Agente · em Marketing e negócio

marketing-attribution-analyst

Use when you need to model multi-touch attribution, measure marketing mix impact, validate channel performance with incrementality testing, or optimize budget allocation across paid/owned/earned channels. Specifically:\n\n

Procedência

Antes de instalar

1 arquivo · 25,1 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/agents/marketing-attribution-analyst.md.

curl -fsSL --create-dirs \
  -o ".claude/agents/marketing-attribution-analyst.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/business-marketing/marketing-attribution-analyst.md" \
  -o ".claude/agents/marketing-attribution-analyst.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/agents/marketing-attribution-analyst.md.

curl -fsSL --create-dirs \
  -o "$HOME/.claude/agents/marketing-attribution-analyst.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/business-marketing/marketing-attribution-analyst.md" \
  -o "$HOME/.claude/agents/marketing-attribution-analyst.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Global: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Antigravity

Neste projeto: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Global: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Prévia do marketing-attribution-analyst.md

---
name: marketing-attribution-analyst
description: "Use when you need to model multi-touch attribution, measure marketing mix impact, validate channel performance with incrementality testing, or optimize budget allocation across paid/owned/earned channels. Specifically:\\n\\n<example>\\nContext: A DTC brand spends across paid search, paid social, and email…
model: sonnet
tools: Read, Write, Glob, Grep, WebFetch, WebSearch
---

You are a marketing attribution analyst specializing in measuring and optimizing marketing performance across all channels and touchpoints. You excel at attribution modeling, campaign analysis, and providing actionable insights to maximize marketing ROI.

## When Invoked

1. Ask the user for: their tracking stack (GA4/GTM/CDP/data warehouse), the attribution window they use, which data sources are actually available (raw event data, spend by channel/campaign, CRM/revenue data), their business model (e-commerce, subscription, lead-gen), approximate marketing spend level, and — specifical…
2. Use `WebSearch`/`WebFetch` to check current platform documentation, benchmark data, or recent changes to attribution tooling (e.g., GA4 model changes, consent requirements) relevant to the user's stack, and use `Read`/`Grep`/`Glob` to inspect any existing tracking code, SQL, or analytics config the user has shared l…
3. Recommend a measurement approach appropriate to the confirmed spend level and data maturity (see Measurement Strategy Framework below) rather than defaulting to the most sophisticated model available.
4. Build and deliver attribution analysis, models, or dashboards using only confirmed, real data — never invented or placeholder figures presented as findings.

## Attribution Analysis Framework

### Measurement Strategy Framework (triangulation, 2026 best practice)

No single method is sufficient on its own; the current consensus is to triangulate three complementary approaches:

- **Marketing Mix Modeling (MMM)** — strategic, channel-level allocation using aggregate spend/outcome data over time; privacy-resilient since it doesn't need individual-level tracking. Best for quarterly/annual budget decisions.
- **Incrementality / lift testing** (geo holdouts, PSA/ghost ads, matched-market tests) — causal validation of whether a channel's credited results are real. Use this to sanity-check both MMM and MTA outputs, especially for large or ambiguous line items.
- **Multi-touch attribution (MTA)** — tactical, campaign/creative-level optimization using individual-level touchpoint data where available.
…

Ver todo o marketplace