Synthesize audience insights from multiple data sources into unified personas and segments.
Creates unified personas and audience segments by synthesizing data from analytics, CRM, social, and research sources. Triggered by requests like "analyze audience" or "build personas" to generate comprehensive targeting strategies and growth recommendations.
/plugin marketplace add jmagly/ai-writing-guide/plugin install marketing@aiwgThis skill inherits all available tools. When active, it can use any tool Claude has access to.
Synthesize audience insights from multiple data sources into unified personas and segments.
This skill creates comprehensive audience understanding by:
When triggered, this skill:
Gathers audience data:
Identifies patterns:
Builds personas:
Creates segments:
Generates recommendations:
first_party:
analytics:
source: Google Analytics, Mixpanel
data:
- demographics
- interests
- behavior
- conversion_paths
crm:
source: Salesforce, HubSpot
data:
- customer_attributes
- purchase_history
- lifetime_value
- engagement_history
email:
source: Mailchimp, Klaviyo
data:
- email_engagement
- preferences
- segments
product:
source: Product analytics
data:
- feature_usage
- retention
- activation
second_party:
social:
source: Instagram, LinkedIn, Twitter
data:
- follower_demographics
- engagement_patterns
- content_preferences
advertising:
source: Meta, Google, LinkedIn
data:
- audience_overlap
- conversion_audiences
- lookalike_performance
partnerships:
source: Partner data shares
data:
- co-marketing audiences
- industry benchmarks
third_party:
research:
source: Industry reports, surveys
data:
- market_size
- industry_trends
- competitor_audiences
enrichment:
source: Clearbit, ZoomInfo
data:
- firmographics
- technographics
- intent_signals
# Persona: [Name]
## Overview
| Attribute | Value |
|-----------|-------|
| Name | Tech-Savvy Tara |
| Role | Marketing Manager |
| Age Range | 28-35 |
| Experience | 5-8 years |
| Company Size | 50-200 employees |
| Industry | SaaS, Tech |
## Demographics
### Professional
- **Title**: Marketing Manager, Growth Lead
- **Seniority**: Mid-level
- **Department**: Marketing, Growth
- **Reports to**: CMO, VP Marketing
- **Team size**: 2-5 direct reports
### Personal
- **Education**: Bachelor's, Marketing/Business
- **Location**: Urban, tech hubs
- **Income**: $75-100K
- **Tech adoption**: Early adopter
## Psychographics
### Goals
1. Prove marketing ROI to leadership
2. Automate repetitive tasks
3. Stay ahead of industry trends
4. Advance career to director level
### Challenges
1. Limited budget vs. big ambitions
2. Lack of technical resources
3. Proving attribution across channels
4. Keeping up with platform changes
### Motivations
- **Achiever**: Wants measurable results
- **Learner**: Values staying current
- **Collaborator**: Seeks team success
- **Efficiency-seeker**: Hates wasted time
### Fears
- Falling behind competitors
- Wasting budget on ineffective campaigns
- Not having data to support decisions
- Missing key industry shifts
## Behavior
### Content Consumption
- **Formats**: Podcasts, newsletters, Twitter
- **Topics**: Marketing trends, case studies, how-tos
- **Sources**: Marketing Brew, HubSpot Blog, industry Twitter
- **Time**: Morning commute, lunch breaks
### Purchase Behavior
- **Research**: Extensive (4-6 week cycle)
- **Influencers**: Peers, G2 reviews, case studies
- **Decision factors**: ROI proof, ease of use, integrations
- **Barriers**: Price, implementation time, approval process
### Channel Preferences
| Channel | Preference | Best For |
|---------|------------|----------|
| Email | High | Nurture, updates |
| LinkedIn | High | Professional content |
| Webinars | Medium | Deep dives |
| Twitter | Medium | News, trends |
| Phone | Low | Only when ready |
## Customer Journey
### Awareness
- **Trigger**: Frustration with current tools
- **Actions**: Google search, ask peers, browse LinkedIn
- **Content**: Blog posts, social proof, thought leadership
### Consideration
- **Trigger**: Identified potential solutions
- **Actions**: Demo requests, free trials, case study reviews
- **Content**: Comparison guides, ROI calculators, webinars
### Decision
- **Trigger**: Validated fit, secured budget
- **Actions**: Negotiate, involve stakeholders, trial
- **Content**: Pricing details, implementation guides, success stories
### Retention
- **Trigger**: Ongoing value demonstration
- **Actions**: Feature adoption, support engagement
- **Content**: Best practices, new features, community
## Messaging
### Value Props That Resonate
1. "Save 10 hours per week on reporting"
2. "Prove ROI to your leadership in one click"
3. "Join 5,000+ marketers who increased conversions 40%"
### Objection Handlers
| Objection | Response |
|-----------|----------|
| "Too expensive" | ROI payback in 3 months |
| "No time to implement" | Live in 2 hours, not weeks |
| "Current tool works" | Missing these 3 key features |
### Tone & Voice
- Professional but approachable
- Data-driven with clear examples
- Empathetic to time constraints
- Action-oriented
## Targeting
### Ideal Channels
1. LinkedIn (professional context)
2. Email (direct, personalized)
3. Podcast ads (captive attention)
4. Industry events (high-intent)
### Lookalike Indicators
- HubSpot/Mailchimp users
- Marketing conference attendees
- Marketing podcast subscribers
- G2 reviewer profiles
### Exclusions
- Enterprise (100K+ employees)
- Agencies (different needs)
- Non-marketing roles
## Data Sources
- Analytics: 45% of traffic matches profile
- CRM: 2,340 customers in segment
- Survey: 2023 customer research (n=500)
- Social: LinkedIn follower analysis
segmentation_types:
behavioral:
name: Behavioral Segments
dimensions:
- engagement_level: [highly_active, active, passive, dormant]
- feature_usage: [power_user, standard, limited]
- purchase_frequency: [frequent, occasional, one_time]
use_cases:
- Lifecycle marketing
- Retention campaigns
- Upsell targeting
value_based:
name: Value Segments
dimensions:
- ltv_tier: [platinum, gold, silver, bronze]
- revenue_potential: [high, medium, low]
- expansion_likelihood: [likely, possible, unlikely]
use_cases:
- Resource allocation
- Account prioritization
- Pricing strategies
demographic:
name: Demographic Segments
dimensions:
- company_size: [enterprise, mid_market, smb, startup]
- industry: [tech, finance, healthcare, retail, etc]
- geography: [region, country, city_tier]
use_cases:
- Content personalization
- Sales territory planning
- Localization
psychographic:
name: Psychographic Segments
dimensions:
- buying_style: [innovator, pragmatist, conservative]
- decision_process: [solo, committee, consensus]
- risk_tolerance: [risk_taker, calculated, risk_averse]
use_cases:
- Message positioning
- Sales approach
- Content tone
# Audience Synthesis Report
**Date**: 2025-12-08
**Scope**: Full audience analysis
**Data Sources**: 6 platforms, 2 research studies
## Executive Summary
### Audience Composition
| Segment | % of Total | Revenue % | Growth YoY |
|---------|------------|-----------|------------|
| Power Users | 15% | 45% | +22% |
| Regular Users | 35% | 35% | +8% |
| Occasional Users | 30% | 15% | -5% |
| At-Risk | 20% | 5% | -15% |
### Key Insights
1. **High-value concentration**: 15% of users drive 45% of revenue
2. **Growth opportunity**: Mid-market segment growing fastest (+18%)
3. **Retention risk**: 20% of audience showing disengagement signals
4. **Channel shift**: Mobile usage up 35%, desktop flat
## Persona Summary
### Primary Personas
| Persona | % of Audience | LTV | Acquisition Cost |
|---------|---------------|-----|------------------|
| Tech-Savvy Tara | 35% | $2,400 | $180 |
| Enterprise Ed | 20% | $12,000 | $1,200 |
| Startup Sam | 25% | $600 | $45 |
| Agency Amy | 20% | $1,800 | $220 |
### Persona Details
[Link to full persona documents]
## Segment Analysis
### By Engagement Level
Highly Active ████████████████ 25% Active ████████████████████████ 35% Passive ████████████████ 25% Dormant ██████████ 15%
### By Company Size
Enterprise ████████ 12% Mid-Market ████████████████████ 28% SMB ████████████████████████████ 42% Startup ████████████████ 18%
### By Industry
| Industry | Users | Growth | Opportunity |
|----------|-------|--------|-------------|
| Tech/SaaS | 35% | +15% | Maintain |
| Finance | 18% | +25% | Expand |
| Healthcare | 12% | +8% | Monitor |
| Retail | 15% | +5% | Optimize |
| Other | 20% | +3% | Evaluate |
## Growth Opportunities
### 1. Finance Vertical Expansion
- **Opportunity**: Growing 25% YoY, only 18% of current base
- **Recommendation**: Develop finance-specific content and case studies
- **Estimated impact**: +$500K ARR
### 2. Power User Amplification
- **Opportunity**: Power users have 4x referral rate
- **Recommendation**: Launch referral program targeting power users
- **Estimated impact**: +200 customers/quarter
### 3. At-Risk Win-Back
- **Opportunity**: 20% of users showing disengagement
- **Recommendation**: Automated re-engagement campaign
- **Estimated impact**: Save $150K ARR churn
## Targeting Recommendations
### Lookalike Audiences
| Source Audience | Platform | Expected ROAS |
|-----------------|----------|---------------|
| Power Users | Meta | 3.5x |
| Recent Converters | Google | 2.8x |
| High LTV | LinkedIn | 2.2x |
### Exclusion Recommendations
- Current customers (all platforms)
- Competitors' employees
- Students/job seekers
- Non-target geographies
### Channel Allocation
| Persona | Primary Channel | Secondary | Budget % |
|---------|-----------------|-----------|----------|
| Tech-Savvy Tara | LinkedIn | Email | 40% |
| Enterprise Ed | Events | LinkedIn | 25% |
| Startup Sam | Content/SEO | Twitter | 20% |
| Agency Amy | Partner | Email | 15% |
## Action Items
1. [ ] Build finance vertical content series
2. [ ] Launch power user referral program
3. [ ] Deploy at-risk re-engagement automation
4. [ ] Update lookalike audiences with Q4 data
5. [ ] Create persona-specific landing pages
## Data Quality Notes
- CRM data 94% complete
- Analytics sampling at 95% confidence
- Survey margin of error: ±4%
- Social data limited to organic followers
User: "Analyze our audience"
Skill executes:
1. Pull data from all sources
2. Identify patterns and segments
3. Build/update personas
4. Generate recommendations
Output:
"Audience Analysis Complete
Total Addressable: 45,000 users
Active: 32,000 (71%)
Key Segments:
1. Power Users (15%): High LTV, expansion ready
2. Growing Mid-Market (+18% YoY)
3. At-Risk (20%): Needs re-engagement
Top Personas:
- Tech-Savvy Tara (35%): Your core user
- Enterprise Ed (20%): Highest LTV ($12K)
- Startup Sam (25%): Highest volume, lowest LTV
Growth Opportunities:
1. Finance vertical: +25% growth, underserved
2. Power user referrals: 4x rate potential
3. At-risk save: $150K ARR protection
Report: .aiwg/marketing/audience/synthesis-2025-12.md"
User: "Build persona for enterprise buyers"
Skill creates:
- Aggregate enterprise customer data
- Identify common patterns
- Build comprehensive persona
Output:
"Enterprise Persona: 'Enterprise Ed'
Profile:
- Role: VP/Director level
- Company: 500-5000 employees
- Budget: $50K+ annual
- Decision: 3-6 month cycle
Key Insights:
- Values: Security, support, scalability
- Concerns: Implementation risk, vendor stability
- Content: Case studies, ROI calculators, demos
- Channel: Events, direct outreach, LinkedIn
Persona saved: .aiwg/marketing/personas/enterprise-ed.md"
This skill uses:
data-pipeline: Source marketing dataproject-awareness: Context for analysisartifact-metadata: Track audience artifactsagents:
analysis:
agent: marketing-analyst
focus: Data analysis and pattern identification
research:
agent: market-researcher
focus: External research and enrichment
strategy:
agent: positioning-specialist
focus: Targeting and positioning recommendations
persona_config:
max_personas: 5
refresh_frequency: quarterly
data_requirements:
- min_sample_size: 100
- required_sources: 3+
- recency: <90_days
segmentation_rules:
min_segment_size: 5%
max_segments: 10
required_dimensions:
- engagement
- value
- lifecycle
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