Ads Performance Analytics
技能 已验证 活跃How to read paid media dashboards without fooling yourself. Attribution models, platform reporting quirks, multi-platform reconciliation, ROAS vs LTV horizon traps, statistical noise in performance metrics, incrementality testing, and the failure modes that produce expensive lessons. Triggers on read paid media dashboard, attribution analysis, ROAS vs LTV, multi-platform reconciliation, ad incrementality, geo holdout, conversion lift study, ghost bidding, paid media reporting, board-deck paid media metrics, blended CAC, MMM, MTA, last-click attribution. Also triggers when a marketer is about to scale, kill, or rebudget a campaign based on platform metrics, or when reconciling platform reports against warehouse revenue.
To equip marketers and analysts with the knowledge and framework to accurately interpret paid media performance data, enabling sounder strategic decisions and preventing wasted budget due to misinterpretation.
功能
- In-depth analysis of attribution models and their biases
- Guidance on reconciling platform data with warehouse truth
- Frameworks for understanding ROAS vs. LTV trade-offs
- Methodologies for incrementality testing (geo holdout, lift studies)
- Identification and analysis of common reporting failures and pitfalls
使用场景
- Interpreting paid media dashboard reports without self-deception
- Reconciling conflicting data between advertising platforms and internal warehouses
- Evaluating the true incremental value of paid media campaigns
- Making strategic budget allocation decisions based on reliable LTV and CAC insights
非目标
- Developing paid media strategy or campaign planning
- Producing creative assets or ad copy
- Operating platform-specific tooling or MCPs directly
- Providing real-time campaign optimization; focuses on analytical interpretation
安装
npx skills add rampstackco/claude-skills通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
已验证类似扩展
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