# Crm Lead Source Quality Audit

> Ranks lead sources by downstream stage, value and close rate. Use when two channels look equally good on cost per lead, or before moving budget between sources.

- Skill: `mardab96/crm-lead-source-quality-audit` (Agent Skill)
- Install (CLI): `npx skillmds@latest add mardab96/crm-lead-source-quality-audit`
- Raw SKILL.md: https://api.skillmd.com/api/skills/mardab96/crm-lead-source-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/crm-lead-source-quality-audit

---


# CRM Lead Source 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 crm lead source 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. Normalize lead source, campaign, channel and timestamp fields before comparing performance.
2. Rank sources by lead volume, MQL rate, SQL rate, opportunity rate, close rate, revenue and sales rejection patterns.
3. Separate source quality from follow-up speed, routing, offer and tracking issues.
4. Flag sources where CPL looks good but qualified pipeline or revenue is weak.
5. Recommend budget, targeting, follow-up or tracking actions by source.

## 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 crm lead source quality audit. What should we change before the next campaign move?"

Assistant should: rank sources by what happened downstream rather than by cost per lead, and stop at a budget reallocation the owner can approve.

## 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.