AI-Powered Multi-Channel Campaigns: A Comparative Study of GPT-4, Claude, and Gemini in Personalized Marketing

Abstract

Large language models (LLMs) have transformed multi-channel marketing and allowed companies to provide highly customised, context-aware, and data-driven campaigns across several platforms. Evaluating GPT-4, Claude, and Gemini's efficacy in AI-powered multi-channel marketing automation, this paper offers a comparison. By means of sophisticated Natural Language Processing (NLP), deep learning, and reinforcement learning (RL), we investigate how these models improve content personalising, consumer engagement, and campaign optimisation.The paper investigates in real-time content creation, sentiment analysis, and adaptive marketing techniques each model's strengths and shortcomings. We evaluate their influence on marketing efficacy using quantitative and qualitative performance measures including click-through rates (CTR), conversion rates, engagement levels, and response coherence. We also go over ethical issues, prejudice avoidance, and data privacy concerns and suggest federated learning and differentiated privacy to guarantee responsible artificial intelligence application in marketing automation.By means of industry-driven applications and experimental case studies, we discover that GPT-4, Claude, and Gemini each have special benefits with different trade-offs in response accuracy, flexibility, and computing efficiency. The study offers a thorough methodology for marketers to deliberately include LLMs into tailored, AI-driven multi-channel campaigns, hence improving customer experience and optimising return on investment (ROI).