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

TL;DR

This paper introduces a new neural model for classifying e-commerce user sessions based on minimal temporal patterns in clickstream data, outperforming existing models and utilizing a novel dataset from a European website.

Contribution

The paper presents a new neural model for clickstream classification, a novel dataset, and a comprehensive evaluation of baseline and state-of-the-art models in e-commerce.

Findings

The proposed neural model outperforms recent architectures.

A new dataset of live shopping sessions is introduced.

Strong baseline models are tested and compared.

Abstract

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.