We are pleased to announce a practical masterclass led by Dmytro Kyiashko, a Software Developer in Test with over a decade of experience in IT and a recognized expert in testing AI-based systems, automated decision-making, as well as data-driven advertising platforms. In this session, Dmytro will focus on how artificial intelligence is reshaping modern advertising through automated creative adaptation, real-time audience analysis, and predictive performance optimization.

Drawing on real production systems and large-scale advertising datasets, this masterclass will demonstrate how AI models analyze audience behavior, segment users, dynamically assemble creatives, and continuously optimize messaging to improve engagement, efficiency, and return on ad spend. The session is designed for professionals seeking to move beyond manual campaign management toward scalable, intelligent advertising systems.

About Dmytro Kyiashko:

Dmytro Kyiashko is a Software Developer in Test with more than 10 years of experience in IT, including extensive work in building engineering teams, mentoring specialists, and leading technical initiatives. Over the past several years, his professional focus has been on testing and validating AI-driven systems, ensuring their reliability, stability, and performance in real-world conditions.

He is a member of IEEE (Institute of Electrical and Electronics Engineers) and has served as an expert jury member at international technology events, including UAtech Venture Night (Web Summit Vancouver). Dmytro is the author of a scientific paper dedicated to testing challenges in multimodal AI-agent systems, as well as the author of the book “AI Systems Testing and Assessment: An Engineering Guide,” which presents an engineering framework for evaluating AI system behavior.

In advertising-related projects, Dmytro has worked on AI-powered platforms that automate campaign creation, creative generation, audience targeting, performance analysis, and predictive optimization across major advertising ecosystems such as Google Ads, Meta (Facebook and Instagram), and YouTube. His expertise lies in understanding not only individual performance metrics, but also complex dependencies between them, enabling earlier detection of inefficiencies and smarter optimization decisions.

Workshop Program: “Next-Gen Ads: Automated Creative Adaptation for Smarter Targeting”

1) How AI Analyzes Audience Behavior

• Behavioral data: clicks, views, scroll depth, interactions
• CRM and LTV signals: customer history, purchase frequency, value
• Contextual signals: content category, keywords, placement context
• Geo, device, and time attributes and their impact on user intent

2) AI Processing Pipeline

• Minimum data requirements
• Transformation of raw metrics into audience signals
• Audience segmentation approaches used in advertising systems
• Detection of relationships between performance metrics

3) AI-Powered Creative Adaptation

• Adaptation of visuals, copy, formats, and CTAs
• Real-time matching of creatives to audience segments
• Dynamic variation selection
• Detection of creative fatigue

4) Measuring Creative Performance

• Core performance metrics: CTR, CVR, ROAS, engagement, VTR
• Limitations of single-metric analysis
• Interpretation of combined performance signals
• Analysis of performance dynamics

5) Predictive Optimization and Decision Support

• Prediction of performance decline
• Automated alerts and recommendations
• Creative refresh and campaign pause decisions
• Budget efficiency signals

6) Continuous Learning and Optimization Loop

• Feedback collection from campaign performance
• Automated testing and iteration
• Knowledge accumulation across campaigns
• Long-term creative performance improvement

7) Q&A Session

• Open discussion
• Real-case questions and expert answers