ads-performance-analytics
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 t
Profile →npx skills add rampstackco/claude-skills --skill ads-performance-analytics --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Gives a structured guide for interpreting paid media dashboards, focusing on attribution models, platform reporting quirks, multi-platform reconciliation, ROAS vs LTV horizons, noise in metrics, incrementality testing, and common failure modes that waste budget.
How it works
Describes multiple concepts and practices for reading dashboards: specify the nine elements a trustworthy result panel should expose; contrast platform-reported figures with warehouse truth; outline different attribution models (Last-click, First-click, Linear, Time-decay, U-shaped, Data-driven, MMM, and an anti-model); explain multi-platform reconciliation patterns and the blended CAC approach; discuss ROAS vs LTV horizons and how to compare using cohort LTV, payback, and LTV-CAC; cover cohort analysis versus daily metrics; detail sources of statistical noise (day-of-week, seasonality, weather, reporting delays) and recommend pre-commit testing via experimentation-analytics; define incrementality testing methods (geo holdout, ghost bidding, conversion lift studies, PSA tests) and suggest quarterly testing.
When to use it
Use any time you are about to scale, kill, or rebudget a campaign based on platform metrics; reconciling platform reports with revenue data; evaluating an agency's reporting; or building a paid media dashboard that will not lie to you.
What it can touch
The skill references and templates touch cross-platform metrics, warehouse data, experimentation frameworks, and references within the provided sections, including mentions of: references/platform-reporting-quirks.md, references/dashboard-reconciliation-patterns.md, references/attribution-model-comparison.md, references/incrementality-testing-playbook.md, and references/cohort-analysis-templates.md.
Caveats
States that attribution models are approximations and that platform-reported conversions are not to be taken as fact; emphasizes reconciliation to a single source of truth (warehouse, GA4, or unified analytics). Mentions the necessity of data volume for sophisticated attribution and notes the opaque nature of some models (e.g., data-driven attribution, ghost bidding math). License: MIT (as per repository metadata).
# Ads Performance Analytics A data-team-mentor's playbook for interpreting paid media dashboards without fooling yourself. The dashboard is the moment of truth for paid media decisions. The numbers on it determine whether you scale, hold, or kill. They also expose every platform's self-attribution bias, every modeled-conversion shortcut, every cross-platform double-count. Most "scale this campaign" decisions trace back to misreading the dashboard. This skill is the discipline that prevents misreading. It assumes the campaign was strategically sound (see `paid-media-strategy`). It assumes the creative was tested properly (see `ads-creative-development`). The hard part is knowing what each number actually means, what it does not, and how to reconcile platform-reported metrics with the truth in your warehouse. When to use this skill: any time you are about to scale, kill, or rebudget a campaign based on platform metrics; reconciling platform reports with revenue data; evaluating an agency's reporting; or building a paid media dashboard that will not lie to you. --- ## What this skill is for This skill spans paid media result interpretation. It does not cover paid media strategy (use `
- What this skill is for
- The result panel: what every paid media platform should expose
- Platform-reported vs reality
- Attribution models in practice
- Multi-platform reconciliation
- ROAS vs LTV: the time horizon trap
- Cohort analysis vs daily metrics
- Statistical noise in performance metrics
- Incrementality testing
- Geo experiments and holdouts
- Platform self-attribution bias
- Common interpretation failures
- The framework: 12 considerations for trustworthy paid media interpretation
- Reference files
What does the ads-performance-analytics skill do?
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 t
How do I install it?
Run `npx skills add rampstackco/claude-skills --skill ads-performance-analytics --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From rampstackco/claude-skills, a repository with 515 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.