ARTIFICIAL INTELLIGENCE IN MARKETING CAMPAIGNS: A WAY TO ADAPT MARKETING TO NEW TECHNOLOGIES

Abstract

The contemporary landscape of Digital Marketing is marked by the incessant demand for personalized and optimized content, which poses a critical challenge to teams: the inefficiency in the manual analysis of information and the subsequent slowness in writing campaigns, limiting the scalability of corporate communication. The aim is to mitigate this limitation through the development of SocialGen-AI, a generative Artificial Intelligence system designed for end-to-end automation of the creation of promotional texts for social media, starting from the structural and semantic analysis of web pages. The implementation methodology is based on Agile Software Engineering, culminating in a solution implemented in Python and structured via API, which integrates with the Ollama framework for the optimized execution of the DeepSeek large language model. The process includes data extraction, contextualized semantic analysis, and the generation of copy with tone and style adjustments. Thus, SocialGen- AI allows for a substantial reduction in content time-to-market, in addition to ensuring high consistency and personalization at scale. In conclusion, an architecture based on local LLMs and dedicated APIs represents a viable and scalable technological solution for optimizing marketing productivity, paving the way for future research focused on native integration with social media platforms and the continuous improvement of AI's contextual accuracy.