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Intelligent finance is a new form of business with deep integration of artificial intelligence technology and financial industry. An important application of intelligent finance is the precise marketing of financial products. As a key link in precision marketing, click through rate(CTR) prediction has made great progress, but there is still room for improvement in multiple features fusion, feature interactions learning and other aspects. In view of these needs and challenges, we propose a CTR prediction model named MCGM, which is used to realize precision marketing of financial products. The main characteristics of the model are as follows: (i) in order to effectively fuse multiple features, we design a hierarchical gated mechanism to select salient feature information at different levels; (ii) in order to fully learn the nonlinear relationship between features, we design a multi-channel feature interactions learning module. Specifically, it adopts factorization machine(FM), improved CrossNet(ICN) and multilayer perceptron(MLP) components to model the feature interactions from high-order to low-order, in order to obtain the abstract features containing rich information. Comprehensive and sufficient experiments on real world datasets show that the proposed model achieves better prediction performance compared with baselines. The proposed model not only has specific application value in the field of financial products marketing, but also provides an idea reference for data-driven marketing modeling. [ABSTRACT FROM AUTHOR]