Prediction is very hard, especially about conversion. Predicting user purchases from clickstream data in fashion e-commerce
This work addresses the problem of real-time next best action policies for e-commerce platforms, but it appears incremental as it builds on existing methods in a specific domain.
The paper tackles the challenge of predicting user purchases from clickstream data in fashion e-commerce, where low conversion rates and noisy browsing data make classification difficult. It introduces a new neural model that outperforms recent neural architectures from Rakuten labs, though no specific performance numbers are provided.
Knowing if a user is a buyer vs window shopper solely based on clickstream data is of crucial importance for ecommerce 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 fashion industry and present three major contributions to the burgeoning field of AI in fashion: first, we collected, normalized and prepared a novel dataset of live shopping sessions from a major European e-commerce fashion 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.