Top 4 Reasons Why Table Games Operators Fail to Leverage Analytics
“Data is to this century is what oil was to the last one: a driver of growth and change. Flows of data have created new infrastructure, new businesses, new monopolies, new politics and — crucially — new economics. Digital information is unlike any previous resource; it is extracted, refined, valued, bought and sold in different ways.” – The Economist, May ‘17.
The data analytics advantage is real and tangible and most operators believe that value can be extracted from data. Then why is it that many aren’t able to exploit their data and turn it into a competitive advantage?
In this blog post, I delve deeper into this question and highlight the four reasons why operations fail to leverage analytics and how they could unlock the full potential of their “new oil” — unmined data.
While the examples are specific to table games operations and yield management, the principles hold true for any department or industry.
1. Value of Data is not Quantified
Most operations already have a Casino Management System (CMS) in place for their hospitality data. When operational data is manually entered by supervisors into the CMS, one of the most common objections in leveraging this data for yield management is “garbage-in-garbage-out” (GIGO).
However, many operators aren’t able to quantify to what extent this is a problem. Advances in data science allow us to study data quality and robustness. Without studying the data, the GIGO theory will remain what it is — an opinion that is not based on data. What’s worse? The same data is already used for patron loyalty programs, which makes it even more imperative to study.
Once the value of the operational data, that is already being collected, is quantified, better capital investment decisions can be made — whether it is a low-cost regular audit process to measure data quality or high-cost sensors to automate data capture.
2. Disproportionate Amount of Time Spent on Reporting vs Analytics
The operators who haven’t quantified the value of the data are hesitant to invest in more sophisticated tools and technology that can mine that data. Instead, they rely on the ubiquitous but inadequate software such as spreadsheets and manual analysis along with a few simple dimensions.
On the other hand, the operators who realize the potential of their data use more advanced tools that allow them to better execute their strategy. These tools typically automate reporting and visualization, while maximizing the benefit from human judgment to make far better decisions.
However, it is important to differentiate between reporting and analytics. Reporting focuses on hindsight i.e. “what happened” while analytics focuses on foresight i.e. “how can we make it happen”. Furthermore, action-oriented analytics are the most valuable e.g. “what specific operational lever can I pull now to improve my table games EBITDA margin by 2% next quarter?” They are also the most difficult because it requires specialized software tools and modeling. We explain this in more depth in this blog post.
The right combination of data and software tools will generate new insights and opportunities, but these decisions are driven by staff.
3. No Access to Information when it’s Relevant
The primary challenge, when it comes to managing table games yield, is having access to the right information at the right time. For example, reporting on a Monday morning that we could have opened 10 more tables, or priced table games 25% higher on the previous Friday night due to an unexpected surge in demand adds no value. This also hurts the guest experience and the resort brand if patrons couldn’t find a seat at the table.
A better approach would be an automated alert sent to the floor manager at 8pm on Friday night indicating that there is a sustained surge in demand and they should either bring in more staff, reallocate existing staff to the tables that are currently closed, or increase price to service the most profitable segment first.
Table games management is already complex and patron demand is dynamic and fluctuates throughout the day. Giving the right people the tools they need to make better data-driven decisions will result in a better and more consistent guest experience and better alignment of management strategy across the operation.
4. HiPPOs: When Opinions Override Data
Lastly and probably the biggest challenge to an organization is the HiPPO – Highest Paid Person’s Opinion; senior executives who come with decades of experience and have a strong influence on decisions. HiPPOs are detrimental when their opinion overrides any data-driven analytics that contradicts their gut feelings. As this approach percolates downstream, associates are taught to rely on their experience even when it is at odds with the data. This culture creates barriers to success.
There are multiple facets of forging a culture where an individual opinion does not dominate over data analysis:
- analytics should be seen as an important tool that facilitates unbiased decision making
- leadership should be willing to override intuition if the data disagrees
- a culture of experimentation should be adopted: test a decision, create success criteria and measure results
- all the departments: marketing, workforce planning, operations, and analytics should be aligned with the same vision and analytics strategy
Leveraging analytics requires effort and breaking old habits. Once the value of data is quantified, the right effort and resources can be allocated to leverage the data. Software and analytics tools should have an equal, if not higher, emphasis on action-oriented decisions instead of just reporting.
Operators that figure out how to effectively combine their expertise with data analytics will reap rich dividends and will pull away from their rivals.
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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.