Bank Telemarketing Forecasting Model Based on t-SNE-SVM

As a low-cost marketing model, telemarketing has always been the most important channel for banks to promote wealth management products. Traditional telemarketing has not only brought intrusiveness to many telephone access customers, but also a waste of resources for the bank itself. In order to improve the success rate of bank telemarketing, it is necessary to predict in advance which customers are most likely to purchase the wealth management product, so as to achieve precision marketing. Aiming at the complex high-dimensional nonlinear characteristics of the factors affecting the success rate of telemarketing, a t-SNE (t-distributed stochastic neighbor embedding) feature extraction method, and then take the extracted low-dimensional features as input, use nonlinear support vector machine (SVM) for training and prediction. The empirical results show that the bank phone based on t-SNE-SVM proposed in this paper. The marketing prediction model has good learning ability and generalization ability, which can provide certain decision-making reference for banks and other industries to achieve precision marketing.