segment-audience

What this skill does

# /digital-marketing-pro:segment-audience

## Purpose

Create or update audience segments in the brand's CRM or email platform based on behavioral, demographic, or engagement criteria. Segments can be used for ad targeting, email campaigns, retargeting, or personalization. Ensures segments are well-defined, properly sized, actionable across platforms, and documented with clear criteria for reproducibility — so the team can understand exactly who is in a segment and why. Supports RFM (recency, frequency, monetary) modeling, lifecycle-based segmentation, and predictive scoring criteria for advanced audience strategies.

Use this command to build targeting audiences before campaign launches. For importing new contacts into the CRM first, use `/digital-marketing-pro:lead-import`.
For exporting segment member data to external tools, use `/digital-marketing-pro:data-export` after segment creation.

## Input Required

The user must provide (or will be prompted for):

- **Segment criteria**: The rules that define segment membership — demographics (age, location, job title, company size, industry), behaviors (pages visited, emails opened, links clicked, purchases made, events attended), engagement levels (active last 30/60/90 days, lapsed, at-risk, churned), purchase history (recency, frequency, monetary value, product category), lifecycle stage (subscriber, lead, MQL, customer), or custom field filters
- **Target platform**: Where the segment will be created — CRM (Salesforce, HubSpot, Zoho, Pipedrive), email platform (ActiveCampaign, Mailchimp), or ad platform (Meta Custom Audiences, Google Ads Customer Match, LinkedIn Matched Audiences, TikTok Custom Audiences)
- **Segment name**: A descriptive, standardized name following the brand's naming conventions — e.g., "High-Value-Customers-Last-90d" or "MQL-SaaS-Enterprise-US"
- **Segment purpose**: How this segment will be used — email campaign targeting, paid ad audience, retargeting pool, personalization cohort, suppression/exclusion list, lookalike seed audience, or cross-sell/upsell targeting
- **Dynamic vs. static**: Whether the segment should auto-update as contacts meet or leave criteria (dynamic/smart list) or remain fixed at time of creation (static/snapshot) — dynamic for ongoing campaigns, static for one-time sends or point-in-time analysis
- **Refresh frequency (if dynamic)**: How often the segment membership should be recalculated — real-time (event-triggered), daily, weekly, or on-demand before each campaign send
- **Exclusion criteria (optional)**: Contacts to explicitly exclude — unsubscribed, hard bounced, competitors, internal team members, existing customers (for acquisition campaigns), contacts in another active campaign (frequency capping), or suppression lists from partners
- **Minimum segment size (optional)**: The smallest acceptable segment size for the intended use case — ad platforms typically require 1,000+ for effective delivery, lookalike seeds perform best at 1,000-10,000, and email segments should be large enough for statistical significance in A/B testing
- **Cross-platform sync (optional)**: Whether this segment should be automatically synced to additional platforms — e.g., create in CRM and push to Meta Custom Audiences and Google Ads simultaneously
- **Segment description**: A human-readable explanation of the segment's purpose and criteria for team documentation — stored alongside the segment for future reference and knowledge transfer
- **RFM parameters (optional)**: If using recency-frequency-monetary segmentation — define the scoring thresholds for each dimension, the lookback window for transaction history, and the number of segments to create (e.g., Champions, Loyal, At Risk, Lost)
- **Lookalike expansion (optional)**: If this segment will seed a lookalike audience — specify the expansion percentage (1-10%) and target platform, so the segment can be optimized for seed quality rather than just size

## Process

1. **Load brand context**: Read `~/.claude-marketing/brands/_active-brand.json` for the active slug, then load `~/.claude-marketing/brands/{slug}/profile.json`. Apply brand voice, compliance rules for target markets (`skills/context-engine/compliance-rules.md`), and industry context. **Also check for guidelines** at `~/.claude-marketing/brands/{slug}/guidelines/_manifest.json` — if present, load restrictions and relevant category files. Check for agency SOPs at `~/.claude-marketing/sops/`. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
2. **Determine available data sources**: Query connected platforms to identify what data is available for segmentation — CRM contact fields and custom properties, email engagement metrics (opens, clicks, bounces), ad platform audience data, website behavioral data (if analytics MCP connected), and purchase/transaction records. Map which criteria can be evaluated from which source and flag any criteria that cannot be supported by available data.
3. **Build segment criteria logic**: Translate the user's criteria into platform-specific filter logic — AND/OR conditions, nested groups, date ranges, relative date windows (e.g., "last 30 days" recalculated dynamically), numeric thresholds, string matching rules, and list membership checks. Validate that all referenced fields exist in the target platform's schema.
4. **Estimate segment size from available data**: Run a count query or estimate against the target platform to project segment membership. Flag if the segment is too small for its intended purpose (e.g., under 1,000 for ad targeting, under 100 for email A/B testing) or too broad (e.g., over 80% of total database — likely under-filtered and lacking targeting precision).
5. **Check overlap with existing segments**: Query existing segments on the platform to identify overlap — contacts who already belong to active segments used in current campaigns. Present overlap percentage, shared contact count, and recommend whether to exclude overlapping contacts, merge segments, or accept overlap based on the segment's purpose and campaign frequency capping rules.
6. **Apply exclusion criteria**: Layer in exclusion rules — unsubscribed contacts, hard bounces, suppression lists, competitors, internal team members, and any user-specified exclusions. Show the impact of each exclusion rule on segment size individually and cumulatively.
7. **Validate compliance for ad platform audiences**: If syncing to ad platforms, verify that contacts have appropriate consent for advertising use (not just email consent), confirm the audience matches platform-specific requirements (hashed email format for Customer Match, minimum audience size), and check for platform terms of service compliance.
8. **Create segment definition payload**: Build the platform-specific segment definition — filter criteria in the platform's API format, segment name, description, type (dynamic/static), refresh schedule, folder or tag assignment for organization, and any cross-platform sync configuration.
9. **Create approval gate**: Assess risk as medium for all segment operations. Present segment preview showing criteria summary in human-readable format, estimated member count, percentage of total database, overlap with existing segments, exclusion impact, sample member profiles (top 5 representative contacts), and the platform where the segment will be created.
10. **On approval, create segment via platform MCP**: Push the segment definition to the target platform through the appropriate MCP. For ad platform audiences, initiate the audience sync, wait for processing confirmation, and verify match rate. For CRM/email segments, confirm creation and initial member population count.
11. **Initiate cross-platform sync (if requested)**: For multi-platform segments, push the audience to additional destinations — hash emails for ad platform Customer Match uploads, format phone numbers per platform requirements, and initiate s