# Analytics Reporter

> Convierte datos en insights accionables. Usa este skill para dashboards, reportes de métricas, análisis de cohortes, funnel analysis, y crear reportes que guíen decisiones de negocio.

- Skill: `leandroomargarcia/analytics-reporter` (Agent Skill)
- Install (CLI): `npx skillmds@latest add leandroomargarcia/analytics-reporter`
- Raw SKILL.md: https://api.skillmd.com/api/skills/leandroomargarcia/analytics-reporter/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: leandroomargarcia (https://skillmd.com/u/leandroomargarcia)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/leandroomargarcia/analytics-reporter

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# Analytics Reporter

Especialista en transformar datos raw en insights que impulsan decisiones. Combina análisis técnico con storytelling para hacer que los números cuenten historias.

## Cuándo Usar Este Skill

- Crear dashboards de métricas clave
- Analizar funnels de conversión
- Hacer análisis de cohortes
- Reportar performance a stakeholders
- Investigar anomalías en datos
- Definir KPIs y tracking

## Métricas Fundamentales

```
ACQUISITION:
- DAU/WAU/MAU
- New users
- Traffic sources
- CAC

ACTIVATION:
- Signup completion
- Time to first action
- Onboarding completion

ENGAGEMENT:
- Session duration
- Feature usage
- DAU/MAU ratio
- Actions per session

RETENTION:
- Day 1/7/30 retention
- Churn rate
- Cohort curves

REVENUE:
- MRR/ARR
- ARPU
- LTV
- Conversion to paid
```

## Dashboard Template

```markdown
## [Product] Dashboard

### Health Metrics (daily check)
| Metric | Current | vs Last Week | Target |
|--------|---------|--------------|--------|
| DAU | X | +Y% | Z |
| New users | X | +Y% | Z |
| Error rate | X% | -Y% | <Z% |
| Uptime | X% | - | >99.9% |

### Growth Metrics (weekly)
| Metric | This Week | Last Week | Trend |
|--------|-----------|-----------|-------|
| Signups | X | Y | ↑/↓ |
| Activations | X | Y | ↑/↓ |
| Conversions | X | Y | ↑/↓ |
| Revenue | $X | $Y | ↑/↓ |

### Funnel Performance
Visitors → Signups → Activated → Paid
[X%]        [Y%]       [Z%]       [W%]

### Top Features Used
1. [Feature] - X users
2. [Feature] - Y users
3. [Feature] - Z users
```

## Cohort Analysis

```
QUÉ ES:
Agrupar usuarios por cuándo se unieron
y trackear su comportamiento over time.

EJEMPLO - Retention por Cohorte:
          Week 0  Week 1  Week 2  Week 4
Jan cohort  100%    45%     30%     20%
Feb cohort  100%    50%     35%     25%
Mar cohort  100%    55%     40%     28%

INTERPRETAR:
- ¿Las cohortes mejoran? (producto mejora)
- ¿Dónde está el mayor drop-off?
- ¿Qué cohortes son mejores? (por qué?)

VARIANTES:
- Retention cohorts
- Revenue cohorts
- Feature adoption cohorts
- Behavioral cohorts
```

## Funnel Analysis

```
FUNNEL TÍPICO:
Landing Page → Signup → Activation → Conversion

ANALIZAR:
1. Drop-off en cada paso
   - ¿Cuál es el mayor leak?
2. Tiempo entre pasos
   - ¿Hay demoras sospechosas?
3. Segmentar por:
   - Fuente de tráfico
   - Device
   - User type

EJEMPLO:
Step          | Users  | Rate   | Drop-off
--------------|--------|--------|----------
Landing       | 10,000 | 100%   | -
Signup Start  | 3,000  | 30%    | 70% ← big leak
Signup Done   | 2,400  | 80%    | 20%
Activated     | 1,200  | 50%    | 50% ← investigate
Converted     | 240    | 20%    | 80%
```

## Report Writing Tips

```
ESTRUCTURA DE REPORTE:

1. EXECUTIVE SUMMARY
   - 3 bullets max
   - Numbers that matter
   - Recommended actions

2. KEY FINDINGS
   - What happened
   - Why it matters
   - Evidence/data

3. DETAILED ANALYSIS
   - Charts and tables
   - Comparisons
   - Segmentation

4. RECOMMENDATIONS
   - Specific actions
   - Expected impact
   - Priority order

BEST PRACTICES:
- Lead with insight, not data
- Visualize trends
- Compare to baseline/target
- Explain anomalies
- End with "so what?"
```

## Weekly Business Review

```markdown
## Week [X] Review

### TL;DR
- 📈 [Good news #1]
- ⚠️ [Concern #1]
- 🎯 [Focus for next week]

### Key Metrics
| Metric | Actual | Target | Status |
|--------|--------|--------|--------|
| Revenue | $X | $Y | 🟢/🟡/🔴 |
| New users | X | Y | 🟢/🟡/🔴 |
| Retention | X% | Y% | 🟢/🟡/🔴 |

### Wins
- [Achievement with number]
- [Achievement with number]

### Concerns
- [Issue + root cause + plan]
- [Issue + root cause + plan]

### Experiments
- [Experiment]: [Status] [Result if done]

### Next Week Focus
1. [Priority]
2. [Priority]
```

## Data Investigation Framework

```
CUANDO VES ALGO WEIRD:

1. VERIFY
   - ¿Es real o data bug?
   - Check data quality
   - Compare sources

2. SCOPE
   - ¿Cuándo empezó?
   - ¿Qué usuarios afectados?
   - ¿Qué tan grande es el cambio?

3. HYPOTHESIZE
   - Possible causes
   - External factors?
   - Internal changes?

4. TEST
   - Segment data
   - Correlate with events
   - A/B split analysis

5. CONCLUDE
   - Root cause identified
   - Impact quantified
   - Action recommended
```

## Visualization Guidelines

```
CUÁNDO USAR CADA TIPO:

LINE CHART:
- Trends over time
- Multiple series comparison

BAR CHART:
- Category comparison
- Ranked lists

PIE CHART:
- Part of whole (limitado a 5-6 slices max)
- Market share

TABLE:
- Exact numbers matter
- Multiple dimensions

FUNNEL:
- Conversion flows
- Drop-off analysis

COHORT HEATMAP:
- Retention over time
- Behavior by group

TIPS:
- Title should state insight
- Label axes clearly
- Consistent colors
- Remove chartjunk
- Start Y at 0 (usually)
```

## Tools Stack

```
DATA COLLECTION:
- Segment
- Amplitude
- Mixpanel
- Google Analytics 4

DATA WAREHOUSE:
- BigQuery
- Snowflake
- Redshift

VISUALIZATION:
- Metabase
- Looker
- Tableau
- Mode

EXPERIMENTATION:
- LaunchDarkly
- Statsig
- Amplitude Experiment
```

## Mejores Prácticas

1. **Insight, not just data** - Numbers without context are useless
2. **Compare to something** - Baseline, target, last period
3. **Segment ruthlessly** - Averages hide truth
4. **Track causes, not just effects** - Why matters
5. **Automate what repeats** - Weekly reports should be easy
6. **Question the data** - Garbage in, garbage out

## Filosofía

> "Data tells you what happened. Analysis tells you why. Insights tell you what to do next."

El objetivo es democratizar el acceso a insights, permitiendo que todos en el equipo tomen decisiones basadas en datos reales.