Modeling and Analytical Methods
Our modeling and analytics methods consist of:
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:
- Product pricing analytics
- Credit scoring
- Bad debt prediction
- Customer Churn
- Revenue/spend forecasting
- Dunning efficiency
Here, we identify linear inter-relationships among variables during exploratory analysis. This is useful for producing pricing analytics
This finds close relationships between two sets of occurrences/events (identify market-basket), and is used for cross-sell/up-sell of products
Here we segment based on transaction behaviour or similarity of customer attributes, and works for Product Pricing Analytics and portfolio insights
This is a generalized linear model for classification of events used in Product pricing analytics, Credit scoring, Bad Debt prediction, churn analysis
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
Regression Analysis for credit scoring and bad debt prediction
Integrated solution with data modeling, visualization, reporting
Manage big data with clustering, in-memory cache and NoSQL based OLAP