AI-Driven SEM Keyword Optimization and Consumer Search Intent Prediction: An Intelligent Approach to Search Engine Marketing
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Abstract
The exponential growth of digital advertising expenditures necessitates sophisticated optimization strategies to maximize search engine marketing (SEM) effectiveness. This research presents an innovative framework integrating artificial intelligence algorithms with consumer search intent prediction to enhance SEM keyword optimization performance. The proposed methodology employs multi-layered clustering techniques and predictive modeling to analyze search patterns and optimize bidding strategies automatically. Experimental validation using e-commerce plat-form data demonstrates significant improvements in key performance indicators, including a 23.5% reduction in cost-per-click (CPC) and a 52.9% increase in return on advertising spend (ROAS). The framework incorporates natural language processing techniques for intent classification and machine learning algorithms for dynamic bid adjustment. Real-time implementation results in-dictate substantial enhancements in campaign efficiency and revenue generation compared to traditional optimization approaches. The research contributes to the advancement of intelligent marketing automation by providing empirical evidence of AI-driven optimization superiority in competitive digital advertising environments.
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