Product Analytics 101: From Question to Decision with AI

Part of AI Analytics Frameworks

Hosted by Shane Butler, Sravya Madipalli, and Hai Guan

Wed, Jul 22, 2026

5:00 PM UTC (1 hour)

Virtual (Zoom)

Free to join

65 students

Invite your network

Go deeper with a course

Automate AI Evals with Claude Code
Shane Butler
View syllabus

What you'll learn

Turn a vague request into a real question

Take a fuzzy ask like "how are we doing" and shape it into a question that has an answer you can act on.

Pick the metric that answers it

Choose the one number that actually maps to the decision, and skip the dashboard full of metrics that do not.

Get to an insight and a recommendation, fast

Go from question to a clear finding and a 3-line recommendation in one sitting, with AI doing the heavy lifting.

Avoid the analysis that goes nowhere

Spot the dead ends early, keep the AI honest, and make sure every analysis ends in a decision.

Why this topic matters

Most analysis never leads to a decision. It stalls in a dashboard, a deck, or a "let me look into it." Getting the answer stopped being the hard part once AI could crunch the data. The hard part is asking the right question and knowing when the answer is good enough to act on. This session is the full workflow from a fuzzy ask to a decision, using AI.

You'll learn from

Shane Butler

Co-founder, AI Analyst Lab

Shane Butler is a Co-Founder of the AI Analyst Lab. Previously he led evaluation strategy for AI product development in the legal tech domain. He has more than ten years of experience in product data science and causal inference, with prior roles at Stripe, Nextdoor, and PwC. Shane is also the co-host of the AI podcast Data Neighbor, where he interviews product, data, and engineering leaders who are pioneering the next generation of data science and analytics in an AI-driven landscape.

Sravya Madipalli

Senior DS Leader (Ex-Microsoft)

Sravya Madipalli is a Senior Manager of Data Science with 14+ years of experience helping teams make better decisions with data. She has built and led data science and product analytics teams at Microsoft, eBay, Nextdoor, and Superhuman (prev. Grammarly), working closely with product, engineering, marketing, and leadership. Her expertise spans experimentation, metrics design, modeling, analytics, and translating complex user behavior into clear, actionable insights.

Hai Guan

Head of Data at Ontra, Ex-LinkedIn

Hai Guan leads the data organization at Ontra, the leading legal tech AI solutions for private markets. He previously led Data Science & Analytics at LinkedIn, Nextdoor, Pinterest, and Meta. He's spent a decade teaching product development teams how to ask questions that actually drive decisions—and now teaches how to combine that judgment with AI to move 10x faster.

See all products from AI

Sign up to join this lesson

By continuing, you agree to Maven's Terms and Privacy Policy.