frequently. To leverage this insight, Harrahs created marketing programs to attract and retain these more money-making customers for them. And accordingly, this insight is reportedly what sent them on the path to becoming the first casino operator to introduce a loyalty program for players.

Other examples of analytics in businesses are abundant and range from small businesses to large corporations. A look at some which show how businesses and even gaming operations could benefit: 

  • Capital One, a credit card company, uses analytics to differentiate customers based on credit risk. They also match customer characteristics with appropriate product offerings.
  • An early analytical competitor, American Airlines, started its analytical approach to "yield management" or optimized pricing in 1985. This approach may have helped to put some upstart competitors out of business and its yield management systems contributed $1.4 billion in a three-year period at the airline.
  • NETFLIX, an online movie service, uses analytics to identify the most logical movies to recommend based on past behaviour. This model has been said to increase loyalty and sales as movie choices are based on customers’ preferences and the experience is customized to each individual.
  • Olive Garden, the Italian restaurant chain, uses its data on store operations to forecast almost every aspect of its restaurants. For example, a predictive guest application produces forecasts for staffing and food preparation down to individual menu items.

All businesses today want to utilize their data and get answers to key questions: Who are our most profitable customers? Who are our most expensive customers?

And once they have those answers, they want to know what can be done to not only better target the most profitable customers but also how non-profitable customers could be converted into to moneymaking ones. 

 The “How” of Analytics

 The analytics is based on how everyone, including customers, is unique in behaviors and habits. As a result, different things appeal to each different. A study of these individual habits can be assembled and in turn, form into a pattern. Interpreting these patterns is key to understanding customers.

Overall, there are usually four key steps to the analytical process:

  • Observe/define the business problem: Analytics begins with observing the phenomenon and setting up the right business problem. It requires understanding the facts, that companies have ready access, and then drawing conclusions from these facts to identify the business problem which needs to be solved.
  • Hypothesis: A hypothesis is a proposed explanation for an observable phenomenon. A trial solution to a problem is termed a hypothesis and often also called an "educated guess" because it provides a suggested solution based on the evidence. Researchers may test and reject several hypotheses before solving the problem.
  • Test/Experimentation: An experiment is the step in the scientific method that arbitrates between competing models or hypotheses. Experimentation is also used to test existing theories or new hypotheses in order to support them or disprove them. An experiment or test can be carried out using the scientific method to answer a question or investigate a problem.
  • Learn: Learning is acquiring new knowledge, behaviors, skills, values, preferences or understanding, and may involve synthesizing different types of information.

While scientists have been utilizing the above mentioned technique for a long time, businesses are just beginning to use it. Largely, analytics requires a strong commitment to the scientific process and a systematic approach to create a TEST & LEARN environment where there is constant testing, learning and evolving to create increased bottom line benefits for a company.

In the next article, the authors will be discussing the concepts like "Who" & "Why" of analytics.