--- title: "E-Commerce Data Teams: Drive Revenue with LLMs | MotherDuck | MotherDuck" description: "See how e-commerce data teams use LLMs and MotherDuck to go from a plain-language business question to a published, interactive analysis in minutes." canonical: "https://motherduck.com/videos/ecommerce-data-teams-llms-revenue/" --- [BACK TO VIDEOS](/videos/) [Webinar](/videos/?category=Webinar#videos-and-webinars-library)[AI, ML and LLMs](/videos/?category=AI,%20ML%20and%20LLMs#videos-and-webinars-library)[BI & Visualization](/videos/?category=BI%20&%20Visualization#videos-and-webinars-library) # How Top E-Commerce Data Teams Drive Revenue with LLMs 2026/05/28Featuring: [Jacob Matson](/authors/jacob-matson/) , [Daniel Pham](/authors/daniel-pham/) TL;DR: MotherDuck and Silver Creek Insights demo going from a plain-language business question to a published, interactive analysis on real e-commerce data — without touching a BI backlog. ## The problem this solves Data teams at e-commerce companies already have what they need to answer hard revenue questions — which promotions actually move margin, whether ad spend is working, where customers drop off in checkout. The holdup isn't the data. It's the queue. Requests pile up in BI backlogs. Spreadsheets get forwarded around. Dashboards get built that nobody opens a week later. ## What the demo covers The session is a live demo: start with a business question in plain language, use LLMs and MotherDuck to generate and run SQL against real e-commerce data, then publish the results as a shareable, interactive analysis. The whole thing takes minutes. The demo runs on actual e-commerce data, not toy examples, so you can see how it handles the messiness you'd find in your own warehouse. ## Where this fits in your existing stack LLM-driven analysis doesn't mean ripping out your current tools. The session covers where it slots in alongside what you already have, what it handles well, what it doesn't, and what infrastructure you need in place. If you use MotherDuck with [MCP workflows](/docs/key-tasks/ai-and-motherduck/mcp-workflows/), setup is straightforward. ## Picking the right first use case Not every business question is a good starting point. The session walks through how to choose one that's high-value and low-risk — something that drives an actual decision rather than producing another report nobody reads. A good first use case typically has a clear business owner, data that's already clean and in your warehouse, and an answer someone will act on. TABLE OF CONTENTS - The problem this solves - What the demo covers - Where this fits in your existing stack - Picking the right first use case Start using MotherDuck now! [Try 7 Days Free](https://auth.motherduck.com/authorize?app_source=web&response_type=code&client_id=bza3KWQpxRAFlTlRFXUo29AOg9xD7zcp&redirect_uri=https%3A%2F%2Fapp.motherduck.com%2F&state=STATE&auth_flow=signup&screen_hint=signup&ext-ph_distinct_id=cf98446c-b274-474a-8fdb-93ac82da8ce0) ## FAQS ### How do LLMs help e-commerce data teams answer business questions faster? LLMs can take a business question in plain English and write the SQL for it, so you don't have to wait for an analyst to get around to it. What used to sit in a BI queue for days comes back in minutes. MotherDuck lets you publish the results as an interactive view that stakeholders can explore on their own, no SQL required. ### What is MotherDuck and how does it work with LLMs? MotherDuck is a serverless analytics database built on DuckDB. It's fast, speaks SQL, and connects to AI tools through the [MCP server protocol](/product/mcp-server/). You can get up and running without much infrastructure. The [getting started guide](/docs/getting-started/) walks you through it. ### How do I pick the right first LLM use case for my data team? Look for a question where someone specific will actually do something with the answer, the data already exists and is clean, and you know what the output should look like. Skip anything that needs weeks of data cleanup or where nobody will change what they're doing based on the result. One concrete win makes the next project easier to justify. ### Do I need to replace my existing BI tools to use LLMs for data analysis? No. LLM-driven analysis works alongside your current stack. It's best for ad hoc questions that would otherwise sit in a backlog, not for replacing scheduled reports or dashboards your team already relies on. Think of it as filling the gap between "I need an answer today" and "we'll get to that ticket next sprint." ### What e-commerce use cases were covered in the demo? The session used real e-commerce data to show how you go from plain-language questions about promotions, ad spend, and customer behavior to a published, interactive analysis. These are the revenue questions that take too long to answer right now, and the demo showed what it actually looks like when you close that gap. ## Related Videos [60:29](/videos/ai-agents-semantic-layer/)[2026-08-26](/videos/ai-agents-semantic-layer/) ### [Do AI Agents Need a Semantic Layer?](/videos/ai-agents-semantic-layer/) Do AI agents need a semantic layer? Evals comparing plain-text context with a Malloy semantic layer show where each wins, and what it costs. 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