A New Prediction Model of Customer Churn Based on PCA Analysis

Customer churns analysis and predication is an important part of Customer Relationship Management (CRM). Customer retention and customer acquisition are two supports which have great influences on the bottom line compared with the increase of market share, the reduction of unit costs, and other competitive tools. Because of the discrepancy of collecting channel and data gather, crude customer data have imprecise, unbalanced and high dimensional characteristics, which degrade model performance. In order to solve this problem the paper addresses a prediction model based on Principal Component Analysis (abbr.PCA) and Least Square Support Vector Machine (abbr. LS-SVM). The procedure includes two steps. The first step uses PCA to compress crude input data and extract features, which can implement de-correlation. The second step uses samples to train LS-SVM and establish customer churn forecasting model. In this way, the two algorithms have combined, whose advantages have been made a full use. Case studies are applied to test the proposed model.