KXEN is:
A business tool for advanced analysis
Descriptive and predictive models for marketing, risk, finance, sales, operations …
Quick, but not dirty
Based on revolutionary mathematical theory and driven by a Scientific Steering Committee
Capable of being integrated to your technologicalenvironment
Databases, text files, SAS, SPSS – almost everything can a data source with no duplication
Easy to learn and easy to understand
Minimum learning curve with results presented in an open and friendly way
A powerful tool to automate activities with low added value
Data preparation, variable selection and model testing can be speed up

Mathematical Theory
Behind KXEN by Michel BERA, KXEN CSO
This document aims at providing a deeper overview of KXEN embedded scientific theory (Learning Theory) and KXEN VC Dimension.
KXEN algorithms are based on Vapnik’s Theory of Modelization, and on Vapnik’s special strategy to build an optimal model: Structural Risk Modelization (SRM).
A model is a function f that will use attributes (X1,..,Xn) to produce a value Y = f(X1,..,Xn).
If Y={1,-1} the model is called a classifier, if Y is a numerical value, then the model is called a regression or a score .

The Challenge
With the increased fragmentation of the consumer market and ever decreasing costs to switch, it is easer than ever for your customers to choose to replace your products and services with those from your competitors. The challenge you face is how to retain the most profitable customers and how to do that at the lowest cost.

The Challenge
When planning a new campaign for new customer acquisition you will naturally want to analyze which customers are most likely to respond to a particular offer. However to maximize profits for the campaign there is the need to do further analysis to optimize the waves of the campaign for the best response across all combinations of customer segment, product, channel and region.

The Challenge
When planning a new campaign for cross-sell or up-sell you will naturally want to analyze which customers are most likely to respond to a particular offer. However to maximize profits for promotions or special offers there is the need to do further analysis to determine who would buy the product without the special offer and who requires the offer in order to buy.