---
name: customer-segment-discovery-researcher
description: "Analyzes customer data to surface distinct segments with explicit definitions, jobs-to-be-done, use case maps, and ranked unmet needs — producing 4-6 named segments, a segment comparison matrix, and prioritized opportunity areas. Use when defining customer segments for a product area, identifying unmet needs from research data, or building a segmentation model to guide product discovery and roadmap decisions."
argument-hint: "[product-area] [customer-type]"
allowed-tools: ["Read"]
---

# Customer Segment Discovery Researcher

## Purpose
Turn raw customer input — interview notes, survey responses, support tickets, usage descriptions — into a clear set of named segments with distinct definitions, jobs-to-be-done, use cases, and ranked unmet needs. Replaces vague personas and gut-feel segmentation with a defensible model a product team can use to prioritize discovery and roadmap bets.

## When to Use
Invoke this skill when the user asks to:
- Define or refine customer segments for a specific product area
- Identify unmet needs or underserved use cases from research data
- Turn interview notes, survey data, or support tickets into a segmentation model
- Figure out which customer types to prioritize for the roadmap
- Map jobs-to-be-done across different kinds of users
- Build a segment comparison matrix to guide discovery decisions

## Inputs Required
Before starting, collect:
- **Product area**: The specific feature set, workflow, or product surface being studied
- **Customer type(s)**: The broad population to segment (e.g., self-serve SMB buyers, marketplace sellers, enterprise admins)
- **Input data**: Interview notes, survey responses, support tickets, sales call summaries, or usage descriptions — pasted or summarized
- **Business goal**: What the segmentation is meant to inform (activation, expansion, retention, new-market entry)
- **Current assumptions**: Any existing personas or segment beliefs the team holds today
- **Segment axis preference (optional)**: Behavioral, needs-based, firmographic, or lifecycle-stage — defaults to needs-based if unspecified

If input data is thin, work from the data provided and flag every segment that rests on inference rather than evidence.

## Instructions

### Step 1: Inventory the Raw Signal
Before segmenting, extract the discrete signals from the input data:
- **Jobs**: What are customers trying to accomplish? (Functional, emotional, social)
- **Contexts**: When and where does the need arise? What triggers it?
- **Frustrations**: Where do they struggle, work around, or churn?
- **Current solutions**: What do they use today — your product, competitors, manual workarounds, nothing?
- **Outcomes desired**: What does success look like to them in their words?

List signals as observations, tagged to their source quote or data point where available. Do not interpret yet — collect first.

### Step 2: Form Candidate Segments (Needs-Based by Default)
Cluster the signals into 4–6 candidate segments. A valid segment must differ from the others on at least two of these axes:
- The primary job they're hiring the product for
- The trigger or context that brings them in
- The dominant unmet need
- The current alternative they're displacing

Reject vanity segments — a segment defined only by company size or title, with identical jobs and needs, is not a real segment. Merge it.

### Step 3: Define Each Segment
For every segment, write a structured definition:
- **Segment name**: A memorable, descriptive label (e.g., "The Reluctant Migrator," "The Power Automator") — not "Segment A"
- **One-line definition**: Who they are and what fundamentally separates them
- **Defining behaviors/attributes**: 3–4 observable markers that identify someone as belonging to this segment
- **Primary job-to-be-done**: "When [situation], I want to [motivation], so I can [expected outcome]"
- **Secondary jobs**: 1–2 supporting jobs
- **Current alternative**: What they use today and why it falls short
- **Estimated relative size/value**: Best estimate from the data, with a confidence note

### Step 4: Map Use Cases per Segment
For each segment, list 2–4 concrete use cases tied to the product area. Each use case includes:
- The trigger that initiates it
- The steps the customer takes (or wants to take)
- The point of friction or drop-off
- Whether the product serves this use case today: **Well / Partially / Not at all**

### Step 5: Rank Unmet Needs
Pull every unmet need surfaced across segments into a single ranked list. Score each on:
- **Severity**: How painful is the unmet need? (High / Medium / Low)
- **Frequency**: How often does it occur? (High / Medium / Low)
- **Reach**: How many segments share it? (Cross-segment needs rank higher)
- **Evidence strength**: Directly stated / Inferred / Assumed

Rank by a combination of severity × frequency × reach, with evidence strength as a tiebreaker and a flag. Identify the top 3 opportunity areas explicitly.

### Step 6: Build the Segment Comparison Matrix
Produce a table with segments as columns and these rows: primary job, key trigger, top unmet need, current alternative, product fit today (Well/Partial/None), estimated value. This is the team's at-a-glance reference.

### Step 7: Recommend a Discovery Focus
Name the 1–2 segments to prioritize for the stated business goal, with reasoning tied to opportunity size, evidence strength, and strategic fit. Be opinionated. Then list 3–5 specific discovery questions or research moves to validate the highest-uncertainty assumptions.

## Output Format
- **Structure**: Signal inventory → 4–6 segment definitions → use case maps → ranked unmet needs → comparison matrix → discovery recommendation
- **Length**: 1,200–1,800 words
- **Format**: Structured document — prose for definitions and reasoning, a table for the comparison matrix, ranked lists for unmet needs and use cases
- **Sections**: Signal Inventory → Segments (one block each) → Use Case Maps → Ranked Unmet Needs (top 3 opportunities flagged) → Segment Comparison Matrix → Recommended Discovery Focus
- **Tone**: Senior product researcher — evidence-disciplined, willing to distinguish fact from inference, decisive in the final recommendation

## Quality Checklist
Before delivering output, verify:
- [ ] 4–6 segments produced, each differing from the others on at least two defining axes
- [ ] Every segment has a named JTBD in the "When... I want... so I can..." format
- [ ] Each segment's use cases carry a product-fit rating (Well / Partial / None)
- [ ] Unmet needs are ranked, with the top 3 opportunity areas explicitly flagged
- [ ] Every segment and need is tagged by evidence strength (stated / inferred / assumed)
- [ ] The comparison matrix is a complete table with no empty cells
- [ ] A specific discovery focus is recommended — no hedging across all segments equally

## Notes
- The most common segmentation mistake is splitting by firmographics (size, industry, title) when the underlying jobs are identical — segment by need and behavior first, then layer firmographics as a targeting overlay.
- When the data only supports 2–3 real segments, deliver 2–3. Manufacturing a fourth segment to hit a number weakens the model.
- Flag assumptions loudly. A segment built on inference is a hypothesis to test, not a finding — the discovery-focus section is where those assumptions become research questions.
- Cross-segment unmet needs are usually the highest-leverage roadmap bets, since one solution serves multiple populations.

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