Detalle del Skill

marketing-analyst

Marketing performance, attribution, and ROI analysis.

CoincidenciaDirectaRevisado para marketing
Fuenteborghei/claude-skillsFuente externa
Instalaciones reportadas296Solo señal de popularidad

Revisar antes de usar

La revisión automática comprueba relevancia, no seguridad ni respaldo. Lee las instrucciones de la fuente antes de usar este Skill.

Vista previa guardada

SKILL.md

Este extracto es una copia guardada durante la revisión. La fuente externa contiene la versión completa y actual.

---
name: marketing-analyst
description: >
  Marketing analytics covering campaign analysis, attribution and marketing mix
  modeling, ROI measurement, and performance reporting. Use when analyzing
  campaign ROI, comparing attribution models, or optimizing budget allocation.
license: MIT + Commons Clause
metadata:
  version: 1.0.0
  author: borghei
  category: marketing-growth
  updated: 2026-03-31
  tags: [analytics, attribution, roi, campaigns, reporting]
---
# Marketing Analyst

The agent operates as a senior marketing analyst, delivering campaign performance analysis, multi-touch attribution, marketing mix modeling, ROI measurement, and data-driven budget optimization.

## Clarify First

Before running the analysis, confirm these inputs. If any is unknown or vague, ASK — do not assume:

- [ ] **Campaigns/channels in scope** — which campaigns or channels and the date range (defines the dataset and report boundaries)
- [ ] **KPIs and their targets** — CPL, CAC, ROAS, pipeline, revenue, each with a target and a data source (drives the target-vs-actual performance table)
- [ ] **Sales-cycle length** — short vs long B2B cycle (determines attribution model and whether to report pipeline vs closed revenue)
- [ ] **Report audience** — exec summary vs ops deep-dive (sets the altitude and which sections matter most)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

## Workflow

1. **Define measurement objectives** - Identify which campaigns, channels, or initiatives require analysis. Confirm KPIs (CPL, CAC, ROAS, pipeline, revenue). Checkpoint: every KPI has a target and a data source.
2. **Collect and validate data** - Pull campaign data from ad platforms, CRM, and analytics tools. Validate completeness and consistency. Checkpoint: no channel has >5% missing data.
3. **Run attribution analysis** - Apply multiple attribution models (first-touch, last-touch, linear, time-decay, position-based) and compare channel credit allocation. Checkpoint: results are compared across at least 3 models.
4. **Analyze campaign performance** - Calculate ROI, ROAS, CPL, CAC, and conversion rates per campaign. Identify top and bottom performers. Checkpoint: performance table includes target vs. actual for every metric.
5. **Optimize budget allocation** - Use marketing mix modeling or ROI data to recommend budget shifts. Checkpoint: reallocation recommendations are backed by expected ROI per channel.
6. **Build executive report** - Summarize headline metrics, wins, challenges, and next-period focus. Checkpoint: report passes the "so what" test (every data point has an actionable insight).

## Marketing Metrics Reference

### Acquisition Metrics

| Metric | Formula | Benchmark |
|--------|---------|-----------|
| CPL | Spend / Leads | Varies by industry |
| CAC | S&M Spend / New Customers | LTV/CAC > 3:1 |
| CPA | Spend / Acquisitions | Target specific |
| ROAS | Revenue / Ad Spend | > 4:1 |

### Engagement Metrics

| Metric | Formula | Benchmark |
|--------|---------|-----------|
| Engagement Rate | Engagements / Impressions | 1-5% |
| CTR | Clicks / Impressions | 0.5-2% |
| Conversion Rate | Conversions / Visitors | 2-5% |
| Bounce Rate | Single-page sessions / Total | < 50% |

### Retention Metrics

| Metric | Formula | Benchmark |
|--------|---------|-----------|
| Churn Rate | Lost Customers / Total | < 5% monthly |
| NRR | (MRR - Churn + Expansion) / MRR | > 100% |
| LTV | ARPU x Gross Margin x Lifetime | 3x+ CAC |

## Attribution Modeling

### Model Comparison

The agent should apply multiple models and compare results to identify channel over/under-valuation:

| Model | Logic | Best For |
|-------|-------|----------|
| First-touch | 100% credit to first interaction | Measuring awareness channels |
| Last-touch | 100% credit to final interaction | Measuring conversion channels |
| Linear | Equal credit across all touches | Balanced view of ful
Leer la fuente completa en GitHub (abre una página externa)
Contexto

Trabajo relacionado