audience-segmentation
Audience segmentation skill used by the analyst and curator agents. Provides subscriber behavior analysis, persona-based content personalization, and segment-specific strategy methodologies. Use for 'audience analysis,' 'segmentation,' 'persona,' 'send optimization,' and similar requests.
Best use case
audience-segmentation is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Audience segmentation skill used by the analyst and curator agents. Provides subscriber behavior analysis, persona-based content personalization, and segment-specific strategy methodologies. Use for 'audience analysis,' 'segmentation,' 'persona,' 'send optimization,' and similar requests.
Teams using audience-segmentation should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/audience-segmentation/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How audience-segmentation Compares
| Feature / Agent | audience-segmentation | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Audience segmentation skill used by the analyst and curator agents. Provides subscriber behavior analysis, persona-based content personalization, and segment-specific strategy methodologies. Use for 'audience analysis,' 'segmentation,' 'persona,' 'send optimization,' and similar requests.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Audience Segmentation — Audience Segmentation Methodology Specialist audience classification knowledge used by the analyst and curator agents when designing content strategy and A/B tests. ## Why Segmentation Matters Sending the same email to 1,000 subscribers produces content that **satisfies nobody on average**. Segmentation is the art of deciding "who gets what." ## Newsletter Segmentation Model: The BEAR Framework ### B — Behavior (Behavior-Based) | Behavior Segment | Definition | Strategy | |-----------------|------------|----------| | **Power Readers** | Opened last 5 issues consecutively | Deep content, exclusive resources | | **Occasional Readers** | Opened 2–3 out of 5 | Optimize subject lines, keep it concise | | **At-Risk** | Missed last 3 consecutive issues | Re-engagement campaign | | **New Subscribers** | Joined within last 30 days | Welcome series, best-of curation | | **Click-Active** | High link-click rate after opening | Deep-dive content, CTA-focused | ### E — Engagement Level Calculate engagement on a 0–100 score: ``` Engagement Score = (Open Rate Weight x 40) + (Click Rate Weight x 35) + (Reply/Share x 25) - 80–100: VIP — Community invite, early access to content - 50–79: Core — Standard newsletter + monthly special content - 20–49: Casual — Key summary version, short format - 0–19: Dormant — Re-engagement sequence → if no response, clean from list ``` ### A — Attribute (Attribute-Based) | Attribute | Classification Criteria | Content Differentiation | |-----------|----------------------|------------------------| | **Role** | Developer / Marketer / Executive / Designer | Adjust examples and terminology level | | **Experience Level** | Beginner / Intermediate / Expert | Include/exclude foundational explanations | | **Interest Topic** | Click history by tag/category | Topic-specific curation | | **Signup Source** | Blog / Social / Referral / Event | Onboarding matched to initial expectations | ### R — Recency-Frequency | Segment | R (Last Open) | F (Open Frequency) | Strategy | |---------|--------------|-------------------|----------| | **Champion** | Within 7 days | Opens every issue | Exclusive content, referral program | | **Loyal Reader** | Within 14 days | 1 in 2+ | Standard content, feedback requests | | **At-Risk** | 14–30 days ago | Declining trend | "Did you miss this?" reminder | | **Dormant** | 30+ days | Near zero | Final re-engagement → remove if no response | ## Segment-Specific Content Strategy ### Welcome Series (New Subscribers Only) | Day | Email | Purpose | |-----|-------|---------| | D+0 | Welcome + self-introduction + expectation setting | First impression, frequency/tone overview | | D+2 | All-time top 3 content | Prove the newsletter's value | | D+5 | "We'd love to know about you" — short survey | Collect segmentation data | | D+10 | Exclusive content or resource | Incentivize long-term subscription | ### Re-engagement Sequence (At-Risk Readers) | Step | Subject Line Pattern | Strategy | |------|---------------------|----------| | 1st | "We know you've been busy — just read this one" | Deliver one compressed top piece | | 2nd | "Did we end up in your spam folder?" | Request whitelisting + technical fix | | 3rd | "Here's an honest case for staying subscribed" | Reaffirm value, offer frequency options | | No response | Remove from list | Maintain list health (protect deliverability) | ## Send Time Optimization Matrix | Subscriber Type | Best Day | Best Time | Rationale | |----------------|----------|-----------|-----------| | B2B Professionals | Tue–Thu | 8–10 AM | Post-arrival email check window | | Developers/Tech | Tue, Thu | 7–8 AM | Early-start habit | | B2C General | Sat, Sun | 10 AM–12 PM | Weekend leisure time | | Executives/Decision-makers | Tue, Wed | 6–7 AM | Pre-day check | | Global Mixed | Tue | 2:00 PM UTC | Optimal cross-timezone intersection | ## Content Personalization Levels | Level | Method | Complexity | Effect | |-------|--------|-----------|--------| | L1 | Name insertion (Hi [Name]) | Low | Open rate +10–15% | | L2 | Reorder sections by interest topic | Medium | Click rate +20–30% | | L3 | Completely different content versions per segment | High | Click rate +40–60% | | L4 | Individual AI-powered recommendation curation | Very High | Click rate +50–80% | ## Newsletter Health Metrics | Metric | Healthy | Caution | Danger | |--------|---------|---------|--------| | Open Rate | 40%+ | 25–39% | Below 25% | | Click-through Rate | 5%+ | 2–4% | Below 2% | | Unsubscribe Rate | Below 0.3% | 0.3–0.5% | 0.5%+ | | Spam Complaint Rate | Below 0.01% | 0.01–0.05% | 0.05%+ | | List Growth Rate | 5%+/month | 1–4% | Below 0% |
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