Predictive analytics uses techniques like data mining, statistics, and game theory to analyze current and historical data to predict future behaviors and outcomes. Predictive models examine patterns in data to identify opportunities and risks. Basic predictive analytics techniques include data profiling/transformations, sequential pattern analysis, and time series tracking. More advanced techniques include time series forecasting, Bayesian analytics, regression, classification, dependency/association analysis, simulation, and optimization. These techniques are used across various domains to predict things like customer purchase behavior, product satisfaction, budget analysis, and workforce projections.

1. Performance management analytics
Predictive analytics is a solution used by many businesses today to gain more value out
of large amounts of raw data by applying techniques that are used to predict future
behaviors within an organization, it's customer base, it's products and services. Predictive
analytics encompasses a variety of techniques from data mining, stastics and game theory
that analyze current and historical facts to make predictions about future events.
Predictive models examine patterns found in historical and transactional data to identify
opportunities and risks. Predictive models capture relationships among many factors to
allow assessment of risk or potential associated with a particular set of conditions,
guiding decision making for candidate transactions.
There are some basic and more complex predictive analytics techniques. Three basic
techniques include:
Data Profiling and Transformations
Sequential Pattern Analysis
Time Series Tracking.
Data profiling and transformations are functions that analyze row and column attributes
and dependencies, change data formats, merge fields, aggregate records, and join rows
and columns.
Sequential pattern analysis discovers relationships between rows of data. Sequential
pattern analysis is used to identify frequently observed sequential occurrence of items
across ordered transactions over time. Such a frequently observed sequential occurrence
of items (called a sequential pattern) must satisfy a user-specified minimum support.
Understanding long-term customer purchase behavior is an example of the sequential
pattern analysis. Other examples include customer shopping sequences, click-stream
sessions, and telephone calling patterns.
Time series tracking tracks metrics that represent key behaviors or business strategies. It
is an ordered sequence of values of a variable at equally spaced time intervals. Time
series analysis accounts for the fact that data points taken over time may have an internal
structure (such as autocorrelation, trend or seasonal variation) that should be accounted
for. Examples include patterning customer sales that indicate product satisfaction and
buying habits, budgetary analysis, stock market analysis, census analysis, and workforce
projections.
More advanced predictive analytics techniques include:

2. Time Series Forecasting
Data Profiling and Transformations
Bayesian Analytics
Regression
Classification
Dependency or Association Analysis
Simulation
Optimization
Time series forecasting predicts the future value of a measure based on past values. Time
series forecasting uses a model to forecast future events based on known past events.
Examples include stock prices and sales revenue.
Data profiling and transformation uses functions that analyze row and column attributes
and dependencies, change data formats, merge fields, aggregate records, and join rows
and columns.
Bayesian analytics capture the concepts used in probability forecasting. It is a statistical
procedure which estimate parameters of an underlying distribution based on the observed
distribution. An example is used in a court setting by an individual juror to coherently
accumulate the evidence for and against the guilt of the defendant, and to see whether, in
totality, it meets their threshold for 'beyond a reasonable doubt'.
Regression analysis is a statistical tool for the investigation of relationships between
variables. Usually, the investigator seeks to ascertain the causal effect of one variable
upon another-the effect of a price increase upon demand, for example, or the effect of
changes in the money supply upon the inflation rate.
Classification used attributes in data to assign an object to a predefined class or predict
the value of a numeric variable of interest. Examples include credit risk analysis,
likelihood to purchase. Examples include acquisition, cross-sell, attrition, credit scoring
and collections.
Clustering or segmentation separates data into homogeneous subgroups based on
attributes. Clustering assigns a set of observations into subsets (clusters) so that
observations in the same cluster are similar. An example is customer demographic
segmentation.
Dependency or association analysis describes significant associations between data items.
An example is market basket analysis. Market basket analysis is a modeling technique
based upon the theory that if you buy a certain group of items, you are more (or less)
likely to buy another group of items.
Simulation models a system structure to estimate the impact of management decisions or
changes. Simulation model behavior will change in each simulation according to the set

3. of initial parameters assumed for the environment. Examples include inventory reorder
policies, currency hedging, military training.
Optimization models a system structure in terms of constraints to find the best possible
solution. Optimization models form part of a larger system which people use to help them
make decisions. The user is able to influence the solutions which the model produces and
reviews them before making a final decision as to what to do. Examples include
scheduling of shift workers, routing of train cargo, and pricing airline seats.
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