Attending to Customer Attention: A Novel Deep Learning Method for Leveraging Multimodal Online Reviews to Enhance Sales Prediction

Although the impacts of helpful online reviews on customers’ purchase decisions and product sales have been widely investigated, review helpfulness has been commonly measured relying on quantitative indicators at the review level. Helpful reviews qualified by such simple indicators, however, may not necessarily yield accurate sales predictions, owing to the ever-evolving review information quality, customer demand, and product attributes. Positing that reviews with higher customer attention should be more influential to customers’ purchase intention and product sales, we propose to leverage customer attention to better realize the potential of multimodal reviews for sales prediction. We conceptualize customer attention at the holistic review set, review subset, individual review, and review element levels, respectively, and induce four indicators of customer attention, that is, timeliness, semantic diversity, voting awareness, and varying multimodal interaction. We then propose a deep learning method, which incorporates these customer attention indicators using neural network attention mechanisms specifically designed for multimodal review–based sales prediction. Empirical evaluation based on a large data set in a case study predicting hotel sales (specifically, monthly occupancy rate) shows that, in terms of both prediction performance and representation learning performance, our proposed method outperformed benchmarked state-of-the-art deep learning methods.

History: Zhiqiang (Eric) Zheng, Senior Editor; Gautam Pant, Associate Editor.

Funding: This work was supported by the National Natural Science Foundation of China [Grants 71971067, 72232009, 71832013, and 72271059] and the China Postdoctoral Science Foundation [Grant 2022M722394].

Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2021.0292.