Why Drop, Win, and Hold % Are Not the Most Useful KPIs to Evaluate Table Games Performance
Key Performance Indicators (KPIs) measure how effectively a business is achieving its objectives and help understand what can be changed to make it better. For table games, the three most common KPIs used are Drop, Win and Hold%. This blog post analyzes these KPIs in terms of their effectiveness in affecting change in table operations.
Let’s begin by outlining possible characteristics of good KPIs:
- Controllable: A good KPI is one that we can directly influence through our actions. The less directly we can affect a KPI, the more problematic it will be to know why it changed.
- Consistent: A good KPI should be consistent, i.e. similar actions should yield similar results. It is hard to learn from a KPI if the same action results in different outcomes. We use the knowledge of how a KPI varies with our actions to learn and make better decisions.
- Quantifiable: A good KPI can be calculated and the calculation needs to be stable and meaningful. If we cannot quantify it, we cannot affect it. However, quantifiable should not be confused with precision because it is better to be approximately right than to be precisely wrong.
- Unmanipulable: Lastly, a good KPI should be difficult to game. If a KPI can be manipulated, it will be as people generally follow the principle of least effort which postulates animals, people, and even well-designed machines will naturally choose the path-of-least resistance or “effort”.
Now that we understand the necessary characteristics of a good KPI, let’s analyze Drop, Win and Hold through that lens.
Drop can be considered the intent to play and depends on the number and quality of players on the floor. As operators, we do not influence how many players, and of what quality, are on the floor. The type of service we provide does impact whether patrons visit us again but incentives, amenities, location, etc. have a far larger influence on the Drop and therefore, it is uncontrollable by operations. There are factors outside of gaming operations like Marketing and Promotions that can influence Drop.
Drop is also not consistent in that a high Drop does not always mean we as operators made the right decisions. An example of that is the occurrence of false Drop, i.e. Drop not intended to play, which happens when a patron buys in, plays for a while, then cashes out their chips and then re-buys in at a later point in time. This is a common occurrence and makes increases in Drop unreliable as a KPI.
Drop is certainly quantifiable. It is audited by Finance and cannot be manipulated. However, given that as operators there is not much we can do to increase Drop and if there is an increase, it is difficult to tie back to specific actions we took. Drop is not a sound KPI.
Win is the sum of all the wagers won by a casino minus all the wagers lost by a casino.
Every bet made in theory has a positive edge. That implies the expected return on every bet made is positive for the casino and negative for the patron. However, that does not mean that we can predict the outcome of any single bet ahead of time. On the contrary, the effect of a single large bet won or lost by a casino may take a long time to undo. This time can be days, weeks or even months.
In short, Win is not directly controllable in the short run and it is certainly not consistent since there is no way to know ahead of time what to expect from the patron betting, i.e. how much they’ll bet, as well as what the outcome of the bet will be.
Although just as in the case of Drop, Win is precisely quantifiable and cannot be manipulated, it still falls short as a good KPI that will drive the right behaviors and lead to better understanding of ways to improve the operation.
As operators, we have control over how much of the Drop we convert into Win when tracked over longer periods on time. This may be why operators prefer tracking Hold % as a KPI.
Hold is simply the ratio of Win to Drop and can be thought of as what proportion of Win translates to Drop. It is by far the most universally used KPI in the industry.
By definition, Hold depends on two variables: Win and Drop. We have shown both to be lacking in effectiveness as KPIs due to insufficient control and inconsistency. So it stands to reason that a KPI derived from two variables, which cannot be controlled and are not consistent, must then be uncontrollable and inconsistent too.
However, unlike Win and Drop, Hold can be manipulated. For example, Hold% can be increased simply by excluding Cage Drop or by measuring Gross Drop vs Net Drop. It is easy to see how we measure Hold has no bearing on how good or bad the operation is. In that sense, Hold is relatively a worse KPI when compared to Drop and Win.
Let us be clear though, it is not that Hold is meaningless; it does tell us about customer quality variances across operations. What we are saying is it is unclear what actions we should take and how they would directly translate into higher Hold and hence as a KPI, it falls short.
To conclude, good KPIs must be controllable, provide consistent feedback to changes being made, be measurable within reason and should be difficult to game. Based on that criteria Win, Drop and Hold fall woefully short.
Operators should focus on variables they control and can measure like capacity, open hours, game-mix and pricing to understand how changing those KPIs will improve financial performance.
A casino is profitable because it follows the process of taking countless bets with a positive expected value. On any given bet, the casino does not know if it will win or lose but following the process makes it profitable over the long run. In the same way, as operators, we should follow the process of measuring and affecting variables we control and improved financial performance will follow.
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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.