| name | audience-segmentation |
| description | 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. |
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% |
