AI-Based Advertisement Optimization and Performance Analytics
The AI-Based Advertisement Optimization and Performance Analytics program aims to revolutionize digital marketing by way of real-time automation and optimization of advertising campaigns using AI. The architecture proposes in making use of advanced machine learning algorithms and data analytics to analyze massive amounts of ad performance data, theirs including and not limited to click-through rates, conversion rates, audience demographics, engagement rates, and temporal patterns, and develop key performance indicators or useful insights on the paved way of real-time automated marketing and optimization of advertising campaigns through AI. The system's other operations engage predictive modeling approaches to provide ad placements, formats, and budgets, recommending them dynamically while maximizing returns and minimizing costs per click, complemented also by audience sentiment estimation involving reviews and feedback input via techniques like natural language processing(NLP) for context-relevant advertising. Reinforcement learning agents attach and hook advertisements continuously trained from fresh data and change strategies accordingly to keep ads flexible and performance-driven. The solution provides stakeholders with interactive dashboards through which they can view and appreciate real-time ad performance across different platforms like Facebook, Instagram, and Google Ads, while analyzing and visualizing each campaign's reach and impact. The research really is a testament to AI being poised to radically change digital advertising by making it more intelligent, effective, and data-driven. It certainly raises the bar for client targeting precision, hence promoting data-based, well-informed marketing decisions and laying a well-considered foundation for independent management of ad campaigns.
Authors
- K. Swetha
- T. Ananth Kumar (ORCID: https://orcid.org/0000-0002-0494-7803)
- P. Kanimozhi
Publication Details
- Journal
- Asian Journal of Applied Science and Technology
- Published
- 2025-01-01
- DOI
- https://doi.org/10.38177/ajast.2025.9202
- Citations
- 1
- Primary Topic
- Digital Marketing and Social Media
- Type
- article
- Field-Weighted Citation Impact
- 4.20