This session at The Cutting Edge Table Games Conference included an educational portion as well as a panel discussion. Ari Mizrachi, Tangam’s VP of Operations Optimization, presented the theory behind yield management and focused on the:

• key drivers for yield management
• right approach to yielding: Cost Minimization vs Profit Maximization
• optimal utilization and drivers
• right metrics to measure yield on

Below is a video clip where Ari introduces yield management for table games and sets the tone for the rest of the session.

After the yield management presentation, the panel discussion began which would be a hands-on discussion on how operators can optimize their approach to table games. The panel was moderated by Ari and included industry experts:

• Mike May, Vice President of Table Operations, Pechanga Resort & Casino
• Brad Waldron, Group Head of Table Game Optimization, Crown Resorts
• Bill Zender, Zender & Associates
• Varun Nayak, SVP of Gaming Strategy, Tangam Systems

The panel was the second half of the session and below is a summary of some of the discussion.Ari: Bill, what are some of the challenges that your clients have faced with yielding and what have you seen differently amongst different operators, both big and small?

Bill: One of the biggest problems was mentioned previously with the phrase ‘garbage in, garbage out’ and all the metrics that are used for making decisions. If you’re not entering accurate data, you’re not going to reach your maximum revenue opportunities, no matter what system you use. Operators need to know the math behind the systems and what they’re trying to accomplish.

Also, if you’re relying on various systems, you have to be able to override them or have it take into consideration real-time changes. If something atypical happens and you need to open a new table, the system needs to be able to make the necessary adjustments.

Many of these systems can be great assets within a property as long as the foundation is in place. What I found traveling all over the country as a consultant is that many of these operators don’t have that foundation and it’s inevitable that it will fail, or at least, not produce the expected results.

Ari: Mike, you manage a large property. How are you able to get the 50+ Pit Managers to execute your strategy?

Mike: I’ve been in this type of role for a long time and have helped develop a variety systems. However, it needs to be in real-time as giving me something a week later doesn’t make any sense.

In my current role, we developed a check and balance type of system and the key to battle the ‘garbage in, garbage out’, was training. By ensuring they had a good understanding of all the metrics and data, they would be able to effectively utilize the systems. To do this, each Pit Manager needs to provide a report before they leave for the day. Within that report is their pricing and utilization for the games that they handled that day and their justification for why they made the changes they did. This forces them to do the work in real-time. But ultimately, whatever system you have, someone has to be looking at it all day like the Director, the Shift Managers, or whoever. You need to be able to look up information and determine, were the correct decisions made but at the same time, be able to manage on your feet on the floor.

Ari: Varun, you worked at two large properties where they relied on the data from supervisors. What is your opinion on this?

Varun: I fully believe that there is a rightful place for making judgment based decisions and the purpose of technology isn’t to replace judgment but to augment judgment in places where technology has no way of providing incremental value. Part of what the previous panel talked about is that you have to decide for yourself what you are as a property and part of that decision is always a judgment. If you want to make more money, there is a way to do it at the expense of something else but if you want to make less money, there is a way to do that. There is a way to quantify that opportunity cost. I do think of this as either choosing A or B and it will depend on what you choose to be and anything you choose to be, you’re going to also give up on something else.

Going back to the comment of ‘garbage in, garbage out’, I think a good comparison to illustrate the point is life insurance. If 100 people in my age group buy life insurance, the company doesn’t know when I’m going to die but in that group of 100 people, x amount of people will die and that is how they base their rates to make money. There is a way to effectively utilize the data that supervisors put in, assume there are errors in it, figure out where the errors and biases are and from there, extract the information such that the information can be used to make better decisions. The second part is that having data that is complete garbage is a problem that should be addressed. But if data is being used for certain pieces but not for yielding, that is also problematic.

Ari: Many people within the casino industry started at the dealer level and progressed upwards. Some went to school, some took additional training, but many at the managerial level have no idea as to what makes money and how much. There are so many distractions on the floor so without the data validating the decisions being made on the floor, there is no way to understand what is working best.

Brad: To add to that point, we do consistent training with employees. For dealers, after six months, we bring them back for a full day’s training. We provide them with more information rather than taking the approach of just deal cards or spin the ball. At the five year mark, they’re brought in for further training and provide more detailed insight into the business. For shift managers and floor staff, I currently have about 330. They are part of presentations where we are very transparent and share many details about the business and why things are done the way they are. We have four levels of presentations from the dealer all the way to the decision makers so that we’re all aligned and understanding what we’re trying to achieve and why.

Any real-time prompts force the floor staff to think of what should be done. Floor staff is directed to use the tools given to them, then look at the floor and make decisions on gut instinct based on what they see and what information they are given by the software tools. There is a piece that technology will never replace and it’s the human element on the floor.

Key Takeaways:

• It’s important to segregate demand by price, either time-constrained or wallet-constrained players. Ideal occupancy isn’t the same for all segments; it depends on many factors (average bet, game speed, player behavior, etc)
• The goal of yielding is to prioritize the right type of demand and align strategy with operations. Profit maximization is an outcome of yielding, rather than a goal
• To effectively yield, operators should consider:
• the four levers of yielding: table capacity, table open hours, table pricing and house edge
• it is crucial to ensure all teams are aligned with the same goal so that managers are effectively utilizing the tools at their disposal to make the best decisions for the floor
• Metrics to measure yield should be both financial (Win, Drop, Hold %) as well as operational such as Win per Open Hour, Win per Table per Day, Open Hours per Table per Day, Average Minimum Bet, Average Wager and Average Utilization