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Week 4: Metric Design and Business Outcome Linkage · Lesson 4.4

Segmentation strategy for AI systems

Where does the system work well, where does it fail, and how do we structure segments to see it?

Retired course. Due to the fast pace of AI, this course was retired before full release. Exercises, datasets, and videos referenced in this lesson are not available. The slide content and frameworks remain free to study.

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Reader Notes

This is Week 4, Lesson 4: Segmentation Strategy. The short version: aggregate metrics lie. Not because the math is wrong, but because they compress away the details that matter. This lesson covers how to catch those lies before shipping. How to break down quality metrics by the dimensions that actually matter for user value and business decisions. How to detect when an overall number that looks fine is hiding catastrophic failure in a specific segment. And how to prioritize which segments to fix first when not everything can be fixed at once. By the end, the deliverable is a Segment Prioritization Schema: a structured, decision-ready artifact that goes straight into a portfolio. It is the thing to bring to a ship meeting instead of a gut feeling. The core question this lesson teaches: "86% for whom?" Three words. That is the question that prevents disasters.

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