  In this paper, we apply neural networks into digital marketing world for the
purpose of better targeting the potential customers. To do so, we model the
customer online behaviours using dedicated neural network architectures.
Starting from user searched keywords in a search engine to the landing page and
different following pages, until the user left the site, we model the whole
visited journey with a Recurrent Neural Network (RNN), together with
Convolution Neural Networks (CNN) that can take into account of the semantic
meaning of user searched keywords and different visited page names. With such
model, we use Monte Carlo simulation to estimate the conversion rates of each
potential customer in the future visiting. We believe our concept and the
preliminary promising results in this paper enable the use of largely available
customer online behaviours data for advanced digital marketing analysis.
