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Analytics in Practice: 4 Types and How to Use Them
Nov 12, 2018
The term “Analytics” has been around for ages and although most operators the importance of using their data, most are not aware of how analytics have evolved with technology. There is an almost unlimited potential for operational improvement using analytics, but it can be hard to know how to use it or where to start.
The key to unlocking your analytics potential is understanding that there are different types for different purposes. Global Gaming Business magazine outlined four types in a recent article and today we cover how understanding and leveraging these types can help your business succeed.
A first step might be just looking at the data you have available. Most technology solutions today will give you basic summaries such as daily operational reports. This would be an example of descriptive analytics. It can be useful for operators to get an overview of the day or week and analyzing the data a little further can yield more value.
Working with your data and analyzing patterns helps answer important questions. For example, you want to know ‘Why did we have fewer guests last week than usual?’. Using diagnostic analytics, you might discover an underlying cause like a competitor’s extraordinary customer offer or inclement weather. Diagnostic analytics is useful when answering managers’ questions about why the operation is doing worse or better than expected. However, it is still focused on historical events and understanding what has already happened. Now let’s look ahead to the next type of analytics.
With the right kind of data analytics tool, aspects of future scenarios can be predicted. Looking at your existing data, the tool might predict how the business might perform next season or even how much a guest is expected to spend during their next visit. Using still newer, intelligent technology, such as machine learning, this process can become even more efficient and accurate.
The last and most advanced is prescriptive analytics. Using the existing data and based on the predictions it can uncover, these solutions are able to make recommendations that would help generate the best possible outcome. A simple example could be a weekend night in a restaurant where the manager is expecting the same number of guests as usual. A prescriptive analytics tool picks up on a pattern that suggests this weekend will be extra busy, and further recommends additional staff. As a result, the restaurant will be running smoother and generating more revenue.