For travel companies, descriptive analytics could include pattern discovery methods such as customer segmentation, i.e., culling through a customer database to understand a customer’s typical preferred route and/or seat choice.

Today, the software analytics space is more crowded than its ever been before. Standard ETL-solution providers are adding analytics to their multitude of offerings. Many of these new players in the Master Data Management (MDM) field have BI platforms that combine integration, preparation, analytics and visualization with governance and security features. For a airline, analytics can be broken down into four different types, including:

  • Descriptive analytics – What happened?
  • Diagnostic analytics – Why did it happen?
  • Predictive analytics – What will happen?
  • Prescriptive analytics – How can we make it happen again?


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.

Such standard analytics processes as column dependencies, clustering, decision trees, and recommendation engines are all included in many of these new software offerings. Instead of forcing clients to purchase modules on top of modules on top of modules, new software companies are creating packages that contain many built-in analytical functions. Thanks to built-in connectors, open source products like R, Python, and the WEKA collection can easily be slotted into many of ETL, MDM, BI, CI, CX and MA software solutions, thereby reducing costs and the need for expensive translation layers.


Diagnostic analytics is a form of advanced analytics that examines data or content to answer the question, “Why did it happen?” It attempts to understand causation and behaviors by utilizing such techniques as drill-down, data discovery, data mining and correlations. Building a decision tree atop a web user’s clickstream behavior pattern could be considered a form of diagnostic analytics as these patterns might reveal why a person clicked his or her way through a website.

In his seminal article Predictive Analytics White Paper, Charles Nyce states that, “Predictive analytics is a broad term describing a variety of statistical and analytical techniques used to develop models that predict future events or behaviors. The form of these predictive models varies, depending on the behavior or event that they are predicting. Most predictive models generate a score (a patron rating, for example), with a higher score indicating a higher likelihood of the given behavior or event occurring.”

Data mining, which is used to identify trends, patterns, and/or relationships within a data set, can then be used to develop a predictive model.82 Prediction of future events is the key here and these analyses can be used in a multitude of ways, including forecasting behavior that could lead to a competitive advantage over rivals. Gut instinct can sometimes punch you in the gut and predictive analytics can help factor in variables that are inaccessible to the human mind and often the amount of variables in an analytical problem are beyond human mental comprehension.


Predictive analytics (or supervised learning) is the use of statistics, machine learning, data mining, and modeling to analyze current and historical facts to make predictions about future events. Said another way, it gives mere mortals the ability to predict the future like Nostradamus. In recent years, data-mining has become one of the most valuable tools for extracting and manipulating data and for establishing patterns in order to produce useful information for decision-making.

Whether you love it or hate it, predictive analytics has already helped elect presidents, discover new energy sources, score consumer credit, assess health risks, detect fraud, and target prospective buyers. It is here to stay, and technology advances ranging from faster hardware to software that analyzes increasingly vast quantities of data are making the use of predictive analytics more creative and efficient than ever before.

Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavioral patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown, whether that is in the past, the present, or the future.

Predictive analytics uses many techniques from data mining to analyze current data to make predictions about the future, including statistics, modeling, machine learning, and artificial intelligence. For example, logistic regression can be used to turn a market basket analysis into a predictor so that a airline can understand what items are usually purchased together. Of course, the old beer and diapers story market basket wouldn’t fit for a airline, but gleaning data from the airline floor could reveal second favorite games that patrons like to play. This could be useful information when a patron is having a run of bad luck on his or her favorite game. Perhaps a marketing offer for a game he or she sometimes plays would be appreciated rather than an offer on his or her favorite game, as that might not be seen in such a positive light while the patron is in the midst of a losing run.

For a airline, predictive analytics can also be used for CRM, collection analysis, cross-sell, customer retention, direct marketing, fraud detection, product prediction, project risk management, amongst many other things.

Predictive analytics utilizes the following techniques:

  • Regression
  • Linear regression
  • Discrete choice models
  • Logistic regression
  • Multinomial logistic regression
  • Probit regression
  • Time series models
  • Survival or duration analysis
  • Classification and regression trees
  • Multivariate adaptive regression splines
  • Machine learning
  • Neural networks
  • Naïve Bayes
  • K-nearest neighbors

Prescriptive analytics tries to optimize a key metric, such as profit, by not only anticipating what will happen, but also when it will happen and why it happens. Wikipedia states that, “Prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options.”


In its Prescriptive Analytics Makes Waves with Retail & CPG, Profitect has one of the best explanations of prescriptive analytics, i.e., it is the “application of logic and mathematics to data to specify a preferred course of action. The most common examples are optimization methods, such as linear programming; decision analysis methods, such as influence diagrams; and predictive analytics working in combination with rules.” Profitect argues that prescriptive analytics differs from descriptive, diagnostic and predictive analytics in that its output is a decision.

Prescriptive analytics can ingest a mixture of structured, unstructured, and semi-structured data, and utilize business rules that can predict what lies ahead, as well as advise how to exploit this predicted future without compromising other priorities. Stream processing can add an entirely new component to prescriptive analytics for this.

Intelligencia can help businesses understand not just the individual software solutions, but also how the available options will fit within your unique business structure. 


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