Authors’ Note: This is part 11 of our 18-part series, “Where’s the Money,” and part three in our subseries on big data. In this article, we look at how money is made by enabling operators to act and that these actions can be facilitated by war room analytics. We then examine how reporting and analytics are very different, and how confusing them is like confusing a motor bike with an 18-wheeler; both can get you to your destination, but the ride is quite different. We describe the art of analytics and how it draws on creative insight and skills that enable a world of collaborative opportunistic profit improvement. Looking deeper into this, we draw on some of the previous case studies of gaming analytics to illustrate how this collaborative opportunistic has been successfully applied.
War rooms are places where teams meet to collaborate. They have existed for centuries and have been, as the name suggests, central to the intelligence activities of many wartime campaigns. The walls are typically plastered with initiatives that are central to the theme of the war room, as war rooms are the end point for what is often a huge data collection process and information consumption is almost always a team effort. This article examines how war rooms can and should play a critical role in the intelligence of business operations and how they are very different to other analytical approaches.
With the explosion of smartphones and tablets over the past few years, software companies have been racing to find ways to present analytics in a smaller and smaller package. A search of any app store will uncover dozens of analytics applications for mobile devices. Judging by all the hype, it appears that this is the wave of the future—a world in which people have their heads buried in their smartphone all day long, trying to uncover hidden truths about their data and emerging at the end of the day with brilliant insights to help drive their business.
There is only one problem with this idea: Analytics and reporting are not the same thing. But rather than launch into a Webster’s Dictionary-based discussion of the differences between the two, let’s consider slot optimization as an illustrative example. (See Figure 1.) As we described in “Where’s the Money? Part 6” (CEM, December 2011), the goal of slot optimization is to increase the player experience in ways that drive incremental gaming revenue. In that article, we outlined the difference between optimization metrics like utilization and spend per hour and outcome metrics like win per unit and hold percentage. The differences between these metrics lie parallel to the differences between reporting and analytics.
As we can see in Figure 1, we have two examples of analytics and two examples of reporting. These examples show how analytics is focused on optimization and how reporting is focused on the outcome or results. There are a few other factors regarding analytics vs. reporting that are important to be aware of, namely, that analytics is big, analytics is complicated, analytics is not an exact science, and analytics is a team effort.
Analytics is BIG
In the example in Step 1 (see Figure 1), one is pouring over thousands of data elements, trying to find areas of opportunity to improve the customer experience.
Analytics is Complicated
In Step 3, one is measuring the impact of perhaps hundreds of games and trying to remove the biases created by seasonality, changes in customer preferences over time, and cannibalization. Reporting, on the other hand, is much simpler and smaller, and thus lends itself well to miniaturization to a smartphone or tablet. We propose that analytics should not be miniaturized, and will make the case below through our argument for the creation of an analytics war room.
Analytics is an Art
You heard it here first! Analytics is an art, not a science. Reporting is a science. If you want to know how many widgets you sold yesterday, you take your source system tracking data, ETL it into a data warehouse, push the data into a front-end business intelligence system, then access that system via a computer, tablet or smartphone, and—voila!—you know how many widgets you sold yesterday. However, if you want to know how you can drive incremental widget sales, the task becomes much more difficult.
Let’s return to our example of slot optimization. Say we want to introduce a hot new game to our floor. We have to first determine which games to remove in order to make room for this hot new game. In the old days, slot operators would simply cull the games that had the lowest WPU. However, we’ve since learned that many other factors are important:
1. Should the new game be placed in a high- or low-traffic area?
2. Will the new game cannibalize nearby games?
3. Will removing old games cause us to lose customers who are loyal to those games and are now frustrated by their disappearance?
There are metrics that can help us answer all of these questions. However, there is no set formula that will tell us the exact location where the hot new game will drive the most incremental gaming revenue. Rather, we have to use our analysis of past moves to understand how customers react to changes we make on the slot floor—and this is a complicated exercise involving multiple metrics on dozens of changes made to thousands of games. In this way, analytics is a healthy combination of both art and science.
Analytics is a Team Effort
It our belief and experience that action is what drives value from analytics, and our experience also shows us that action normally requires a team effort. Collaboration regarding analytics is about meeting as a team, deciding which actions to take, and then taking those actions. This collaboration then extends to the operationalization of decisions made.
When looking at optimization metrics, the analytical approach is innovative and insightful; when looking at reporting outcomes, we need a structured review. The table in Figure 2 summarizes the relationship between the type of metric and the collaboration required to act on those metrics.
The War Room
The next question is what is the best setting for the analytics team? Enter the war room.
But what should the war room look like? Should it be virtual, with team members sitting behind their own computers on a conference call? Should they all physically be in a room together, but tapping furiously on their own tablets or laptops? Or looking at a PowerPoint presentation displayed on a projector? We personally agree that none of these options are conducive to team analytics.
Instead, we believe, and we have discovered in practice, that the best approach is to go big. One great way to achieve this is to make massive printouts using a plotter, which allow you to step back and see the big picture, then zoom in (by shuffling your feet forward) to see the details. Hang these printouts on the walls of your war room and multiple people can look, point, write and debate, making the best use of the information available.
Going back to our slot floor example, a great printout to make would be a heat map of your slot floor, with all the necessary metrics written in detail as well. Each slot machine gets represented by a square on your map. Each square can then be colored based on whatever metrics you choose—and it is possible to use more than one color at a time. For example, you could have a color that represents utilization and a color that represents win per unit. Inside each square, you can then include more detailed data, such as the name of the game, the game type and cabinet, the utilization, win per unit, spend per hour, devotion score, and hold percentage. With this approach, you can stand back and look for patterns in the heat map, then move closer to see the detail in each game. This is one of the strategies put into action at the real-life analytics war room at Silverton Casino.
Troy Freet, Teradata database administrator at Silverton, says, “The war room has become a central focus for our analytics. Combining our atomic level data with high-resolution visualizations allows the users endless flexibility and interaction in a highly collaborative environment. The analytic environment provided by the war room has drawn senior management into the analytics process with very positive results.”
The best war rooms will make the analytics immersive (in that you physically walk into the room) and the analytics collaborative by holding strategy meetings in the room. Drive actions from your war room by making sure that decision-makers are being fed both the collaborative and the creative insight opportunities to make good decisions.
Show Me the Money
According to Thomas H. Davenport: “Analytics competitors are more than simple number-crunching factories. Certainly, they apply technology—with a mixture of brute force and finess—to multiple business problems. But they also direct their energies toward finding the right focus, building the right culture, and hiring the right people to make optimal use of the data they constantly churn. In the end, people and strategy, as much as information technology give organizations strength.”1
As Davenport so clearly says, people and strategy are central to organizational strength. And while there are likely to be many paths to this strength, war rooms focused on optimization metrics are a powerful way to bring forth strategies and to unite people in a culture that enables change and innovation. This is particularly relevant when we consider the turbulence in business today, whereby innovation is ever more important.2 In weathering this kind of turbulence, the role of analytics has become a central part of many decision-making processes and businesses. In addition, operators are often faced with the difficult task of throwing away hard-earned business practices to innovate in different directions entirely. The role of analytics is central to the discovery of these new directions and their team-based implementation.
1 “Competing on Analytics,” by Thomas H. Davenport. The Harvard Business Review. Retrieved from www2.mccombs.utexas.edu.
2 “Innovation in Turbulent Times,” by Darrell K. Rigby, Kara Gruver and James Allen. The Harvard Business Review. Retrieved from http://hbr.org.
Andrew Cardno has more than 16 years of experience in business analytics, ranging from modeling health care drive times to casino gaming floor analytics. He often presents on the future of analytics across the world and has spent the last seven years living in the United States and working with corporations around the world. He can be reached at andrewcardno[at]yahoo.com.
Dr. Ralph Thomas is Vice President of Strategic Analytics and Database Marketing for Seminole Gaming. During his years in the casino industry, Thomas has focused on maximizing profitability by applying statistical analysis to the company database. Previously, Thomas spent 15 years in academia, as both a student and a lecturer of mathematics. He can be reached at ralph.thomas[at]stofgaming.com.