ads-audience

# ads-audience — Audience Persona Builder > **Portability note:** Self-contained — uses only `WebFetch` + `WebSearch` and writes one Markdown file to the CWD. No VPS-only infra. Runs anywhere Claude Code runs. ## 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 ## Dynamic Workflow orchestration The unit of fan-out here is **one persona** (plus two cross-cutting units: negative audiences, cross-persona synthesis). Personas are file-disjoint research tasks — parallelize them; the synthesis is your own job. 1. **Plan.** Run Step 1 (Business Intelligence) + Step 2 (Industry Intelligence) ONCE, in the orchestrator. Output: a shared brief (business facts, price tier, geo, customer evidence) that every persona branch reuses. From it, name the 5-7 candidate personas before building any. 2. **Parallel fan-out.** Build the 5-7 personas concurrently — each branch owns ONE persona and fills the full template (Step 3) from the shared brief + persona-specific `WebSearch`. No branch may invent business facts; it pulls them from the shared brief or cites its own search. 3. **Adversarial verify (2-of-3).** Before accepting a persona, falsify it through 3 independent lenses; a persona ships only if **≥2 of 3 pass**: - **Targetability lens:** Are Meta interests / Google keywords / LinkedIn titles REAL, selectable parameters (not invented)? Fail = hallucinated targeting. - **Evidence lens:** Do demographics/pain points trace to a source (testimonial, review, industry data) and not just vibes? Fail = uncited assertion. - **Distinctness lens:** Is this persona meaningfully different from the others (not the same buyer relabeled)? Fail = duplicate segment — merge or replace. 4. **Loop-until-dry.** If a persona fails ≥2 lenses, regenerate or replace it (re-search, don't retry blind). Stop when you hold 5-7 personas that each pass — never pad to 7 with weak duplicates. 5. **Synthesize.** YOU write Steps 4-6 (scoring matrix, negative audiences, cross-persona insights) over the verified set — never paste a branch's summary as the verdict. The matrix must rank the personas you actually kept. > Single-pass fallback: if running branches isn't available, do the same sequentially — build → run the 3 lenses → keep/replace — one persona at a time. The verify gate is mandatory either way. ## 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 ```markdown ### 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] | #### Content Consumption Habits | 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] | #### Platform Targeting Parameters **Meta (Facebook/Instagram):** - 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] **Google Ads:** - 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] **LinkedIn:** - 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] **TikTok:** - Interest categories: [TikTok's interest targeting] - Behavioral targeting: [video interaction types] - Creator categories: [types of creators they follow] - Hashtag targeting: [relevant hashtags] **Pinterest:** - Interest targeting: [Pinterest interest categories] - Keyword targeting: [search terms on Pinterest] - Actalike audiences: [seed audience description] #### The Perfect Ad for This Persona - **Hook angle:** [what opening line would stop their scroll] - **Emotional trigger:** [the core emotion to tap into] - **Proof type:** [what evidence convinces them — stats, testimonials, demos, case studies] - **CTA style:** [soft ask vs. hard ask, what language works] - **Creative format:** [video/image/carousel + style — UGC, polished, meme, etc.] ``` ### Step 4: Persona Scoring Matrix After building all personas, create a comparison matrix: ```markdown ## 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. ```markdown ## 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 ```markdown ## 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 |