Paid Ads Account Audit Framework
Run a paid-advertising account audit across Google, Meta, TikTok, and nine other platforms with real evidence discipline — a standardized finding schema, honest completeness states (complete/provisional/partial/insufficient-evidence) instead of a fake 100% coverage claim, correct weight renormalization when a platform's data fails to load, and strict separation between observations, diagnoses, recommendations, and draft account-change proposals.
A paid-ads audit that scores portfolio health at 82% after one platform's authentication silently failed isn't reporting 82% — it's reporting a number computed from 11 platforms while claiming coverage of 12, and the honest fix is a completeness state that says Partial, not a health score that pretends nothing went wrong.
Who it's for: marketing operations teams auditing paid ad accounts across multiple platforms, agencies needing a standardized, evidence-graded finding format instead of a prose summary a client can't verify, anyone who's had an AI-generated ads audit invent a score for a platform it couldn't actually read, teams that need proposed account changes kept as reviewable drafts instead of silently applied
Example
"Audit our Google, Meta, and TikTok ad accounts for opportunities and risks" → Each platform's data normalized into a snapshot with source lineage preserved, findings validated against a common schema (severity, confidence, evidence reference, source classification) instead of a loose bullet list, an honest completeness state when TikTok's data comes back only 65% covered, and every recommendation kept separate from any proposed account mutation — which stays a draft until explicitly reviewed
CLAUDE.md Template
New here? 3-minute setup guide → | Already set up? Copy the template below.
npx ccplaybooks add paid-ads-account-audit# Paid Ads Account Audit
Run a source-grounded, evidence-disciplined audit of a paid advertising account or portfolio across Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, and X. Produce a versioned findings bundle first, then render human-readable deliverables from that bundle — never aggregate prose-only summaries or claim coverage for a platform whose required data was actually missing.
## Context Intake
Before auditing, extract what's already been supplied, and ask only for what materially changes the work:
- Business model, industry, offer, geography, and regulated category.
- Objective and primary conversion, including value and attribution definition.
- Monthly and per-platform spend, plus target CPA, ROAS, MER, or LTV:CAC.
- Active platforms, account age, campaign age, pixel/conversion-signal history, and recent material changes.Get new playbooks like this one
One email a week with new Claude Code workflows. Free, like everything here.
No spam. Unsubscribe anytime.
What This Does
An evidence-disciplined framework for auditing paid advertising accounts across twelve platforms — Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, and X — built around a rigor that most AI-generated marketing audits skip entirely: every finding follows a standardized schema (platform, control ID, pass/fail/unknown result, severity, confidence, source classification, the actual evidence it's based on, and a recommendation), and the audit's overall completeness gets stated honestly as Complete, Provisional, Partial, or Insufficient Evidence rather than silently presenting a number as if every platform's data loaded cleanly when it didn't.
The rigor shows up most clearly in how a failed data source gets handled: if one platform's authentication fails mid-audit, the framework doesn't stop analyzing the others, doesn't assign that platform a zero, doesn't carry forward a stale historical score, and doesn't fold its missing weight into the denominator anyway — it renormalizes the portfolio score only across platforms that actually succeeded, and marks the whole bundle Partial. It also enforces a strict four-layer separation in the final report — observations directly supported by data, diagnoses inferred with a stated confidence level, recommendations with an owner and success measure, and proposed account mutations that remain drafts requiring explicit approval — so a client or stakeholder can tell the difference between "here's what we found" and "here's what we're about to change in your live account."
Quick Start
Step 1: Create a Project Folder
mkdir ads-audit && cd ads-audit
Step 2: Download the Template
Click Download above, then:
mv ~/Downloads/CLAUDE.md ./
Step 3: Run an Audit
claude
Provide account exports, screenshots, or authenticated access for the platforms you want audited, along with business context (objective, spend, target CPA/ROAS). Claude will normalize the data into a snapshot, validate findings against the standard schema, and produce a report with an honest completeness state — never claiming coverage it doesn't actually have.
Tips & Best Practices
- Always supply the primary conversion definition and its value up front — without it, findings about ROAS or CPA efficiency have nothing real to be measured against, and the audit should flag that gap rather than guess.
- Treat a Provisional or Partial completeness state as real information, not a failure of the audit — it tells you exactly where to focus follow-up data collection before trusting the portfolio-level numbers.
- Keep proposed account changes (mutations) explicitly separate from recommendations in whatever you share onward — a stakeholder reading a combined list can't tell what's already been decided from what still needs their sign-off.
Limitations
- A framework for structuring and grading an audit's findings — it doesn't include platform-specific benchmark data or scoring thresholds, which change too often to hardcode reliably and should come from current platform documentation or your own historical baselines.
- Assumes account data arrives via export, screenshot, or authenticated API access; the audit is only as complete as what's actually supplied, and the completeness-state discipline exists specifically to make that limitation visible rather than hidden.
- Best suited to a structured, multi-platform review — a quick single-metric question ("is our Google CPA too high?") doesn't need this level of process overhead.