From Dashboards to Decisions: Building Agentic Marketing Analytics with GenAI

While much of the buzz around AI in marketing focuses on using ChatGPT for copywriting, a quieter but more transformative shift is happening in marketing analytics—AI agents that can analyze data, not just generate content. This session explores how to build autonomous analytics systems that convert natural language questions into actionable insights, eliminating the need for manual SQL writing. You'll learn about the architecture behind agentic analysis—how large language models (LLMs) combined with structured data can automate insight generation—and see a live demo of a marketing mix modeling agent that updates itself daily. We’ll discuss why having clean, centralized data is essential for effective AI analytics, and walk through the process of moving from a proof-of-concept to production-ready systems. A case study will show how agentic workflows enabled faster, more cost-effective campaign analysis. Attendees will gain a technical blueprint for implementing agentic analytics on their current data stack, a clear understanding of where AI agents outperform traditional BI tools, practical code examples to get started, strategies for managing risk in AI-supported decision-making, and a roadmap to move from reactive reporting to truly proactive analysis.