Research on Marketing Prediction Model for Cross-Border E-Commerce Enterprises Based on Artificial Intelligence Algorithms

Understanding consumer behavior and predicting market trends are critical for enterprises looking to advance their innovation in the rapidly changing world of Cross-border E-commerce (CBEC). To advance the logistical facility occurrence for consumers and optimize inventory organization, e-commerce businesses are focused on using Artificial Intelligence (AI) approach to amplify the accuracy of sales prediction. This study proposes a marketing prediction model that employs Enhanced Tabu Search Optimized Dynamic Gradient Boosting Machines (ETSO-DGBM) to aid CBEC businesses in building enhanced assessments. The CBEC enterprises’ data were collected and used for predicting their marketing approach. To advance the capability of the model to predict, pre-processing procedures, including normalization, are used to certify data stability and Independent Component Analysis (ICA) assists in extracting features. The result illustrates that the ETSO-DGBM marketing prediction model develops forecasting accuracy significantly. When assessing with traditional techniques, the proposed ETSO-DGBM model performs better. The method assists organizations in predicting their marketing strategy to gather the diverse demands of different segments by providing perceptive information about customer behavior. It uses AI approach to extend adaptable marketing programs for CBEC companies, facilitating informed decision-making and promoting expansion and profitability in international markets. It underscores the significance of AI in marketing strategies to handle cross-border customer behavior.