Predictive
Analysis is the process of making predictions about future or unknown events
using a variety of techniques from statistics, modelling, machine learning, and
data mining that analyse current and historical facts. Using historical data, a
predictive model can identify risks and opportunities for us. Relationship
between various factors can be captured which helps in the assessment of risk
or potential associated with a particular set of conditions, guiding decision
making.
Generally,
the term predictive analytics is used to mean predictive modelling, "scoring" data
with predictive models, and forecasting.
However, people are increasingly using the term to refer to related analytical
disciplines, such as descriptive modelling and decision modelling or
optimization. These disciplines also involve rigorous data analysis, and are
widely used in business for segmentation and decision making but have different
purposes and the statistical techniques underlying them vary.
Predictive
models analyse past performance to assess how likely a customer is to exhibit a
specific behaviour in order to improve marketing effectiveness. Predictive
models often perform calculations during live transactions in order to guide a
decision.
Descriptive
models quantify relationships in data in a way that is often used to classify
customers or prospects into groups. Unlike predictive models that focus on
predicting a single customer behaviour (such as credit risk), descriptive
models identify many different relationships between customers or products.
Decision
models describe the relationship between all the elements of a decision — the
known data (including results of predictive models), the decision, and the
forecast results of the decision — in order to predict the results of decisions
involving many variables.
Predictive
analytics has many applications. Some of them are
- · Analytical Customer Relationship Management
- · Clinical Decision Support Systems
- · Collection Analytics
- · Cross-sell
- · Customer retention
- · Direct marketing
- · Fraud detection
- · Portfolio, product or economy level prediction
- · Risk management
- · Underwriting
The
approaches and techniques used to conduct predictive analytics can broadly be
grouped into regression techniques and machine learning techniques.
- · Regression techniques
- · Linear regression models
- · Discrete choice models
- · Logistic regression
- · Multinomial logistic regression
- · Probit regression
- · Logit vs probit
- · Time series models
- · Survival or duration analysis
- · Classification of regression trees
- · Multivariate adaptive regression splines
- · Machine learning techniques
- · Neural networks
- · Multilayer perceptron
- · Radial basis functions
