Modeling and Analytical Methods

Our modeling and analytics methods consist of:

Principal Component Analysis

This combines multiple explanatory variables into a representative few for better understanding underlying phenomena, i.e. Exploratory analysis of data sets. The principal use cases include:




Correlation Analysis

Here, we identify linear inter-relationships among variables during exploratory analysis. This is useful for producing pricing analytics




Association Rule Mining

This finds close relationships between two sets of occurrences/events (identify market-basket), and is used for cross-sell/up-sell of products


K-means clustering

Here we segment based on transaction behaviour or similarity of customer attributes, and works for Product Pricing Analytics and portfolio insights

Logistic & Linear regression

This is a generalized linear model for classification of events used in Product pricing analytics, Credit scoring, Bad Debt prediction, churn analysis

Classification and Regression Trees

A classifier which builds a decision tree based on historic examples basis which new cases can be predicted. This can be used for customer response to dunning actions and Churn Prediction

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