Audience Segmentation

Use this when a client needs to define, refine, or formalize their audience segments for marketing, product, or sales purposes. Works for both B2B and B2C, whether the client has rich customer data or is building segments from market intuition and early signals.

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How it works

1. You provide a description of customer data, the business model, any current segments, and relevant context
2. The skill builds a multi-dimensional segmentation model covering behavioral, demographic, psychographic, and firmographic dimensions, with activation criteria and channel preferences per segment
3. It returns segment cards the client's marketing, sales, and product teams can use immediately for targeting, messaging, and prioritization

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Prompt

You are building an audience segmentation model for a Kate Makrigiannis consulting engagement. Kate helps clients move from vague "we target everyone" positioning to sharp, actionable segments that drive real marketing and product decisions. Before writing, read knowledge/voice-tone-guide.md -- use the client-facing voice.

Inputs I will provide:

• Customer data description: {{CUSTOMER_DATA}} (what data is available -- e.g., "CRM export with 2,000 customers, usage analytics, NPS survey results," or "we have 50 customers and some anecdotal patterns," or "pre-launch, working from market research and interviews")
• Business model: {{BUSINESS_MODEL}} (how the business makes money -- e.g., "B2B SaaS, $50K ACV, 12-month contracts," "DTC subscription, $29/month," "marketplace with buyer and seller sides")
• Current segments (optional): {{CURRENT_SEGMENTS}} (existing segmentation, formal or informal -- e.g., "we loosely target SMBs and enterprise," "we think of our users as power users and casual users")
• Context (optional): {{CONTEXT}} (why this work is happening now -- e.g., "preparing for GTM launch," "CAC is rising and we need to focus," "launching a new product line," "sales team keeps saying all leads look the same")

Step 1: Assess available data and choose segmentation approach

Evaluate the inputs and determine the right segmentation methodology:

Data Richness	Approach
Rich data (CRM, usage analytics, purchase history)	Data-driven segmentation: cluster analysis patterns, behavioral cohorts, value-based tiers
Moderate data (some customers, qualitative research)	Hybrid approach: pattern recognition from data + market logic
Thin data (pre-launch, few customers)	Hypothesis-driven: build segments from market research, competitor analysis, and jobs-to-be-done

State which approach you are using and why: "Based on the available data, I'm using [approach] because [reason]."

If data is thin, flag:

Step 2: Identify segmentation dimensions

Analyze the data across four dimensions:

Behavioral Segmentation

• Usage patterns (frequency, depth, features used)
• Purchase behavior (AOV, frequency, recency)
• Engagement signals (content consumption, support tickets, community participation)
• Adoption stage (evaluating, onboarding, active, power user, churning)

Demographic Segmentation

• For B2C: age, income, education, location, life stage, household composition
• For B2B: job title, department, seniority, team size, decision-making authority

Psychographic Segmentation

• Values and motivations (efficiency-driven, innovation-seeking, risk-averse, status-conscious)
• Attitudes toward the product category (early adopter, pragmatist, skeptic)
• Pain point intensity (acute vs. mild, urgent vs. eventual)
• Information preferences (data-driven, peer-influenced, expert-guided)

Firmographic Segmentation (B2B only)

• Company size (employees, revenue)
• Industry vertical and sub-vertical
• Company stage (startup, growth, mature, enterprise)
• Technology stack and maturity
• Budget authority and procurement process

Geographic & Linguistic Segmentation (if multi-market)

• Primary language and secondary languages spoken
• Cultural context (individualist vs. collectivist markets, communication style preferences, formality expectations)
• Regulatory environment (data privacy, compliance requirements by region)
• Payment preferences and purchasing power parity
• Content consumption habits by region (preferred channels, platforms, formats)
• Local competitors and market dynamics

Step 3: Define segments

Based on the dimensional analysis, define 3-6 distinct segments. More than 6 is usually too many to activate meaningfully. Fewer than 3 suggests the market is not well understood.

For each segment, create a full segment card:

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Segment [N]: [Segment Name]

[One-sentence description of who this segment is in plain language]

Size estimate: [Approximate percentage of total market or customer base, and absolute numbers if available]

. Validate with market sizing data if available.]`

Profile

Dimension	Detail
Behavioral markers	[How they use the product or engage with the market]
Demographics / Firmographics	[Who they are]
Psychographic profile	[What drives them, how they think]
Adoption stage	[Where they are in the customer journey]
Language / Locale	[Primary language, region, cultural context -- if multi-market]

Needs and Pain Points

Priority	Need / Pain Point	Intensity
1	[Primary need]	[Acute / Moderate / Mild]
2	[Secondary need]	[Intensity]
3	[Tertiary need]	[Intensity]

Behavioral Triggers

Events or conditions that make this segment ready to buy, engage, or upgrade:

• Trigger 1: [event or condition] -- [how to detect it]
• Trigger 2: [event or condition] -- [how to detect it]
• Trigger 3: [event or condition] -- [how to detect it]

Activation Criteria

How to identify this segment in your data:

Signal	Source	Threshold
[Signal 1]	[CRM / analytics / survey / external data]	[specific criteria]
[Signal 2]	[Source]	[Threshold]

Messaging Guidelines

• Key message: [The primary value proposition for this segment, one sentence]
• Proof points to lead with: [2-3 proof points most relevant to this segment]
• Tone adjustment: [How messaging shifts for this segment -- more technical, more outcome-focused, more aspirational]
• Words that resonate: [3-5 terms or phrases this segment responds to]
• Words to avoid: [Terms that alienate or confuse this segment]

Channel Preferences

Channel	Effectiveness	Rationale
[Channel 1]	[High / Medium / Low]	[Why this channel works or doesn't for this segment]
[Channel 2]	[Effectiveness]	[Rationale]
[Channel 3]	[Effectiveness]	[Rationale]

Best channel mix: [Top 2-3 channels for reaching and converting this segment]

Recommended Tactics

• Acquisition: [Specific tactics to attract this segment]
• Activation: [How to get first value]
• Retention: [How to keep them engaged]
• Expansion: [Upsell or cross-sell opportunity]

Segment Economics

Factor	Estimate	Confidence
Expected LTV	[$X range]	[High / Medium / Low]
Expected CAC	[$X range]	[High / Medium / Low]
LTV:CAC ratio	[X:1]	[High / Medium / Low]
Payback period	[X months]	[High / Medium / Low]

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Repeat for each segment.

Step 4: Segment prioritization

Segment Priority Matrix

Segment	Market Size	Revenue Potential	Acquisition Ease	Strategic Fit	Priority
[Segment 1]	[S/M/L]	[$/$$/$$$]	[Easy / Moderate / Hard]	[High / Medium / Low]	[P1 / P2 / P3]
[Segment 2]	[Size]	[Revenue]	[Ease]	[Fit]	[Priority]

Recommended primary segment: [Segment name] because [1-2 sentence rationale grounded in data]

Recommended secondary segment(s): [Segment name(s)] because [rationale]

Step 5: Activation playbook

Immediate Actions (Next 30 Days)

1. [Specific action to start activating the primary segment]
2. [Action to validate segment hypotheses]
3. [Action to set up tracking for segment-specific metrics]

Medium-Term Actions (30-90 Days)

1. [Build segment-specific campaigns]
2. [Test messaging variations by segment]
3. [Refine segment definitions based on early data]

Data Gaps to Close

Gap	Impact	How to Close
[Missing data point]	[Why it matters for segmentation]	[Survey / interview / analytics setup / purchase external data]

Related skills: Pairs with /ideal-customer-profile for deeper profiling of the primary segment. Feeds into /email-campaign-builder for segment-specific campaigns. Feeds into /brand-messaging-framework for audience-tailored messaging. Uses /marketing-roi-analyzer for validating segment economics with real spend data.

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