Predictive analytics U.S. Global Marketing Competitiveness using AI

Abstract

The competitiveness in global marketing is gradually being driven by the use of data-driven decisions and the capability of businesses to foresee the consumer needs in dynamic market conditions. The United States is well-established in the global trade and marketing segment, but there are leading-edge applications of artificial intelligence (AI) and predictive analytics emerging around the globe, and which require more facilitation of digital transformation in order to maintain and augment this leadership. What this study aims to explore is how predictive analytics using AI can help the U.S. become more competitive in the global market, by considering consumer purchasing behavior and decision-making patterns in relation to marketing. Based on the available dataset related to consumer behavior and shopping habits, obtained on Kaggle, the research examines the demographic factors, how often people buy a product, the type of product, seasonal patterns, sensitivity to discounts, and satisfaction with the review to determine the main marketing drivers. Classification models and clustering are used as machine learning methods to forecast consumer-related outcomes like the high-value spending behavior and the likelihood of loyalty. Categorical encoding, normalization, and the exploratory analysis are the steps of data preprocessing that guarantee the model accuracy and generalization. The results show that pricing, promotional involvement, and product type preferences play a major role in determining purchasing decisions and age and frequency of shopping are good predictors of high value customer segments. The information based on the segmentation of consumers and predictive patterns shows that marketing activities in the U.S. can be enhanced by focusing on localized customer requirements, better personalized marketing of products, and product positioning strategies. The paper finds that the adaptation of predictive analytics allows American companies to become more competitive in the global market due to better consumer satisfaction, more revenue-generating possibilities, and the maintenance of a strategic position in the market. The future research can incorporate more international data sets to generalize competitiveness study on international consumer markets. This study offers a viable and evidence-based method of using AI-based predictive analytics to promote the strategic development of American companies in competitive global markets.