In the marketing field, understanding consumer heterogeneity, which is the internal or psychological difference among consumers that cannot be captured by behavioral logs, has long been a critical challenge. However, a number of consumers today usually post their evaluation on the specific product on the online platform, which can be the valuable source of such unobservable differences among consumers. Several previous studies have shown the validity of the analysis on text modality, but on the other hand, such analyses may not necessarily demonstrate sufficient predictive accuracy for text alone, as they may not include information readily available from cross-sectional data, such as consumer profile data. In addition, recent advances in machine learning techniques, such as large-scale language models (LLMs) and multimodal learning have made it possible to deal with the various kind of dataset simultaneously, including textual data and the traditional cross-sectional data, and the joint representations can be effectively obtained from multiple modalities. Therefore, this study constructs a product evaluation model that takes into account consumer heterogeneity by multimodal learning of online product reviews and consumer profile information. We also compare multiple models using different modalities or hyper-parameters to demonstrate the robustness of multimodal learning in marketing analysis.

翻译：在市场营销领域，理解消费者异质性——即行为日志无法捕捉的消费者内部或心理差异——长期以来一直是一个关键挑战。然而，如今许多消费者通常在在线平台上发布对特定产品的评价，这成为获取这种不可观测消费者差异的宝贵来源。以往多项研究已证明文本模态分析的有效性，但另一方面，仅依赖文本分析未必能展现出足够的预测精度，因为这可能未包含横截面数据中易于获取的信息（如消费者特征数据）。此外，近年来机器学习技术的进步，例如大规模语言模型与多模态学习，使得同时处理包括文本数据和传统横截面数据在内的多种数据集成为可能，并能够从多模态数据中有效获取联合表征。因此，本研究通过在线产品评论与消费者特征信息的多模态学习，构建了一个考虑消费者异质性的产品评估模型。同时，我们比较了使用不同模态或超参数的多个模型，以验证多模态学习在营销分析中的鲁棒性。