INTICS - Intelligent Analytics
INTICS - Intelligent Analytics
INTICS - Intelligent Analytics
  About Us
INTICS - Intelligent Analytics Technology
INTICS - Intelligent Analytics Outsourcing
INTICS - Intelligent Analytics Consultancy
INTICS - Intelligent Analytics Predictive modelling
INTICS - Intelligent Analytics Econometrics
INTICS - Intelligent Analytics Decisioning
INTICS - Intelligent Analytics INTICS - Intelligent Analytics Risk Management
INTICS - Intelligent Analytics Securities
INTICS - Intelligent Analytics CRM
INTICS - Intelligent Analytics UK
INTICS - Intelligent Analytics Bulgaria
INTICS - Intelligent Analytics
INTICS - Intelligent Analytics

Predictive Modelling - Overview

Predictive analytics is a data mining technology that uses your customer data to build a predictive model specialised for your business. This process learns from the experience by leveraging history of customer purchases, behaviour and demographics. Predictive modelling is the process by which extrapolating the present a model is created or chosen to try to best predict the probability of an outcome, decision or event.

Predictive analytics applications come in a variety of forms, but most business applications fall under one of the categories described below.


Decision Trees

Decision Trees are powerful tools in decision making. They can be used for both classifying objects into pre-defined groups, or to predict the outcome of a target variable that takes on numeric values - all known as Classification and Regression Trees (CART). These techniques successively partition the data to predict the desired outcome. The resulting tree contains a set of nodes, where observations within the nodes are similar based on the specified measure, whereas observations in different nodes are dissimilar with respect to the same measure. The objective for a decision tree is stated in terms of identifying subsets of data that are most dissimilar along some outcome variables. An alternative to using classification trees would be logistic regression. Logistic performs the same classification functions but also indicates the significance of a variable by the value of corresponding coefficients.


Clustering models are used to group objects into clusters so that objects in the same clusters are more similar to each other than to objects in other clusters. These models are useful when there are competing patterns in data, making it hard to spot any single pattern. They can also be used as a precursor to developing other models. Data can be segmented into clusters of similar records, thus reducing complexity within clusters so that other data mining techniques are likely to succeed. Clustering is typically used to segment customer data into groups of similar customers, based on their purchase patterns, demographics or attitudes. Customized scorecards can then be built for each of these segments.


Scorecards are commonly used to rank order customers based on their likelihood of exhibiting a specific behaviour. Companies can devise business rules to use the scorecards in automating a business process. For instance, threshold scores can be set to automate approval of credit limit increases. Customers scoring below a certain pre-set threshold can be referred to collections for proactive collections action.

Neural Networks

Neural Networks are learning models that can make predictions at incredible speeds based on quantification and replication of highly complex, non-linear patterns in the data. They have predominantly been used in situations that require real-time or near real-time response in identifying subtle data patterns, where less emphasis is placed on what the model is doing than on how well it is doing it. Neural Networks are often used by our consultants in applications that require real time response to unknown behavioural situations.

INTICS Group © 1997-2017
All rights reserved.
Terms and Conditions | Home | Contact