Metrics 101: Define a North Star with AI

Part of Build Your AI Product Analyst

Hosted by Shane Butler, Sravya Madipalli, and Hai Guan

Wed, Oct 7, 2026

5:00 PM UTC (1 hour)

Virtual (Zoom)

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Automate AI Evals with Claude Code
Shane Butler
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What you'll learn

Run the 7-question checklist

The quick test that tells you whether a candidate North Star actually predicts value, or just looks good on a dashboard.

Tell a leading metric from a lagging one

Spot the metric that moves before revenue does, so you can steer instead of reporting the score after the game.

Break it into the levers you can move

Decompose your North Star into the two or three inputs a team can actually act on this quarter.

Avoid the classic vanity traps

Catch the metrics that go up while the business goes nowhere, before they end up in a goal.

Why this topic matters

Most North Star metrics are vanity metrics in disguise. They climb on a slide while retention sits flat, and a whole quarter gets aimed at the wrong number. AI can pressure-test yours in minutes: it checks the definition, tests whether it leads or lags, and breaks it into levers you can move. This session is the workflow for defining a North Star that holds up, and you leave with a checklist.

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.

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