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watch or what book you buy, and they often determine whether you are
included in a marketing campaign by your favorite retailer.
Taken together, this field is one the primary driving factors for interest in
big data and offers huge opportunities for businesses that capitalize on it
(by allowing those businesses to transform their data into a competitive
differentiator). To paint a clearer picture, let's look at three types of models
used in predictive analytics: predictive models, descriptive models, and
decision models.
Predictive Models
Predictive models use historic data to make predictions on how likely a
defined behavior or action is to occur. These models are often used to
improve marketing outcomes by predicting buying behaviors and are also
often used to assess credit risks and in fraud-detection systems.
Descriptive Models
Often, a business will want to identify how groups of customers behave.
Behavior in this regards is often used to identify customers who are loyal,
profitable,oratriskforcancellingyourproductorservice.Thisisacommon
scenario used to predict and proactively handle customer churn and is one
example where descriptive models excel. Descriptive models, instead of
predicting the actions of a single customer, can identify multiple
relationships between products and customers and are used to categorize
like customers together.
Decision Models
The basis of a decision model is to improve the outcome of some
well-defined business decision, taking into account multiple variables. This
model uses well-known data (often included historical decision-making
data), the business decision and the probability of some result based on the
given variables or factors. The results of these models are often used to write
business rules found in the line-of-business applications.
With a high-level understanding of the core concepts behind data mining
andpredictiveanalytics,wecanturnourattentiontolookingathowMahout
allows you to easily integrate some of these techniques into your big data
architecture.
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