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Let Clickstream Talk: A Graph Neural Network Approach to Sales Forecasting
Journal article   Peer reviewed

Let Clickstream Talk: A Graph Neural Network Approach to Sales Forecasting

Rong Liu, Zihan Chen, Denghui Zhang, Feng Mai and Xuying Zhao
Production and operations management
09/16/2026
DOI: 10.1177/10591478261490953

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Abstract

This paper proposes ForecastClickGraph, a deep learning framework that extracts cross-product relationships and demand information from clickstream data for probabilistic sales forecasting. ForecastClickGraph models stages in consumer shopping journeys as a dynamic graph, where nodes represent individual product-stage combinations, such as product A viewed or added to cart, and directed edges capture the transitions between stages. A customized graph neural network learns product representations that incorporate both product-level temporal patterns and cross-product associations. Another graph-based module further detects demand spikes using early signals from related products. ForecastClickGraph cross-learns demand patterns across large-scale products and estimates demand distributions through quantile regression. Extensive experiments on a real-world dataset show that ForecastClickGraph outperforms state-of-the-art benchmark models by 10-28% in forecast accuracy, with particularly strong performance in predicting promotional sales bursts. It also yields superior probabilistic forecasts with substantially lower quantile loss and improved calibration relative to benchmarks.
 Sales Forecasting Graph Neural Network Clickstream Product Relationship Sales Burst

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