Analysis of the KDD cup 2009: Fast scoring on a large orange customer database

Abstract

We organized the KDD cup 2009 around a marketing problem with the goal of identifying data mining techniques capable of rapidly building predictive models and scoring new entries on a large database. Customer Relationship Management (CRM) is a key element of modern marketing strategies. The KDD Cup 2009 offered the opportunity to work on large marketing databases from the French Telecom company Orange to predict the propensity of customers to switch provider (churn), buy new products or services (appetency), or buy upgrades or add-ons proposed to them to make the sale more profitable (up-selling). The challenge started on March 10, 2009 and ended on May 11, 2009. This challenge attracted over 450 participants from 46 countries. We attribute the popularity of the challenge to several factors: (1) A generic problem relevant to the Industry (a classification problem), but presenting a number of scientific and technical challenges of practical interest including: a large number of training examples (50,000) with a large number of missing values (about 60%) and a large number of features (15,000), unbalanced class proportions (fewer than 10 % of the examples of the positive class), noisy data, presence of categorical variables

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