Multimodal foundation models (MFMs) have demonstrated strong capabilities in e-commerce by effectively leveraging multimodal data to enhance product understanding and user experienceHowever, the development of e-commerce MFMs is hindered by two challenges: (1) the scarcity of large-scale, high-quality multimodal benchmark datasets; and (2) the lack of effective multimodal information integration methods in e-commerce. To address these challenges, we introduce MMECInstruct, the first large-scale, high-quality multimodal instruction dataset designed specifically for e-commerce MFMs. MMECInstruct comprises 75,000 samples covering 7 real-world e-commerce tasks, supporting both in-domain (IND) and out-of-domain (OOD) evaluations. Leveraging MMECInstruct, we develop CASLIE, a lightweight framework that enhances multimodal information understanding and integration for e-commerce. Our comprehensive evaluation demonstrates that MMECInstruct endows CASLIE with advanced capability and strong generalizability in e-commerce applications. MMECInstruct and CASLIE models are publicly accessible through https://github.com/ninglab/CASLIE.

E-commerce platforms are rich in multimodal data, featuring a variety of images that depict product details. However, this raises an important question: do these images always enhance product understanding, or can they sometimes introduce redundancy or degrade performance? Existing datasets are limited in both scale and design, making it difficult to systematically examine this question. To this end, we introduce EcomMMMU, an e-commerce multimodal multitask understanding dataset with 406,190 samples and 8,989,510 images. EcomMMMU is comprised of multi-image visual-language data designed with 8 essential tasks and a specialized VSS subset to benchmark the capability of multimodal large language models (MLLMs) to effectively utilize visual content. Analysis on EcomMMMU reveals that product images do not consistently improve performance and can, in some cases, degrade it. This indicates that MLLMs may struggle to effectively leverage rich visual content for e-commerce tasks. Building on these insights, we propose SUMEI, a data-driven method that strategically utilizes multiple images via predicting visual utilities before using them for downstream tasks. Comprehensive experiments demonstrate the effectiveness and robustness of SUMEI. The data and code are available through https://github.com/ninglab/EcomMMMU.