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Knowing if a user is a buyer or window shopper solely based on clickstreamdata is of crucial importance for e-commerce platforms seeking to implementreal-time accurate NBA (next best action) policies. However, due to the lowfrequency of conversion events and the noisiness of browsing data, classifyinguser sessions is very challenging. In this paper, we address the clickstreamclassification problem in the eCommerce industry and present three majorcontributions to the burgeoning field of AI-for-retail: first, we collected,normalized and prepared a novel dataset of live shopping sessions from a majorEuropean e-commerce website; second, we use the dataset to test in a controlledenvironment strong baselines and SOTA models from the literature; finally, wepropose a new discriminative neural model that outperforms neural architecturesrecently proposed at Rakuten labs.

Predicting e-commerce customer conversion from minimal temporal patterns on symbolized clickstream trajectories