analytics-product

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SKILL.md (first lines)

# ANALYTICS-PRODUCT — Decida com Dados

## Overview

Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto. Ativar para: configurar tracking de eventos, criar funil de conversao, analise de cohort, retencao, DAU/MAU, feature flags, A/B testing, north star metric, OKRs, dashboard de produto.

## When to Use This Skill

- Use para definir um evento de ativacao, investigar queda de funil ou calcular retencao com denominador e janela explicitos.
- Antes de instrumentar, registre a decisao de produto, a fonte de dados, o consentimento aplicavel, o fuso horario e a unidade de analise.

## Do Not Use This Skill When

- The task is unrelated to analytics product
- A simpler, more specific tool can handle the request
- The user needs general-purpose assistance without domain expertise

## How It Works

```
[objeto]_[verbo_passado]

Correto:   user_signed_up, conversation_started, upgrade_completed
Errado:    signup, click, conversion
```

## Analytics-Product — Decida Com Dados

> "In God we trust. All others must bring data." — W. Edwards Deming

---

## Exemplo ilustrativo: eventos de um assistente

```python
AURI_EVENTS = {
    # Aquisicao
    "user_signed_up":        {"props": ["source", "medium", "campaign"]},
    "onboarding_started":    {"props": ["step_count"]},
    "onboarding_completed":  {"props": ["time_to_complete", "steps_skipped"]},

    # Ativacao
    "first_conversation":    {"props": ["intent", "response_time"]},
    "aha_moment_reached":    {"props": ["trigger", "session_number"]},
    "feature_discovered":    {"props": ["feature_name", "discovery_method"]},

    # Retencao
    "conversation_started":  {"props": ["intent", "user_tier", "device"]},
    "conversation_completed":{"props": ["messages_count", "duration", "rating"]},
    "session_started":       {"props": ["days_since_last", "platform"]},

    # Receita
    "upgrade_viewed":        {"props": ["trigger", "current_tier"]},
    "upgrade_started":       {"props": ["target_tier", "trigger"]},
    "upgrade_completed":     {"props": ["tier", "plan", "revenue"]},
    "subscription_canceled": {"props": ["reason", "tier", "tenure_days"]},
    "payment_failed":        {"props": ["attempt_count", "error_code"]},
}
```

## Implementacao Posthog (Python)

```python
from posthog import Posthog
import os

posthog = Posthog(
    project_api_key=os.environ["POSTHOG_API_KEY"],
    host=os.environ.get("POSTHOG_HOST", "https://app.posthog.com")
)

def track(user_id: str, event: str, properties: dict = None):
    posthog.capture(
        distinct_id=user_id,
        event=event,
        properties=properties or {}
    )

def identify(user_id: str, traits: dict):
    posthog.identify(
        distinct_id=user_id,
        properties=traits
    )

## Uso:

track("user_123", "conversation_started", {
    "intent": "business_advice",
    "device": "alexa",
    "user_tier": "pro"
})
```

---

## Funil ilustrativo de ativacao (numeros hipoteticos)

```
Visita landing page          (100%)
    | [meta: 40%]
Clicou "Experimentar"         (40%)
    | [meta: 70%]
Completou cadastro            (28%)
    | [meta: 60%]
Fez primeira conversa         (17%)  <- AHA MOMENT
    | [meta: 50%]
Voltou no dia seguinte        (8.5%)
    | [meta: 40%]
Usou 3+ dias na semana        (3.4%)
    | [meta: 20%]
Converteu para Pro            (0.7%)
```

## Otimizando O Funil

```
Para cada drop-off > benchmark:
1. Identificar: onde exatamente o usuario sai?
2. Entender: por que? (session recordings, surveys)

From sickn33/agentic-awesome-skills, MIT.

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