Marketing Analytics
SkillDatabases & dataMeasure CAC, conversion rates, attribution, ROAS, CPL, and pipeline contribution from marketing data. Use to evaluate channels and build marketing reports.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Marketing Analytics skill
What this skill tells your AI
The instructions your AI receives, as published by navinspire-ia/navin in navin/skills/marketing-analytics/SKILL.md and read by ahel’s review.
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.
Signals
- GitHub stars
- 22
- Forks
- 4
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
marketing-analytics-navinspire-ia- Source
- github.com/navinspire-ia/navin