Claude
Skill
marketing-analytics
Measure CAC, conversion rates, attribution, ROAS, CPL, and pipeline contribution from marketing data. Use to evaluate channels and build marketing reports.
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Download
navinspire-ia-navin-navin_skills_marketing-analytics-e9c73a3.zip · 1 KB
Install
skills CLI
npx skills add https://github.com/Navinspire-ia/navin/tree/main/navin/skills/marketing-analytics
Claude Code
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install navinspire-ia-navin@llmmart
Git
git clone https://github.com/Navinspire-ia/navin.git
The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole navinspire-ia/navin collection as a plugin from our marketplace. Git is the plain clone.
Skill manifest
Marketing Analytics
Overview
Turn exports (GA4, ads platforms, CRM, spreadsheets) into decisions: which channel earns its budget, where the funnel leaks.
Core metrics
| Metric | Formula |
|---|---|
| CPL | spend ÷ leads |
| CAC | spend ÷ new customers |
| Conversion rate | step N+1 ÷ step N |
| ROAS | revenue ÷ ad spend |
| Payback | CAC ÷ monthly gross margin per customer |
| Pipeline velocity | opportunities × win rate × deal size ÷ cycle length |
Workflow
- Get the data: user exports CSVs (GA4, ads, CRM) into the workspace, or connect via available tools.
- Analyze with
exec+ Python (pandas): clean, join on UTM/campaign, compute the metrics table. - Build the funnel: visitors → leads → MQL → opportunities → won, with conversion % per step.
- Attribution honestly: first-touch and last-touch views side by side; flag dark-social gaps.
- Deliver: monthly scoreboard + 3 insights + 3 recommended actions (
kpi-reporterfor recurring versions).
Report skeleton
## Marketing scoreboard - <month>
| Channel | Spend | Leads | CPL | Opps | Won | CAC | Notes |
### Insights
### Actions
Rules
- Distinguish correlation from causation explicitly.
- If data is missing or dirty, say so - no invented precision.
- Trends over single data points; always show the previous period.
Files (navin)
-
SKILL.md 1.6 KB
--- name: marketing-analytics description: Measure CAC, conversion rates, attribution, ROAS, CPL, and pipeline contribution from marketing data. Use to evaluate channels and build marketing reports. metadata: {"navin":{"emoji":"📊","category":"marketing"}} --- # Marketing Analytics ## Overview Turn exports (GA4, ads platforms, CRM, spreadsheets) into decisions: which channel earns its budget, where the funnel leaks. ## Core metrics | Metric | Formula | |--------|---------| | CPL | spend ÷ leads | | CAC | spend ÷ new customers | | Conversion rate | step N+1 ÷ step N | | ROAS | revenue ÷ ad spend | | Payback | CAC ÷ monthly gross margin per customer | | Pipeline velocity | opportunities × win rate × deal size ÷ cycle length | ## Workflow 1. Get the data: user exports CSVs (GA4, ads, CRM) into the workspace, or connect via available tools. 2. Analyze with `exec` + Python (pandas): clean, join on UTM/campaign, compute the metrics table. 3. Build the funnel: visitors → leads → MQL → opportunities → won, with conversion % per step. 4. Attribution honestly: first-touch and last-touch views side by side; flag dark-social gaps. 5. Deliver: monthly scoreboard + 3 insights + 3 recommended actions (`kpi-reporter` for recurring versions). ## Report skeleton ```markdown ## Marketing scoreboard - <month> | Channel | Spend | Leads | CPL | Opps | Won | CAC | Notes | ### Insights ### Actions ``` ## Rules - Distinguish correlation from causation explicitly. - If data is missing or dirty, say so - no invented precision. - Trends over single data points; always show the previous period.
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