Summary  
This chapter introduces data segmentation, showing how slicing metrics by relevant dimensions (e.g., customer type, plan) uncovers actionable insights that aggregates hide.  

General domain of usage  
Business metrics analysis

A metric, a window, a baseline — and the number can still hide things. Aggregate numbers are averages of opposites.

"MRR up 4%" can mean enterprise up 14% and SMB down 6%. The aggregate is the lie. The segment is the answer.

## Why Segments Matter More Than Aggregates

| Aggregate Says | Segment Reveals |
|---|---|
| Revenue up 4% | Enterprise +14%, SMB −6% |
| Churn flat | Annual plans steady, monthly plans churning fast |
| Sign-ups stable | Paid channel collapsed, organic compensating |

Without a segment, you can't act on a number — you can only describe it.

>## Task
>
>Bring Claude a question that already has a metric, a window, and a baseline locked:
>
>><b>Our MRR grew 4% in Q1 versus Q4. What does that tell us?
>
>Claude will refuse to interpret it until you pick a specific segment and explain why that slice matters more than the aggregate. "All customers" doesn't count.
>
>Done when you've picked a segment and named why it's the right one to look at.

A hands-on course for non-technical managers and operators who need to turn raw business data into decisions, not summaries. You'll learn how to frame analytical questions sharply, read spreadsheets and internal docs through AI, combine internal numbers with market context, and build your own analyst agent that produces decision-ready briefs end-to-end.

Introduction to business analytics through the lens of AI — why raw data doesn't make decisions, the three layers of analytical questions, and where an AI assistant actually fits in the workflow.



Train the framing reflex. Turn vague business questions into sharp ones, pick the metric that actually matters, and lock down time windows, segments, and comparisons before touching any data.



Hands-on labs with the three sources every operator hits: sales spreadsheets, strategy docs in Notion, and market context from the web. Single-tool labs first, then a combined investigation.



Move from running labs to building your own. Write the persona, wire up the tools, and create an agent that delivers weekly KPI briefs and pre-meeting decision packs — finishing with a capstone Quarterly Business Review.

