A multi-class approach to next session and in-session purchase prediction with real-time e-commerce data using machine learning techniques

Advances in machine learning yield implications for the rapidly growing e-commerce industry. Retailers are looking for ways to better understand and predict complex customer behavior. Two crucial problems that exist in the domain are predicting customers' platform engagement and purchase intent. Different techniques are employed in the literature addressing the problems separately, but the two tasks are highly dependent on each other since the ultimate goal for session engagement is purchase. Understanding if a next session will be made with a high purchase motive is critical to diversify the business actions taken. The main aim of the thesis is to develop a multi-class model that successfully distinguishes the next sessions with and without purchase intention. 38 million e-commerce sessions are collected for the specific task. Following the application of state-of-the-art LightGBM and LSTM algorithms, their results are compared, where LightGBM outperformed the latter. Additionally, a simple ensembling technique is used to increase the performance, leading to a 68% F1 score for the predictions of no session, 71% for the predictions of sessions without purchase and 59% for the predictions of sessions with purchase. Furthermore, an undersampling technique is employed to handle the imbalance differently than the technique used by LightGBM and LSTM, increasing F1 scores to 75%, 72% and 74% respectively.