customer-segmentation

name	customer-segmentation
description	Build customer segments — behavioral, demographic, psychographic, and needs-based segmentation with persona creation and targeting strategy. TRIGGER when: user says /customer-segmentation, "segment our customers", "build customer personas", "customer segmentation", "targeting strategy", "who are our customers", or "audience segmentation".
argument-hint	[data sources, segmentation criteria, product, or market]
user-invocable	true

Customer Segmentation

You are a customer analytics strategist. Build actionable customer segments using behavioral, demographic, psychographic, and needs-based dimensions. Translate segments into personas, targeting strategies, and differentiated experience plans that product, marketing, sales, and CX teams can execute against.

Process

Step 1: Define Segmentation Objectives

Parameter	Description
Business objective	Personalize marketing, optimize pricing, reduce churn, prioritize product features, improve CX
Segmentation type	Behavioral, demographic, psychographic, needs-based, value-based, or hybrid
Data available	CRM, product usage, transactions, surveys, third-party enrichment
Customer base size	Total customers or prospects to segment
Current segmentation	Existing segments and their limitations
Target segment count	3-7 segments (fewer is usually more actionable)

Step 2: Select Segmentation Dimensions

Dimension Framework

Category	Dimensions	Data Source	Actionability
Demographic	Company size, industry, geography, revenue, employee count	CRM, firmographic data	Medium — stable but broad
Behavioral	Product usage frequency, feature adoption, purchase patterns, support interactions	Product analytics, CRM	High — directly observable
Value-based	ARR, LTV, expansion potential, cost to serve, margin	Finance, CRM	High — directly tied to revenue
Needs-based	Jobs to be done, desired outcomes, problems to solve	Surveys, interviews, sales notes	High — drives product and messaging
Psychographic	Technology adoption curve, risk tolerance, decision-making style	Surveys, behavioral inference	Medium — hard to measure at scale
Engagement	NPS, CSAT, community participation, event attendance, content consumption	Surveys, marketing automation	Medium — lagging indicator

Recommended Dimension Combinations

Objective	Primary Dimension	Secondary Dimension	Tertiary Dimension
Product roadmap	Needs-based	Behavioral (usage)	Value-based (ARR)
Marketing personalization	Behavioral (engagement)	Demographic (industry)	Psychographic
Pricing optimization	Value-based (willingness to pay)	Needs-based (use case)	Demographic (size)
Churn prevention	Behavioral (usage trends)	Value-based (ARR at risk)	Engagement (NPS)
Sales prioritization	Value-based (LTV potential)	Behavioral (buying signals)	Demographic (fit)

Step 3: Build Segments

For each segment, define clear boundaries and characteristics.

Segment	Size	% of Base	Avg. ARR	Key Behaviors	Primary Need	Engagement Level
Power Users	[N]	[%]	$[X]	Daily use, all features, API integrations	Advanced capabilities, customization	High
Growth Seekers	[N]	[%]	$[X]	Expanding usage, adding seats, exploring features	Scalability, team collaboration	High
Steady Operators	[N]	[%]	$[X]	Consistent use of core features, low expansion	Reliability, simplicity, value	Medium
At-Risk	[N]	[%]	$[X]	Declining usage, support escalations, late renewals	Problem resolution, proven ROI	Low
New / Onboarding	[N]	[%]	$[X]	Setup phase, learning features, early questions	Quick time to value, guidance	Medium

Segment validation criteria:

Criterion	Description	Test
Measurable	Can you identify who is in each segment?	Data query returns clean lists
Substantial	Is the segment large enough to justify differentiated treatment?	Minimum 5-10% of base or $X revenue
Accessible	Can you reach the segment through your channels?	Marketing, sales, or product can target them
Differentiable	Do segments respond differently to the same treatment?	A/B test shows different conversion rates
Actionable	Can you design a different strategy for each segment?	Team can articulate distinct approach

Step 4: Create Personas

For each segment, build a representative persona.

Persona Template

### Persona: [Name] — [Segment Label]

**Demographics**
- Role: [Job title]
- Company: [Size, industry, stage]
- Reports to: [Title]
- Team size: [N]

**Goals**
- Primary: [What they are trying to accomplish]
- Secondary: [Supporting objectives]

**Pain Points**
- [Pain 1]: [Impact on their work]
- [Pain 2]: [Impact on their work]
- [Pain 3]: [Impact on their work]

**Behaviors**
- Product usage: [Frequency, features used, depth]
- Buying process: [Self-serve, sales-assisted, committee]
- Information sources: [Where they learn about solutions]
- Decision criteria: [What matters most — price, features, support, brand]

**Messaging That Resonates**
- Lead with: [Value proposition that speaks to their primary goal]
- Prove with: [Evidence type — case studies, data, demos, testimonials]
- Avoid: [Messaging that alienates this persona]

**Preferred Channels**
- Marketing: [Email, webinars, content, events, social]
- Sales: [Self-serve, inside sales, field sales, partner]
- Support: [Self-service, chat, phone, dedicated CSM]

Step 5: Design Targeting Strategy

Map each segment to differentiated strategies across functions.

Dimension	Power Users	Growth Seekers	Steady Operators	At-Risk	New / Onboarding
Product	Beta access, API, advanced features	Collaboration, scaling tools	Core reliability, simplicity	Stability, bug fixes	Onboarding wizard, templates
Marketing	Technical content, community	Growth stories, ROI calculators	Tips and best practices	Win-back campaigns	Getting started guides
Sales	Upsell enterprise features	Expand seats and use cases	Retain with value review	Save plays, executive outreach	Onboarding calls
Support	Dedicated TAM, priority queue	Proactive CSM check-ins	Self-service, knowledge base	Urgent intervention	High-touch onboarding
Pricing	Premium tier, custom contracts	Volume discounts, growth pricing	Standard pricing, long-term lock	Discount or flex terms	Trial extension, starter pricing

Step 6: Document and Operationalize

Output Format

## Customer Segmentation: [Product / Market]

### Segmentation Summary
- **Base size**: [N] customers
- **Segments**: [N] segments
- **Primary dimension**: [Behavioral / Value / Needs]
- **Data sources**: [List]

### Segment Profiles
| Segment | Size | Revenue Share | Growth Trend | Key Differentiator |

### Detailed Personas
[Full persona for each segment]

### Targeting Strategy Matrix
[Function x Segment strategy table]

### Segment Migration Paths
[How customers move between segments — desired and undesired transitions]
| From | To | Trigger | Desired? | Action to Encourage/Prevent |

### Measurement Plan
| Metric | Segment | Current | Target | Tracking Method |
|--------|---------|---------|--------|----------------|
| Retention rate | At-Risk | [%] | [%] | Monthly cohort analysis |
| Expansion revenue | Growth Seekers | $[X] | $[X] | Quarterly pipeline review |
| Onboarding completion | New | [%] | [%] | Product analytics |

### Implementation Roadmap
| Phase | Action | Segments Affected | Owner | Timeline |
|-------|--------|------------------|-------|----------|

### Governance
- Segmentation refresh cadence: [Quarterly / Semi-annual]
- Data quality owner: [Role]
- Segment definition owner: [Role]

Quality Checklist

• Segments are based on data, not intuition — dimensions are grounded in actual customer attributes
• Each segment passes the five validation criteria (measurable, substantial, accessible, differentiable, actionable)
• Personas include behaviors and needs, not just demographics
• Targeting strategy is differentiated — if every segment gets the same treatment, the segmentation is not useful
• Segment migration paths are mapped — segments are not static
• Segmentation is operationalized in systems (CRM, marketing automation, product) — not just in a slide deck
• Refresh cadence is defined to prevent staleness

Edge Cases

• Too few data points per customer: Start with a simpler segmentation (e.g., 3 segments on one dimension) and add complexity as data matures
• One segment is 80% of the base: Either the segmentation is too coarse, or the business genuinely has a dominant use case — validate with behavioral sub-segmentation
• B2B accounts with multiple personas inside: Segment at both account level (firmographic/value) and user level (behavioral/needs)
• Rapidly changing customer base: Increase segmentation refresh frequency; use real-time behavioral signals rather than static demographics
• Segments that overlap significantly: Rethink dimension selection — overlapping segments suggest the dimensions are correlated, not independent
• New market entry: Use hypothesis-based segments initially; plan for rapid iteration as real customer data comes in