# Google Ads Lead Quality Audit

> Separates search intent quality from keyword bloat and match-type leaks. Use when Google Ads volume is fine and quality is not, or when the search terms report stops matching who you sell to.

- Skill: `mardab96/google-ads-lead-quality-audit` (Agent Skill)
- Install (CLI): `npx skillmds@latest add mardab96/google-ads-lead-quality-audit`
- Raw SKILL.md: https://api.skillmd.com/api/skills/mardab96/google-ads-lead-quality-audit/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Security
- Author: mardab96 (https://skillmd.com/u/mardab96)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/mardab96/google-ads-lead-quality-audit

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# Google Ads Lead Quality Audit

Use the shared quality bar in `../references/output-standard.md` and `../references/skill-design-principles.md` when those files are available.

## Use this skill when

- the user shares lead source, CRM stage, sales note, form, landing page or campaign data tied to google ads lead quality audit.
- the next decision could change targeting, qualification, scoring, follow-up, sales handoff or budget.
- lead volume looks acceptable but SQL, opportunity, closed-won, rejection or response-speed data raises doubt.

Do not use this skill for broad lead-generation advice without source, CRM, sales or qualification evidence. Use it when a real B2B lead quality decision is on the table.

## Required input

- business model, ICP, offer, ACV or deal value range, sales cycle and main conversion goal.
- ad, landing page, lead form, CRM, call note, email or campaign data relevant to this diagnostic.
- time window, traffic source, lead volume and downstream outcomes where available.
- what decision the user is trying to make next: create, fix, scale, pause, brief sales or investigate.
- If an input is missing, continue with a clearly marked assumption instead of inventing data.

## Analysis workflow

1. Group leads by campaign, keyword/search term, match type, landing page, device and conversion action.
2. Compare query intent with downstream qualification, sales notes and opportunity rate.
3. Flag broad match, PMax/search themes, competitor, informational and low-intent query leaks.
4. Check whether the conversion action optimizes for form fills instead of qualified pipeline.
5. Recommend negatives, intent splits, landing page changes or conversion signal changes.

## Decision rules

- If the data does not connect to revenue, pipeline, qualified leads or conversion quality, label the recommendation as a hypothesis.
- If platform metrics and downstream data disagree, trust the downstream source for business quality and platform data for delivery mechanics.
- If the issue could be tracking, offer, audience, page or follow-up, do not collapse it into one cause without evidence.
- Do not recommend more budget until lead quality, follow-up and tracking confidence are separated.

## Output format

| Finding | Evidence | Lead quality impact | Recommended action | Confidence |
|---|---|---|---|---|
| Specific diagnostic claim | Data, screenshot, report, note or missing-data marker | Business or signal consequence | Smallest useful next step and owner | High / Medium / Low |

End with:

- `Decision:` fix / test / monitor / ask for data / do not act yet
- `Approval needed:` yes/no and what would change if approved
- `Missing data:` only the inputs that would materially change the recommendation

## Practical example

User: "Here are CRM stages, source data and sales notes for google ads lead quality audit. What should we change before the next campaign move?"

Assistant should: separate intent problems from match-type leaks, and stop at a negative keyword and match-type plan awaiting approval.

## Guardrails

- Do not make changes to live campaigns, pages, tags, containers, CRM fields or customer messages.
- Do not claim performance impact without evidence.
- Mark missing data clearly.
- Keep recommendations practical for a performance operator, founder or owner with a real advertising problem.