Newsletter Audience Intelligence

Use when the user asks to understand newsletter audience quality, segment readers, summarize survey/reply/click data, prove sponsor value, create media-kit proof points, compare subscriber source quality, or decide what audience data to collect next.

Turn subscriber, reply, survey, source, and click data into sponsor-ready audience proof and operating decisions.

Core Rule

Use connected analytics, subscriber data, source attribution, survey answers, replies, sponsor history, and issue history when available. Do not invent audience metrics, demographics, reader quotes, locations, job titles, or intent signals.

Inputs

• Newsletter name, category, audience, geography or market
• Current subscriber count, opens, clicks, replies, source attribution, and survey data if available
• Reader segments or tags already used
• Sponsor, paid conversion, growth, or content-quality goal
• Known acquisition sources: Meta, Reddit, X, LinkedIn, TikTok, referrals, swaps, events, search, or organic
• Connected workspace, analytics export, or manual data if available

Workflow

1. Separate known data from assumptions and missing data.
2. Identify useful audience segments by source, geography, role, interest, behavior, lifecycle, or purchase intent.
3. Compare segment quality using engagement, clicks, replies, conversions, and sponsor fit.
4. Extract sponsor-proof bullets without overclaiming attribution.
5. Flag weak or missing proof needed before selling sponsors or scaling acquisition.
6. Recommend 3-7 survey, onboarding, reply, or tagging improvements.
7. Save or hand off audience insights, segment notes, and next data collection steps for the connected workspace.

Output Format

When a reusable artifact is useful, follow templates/audience-proof.md.

Include:

• Audience-quality summary
• Known data vs missing data
• Segment table
• Sponsor-proof bullets
• Weak proof or risk notes
• Data collection plan
• Connected-workspace handoff notes

Segment table columns:

Segment	Evidence	Value to operator	Sponsor relevance	Confidence	Next data to collect

Guardrails

• Do not infer demographics from stereotypes or category assumptions.
• Do not treat subscriber count as sponsor proof by itself.
• Keep exact metrics separate from qualitative signals.
• If data is thin, output a collection plan instead of pretending there is a strong audience story.
• Do not export, delete, tag, or modify subscribers without explicit approval and an available workspace/API tool.