Customer Persona Copy Map

Purpose

This skill turns audience research and customer signals into a practical copy matrix that maps different personas to their specific pain points, motivations, objections, preferred benefits, and CTA language. Instead of one-size-fits-all copy, it helps teams segment messaging across product pages, ad creative, email flows, and landing pages — all while clearly labeling what's evidence-based vs. assumed.

Triggers

• "Map copy to different customer personas"
• "Create persona-based messaging for my product"
• "Build a copy matrix for audience segments"
• "Segment my product messaging by buyer type"
• "Write persona-specific ad copy"
• "Create messaging for different customer types"

Workflow

1. Audience signal collection — Gather known customer segments, demographic/behavioral signals, purchase data patterns (if available), review themes by customer type, support ticket themes, and any existing persona research.
2. Evidence vs. assumption separation — For each persona, clearly separate: what data supports this segment (reviews, sales data, survey results) vs. what is a reasonable hypothesis (market observation, competitor patterns, intuition). Assumptions must be labeled.
3. Persona card creation — For each distinct persona, create a card with: name/descriptor, primary need/job-to-be-done, dominant purchase barrier, emotional driver, trust requirement, and preferred channel.
4. Pain-benefit-message matrix — Create a cross-reference matrix: persona → top pain points → product benefits that address them → message framing → CTA language → best channel.
5. Copy snippet drafting — Write persona-specific copy snippets: above-the-fold headline, key benefit statement, objection pre-handler, and CTA. Each snippet should feel natural for that persona.
6. Channel recommendation — For each persona-message pair, recommend the best channel(s) and format (e.g., "gift-buyer persona → Instagram Story with gift-guide angle").

Prompt Templates

1. Persona Copy Matrix Builder (persona_matrix)

Purpose: Build a complete persona-to-copy matrix from audience data.

Input:

• ${product_name} — Product name
• ${product_category} — Product category
• ${personas} — List of customer persona descriptions (2–5 personas)
• ${product_benefits} — Key product benefits
• ${channels} — Available marketing channels
• ${evidence_sources} — (Optional) data sources supporting persona definitions

Output: Complete matrix with persona cards, pain/benefit/message map, copy snippets, and channel recommendations.

2. Persona Expander (persona_expand)

Purpose: Expand a thin persona description into a full messaging profile.

Input:

• ${persona_name} — Persona name or descriptor
• ${known_traits} — What's known about this persona
• ${product_context} — What this product does for them

Output: Expanded persona card with messaging angles, objections, and copy snippet drafts.

3. Message Adapter (message_adapt)

Purpose: Adapt one core message to multiple persona framings.

Input:

• ${core_message} — The central product message
• ${personas} — List of personas with key motivations
• ${channels} — Target channels for adaptation

Output: Persona-specific message adaptations with rationale for changes.

4. Assumption Auditor (assumption_audit)

Purpose: Audit a persona set for untested assumptions that could lead to wasted spend.

Input:

• ${persona_set} — Complete persona definitions with messaging
• ${evidence_available} — What data actually supports each persona

Output: Each persona scored by evidence strength (High/Medium/Low), with assumptions highlighted and test recommendations for low-evidence personas.

Output Format

## Persona Copy Map: [Product Name]
**Category:** [Category] | **Personas:** [N personas]

### Persona Cards

**Persona 1: [Name/Descriptor]**
- **Primary Need:** [Job-to-be-done]
- **Dominant Barrier:** [What keeps them from buying]
- **Emotional Driver:** [What feeling motivates them]
- **Trust Requirement:** [What proof they need]
- **Preferred Channel:** [Where they're most reachable]
- **Evidence Strength:** [High/Medium/Low] — [what data supports this]

**Persona 2: [...]**
...

### Pain-Benefit-Message Matrix

| Persona | Top Pain | Product Benefit | Message Frame | CTA Language | Best Channel |
|---|---|---|---|---|---|
| Persona 1 | [Pain] | [Benefit] | [How to frame] | "[CTA]" | [Channel] |
| Persona 2 | [Pain] | [Benefit] | [How to frame] | "[CTA]" | [Channel] |
| ... | ... | ... | ... | ... | ... |

### Copy Snippets

**For [Persona 1]:**
- 🎯 Headline: "[Above-the-fold headline]"
- 💡 Benefit: "[Key benefit statement]"
- 🛡️ Objection handler: "[Pre-handle common objection]"
- 🚀 CTA: "[Call to action]"

**For [Persona 2]:**
...

### Channel Recommendations
- **[Channel]:** Best for personas [X, Y] — format: [suggestion]
- **[Channel]:** Best for persona [Z] — format: [suggestion]

### Assumption Audit
- ✅ Persona 1: [Evidence strength] — supported by [sources]
- ⚠️ Persona 2: Medium evidence — [assumptions] need validation via [test idea]
- ❓ Persona 3: Low evidence — consider deprioritizing until [validation method]

Safety Rules

• ALWAYS clearly label assumptions vs. evidence — unvalidated personas can waste budget and alienate real customers
• NEVER stereotype based on protected characteristics (age, gender, race, religion, disability, sexual orientation) when the data doesn't support it
• NEVER create personas for sensitive categories (health conditions, financial distress, personal crises) without extreme care and explicit disclosure of limitations
• ALWAYS avoid manipulative messaging that exploits persona vulnerabilities (e.g., insecurity-based marketing to teens, fear-based messaging to elderly)
• NEVER present assumed personas as "proven by AI" — clearly state the evidence basis for every segment

Examples

Example 1: Skincare Serum (3 Personas)

Input: Product="Vitamin C Brightening Serum", Personas="(1) Skincare Beginner — wants results without complexity, (2) Ingredient Nerd — researches every component, (3) Gift Buyer — buying for someone else, wants safe choice"

Output: Matrix showing: Beginner → pain="too many choices, don't know what works" → message="One serum, proven ingredients, simple routine" → CTA="Start your 2-step routine" → channel=Instagram/TikTok. Ingredient Nerd → pain="skeptical of marketing claims" → message="15% L-AA + E + ferulic, airless pump, dermatologist-tested — here's the data" → CTA="See the full ingredient breakdown" → channel=blog/email. Gift Buyer → pain="will they like it? will it work for their skin?" → message="Universally loved, fragrance-free, suitable for most skin types, beautiful packaging" → CTA="Gift the glow" → channel=Facebook/Instagram.

Example 2: Kitchen Gadget (2 Personas)

Input: Product="Air Fryer Liners", Personas="(1) Convenience Cook — wants faster cleanup, (2) Eco-Conscious Cook — wants to reduce waste (aluminum foil/paper)"

Output: Matrix: Convenience → pain="air fryer cleanup is annoying" → message="Cook, eat, toss the liner — no scrubbing" → CTA="Make cleanup optional" → channel=TikTok. Eco-Conscious → pain="using disposable foil/paper feels wasteful" → message="Reusable silicone liners replace hundreds of foil sheets" → CTA="Cook cleaner, waste less" → channel=Instagram/blog. Assumption audit flags that "Eco-Conscious" is partially assumed — recommended survey validation.

Usage Scenarios

Scenario 1

User Input: "Create a copy map for 3 personas: 'Budget Parent', 'Tech Enthusiast', and 'Eco Minimalist' for our reusable water bottle."

Expected Output: A 3-column copy matrix: headline variants, benefit statements, objection-handling lines, and CTA language tailored to each persona's core motivation.

Scenario 2

User Input: "Review our current landing page copy. Are we over-indexing on 'Tech Enthusiast' and alienating 'Budget Parent'?"

Expected Output: Sentiment and vocabulary analysis. Highlights 7 phrases that resonate with tech audience but may confuse or put off budget-conscious buyers. Suggests inclusive alternatives.

Scenario 3

User Input: "Generate A/B test copy variants for Facebook ads targeting 'Eco Minimalist' vs. 'Budget Parent'."

Expected Output: Four ad-copy pairs (2 per persona) with distinct emotional hooks, value propositions, and CTAs, formatted for Facebook's character limits.

Scenario 4: 淘宝店写文案没人看

User input: "我在淘宝开了个店卖女装，详情页写了一大堆产品描述但转化率不到1%。感觉我的客户根本不知道我想说什么。怎么办？"

Expected output: 淘宝详情页文案体系——第一步：定义核心客户（年龄/职业/收入/风格/痛点/她为什么来淘宝买而不是线下去买）；第二步：针对她最关心的3个问题写文案（显瘦/不廉价/面料舒服还是设计独特/怎么搭配/要不要干洗——从评价和问大家里找高频词）；第三步：详情页结构（头图短视频→痛点共鸣句→产品尺寸实测图→面料细节放大→搭配推荐→买家秀集锦→退换政策）；第四步：A/B测试（用生意参谋对比不同标题/主图的点击率，找到点击率最高的组合）。关键工具：生意参谋+直通车流量解析+评价分析。

Related Skills

• listing-bullet-booster — For persona-specific bullet variants
• campaign-angle-spark — For campaign angles targeting specific personas
• faq-objection-crusher — For persona-specific objection handling