Customer churns analysis and predication is an important part of customer relationship management (CRM). Because of the discrepancy of collecting channel and data gathering, raw customer data have imprecise, unbalanced and high dimensional characteristics, which degrade model performance. 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. 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. In the first step PCA is used to compress raw input data and extract features, which can implement de-correlation. In the second step samples are used 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.