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
- Group leads by campaign, keyword/search term, match type, landing page, device and conversion action.
- Compare query intent with downstream qualification, sales notes and opportunity rate.
- Flag broad match, PMax/search themes, competitor, informational and low-intent query leaks.
- Check whether the conversion action optimizes for form fills instead of qualified pipeline.
- 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 yetApproval needed: yes/no and what would change if approvedMissing 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.
1---2name: google-ads-lead-quality-audit3description: 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.4---56# Google Ads Lead Quality Audit78Use the shared quality bar in `../references/output-standard.md` and `../references/skill-design-principles.md` when those files are available.910## Use this skill when1112- the user shares lead source, CRM stage, sales note, form, landing page or campaign data tied to google ads lead quality audit.13- the next decision could change targeting, qualification, scoring, follow-up, sales handoff or budget.14- lead volume looks acceptable but SQL, opportunity, closed-won, rejection or response-speed data raises doubt.1516Do 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.1718## Required input1920- business model, ICP, offer, ACV or deal value range, sales cycle and main conversion goal.21- ad, landing page, lead form, CRM, call note, email or campaign data relevant to this diagnostic.22- time window, traffic source, lead volume and downstream outcomes where available.23- what decision the user is trying to make next: create, fix, scale, pause, brief sales or investigate.24- If an input is missing, continue with a clearly marked assumption instead of inventing data.2526## Analysis workflow27281. Group leads by campaign, keyword/search term, match type, landing page, device and conversion action.292. Compare query intent with downstream qualification, sales notes and opportunity rate.303. Flag broad match, PMax/search themes, competitor, informational and low-intent query leaks.314. Check whether the conversion action optimizes for form fills instead of qualified pipeline.325. Recommend negatives, intent splits, landing page changes or conversion signal changes.3334## Decision rules3536- If the data does not connect to revenue, pipeline, qualified leads or conversion quality, label the recommendation as a hypothesis.37- If platform metrics and downstream data disagree, trust the downstream source for business quality and platform data for delivery mechanics.38- If the issue could be tracking, offer, audience, page or follow-up, do not collapse it into one cause without evidence.39- Do not recommend more budget until lead quality, follow-up and tracking confidence are separated.4041## Output format4243| Finding | Evidence | Lead quality impact | Recommended action | Confidence |44|---|---|---|---|---|45| Specific diagnostic claim | Data, screenshot, report, note or missing-data marker | Business or signal consequence | Smallest useful next step and owner | High / Medium / Low |4647End with:4849- `Decision:` fix / test / monitor / ask for data / do not act yet50- `Approval needed:` yes/no and what would change if approved51- `Missing data:` only the inputs that would materially change the recommendation5253## Practical example5455User: "Here are CRM stages, source data and sales notes for google ads lead quality audit. What should we change before the next campaign move?"5657Assistant should: separate intent problems from match-type leaks, and stop at a negative keyword and match-type plan awaiting approval.5859## Guardrails6061- Do not make changes to live campaigns, pages, tags, containers, CRM fields or customer messages.62- Do not claim performance impact without evidence.63- Mark missing data clearly.64- Keep recommendations practical for a performance operator, founder or owner with a real advertising problem.
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