Descriptive analytics is a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis. Data aggregation and data mining methods organize the data and make it possible to identify patterns and relationships in it that would not otherwise be visible. Querying, reporting and data visualization may be applied to yield more insight. Descriptive analytics is sometimes said to provide information about what happened. You might see, for example, an increase in Twitter followers after a particular tweet. 

For a casino company, descriptive analytics could include pattern discovery methods such as customer segmentation, i.e., culling through a patron database to understand a patron’s preferred game of choice.

Simple cluster segmentation models could divide customers into their preferred choice of games. This information could be given to the marketing department to create lists of baccarat players for a baccarat tournament, for example.

Market basket analysis, which utilizes association rules, would also be considered a descriptive analytics procedure. Casinos should use market basket analysis to bundle and offer promotions as well as gain insight into its patrons’ gaming habits. Detailed patron shopping and purchasing behavior could also be used to develop future products, whether they be gaming or retail related. I will go into full detail on this topic in chapter two.

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