MCP Server 101: Moving Your Analytics and Marketing Reporting to LLMs — Do’s and Don’ts
with Nadia Davis, Joe Schattschneider, and Nolan Garrido
About the Event
In the eighth episode of Season 3 and 2026 on Hitchhiker’s Guide to Marketing Analytics, Nadia Davis is joined by colleagues Joe (marketing data strategy) and Nolan (AI & engineering) to discuss everything marketing analytics pros need to know to get started with LLMs for marketing analytics via MCP servers.
Everyone’s excited about using AI for marketing analytics, and the fastest path looks obvious: export a CSV, upload it to Claude, get a nice visualization, maybe even host it on GitHub. Done, right? Not so fast…. Within minutes your data is stale, your dashboard is a snapshot of the past, and you’re back to the same manual refresh cycle you were trying to escape.
In this session of our award-winning Hitchhiker’s Guide to Marketing Analytics we will walk you through what that “quick and easy” route actually looks like in practice — and why it breaks down fast. From there, we’ll make the case for what actually matters: clean data pipelines feeding a modeled, standardized marketing data warehouse, connected to your LLM interface in real time through something called an MCP server.
If you’ve heard the term “MCP” thrown around and nodded along without fully getting it, this is the session for you. We’ll break down what a Model Context Protocol server is, how it works as the bridge between your data warehouse and tools like Claude or Chat GPT, and why it’s the infrastructure layer that makes AI-powered reporting actually reliable. The goal of this session is to demystify the concept and let you walk away with a full understanding of the MCP server functionality and role: so you leave understanding the architecture well enough to explain it to your team, your boss, or your IT department when they ask what’s involved.
If you are underway or just starting to explore LLMs for reporting (or if you’ve already hit the wall with the CSV-upload approach), this session gives you a clear picture of the do’s, the don’ts, and the path from demo to production.
