Marketing Analytics

---
context: fork
name: marketing-analytics
description: "Analyzes marketing performance with KPI dashboards, channel attribution, campaign ROI measurement, forecasting, and industry benchmarking. Use when user asks about marketing analytics, KPI dashboard, attribution, ROAS, CAC, LTV, channel performance, campaign ROI, 마케팅 분석, KPI 대시보드, 어트리뷰션, or ROAS."
lang: [en, ko]
platforms: [claude-code, gemini-cli, codex-cli, cursor]
level: 3
triggers:
  - "marketing analytics"
  - "ROI"
  - "attribution"
  - "conversion tracking"
  - "marketing metrics"
agents:
  - "code-reviewer"
  - "performance-engineer"
tokens: "~4K"
category: "marketing"
source_hash: 142ec718
whenNotToUse: "Product telemetry, infrastructure monitoring, or engineering performance metrics — use observability or data-analysis skill instead when the domain is not marketing spend and campaign ROI."
---

# Marketing Analytics

## When This Skill Applies
- Building marketing KPI dashboards
- Analyzing channel performance and attribution
- Measuring campaign ROI and ROAS
- Creating marketing forecasts and projections
- Benchmarking performance against industry standards

## Core Guidance

### 1. Analytics Process
```
Define KPIs -> Instrument Tracking -> Collect Data -> Analyze Performance -> Attribute Results -> Generate Insights -> Recommend Actions -> Forecast
```

### 2. Marketing Metrics Hierarchy

```
Level 1 (Business): Revenue, Profit, Market Share
  |
Level 2 (Marketing): CAC, LTV, LTV:CAC, Marketing ROI
  |
Level 3 (Channel): Channel ROAS, Channel CAC, Channel CVR
  |
Level 4 (Campaign): Campaign CPA, Campaign CTR, Campaign Revenue
  |
Level 5 (Tactical): Ad CTR, Email Open Rate, Page Bounce Rate
```

### 3. Channel Performance Framework

| Channel | Key Metrics | Benchmarks | Attribution Role |
|---------|-----------|-----------|-----------------|
| Organic Search | Traffic, rankings, conversions | 2-5% CVR | Awareness + Conversion |
| Paid Search | CPC, CTR, ROAS, quality score | 3-5% CTR | Conversion |
| Social Organic | Engagement, reach, followers | 1-3% engagement | Awareness |
| Social Paid | CPM, CPC, CPA, ROAS | $5-$15 CPM | Awareness + Conversion |
| Email | Open rate, CTR, revenue per email | 20-30% open | Nurture + Conversion |
| Content | Traffic, time on page, leads | 2-5% CTR | Awareness + Nurture |
| Referral | Referral traffic, CVR | 3-7% CVR | Conversion |
| Direct | Direct visits, branded search | Varies | Brand strength indicator |

### 4. Attribution Models Comparison

| Model | Logic | Pros | Cons |
|-------|-------|------|------|
| First-touch | 100% to first channel | Simple, awareness focus | Ignores nurture |
| Last-touch | 100% to last channel | Simple, conversion focus | Ignores awareness |
| Linear | Equal across all touches | Balanced | Oversimplified |
| Time-decay | More to recent touches | Recency-weighted | Devalues awareness |
| Position-based | 40/20/40 first/mid/last | Good B2B model | Arbitrary weights |
| Data-driven | ML-weighted by impact | Most accurate | Requires high volume |

**Recommendation**: Start with Position-based for B2B, Last-touch for B2C with short cycles, Data-driven when volume supports it (10K+ conversions/month).

### 5. Marketing Funnel Metrics

| Funnel Stage | Volume Metric | Efficiency Metric | Cost Metric |
|-------------|--------------|-------------------|------------|
| Impressions | Total impressions | -- | CPM |
| Clicks | Total clicks | CTR | CPC |
| Visits | Sessions | Bounce rate | Cost per visit |
| Leads | MQLs | Visit-to-lead % | CPL |
| Opportunities | SQLs | MQL-to-SQL % | Cost per SQL |
| Customers | New customers | SQL-to-close % | CAC |
| Revenue | Total revenue | ARPU | LTV:CAC |

### 6. Forecasting Methods

| Method | Best For | Data Required |
|--------|---------|--------------|
| Trend Extrapolation | Stable growth patterns | 12+ months historical |
| Seasonal Adjustment | Seasonal businesses | 2+ years data |
| Cohort-Based | Subscription businesses | Cohort retention data |
| Regression | Multi-variable prediction | Large datasets |
| Scenario Modeling | Strategic planning | Assumptions + baselines |

**Forecast Template**:
```
Scenario     | Q1       | Q2       | Q3       | Q4       | Annual
-------------|----------|----------|----------|----------|--------
Conservative | [value]  | [value]  | [value]  | [value]  | [total]
Base Case    | [value]  | [value]  | [value]  | [value]  | [total]
Optimistic   | [value]  | [value]  | [value]  | [value]  | [total]
```

### 7. Industry Benchmarks

| Metric | B2B SaaS | E-commerce | Media | Marketplace |
|--------|----------|-----------|-------|-------------|
| CAC | $200-$1000 | $20-$100 | $5-$30 | $50-$300 |
| LTV:CAC | 3:1 - 5:1 | 3:1 - 4:1 | 2:1 - 3:1 | 3:1 - 5:1 |
| Churn (monthly) | 3-7% | N/A | 5-10% | 5-8% |
| Email Open Rate | 20-25% | 15-20% | 18-22% | 17-21% |
| Landing Page CVR | 3-5% | 2-4% | 5-10% | 3-6% |
| ROAS (Paid) | 3x-5x | 4x-8x | 2x-4x | 3x-6x |

### 8. Analytics Maturity Model

| Level | Capability | Tools |
|-------|-----------|-------|
| L1: Basic | Page views, session tracking | GA4 basic setup |
| L2: Standard | Event tracking, goal tracking | GA4 + tag manager |
| L3: Advanced | Multi-touch attribution, cohort | GA4 + CDP + BI tool |
| L4: Predictive | Forecasting, propensity models | ML models + data warehouse |
| L5: Prescriptive | Automated optimization, real-time | Full MarTech stack |

## Output Format
```
MARKETING ANALYTICS REPORT
===========================
Period:     [date range]
Channels:   [scope]
Model:      [attribution model]

KPI SCORECARD
-------------
Metric | Current | Target | Delta  | Trend | Status
-------|---------|--------|--------|-------|-------
[KPI]  | [value] | [value]| [+/-]  | [dir] | [status]

CHANNEL PERFORMANCE
-------------------
Channel  | Spend    | Revenue  | ROAS | CAC    | CVR
---------|----------|----------|------|--------|-----
[channel]| [spend]  | [revenue]| [Xx] | [cost] | [%]

ATTRIBUTION
-----------
Model      | Top Channel | Revenue Share
-----------|-------------|-------------
[model]    | [channel]   | [%]

FORECAST
--------
Scenario     | Next Period | Confidence
-------------|------------|----------
[scenario]   | [value]    | [%]

RECOMMENDATIONS
---------------
Priority | Action          | Expected Impact
---------|----------------|----------------
P1       | [action]       | [metric lift]
```

## Quick Reference

**Metrics Hierarchy**: Business -> Marketing -> Channel -> Campaign -> Tactical
**Attribution**: Position-based (B2B), Last-touch (B2C short cycle), Data-driven (high volume)
**Forecasting**: Trend, Seasonal, Cohort, Regression, Scenario
**Maturity**: Basic (L1) -> Standard (L2) -> Advanced (L3) -> Predictive (L4) -> Prescriptive (L5)

---

## References

- See `${CLAUDE_SKILL_DIR}/references/metrics-hierarchy.md` for metrics hierarchy
- See `${CLAUDE_SKILL_DIR}/references/attribution-models-selection.md` for attribution models selection guide


## Rationalizations

The following table captures common excuses agents make to skip the rigor of this marketing practice, paired with factual rebuttals.

| Excuse | Rebuttal |
|--------|----------|
| "The dashboard shows green, we're good." | Green dashboards without significance testing can hide noise as trend; always annotate with variance and confidence band. |
| "We can't attribute, so we'll go with gut feel." | Imperfect attribution is still better than none; use MMM, incrementality tests, or geo-holdouts when MTA fails. |
| "YoY growth is proof of channel health." | YoY growth can be driven by market tailwinds, not channel quality; normalize vs. category index before declaring wins. |
| "Vanity metrics are fine for the exec deck." | Exec metrics set organizational priorities; vanity metrics in exec reporting misdirect budget across quarters. |
| "One data source is the source of truth." | Single-source reporting hides ingestion errors and ad platform discrepancies; triangulate GA, ad platforms, and CRM. |