Revolutionary AI-Driven Bid Optimization in Retail Media: A Technical Deep Dive

• Published in: European Journal of Computer Science and Information Technology Vol. 13; no. 50; pp. 27 - 33
• Main Author: Gokhale, Ameya
• Format: Journal Article
• Language: English
• Published: 26.06.2025
• ISSN: 2054-0957; 2054-0965
• DOI: 10.37745/ejcsit.2013/vol13n502733

The future of retail advertising is being revolutionized by AI-driven dynamic bid pricing, leveraging optimized algorithmic real-time bidding (RTB) to maximize advertiser efficiency, retailer profitability, and consumer engagement. Traditional bid pricing strategies in retail advertising have relied on static rules and manual optimization, failing to effectively target specific business goals such as awareness, consideration, clicks, or conversions, which results in inefficiencies and an uneven competitive landscape. This technical analysis explores how AI-powered real-time bid optimization offers a transformative solution by dynamically adjusting bids to achieve multiple advertiser campaign goals. By implementing machine learning algorithms, including reinforcement learning, multi-agent AI systems, and deep neural networks, advertisers can automate real-time bid strategies, ensuring goal-based campaign management and optimal values for each ad impression and click. The article examines the core technical frameworks, advantages, implementation benefits, challenges, and future developments in AI-driven bidding systems, while addressing privacy concerns and bias mitigation strategies.