audience-persona-builder

name	Audience Persona Builder
description	Builds 5-7 detailed audience personas from a URL with demographics, psychographics, pain points, buying triggers, platform-specific targeting parameters, persona scoring, and negative audiences

Audience Persona Builder

Skill Purpose

Build 5-7 hyper-detailed audience personas from a business URL. Each persona goes far beyond basic demographics — it maps psychographic profiles, buying triggers, objections, content consumption habits, platform presence, and ready-to-use targeting parameters for Meta, Google, LinkedIn, TikTok, and Pinterest. Includes persona relevance scoring (1-5) and a negative audience section defining who NOT to target. Produces a single, copy-paste-ready deliverable that an ad buyer can immediately use to build campaigns.

When to Use

• User runs /ads audience <url>
• User asks to build audience personas, customer profiles, or targeting research
• Called as a subagent from /ads strategy (the main orchestrator)
• User wants to know "who should I target?" for a business
• User needs platform-specific targeting parameters for campaign setup

Input Requirements

• Required: A business URL to analyze
• Optional: Industry context, existing customer data, geographic focus, budget range

How to Execute

Step 1: Business Intelligence Gathering

Fetch the business URL using WebFetch and extract:

Data Point	Where to Find
Business name	Page title, logo, about page
Industry/category	Services offered, product types
Value proposition	Hero section, tagline, about page
Price positioning	Pricing page, product prices, "starting at" language
Geographic focus	Service areas, locations, shipping info
Current customers	Testimonials, case studies, reviews
Product/service types	Product pages, service descriptions
Brand tone	Copy style, imagery, color palette
Trust signals	Certifications, awards, years in business, client logos
Content topics	Blog posts, resources, FAQ sections

Run supplementary searches:

WebSearch: "[Business Name]" reviews
WebSearch: "[Business Name]" customers testimonials
WebSearch: "[Industry]" target audience demographics
WebSearch: "[Industry]" buyer persona research 2025
WebSearch: "[Competitor]" "who buys" OR "target market" OR "customer profile"

Step 2: Industry Audience Intelligence

Based on the detected industry, pull standard audience benchmarks:

SaaS/Software:

• Decision makers: CTOs, VPs Engineering, Product Managers, IT Directors
• Influencers: Individual contributors who discover tools
• Budget holders: CFOs, COOs, department heads
• Research behavior: G2 reviews, Product Hunt, Reddit, comparison articles

E-commerce:

• Impulse buyers vs. researchers
• Price-sensitive vs. quality-focused segments
• Brand loyal vs. deal hunters
• Social commerce behavior: Instagram shops, TikTok shop, Pinterest

Local Services:

• Emergency/urgent need buyers
• Planned purchase/project buyers
• Referral-driven customers
• Neighborhood/community-oriented segments

Agency/Professional Services:

• Decision timeline: 30-90 day sales cycles
• Committee buyers vs. solo decision makers
• Budget-constrained vs. ROI-focused
• Relationship-driven vs. results-driven

Creator/Course:

• Aspiration-driven buyers
• Career changers vs. skill upgraders
• DIY vs. guided learning preference
• Community seekers vs. content consumers

Step 3: Build Persona Profiles

Build 5-7 personas following this exact structure for each:

---

Persona Template

### Persona [Number]: [Persona Name] — "[Memorable Tagline]"

**Relevance Score:** [1-5 stars] ★★★★☆
**Revenue Potential:** [Low / Medium / High / Very High]
**Estimated Audience Size:** [Small / Medium / Large]
**Acquisition Difficulty:** [Easy / Moderate / Hard]
**Recommended Priority:** [Primary / Secondary / Tertiary]

---

#### Demographics
| Attribute | Detail |
|---|---|
| Age range | [range] |
| Gender split | [percentage breakdown] |
| Income level | [range and bracket] |
| Education | [level] |
| Job titles | [3-5 specific titles] |
| Company size | [employee range or N/A] |
| Location type | [urban/suburban/rural + specific geos if applicable] |
| Family status | [single/married/parent + relevance] |
| Device usage | [mobile-first / desktop-heavy / multi-device] |

#### Psychographics
| Attribute | Detail |
|---|---|
| Core values | [3-4 values] |
| Aspirations | [what they want to become/achieve] |
| Fears | [what keeps them up at night] |
| Identity | [how they see themselves] |
| Decision style | [analytical/emotional/social proof/authority-driven] |
| Brand affinities | [brands they already buy from] |
| Media consumption | [podcasts, YouTube channels, newsletters, blogs] |
| Social behavior | [lurker/engager/creator + which platforms] |

#### Pain Points (ranked by intensity)
1. **[Pain Point 1]** — [1-2 sentence description of the pain and its impact]
2. **[Pain Point 2]** — [description]
3. **[Pain Point 3]** — [description]
4. **[Pain Point 4]** — [description]
5. **[Pain Point 5]** — [description]

#### Buying Triggers
What makes this persona pull out their wallet RIGHT NOW:
- **Trigger 1:** [specific event or realization]
- **Trigger 2:** [specific event or realization]
- **Trigger 3:** [specific event or realization]
- **Trigger 4:** [specific event or realization]

#### Objections & Hesitations
What stops them from buying:
| Objection | Severity | How to Overcome |
|---|---|---|
| [objection 1] | High/Med/Low | [counter-strategy for ad copy] |
| [objection 2] | High/Med/Low | [counter-strategy] |
| [objection 3] | High/Med/Low | [counter-strategy] |
| [objection 4] | High/Med/Low | [counter-strategy] |


| Platform | Behavior | Content Types They Engage With |
|---|---|---|
| YouTube | [how they use it] | [specific content types] |
| Instagram | [how they use it] | [specific content types] |
| TikTok | [how they use it] | [specific content types] |
| LinkedIn | [how they use it] | [specific content types] |
| Podcasts | [which ones] | [topics] |
| Newsletters | [which ones] | [topics] |
| Reddit | [subreddits] | [discussion types] |
| Google Search | [what they search for] | [query patterns] |




 Interests: [10-15 specific targetable interests]
 Behaviors: [5-7 behavioral targeting options]
 Lookalike source: [what custom audience to seed from]
 Exclusions: [who to exclude within this targeting]


 Search keywords: [10-15 keywords this persona would search]
 In-market audiences: [Google's in-market segments]
 Affinity audiences: [Google's affinity segments]
 Custom intent keywords: [5-7 high-intent keywords]


 Job titles: [5-7 exact titles]
 Job functions: [2-3 functions]
 Industries: [3-5 industries]
 Company sizes: [ranges]
 Seniority levels: [levels]
 Skills: [5-7 skills to target]
 Groups: [relevant LinkedIn groups]


 Interest categories: [TikTok's interest targeting]
 Behavioral targeting: [video interaction types]
 Creator categories: [types of creators they follow]
 Hashtag targeting: [relevant hashtags]


 Interest targeting: [Pinterest interest categories]
 Keyword targeting: [search terms on Pinterest]
 Actalike audiences: [seed audience description]


  [what opening line would stop their scroll]
  [the core emotion to tap into]
  [what evidence convinces them — stats, testimonials, demos, case studies]
  [soft ask vs. hard ask, what language works]
  [video/image/carousel + style — UGC, polished, meme, etc.]

Step 4: Persona Scoring Matrix

After building all personas, create a comparison matrix:

## Persona Scoring Matrix

| Persona | Relevance (1-5) | Revenue Potential | Audience Size | Acquisition Cost | Priority |
|---|---|---|---|---|---|
| [Persona 1] | ★★★★★ | Very High | Medium | Moderate | Primary |
| [Persona 2] | ★★★★☆ | High | Large | Easy | Primary |
| [Persona 3] | ★★★★☆ | Medium | Large | Easy | Secondary |
| [Persona 4] | ★★★☆☆ | High | Small | Hard | Secondary |
| [Persona 5] | ★★★☆☆ | Medium | Medium | Moderate | Tertiary |
| [Persona 6] | ★★☆☆☆ | Low | Large | Easy | Tertiary |

Scoring criteria:

• Relevance (1-5): How closely this persona matches the business's ideal customer
  • 5 = Perfect match, highest conversion probability
  • 4 = Strong match, proven buyer profile
  • 3 = Moderate match, needs nurturing
  • 2 = Weak match, low conversion expected
  • 1 = Marginal match, only target if budget allows
• Revenue Potential: Expected lifetime value of this persona
• Audience Size: How large is this segment on ad platforms
• Acquisition Cost: Estimated relative cost to acquire this persona
• Priority: Primary (target first), Secondary (expand to), Tertiary (test with remaining budget)

Step 5: Negative Audiences

Define who NOT to target. This section saves ad spend and improves ROAS.

## Negative Audiences — Who NOT to Target

### Hard Exclusions (always exclude)
| Audience | Why Exclude | Platform Exclusion Method |
|---|---|---|
| [audience 1] | [reason — tire kickers, wrong intent, etc.] | [how to exclude on Meta, Google, etc.] |
| [audience 2] | [reason] | [exclusion method] |
| [audience 3] | [reason] | [exclusion method] |
| [audience 4] | [reason] | [exclusion method] |
| [audience 5] | [reason] | [exclusion method] |

### Soft Exclusions (exclude in early campaigns, test later)
| Audience | Why Consider Excluding | When to Test |
|---|---|---|
| [audience 1] | [reason] | [conditions for testing] |
| [audience 2] | [reason] | [conditions] |
| [audience 3] | [reason] | [conditions] |

### Negative Keyword Themes (Google Ads)
- [theme 1]: [list of negative keywords]
- [theme 2]: [list of negative keywords]
- [theme 3]: [list of negative keywords]
- [theme 4]: [list of negative keywords]

### Audience Suppression Lists
- **Existing customers:** Suppress from acquisition campaigns (upload customer email list)
- **Past converters:** Suppress from top-of-funnel (use pixel data)
- **Job seekers:** Exclude "[company name] jobs/careers" searches
- **Competitors' employees:** Exclude unless running competitive conquesting
- **Students/researchers:** Exclude unless product is education-focused

Step 6: Cross-Persona Insights

## Cross-Persona Insights

### Shared Pain Points Across All Personas
1. [pain point that appears in 3+ personas]
2. [pain point that appears in 3+ personas]
3. [pain point that appears in 3+ personas]

### Universal Buying Triggers
1. [trigger that works across most personas]
2. [trigger that works across most personas]

### Platform Priority Ranking
Based on where these personas spend time:
1. **[Platform]** — Reaches [X] of [Y] personas, best for [objective]
2. **[Platform]** — Reaches [X] of [Y] personas, best for [objective]
3. **[Platform]** — Reaches [X] of [Y] personas, best for [objective]
4. **[Platform]** — Reaches [X] of [Y] personas, best for [objective]

### Campaign Structure Recommendation
- **Campaign 1 (Primary):** Target Personas [X, Y] on [Platform] — [objective]
- **Campaign 2 (Secondary):** Target Personas [X, Y] on [Platform] — [objective]
- **Campaign 3 (Testing):** Target Persona [X] on [Platform] — [objective]

### Messaging Theme Matrix
| Theme | Persona 1 | Persona 2 | Persona 3 | Persona 4 | Persona 5 |
|---|---|---|---|---|---|
| [theme 1] | Strong | Moderate | Weak | Strong | Moderate |
| [theme 2] | Weak | Strong | Strong | Moderate | Weak |
| [theme 3] | Moderate | Moderate | Strong | Strong | Strong |

Output Format

Save the complete analysis to ADS-AUDIENCE.md in the current working directory.

File structure:

# Audience Persona Report: [Business Name]
> Generated [date] | Source: [URL]
> Ad Readiness — Audience Clarity Score: [X/100]

## Executive Summary
[3-4 sentences: who the ideal customers are, which platforms to prioritize, key insight]

## Persona 1: [Name]
[full persona template]

## Persona 2: [Name]
[full persona template]

[...continue for all 5-7 personas]

## Persona Scoring Matrix
[comparison table]

## Negative Audiences
[full negative audience section]

## Cross-Persona Insights
[shared insights and recommendations]

## Next Steps
1. Start with Persona [X] on [Platform] — highest relevance + largest audience
2. Build lookalike audiences from [seed source]
3. Run `/ads copy <platform>` to generate ad copy tailored to top personas
4. Run `/ads hooks` to generate scroll-stopping hooks for each persona
5. Set up A/B tests between Persona [X] and Persona [Y] messaging

Quality Checklist

Before delivering the output, verify:

• At least 5 personas built with full detail
• Every persona has all sections filled (demographics, psychographics, pain points, triggers, objections, content habits, platform targeting)
• Platform targeting parameters are specific and actionable (not generic)
• Meta interests are real targetable interests in Ads Manager
• Google keywords are actual search terms people use
• LinkedIn titles are real job titles
• Relevance scoring is applied and justified
• Negative audiences section is complete
• Cross-persona insights identify patterns
• Campaign structure recommendation is included
• Output is saved to ADS-AUDIENCE.md

Audience Clarity Score (0-100)

Calculate and report at the top of the file:

Factor	Weight	Scoring
Persona specificity	25%	Generic (0-40) / Detailed (41-70) / Hyper-specific (71-100)
Targeting actionability	25%	Vague (0-40) / Usable (41-70) / Copy-paste ready (71-100)
Pain point depth	20%	Surface-level (0-40) / Researched (41-70) / Customer-voice (71-100)
Platform coverage	15%	1-2 platforms (0-40) / 3-4 platforms (41-70) / 5+ platforms (71-100)
Negative audience quality	15%	Missing (0) / Basic (1-40) / Detailed with methods (41-100)

Composite Score = Weighted average across all factors