This paper covers a sales forecasting problem on e-commerce sites. To predict product sales, we need to understand customers' browsing behavior and identify whether it is for purchase purpose or not. For this goal, we propose a new customer model, B2P, of aggregating predictive features extracted from customers' browsing history. We perform experiments on a real world e-commerce site and show that sales predictions by our model are consistently more accurate than those by existing state-of-the-art baselines.
Yeo et al. REF predict product sales by identifying customers purchase purpose from their browsing behavior.
Browsing2purchase: Online Customer Model for Sales Forecasting in an E-Commerce Site
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Computer Science