Audience Intelligence
Research target audiences.
Marketing teams lose hours to ad-hoc, inconsistent audience intelligence work — Research target audiences. Use when: buyer personas, segmentation, Jobs-to-Be-Done, psychographic profiling, audience deep-dive. This playbook turns the process into a repeatable, brand-aware workflow.
Who it's for: competitive intelligence analysts, strategists, marketing leaders
Example
"Run /audience-intelligence for our brand" → Audience Intelligence workflow output with brand context, structured inputs captured, process steps executed, and a complete deliverable ready for review.
CLAUDE.md Template
New here? 3-minute setup guide → | Already set up? Copy the template below.
# Audience Intelligence
# Audience Intelligence
## When to Use This Skill
Activate this module when the user's request involves any of the following:
- **Buyer Persona Creation**: Building detailed profiles of ideal customers for marketing and product decisions
- **Audience Research**: Understanding who a brand's customers or prospects are at a demographic, psychographic, and behavioral level
- **Segmentation Strategy**: Dividing an audience into meaningful groups for targeted marketing
- **Jobs-to-Be-Done (JTBD) Analysis**: Identifying the functional, social, and emotional jobs customers hire a product to doGet new playbooks like this one
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What This Does
Research target audiences.
Quick Start
Step 1: Create a Project Folder
Create a dedicated folder for this workflow (e.g. ~/marketing/audience-intelligence).
Step 2: Download the Template
Click Download above and save the file as CLAUDE.md in that folder.
Step 3: Run the Workflow
Open the folder in Claude Code and describe your goal. Claude will prompt you for any missing inputs, follow the structured process, and produce a complete deliverable.
Inputs You'll Need
Claude will prompt for the brand context, objective, and any asset references needed to produce the deliverable.
How It Works
Primary Workflow: Persona Development & Segmentation
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Discovery & Data Collection
- Gather all available customer data (analytics, CRM exports, survey results, interview transcripts)
- Review existing marketing materials, landing pages, and ads for implicit audience assumptions
- Analyze competitor targeting (who are they going after? what messaging do they use?)
- If no data exists, conduct a market analysis to build hypothesis personas
- Document the data quality level: data-rich, data-limited, or hypothesis-only
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JTBD Analysis
- Identify the core job the customer is hiring the product to do
- Map functional jobs: What task needs to be accomplished?
- Map social jobs: How does the customer want to be perceived by others?
- Map emotional jobs: How does the customer want to feel?
- Identify the "struggling moment" — what triggers the search for a solution?
- Document competing solutions (including non-consumption and manual workarounds)
- Define desired outcomes and how customers measure success
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Persona Construction
- Build 3-5 primary personas (avoid persona proliferation)
- For each persona, complete all six dimensions:
- Demographic profile: Concrete characteristics with ranges, not single points
- Psychographic profile: Values, beliefs, lifestyle factors that influence purchase decisions
- Behavioral profile: How they buy, where they spend time, what content they consume
- Need-state profile: Specific pain points, urgency drivers, and desired outcomes
- Information profile: Research behavior, trusted sources, content preferences
- Decision profile: Criteria, objections, influencers, and timeline
- Give each persona a memorable name and narrative (but avoid stereotyping)
- Assign estimated segment size and revenue potential
- Prioritize personas by business impact
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Anti-Persona Development
- Define 1-3 anti-personas: people who may seem like targets but are poor fits
- Common anti-persona types: price-sensitive bargain hunters (for premium brands), feature-seekers who will never buy (tire kickers), wrong company size or industry
- Document specific signals that identify anti-personas in your data
- Create exclusion criteria for ad targeting and lead qualification
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Segmentation Strategy
- Select the segmentation approach based on available data and business needs:
- RFM: When transaction data is available — score by recency, frequency, monetary value
- Behavioral: When usage/engagement data exists — group by behavior patterns
- Value-based: When LTV data is available — prioritize high-value segments
- Lifecycle: When customer journey stage data exists — customize by stage
- Needs-based: When qualitative research is available — group by pain point
- Define segment boundaries and naming conventions
- Map segments to personas (segments are data-driven groups; personas are the human stories within them)
- Assign channel and messaging strategies per segment
- Select the segmentation approach based on available data and business needs:
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Activation Planning
- For each persona/segment, define:
- Priority channels for reaching them
- Messaging themes and value propositions that resonate
- Content types and formats they prefer
- Lookalike audience seed criteria for paid platforms
- Lead scoring rules based on persona fit
- Create a persona-to-campaign mapping guide
- Build a validation plan to test persona hypotheses with real campaign data
- For each persona/segment, define:
What You Get
A complete, brand-aligned deliverable ready to review, share, or hand off to execution.