Transformer-Based Model for Predicting Customers’ Next Purchase Day in e-Commerce

Abstract

The paper focuses on predicting the next purchase day (NPD) for customers in e-commerce, a task with applications in marketing, inventory management, and customer retention. A novel transformer-based model for NPD prediction is introduced and compared to traditional methods such as ARIMA, XGBoost, and LSTM. Transformers offer advantages in capturing long-term dependencies within time series data through self-attention mechanisms. This adaptability to various time series patterns, including trends, seasonality, and irregularities, makes them a promising choice for NPD prediction. The transformer model demonstrates improvements in prediction accuracy compared to the baselines. Additionally, a clustered transformer model is proposed, which further enhances accuracy, emphasizing the potential of this architecture for NPD prediction.