Linkedin Ads Lead Quality Audit

Linkedin 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 linkedin 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. Map LinkedIn campaign objective, audience, form, offer, fields and lead source into one flow.
2. Compare CPL with sales acceptance, role seniority, company fit, meeting rate and opportunity rate.
3. Check whether lead gen forms are too easy for the offer value and buyer stage.
4. Review job titles, company size, industries, exclusions and form questions against ICP.
5. Recommend audience, form, offer or follow-up changes before judging LinkedIn by CPL alone.

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

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 linkedin ads lead quality audit. What should we change before the next campaign move?"

Assistant should: pair campaign data with CRM feedback, report cost per qualified lead rather than cost per lead, and stop at scale, fix or pause.

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.