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Newsletter Audience Intelligence

该 Skill 面向 newsletter 运营者:把订阅者数量、打开、点击、回复、来源归因和问卷等数据整理成受众质量总结、已知与缺失数据对比、分群表、赞助商证明要点、弱势证据与风险提示以及数据收集计划;数据不足时改为输出采集方案,不虚构受众指标。

freeman14
freeman14
热度 24.84
最近更新 2026-06-04 01:41:04
分类:营销依赖:纯提示词应用场景:市场分析 / 报告撰写 / 研究 / 推荐

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add newsletter-audience-intelligence
Metadata
name
newsletter-audience-intelligence
description
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.

Newsletter Audience Intelligence

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.