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E-commerce web applications are almost ubiquitous in our day to day life,however as useful as they are, most of them have little to no adaptation touser needs, which in turn can cause both lower conversion rates as well asunsatisfied customers. We propose a machine learning system which learns theuser behaviour from multiple previous sessions and predicts useful metrics forthe current session. In turn, these metrics can be used by the applications tocustomize and better target the customer, which can mean anything from offeringbetter offers of specific products, targeted notifications or placing smartads. The data used for the learning algorithm is extracted from GoogleAnalytics Enhanced E-commerce, which is enabled by most e-commerce websites andthus the system can be used by any such merchant. In order to learn the userpatterns, only its behaviour features were used, which don't include names,gender or any other personal information that could identify the user. Thelearning model that was used is a double recurrent neural network which learnsboth intra-session and inter-session features. The model predicts for eachsession a probability score for each of the defined target classes.

Predicting next shopping stage using Google Analytics data for E-commerce applications