# Research on Multimodal Prediction of E-Commerce Customer Satisfaction Driven by Big Data
###### Abstract  
This study deeply integrates multimodal data analysis and big data technology, proposing a multimodal learning framework that consolidates various information sources, such as user geographic location, behavior data, and product attributes, to achieve a more comprehensive understanding and prediction of consumer behavior. By comparing the performance of unimodal and multimodal approaches in handling complex cross-border e-commerce data, it was found that multimodal learning models using the Adam optimizer significantly outperformed traditional unimodal learning models in terms of prediction accuracy and loss rate. The improvements were particularly notable in training loss and testing accuracy. This demonstrates the efficiency and superiority of multimodal methods in capturing and analyzing heterogeneous data. Furthermore, the study explores and validates the potential of big data and multimodal learning methods to enhance customer satisfaction in the cross-border e-commerce environment. Based on the core findings, specific applications of big data technology in cross-border e-commerce operations were further explored. A series of innovative strategies aimed at improving operational efficiency, enhancing consumer satisfaction, and increasing global market competitiveness were proposed.  
multimodal learning; customer satisfaction; cross-border e-commerce; big data; intelligent business +
Footnote †: journal:  
Predicting customer satisfaction can better serve downstream distributors by accurately identifying consumer needs based on the accumulation of big data in cross-border e-commerce. This capability assists cross-border e-commerce sellers in selecting and stocking products, facilitating the integration of global resources in manufacturing, logistics, marketing, and services and effectively addressing a series of issues from production to post-sale service [1]. Traditional satisfaction prediction requires the establishment of an indicator system and surveys consumer subjective evaluations through questionnaires. This approach is costly and time-consuming, and its accuracy depends on the design of the questionnaire and the precision of the survey sample [2]. E-commerce platforms typically rely on customer satisfaction ratings post-purchase, which is not applicable to products that are still in the testing phase or are newly launched with few consumers. New products often lack historical data, making the use of multimodal big data--such as product positioning and attributes--as feature variables for decision-making particularly suitable. This approach is not only faster and more cost-effective but also retains more sample information, enhancing decision-making capabilities. This study integrates multiple attributes and complex features of cross-border e-commerce sales data to develop a multimodal learning model that combines product and market attributes. This model offers a more comprehensive, precise, and in-depth understanding of consumer behavior, habits, preferences, and characteristics [3]. It rapidly collects extensive information on apparel features, enabling brands to accurately assess which products are suitable for market expansion and estimate consumer satisfaction levels. The model assists cross-border e-commerce operators in making well-informed decisions regarding product listings, selection, and development, allowing them to estimate sales and profits, optimize customer experiences, and support the internationalization and intelligent growth of cross-border e-commerce operations [4].