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Effects of Changing Table Minimums on Profits


The previous blogs highlighted a yielding framework and why operators are able to maximize profits by effective yielding. The yielding framework is reproduced below for reference.

Variables that Can Be Controlled – Dynamic or Scheduled

Impact to Financial Performance

 

In this post, we will delve deeper into how changes to one variable – namely table minimums which is a proxy for price – affect our bottom-line and player behavior, assuming all other variables remain unchanged.

We will start with a simple scenario: assume there is one Non-Commission Baccarat table open at a minimum of $5, employing a dealer who is paid $5 per hour and has eight patrons playing, wagering the following average bets.

 

 

If we assume to get 30 wagers per player (i.e. 30 rounds per hour) when eight patrons are playing, what hourly profit do we expect to make in the above scenario?

Let’s begin by calculating the total amount wagered:

  • patrons 1-4 will each wager $5*30 = $150
  • player 5-7 will each wager $25*30 = $750
  • player 8 will wager $50*30 = $1500


The resulting total hourly wagered amount is $4,950 (4*$150 + 3*$750 + $1,500). Assuming a house edge of 1.5% we can expect to have theoretical revenue of $65.25.

 

 

Now suppose we raise the minimum at this table to $25. What should we expect will happen to the patrons and how should we expect our profit to change?

First of all, patrons wagering $5 have a choice to make … do I play at a higher minimum or do I leave? If they all raised their average bet, the amount wagered from those patrons would increase by 2.5 times. It is plain to see that this outcome is unlikely and were it to happen, it is not difficult to see how our profits would increase.  

The more likely outcome is that they will leave the table (and this one table casino!) given that they now have to wager five times their current wager. Hence, it is useful to examine that scenario where all four patrons choose to leave and we lose all their wagers. We will begin by exploring the effects the bottom-line and tackle the “player experience” issue later in the post.  

With only four patrons at the table, we should see an increase in game speed – assume it is now 40 rounds per hour. Note that although the number of patrons has dropped by half, the game speed has not doubled because deal time per person is a combination of fixed time spent and time spent per player. The table below contrasts the new scenario with the original:

 

 

A few things should stand out when we compare the new outcome vs the old.

    • Although number of patrons dropped by 50%, the total amount wagered increased by about 15%
    • The percentage increase in hourly profit of 24% was larger than the percentage increase in theo of 15%

 

This second point is extremely important to understand. Every incremental dollar of increased revenue flows through directly to the bottom line. In other words, there is tremendous operating leverage in price effects and this leverage gets amplified when we add effects of gaming taxes.

 

 

Assuming a 25% gaming tax, the same increase of 15% in the amount wagered results in a 31% increase in after-tax profits.

This approach also works for price reductions. If instead of $5, we had the table at a $50 minimum. Would that be more profitable? Assuming a further increase in game speed due to lower occupancy of 60 wagers / player / hour, here is what that configuration would look like vs the $25 minimum scenario.

 

 

This scenario is less profitable indicating that there is a limit to how high tables could be priced. The 50% higher game speed at a really high average bet, is not enough to offset the total amount wagered. Clearly lowering the price would be beneficial in this scenario. Now that I have illustrated that there is an optimal price – which is different from the highest price – let me come back to the issue of turning patrons away.

Remember when we raised our minimum from $5 to $25 the four $5 average bet patrons had to choose between raising their average wager by five times or stop playing? We know that decision was more profitable for us but was it a good idea from a “player experience” standpoint?

The short answer is it depends.

As far as the four patrons with the $5 average bet who left because they could no longer play, it’s safe to assume that from their point of view it was “a bad experience”. However, the other four patrons were able to continue to play at their average bets. In fact, their game was faster than before so required less waiting. I would argue that counts as “better game experience”.

So the question becomes do you want to sacrifice the game experience of your high-value patrons for improving the gaming experience of your lower value patrons? We have ignored scenarios where the $5 table is packed with $5 patrons when the three $25 patrons walk in and do not get a seat at the table. How does that impact the business? How easy will it be to persuade those patrons to come back?

Unfortunately, there are no simple answers to these questions. Rather, as operators:

  • We can only make educated trade-offs and should be prepared to test, measure, and react appropriately to our patron demand.
  • Effective changes in table limits (or price) is the only tool in our arsenal that is always available to us. Unlike capacity and labor, there is no constraint on price, other than having a way to know what the right price to be set is in any scenario.
  • How we set the price can make a drastic difference in not only our bottom line but also ensuring we do not unintentionally sacrifice the gaming experience of higher value patrons for that of the low-value patrons.

 


Author:

Varun Nayak
As Tangam’s Senior Vice President of Gaming Strategy, Varun has over 15 years of experience in technology, finance, and casino analytics. Previously, he oversaw gaming optimization and established analytical processes to measure and improve profitability at Sands China in Macau and Caesars Entertainment in Las Vegas. Varun is a computer scientist by training and received his MBA from UCLA Anderson in Finance and Strategy.