Approaches to Yielding
In the last blog, we made the case for the strategic benefits of yielding. Those were qualitative arguments for why yielding is a good idea to prioritize high profitability segments in times of constraints. In this post, we want to lay out a framework for yielding.
In our experience, operators have one of the following approaches to yielding a gaming floor:
Labor Minimization: In this approach, an operator tries to minimize labor used. The result is that operators aim to maximize occupancy per table by keeping tables full. The rationale is that labor savings do not affect revenue.
Revenue Maximization: In this approach, for a given demand, the operator tries to generate the most revenue. This is usually accomplished by spreading patrons out as much as possible – essentially minimizing occupancy per table. The implicit assumption is that more revenue will generate more profits.
Profit Maximization: This approach aims to generate the most profit for a given demand. It is based on the idea that there is an optimal utilization or occupancy for each segment, that if achieved, will generate maximum profits. It combines the above two approaches with an important caveat; labor is not reduced if it reduces revenue by more than gains from saved labor and revenue is not maximized unless there is a commensurate incremental profit.
Let us contrast these approaches using an example.
Assume that a casino has the following demand by price for a given hour:
Furthermore, to keep things simple, assume it is the same game - Blackjack - with a house edge of 1.1%, maximum occupancy of 6, and following rounds per hour based on occupancy.
Rounds per hour is a function of both the game type and the SOPs in place. If the cost to open a table for one hour is $25 and ignoring gaming taxes to keep things simple, we get the following scenarios for each of the approaches outlined above:
Some key insights should stand out from the above comparison:
1. It is clear what action to take in the labor minimization approach (don’t open more tables unless current tables are full) and in the revenue maximization approach (have as many tables open as possible) but not so much in the profit maximization approach. This is an important distinction which creates the need for specialized tools to manage a profit maximization approach.
2. Labor minimization approach leads to the worst outcome. This is because the gains to be had by spreading patrons and improving the number of decisions, and hence, turnover are not directly visible. The cost savings on the other hand are, and that is what prompts a lot of operators to take this approach.
3. Profit maximization approach is able to capture almost all the revenue without sacrificing costs. That is not a coincidence. Rather, the approach dictates that any action taken generate incremental marginal profit which forces opening/closing tables such that the action generates maximum total profit.
4. The above analysis assumes that time on device is not a function of occupancy – a necessary assumption to simplify the analysis. The reality is that wallet-constrained players time on device is far more sensitive to changes in occupancy compared to that of time-constrained players which makes the profit maximization approach even more favorable. We’ll explore this topic in future posts.
The logic behind the profit maximization approach is fairly easy to understand and can be visualized using the following framework:
Variables that Can Be Controlled – Dynamic or Scheduled
Impact to Financial Performance
It is important to keep in mind that the choices we make in one area affect other variables including player behavior. For example, the type of games, number of tables, and average minimum affects the occupancy and the average bet. The occupancy affects the game pace and players’ time on device, which in turn, affects total revenue. The financial performance is the net result of how these variables interact with an addition of some volatility (more on that in future posts).
In our next post, we’ll delve deeper into how changes to each variable affect the bottom line. However, at this point suffice it to say that as operators we can control four levers – namely – price, capacity, labor, and house edge (product) and how we manage those four variables directly impacts the financial performance of our gaming floors.
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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.
As one of the founding executives, Maulin currently serves as the President of Tangam Systems. With over fifteen years of experience helping operators improve business performance with data science, Maulin has overseen Tangam’s growth to the global leader in Table Games analytics. He received his Computer Engineering Bachelor’s and Master’s from the renowned McGill University in Montreal, specializing in Artificial Intelligence.