audience-intelligence

Files (6)

• SKILL.md
• customer-research-methods.md
• jtbd-framework.md
• persona-builder.md
• psychographic-profiling.md
• segmentation.md

SKILL.md (first lines)

# Audience Intelligence

> **Script location.** If your host does not set `${CLAUDE_PLUGIN_ROOT}`, the scripts are in this plugin's `scripts/` folder, next to `skills/`. Run them with Python 3.10 or newer: `python`, or `python3` where `python` is not found (macOS, many Linux systems).

## 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 do
- **Psychographic Profiling**: Understanding audience values, attitudes, interests, lifestyles, and motivations
- **Anti-Persona Definition**: Defining who is NOT the target customer to prevent wasted spend
- **Audience Sizing & TAM Estimation**: Estimating the size of addressable audience segments

**Trigger phrases**: "buyer persona," "target audience," "who are our customers," "customer profile," "segmentation," "audience segments," "Jobs-to-Be-Done," "JTBD," "psychographic," "ideal customer profile," "ICP," "anti-persona," "lookalike audience," "audience research," "buying committee," "customer avatar"

## Brand Context (Auto-Applied)

Before producing any marketing output from this module:

1. **Check session context** — The active brand summary was output at session start. Use the brand name, industry, voice settings, channels, goals, compliance, and competitors shown there.
2. **If you need the full profile**, read: `~/.claude-marketing/brands/{slug}/profile.json`
3. **Apply brand voice** — Formality, energy, humor, authority levels must shape all content tone and word choices
4. **Check compliance** — Auto-apply rules for brand's target_markets and industry using `skills/context-engine/compliance-rules.md`
5. **Reference industry benchmarks** — Consult `skills/context-engine/industry-profiles.md` for the brand's industry
6. **Use platform specs** — Reference `skills/context-engine/platform-specs.md` for character limits and format requirements
7. **Check campaign history** — Run `python "${CLAUDE_PLUGIN_ROOT}/scripts/campaign-tracker.py" --brand {slug} --action list-campaigns` before planning new work
8. **If no brand exists**, say: "No brand profile found. Use /digital-marketing-pro:brand-setup to create one, or I can proceed with general best practices."
9. **Check brand guidelines** — If `~/.claude-marketing/brands/{slug}/guidelines/_manifest.json` exists, load and enforce: `restrictions.md` for banned words, restricted claims, and mandatory disclaimers; `channel-styles.md` for channel-specific tone overrides (may differ from base voice); `messaging.md` for approved key messages, taglines, and positioning language; `voice-and-tone.md` for detailed voice rules beyond the 4 numeric scores. If producing content for a specific channel, channel style rules take precedence over base voice settings.

Do not ask the user for information that already exists in their brand profile.

## Required Context

Before executing audience intelligence work, gather:

1. **Business Description**: What does the company sell, to whom, and what problem does it solve?
2. **Existing Customer Data**: Any analytics, CRM data, survey results, or customer interviews available
3. **Product/Service Details**: Features, pricing, positioning, and key differentiators
4. **Current Audience Assumptions**: Who does the team think their customers are today?
5. **Market Context**: Industry, competitive landscape, market maturity
6. **Geographic Scope**: Local, regional, national, or global audience
7. **Business Model**: B2B, B2C, B2B2C, D2C — this fundamentally shapes persona structure
8. **Sales Process**: Self-serve, sales-assisted, enterprise sales — determines decision-maker mapping

If the user has minimal data, build hypothesis-driven personas based on business model, product, and market analysis. Label these clearly as hypotheses to be validated.

## Capabilities

- **Multi-Dimensional Persona Building**: Personas built across six dimensions:
  - **Demographic**: Age, gender, location, income, education, job title, company size
  - **Psychographic**: Values, attitudes, lifestyle, personality traits, motivations
  - **Behavioral**: Purchase patterns, channel preferences, content consumption, decision-making style
  - **Need-State**: Current pain points, unmet needs, desired outcomes, urgency level
  - **Information**: Where they research, who they trust, content format preferences, information journey
  - **Decision**: Decision criteria, objections, influencers, timeline, risk tolerance
- **JTBD Framework**: Mapping functional jobs (what they need done), social jobs (how they want to be perceived), and emotional jobs (how they want to feel) with outcome-driven innovation metrics
- **RFM Segmentation**: Recency, Frequency, Monetary value analysis for customer base segmentation
- **Behavioral Segmentation**: Grouping by usage patterns, engagement levels, and purchase behavior
- **Value-Based Segmentation**: Grouping by customer lifetime value and profitability potential
- **Lifecycle Segmentation**: Grouping by customer lifecycle stage (prospect, new, active, at-risk, churned, win-back)
- **Lookalike Audience Guidance**: Defining seed audience characteristics for platform-based lookalike targeting
- **Anti-Persona Definition**: Explicitly defining who should be excluded from targeting to prevent wasted spend and misaligned messaging
- **Buying Committee Mapping**: For B2B, mapping all roles involved in purchase decisions with their individual motivations and objections

## Process

**Primary Workflow: Persona Development & Segmentation**

1. **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

2. **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

3. **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

4. **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

5. **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

6. **Activation Planning**
   - For each persona/segment, define:

From indranilbanerjee/digital-marketing-pro, MIT.

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