HOW TO MAKE DATA-DRIVEN DECISIONS - PART II
Here are three easy steps to begin making data-driven decisions:
1. Pick the right metrics.
Most casinos get buried in the detail of 30 to 300 metrics and lose sight of the big picture. You would be shocked by how many people get so caught up in looking at the details that they forget to check to see if they generated revenue and made a profit. At the end of the day, that is what matters for the casino overall. One of the most important steps is to pick the right metrics to look at. If you aren’t sure what these are specific to your department, that’s okay. Get help from someone you know and trust. If you are the one expected to know, reach out to a friend that holds the same position at another property. Ask them, “If you could only pick three key data points on your reports to consider for operating your business, what would they be?” Don’t stop there. Ask, “Why those? What do they tell you? What is an example of those metrics raising a red flag? What is an example that shows you are operating your department well?”
Be cognizant of the size of the casino you’re inquiring about in relation to your own. Make sure you pick numbers you have access to daily — you don’t want to wait until the middle of next month to know how your department is doing this month; it will be too late to adjust and do anything about it. The rule of thumb is to make sure your metrics are tracking something your business cares about — like that it is operating as efficiently as possible, or that you are getting the most out of your departments (covers, slot fills, calls taken) while spending the least amount of money possible.
2. Ask the right questions.
Asking questions is a sign of brilliance and one of the critical steps in driving better results for your casino. Don’t let the analysts scare you! Here is a secret: asking analysts questions often makes them uncomfortable, because they are worried you will spot a mistake in their numbers. Push through the discomfort, be brave, and ask them questions until you understand what you need to. It is okay to push back on analysts’ conclusions. Here are some good questions to ask:
- What is the source of your data? If the data isn’t accurate, then you shouldn’t be going “all in” on the decisions you make from it. Knowing where the data comes from will help you get a feel for how accurate the data is. For example, if the daily revenue number is pulled from your POS system, there is a high likelihood of accuracy. If it is pulled from printed receipts and tabulated by the accounting department because the power went out that day, you might need to omit that day from the data.
- What assumptions are behind the analytics? There may be conditions that might make the analytics invalid or misleading. It is important that you know. For example, if there was a blizzard last year so massive that it shut down the casino for three days, the group shouldn’t be celebrating the big year-over-year increase shown by the data and planning to execute the same marketing efforts next year as a result.
- Does the data include outliers? If so, understand how the outliers affect the results. For example, if you had a player that hit Megabucks at your property, and it tanked revenue in February, it is important to know if that loss is included in the analysis, or if it was removed before you go celebrating this year’s results.
3. Is what I see in the data legit?
Most of us have heard the phrase “correlation is not causation,” but figuring out just what that implies when evaluating data isn’t that easy. Back to the weather example: if the property shuts down for three days and revenue plummets because of a blizzard, clearly the blizzard caused it (i.e., the relationship between the blizzard and the revenue drop is causation based). When the marketing team give away free lobster tails to players in March and sees a 7% increase in revenue, it might be causation based, or it might not. Be careful before you draw conclusions. Ask yourself, “What else could be the reason for the increase? Is the economy up as a whole? Is a competitor’s casino temporarily shut down?"
The best way to know for sure is to maintain a control group when executing marketing programs — that is a group you don’t market to — so you can get a feel for how many players would have come anyway. I understand that this is easier said than done, so, at the very least, consider that the increases or decreases in revenue and profit can be caused by things other than the obvious, so list out what those causes might be for the team to consider before you make a final decision. In other words, when is it reasonable to act based on a correlation?
Now you are armed with the basics of how to make a data-driven decision!
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About the author
Sarah helps clients meld data science and human behavior to grow revenue and increase profit. Sarah will be a panelist for the session "Predictive Analytics and Modeling - What is it? Who is Doing it and Why You Need to get there to outpace your competitors?" during Raving's 20th National Indian Gaming Analytics & Marketing Conference.