Logistic Regression Ensemble for Predicting Customer Defection with Very Large Sample Size

Abstract

Predicting customer defection is an important subject for companies producing cloud based software. The studied company sell three products (High, Medium and Low Price), in which the consumer has choice to defect or retain the product after certain period of time. The fact that the company collected very large dataset leads to inapplicability of standard statistical models due to the curse of dimensionality. Parametric statistical models will tend to produce very big standard error which may lead to inaccurate prediction results. This research examines a machine learning approach developed for high dimensional data namely logistic regression ensemble (LORENS). Using computational approaches, LORENS has prediction ability as good as standard logistic regression model i.e. between 66% to 77% prediction accuracy. In this case, LORENS is preferable as it is more reliable and free of assumptions.