  Knowing if a user is a buyer or window shopper solely based on clickstream
data is of crucial importance for e-commerce platforms seeking to implement
real-time accurate NBA (next best action) policies. However, due to the low
frequency of conversion events and the noisiness of browsing data, classifying
user sessions is very challenging. In this paper, we address the clickstream
classification problem in the eCommerce industry and present three major
contributions to the burgeoning field of AI-for-retail: first, we collected,
normalized and prepared a novel dataset of live shopping sessions from a major
European e-commerce website; second, we use the dataset to test in a controlled
environment strong baselines and SOTA models from the literature; finally, we
propose a new discriminative neural model that outperforms neural architectures
recently proposed at Rakuten labs.
