Panoramic sales insight: Using multimodal fusion to improve the effectiveness of flash sales

Abstract

Flash sales are a widely adopted e-commerce marketing strategy that operate over a brief period, offering limited-time discounts, special promotions, or clearance items to create a sense of urgency and promote rapid sales. This study proposes panoramic sales insight (PSI), a multimodal revenue forecasting framework designed to improve the accuracy of revenue predictions for flash sales. Using historical flash sales data from the fast fashion retailer Shein, the proposed PSI framework integrates both structured and unstructured data, utilizing a text–image fusion module to fuse features from product images and text descriptions and a deep neural network to forecast revenue. The text features are extracted using bidirectional encoder representations from transformers (BERT), the product image features are extracted using a vision transformer (ViT), and review keyword extraction is conducted using Fumeus. Multimodal fusion then integrates these features to deliver accurate revenue forecasting. Controlled experiments evaluate the performance of each module within the PSI framework, while ablation analysis confirms the robustness of PSI. This study provides valuable insights for managers, enabling more accurate revenue forecasting and improving the effectiveness of flash sales.