With the development of 4G and 5G networks, more and more industries in China are borrowing O2O coupons to drive offline consumption; however, many merchants randomly issue coupons leading to immeasurable marketing costs for brands, and at the same time, there is a certain interference with consumers' choice of coupons for underwriting, and there is no more scientific method to improve the redemption rate. Therefore, this paper adopts the actual data provided in the Aliyun Tianchi competition, preprocesses and feature extraction and selection of the dataset, and constructs three prediction single models based on GBDT, XGBoost and LightGBM, respectively. Through experiments, it is concluded that XGBoost has the best prediction effect and the best stability among the single models, thus XGBoost is selected as the prediction model for judging coupon usage, thus helping merchants to predict the probability of consumers using coupons, and thus realizing personalized coupon placement and improving merchants' marketing ability.

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This study focuses on the phenomenon of hidden editions in blind boxes, constructs a framework of consumers' psychological decision-making, and explores the optimization path of corporate marketing strategies. Through research, it is found that scarcity, cognition and interest, and regret psychology are the main psychological driving factors affecting blind box consumption. Furthermore, based on the psychological level of consumers, this paper puts forward targeted marketing strategy suggestions from the aspects of product, price, channel and promotion. The research in this paper provides a theoretical basis for the development of the blind box market.

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The rapid development of artificial intelligence (AI) is reshaping corporate strategic management. In today’s dynamic global environment, organizations face increasing challenges in decision-making, resource allocation, and operational efficiency. Against this background, AI offers significant potential to enhance the speed, accuracy, and effectiveness of strategic management practices. This study examines the applications of AI in corporate strategy, with a focus on how technologies such as machine learning, natural language processing, and automation contribute to improving strategic decision-making. The research employs a non-empirical design, combining literature review and conceptual analysis. The study objects include enterprises across manufacturing, high-tech industries, and SMEs, as represented in existing scholarly works and industry reports. Analytical tools are theoretical models of AI integration, and data sources consist mainly of secondary materials such as peer-reviewed articles, case documentation, and industry analyses. The findings indicate that AI improves strategic accuracy and efficiency, but challenges remain regarding data quality, organizational resistance, and ethical concerns. The study concludes that successful adoption requires aligning technological advances with organizational adaptation. Furthermore, the integration of AI with big data, cloud computing, and blockchain is expected to play a central role in shaping the future of strategic management.

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This paper focuses on consumers' online shopping decision-making, adopting a behavioral economics perspective and analyzing it based on three mechanisms of prospect theory. Building upon a review of relevant literature, the core tenets of prospect theory are summarized, followed by an exploration of how these theoretical perspectives influence consumers' behavioral decisions across various stages, including information gathering, product evaluation, purchase intention, and post-purchase evaluation. The research findings are synthesized to highlight the significance of understanding consumer online shopping decisions through the lens of prospect theory, offering insights for academic innovation, business practices, policy formulation, and societal benefit enhancement.

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