name: marketing-analyst description: Expert marketing analytics covering campaign analysis, attribution modeling, marketing mix modeling, ROI measurement, and performance reporting. version: 1.0.0 author: Claude Skills category: marketing-growth tags: [analytics, attribution, roi, campaigns, reporting]

Marketing Analyst

Expert-level marketing analytics for data-driven decisions.

Core Competencies

Campaign performance analysis
Attribution modeling
Marketing mix modeling
ROI measurement
Customer analytics
Channel optimization
Forecasting
Reporting and visualization

Marketing Metrics Framework

Acquisition Metrics

Metric	Formula	Benchmark
CPL (Cost per Lead)	Spend / Leads	Varies by industry
CAC (Customer Acquisition Cost)	S&M Spend / New Customers	LTV/CAC > 3:1
CPA (Cost per Acquisition)	Spend / Acquisitions	Target specific
ROAS (Return on Ad Spend)	Revenue / Ad Spend	> 4:1

Engagement Metrics

Metric	Formula	Benchmark
Engagement Rate	Engagements / Impressions	1-5%
Click-Through Rate	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
Retention Rate	1 - Churn Rate	> 95% monthly
NRR (Net Revenue Retention)	(MRR - Churn + Expansion) / MRR	> 100%
LTV (Lifetime Value)	ARPU × Gross Margin × Lifetime	3x+ CAC

Attribution Modeling

Model Comparison

import pandas as pd

def calculate_attribution(touchpoints, model='linear'):
    """
    Calculate attribution credit for each touchpoint

    touchpoints: List of touchpoint events
    model: 'first', 'last', 'linear', 'time_decay', 'position'
    """

    n = len(touchpoints)
    credits = {}

    if model == 'first':
        credits[touchpoints[0]] = 1.0

    elif model == 'last':
        credits[touchpoints[-1]] = 1.0

    elif model == 'linear':
        credit = 1.0 / n
        for tp in touchpoints:
            credits[tp] = credits.get(tp, 0) + credit

    elif model == 'time_decay':
        # More recent = more credit
        decay_rate = 0.7
        total_weight = sum([decay_rate ** i for i in range(n)])
        for i, tp in enumerate(reversed(touchpoints)):
            weight = (decay_rate ** i) / total_weight
            credits[tp] = credits.get(tp, 0) + weight

    elif model == 'position':
        # 40% first, 40% last, 20% middle
        if n == 1:
            credits[touchpoints[0]] = 1.0
        elif n == 2:
            credits[touchpoints[0]] = 0.5
            credits[touchpoints[-1]] = 0.5
        else:
            credits[touchpoints[0]] = 0.4
            credits[touchpoints[-1]] = 0.4
            middle_credit = 0.2 / (n - 2)
            for tp in touchpoints[1:-1]:
                credits[tp] = credits.get(tp, 0) + middle_credit

    return credits

Attribution Report

┌─────────────────────────────────────────────────────────────┐
│               Attribution Analysis - [Period]                │
├─────────────────────────────────────────────────────────────┤
│  Model Comparison (Revenue Attribution)                      │
│                                                              │
│  Channel      First    Last    Linear   Position  Data-Driven│
│  Paid Search  $250K    $180K   $210K    $220K     $215K      │
│  Email        $120K    $200K   $160K    $165K     $170K      │
│  Social       $80K     $50K    $65K     $60K      $58K       │
│  Organic      $150K    $170K   $165K    $155K     $157K      │
├─────────────────────────────────────────────────────────────┤
│  Avg Touches to Conversion: 4.2                              │
│  Avg Days to Conversion: 18                                  │
│  Most Common Path: Paid → Email → Organic → Direct           │
└─────────────────────────────────────────────────────────────┘

Campaign Analysis

Campaign Performance Template

# Campaign Analysis: [Campaign Name]

## Overview
- Type: [Type]
- Duration: [Dates]
- Budget: $[Amount]
- Spend: $[Amount]

## Performance Summary

| Metric | Target | Actual | vs Target |
|--------|--------|--------|-----------|
| Impressions | X | Y | +/-% |
| Clicks | X | Y | +/-% |
| Leads | X | Y | +/-% |
| MQLs | X | Y | +/-% |
| Pipeline | $X | $Y | +/-% |
| Revenue | $X | $Y | +/-% |

## Channel Breakdown

| Channel | Spend | Leads | CPL | Pipeline |
|---------|-------|-------|-----|----------|
| [Channel] | $X | Y | $Z | $W |

## Creative Performance

| Variant | Impressions | CTR | Conv Rate |
|---------|-------------|-----|-----------|
| A | X | Y% | Z% |
| B | X | Y% | Z% |

## Audience Insights

### Top Performing Segments
1. [Segment]: [Performance]
2. [Segment]: [Performance]

### Underperforming Segments
1. [Segment]: [Performance]

## Key Learnings
- [Learning 1]
- [Learning 2]

## Recommendations
1. [Recommendation]
2. [Recommendation]

A/B Test Analysis

from scipy import stats
import numpy as np

def analyze_ab_test(control_conversions, control_total,
                    treatment_conversions, treatment_total,
                    alpha=0.05):
    """
    Analyze A/B test results for statistical significance
    """
    # Conversion rates
    p_control = control_conversions / control_total
    p_treatment = treatment_conversions / treatment_total

    # Pooled probability
    p_pool = (control_conversions + treatment_conversions) / \
             (control_total + treatment_total)

    # Standard error
    se = np.sqrt(p_pool * (1 - p_pool) *
                 (1/control_total + 1/treatment_total))

    # Z-score
    z = (p_treatment - p_control) / se

    # P-value
    p_value = 2 * (1 - stats.norm.cdf(abs(z)))

    # Lift
    lift = (p_treatment - p_control) / p_control

    return {
        'control_rate': p_control,
        'treatment_rate': p_treatment,
        'lift': lift,
        'lift_pct': lift * 100,
        'z_score': z,
        'p_value': p_value,
        'significant': p_value < alpha,
        'confidence': 1 - alpha
    }

Marketing Mix Modeling

MMM Framework

SALES = β₀ + β₁(TV) + β₂(Digital) + β₃(Print) + β₄(Seasonality) + ε

Components:
- Base: Organic demand without marketing
- TV: Television advertising impact
- Digital: Digital channels (paid, social)
- Print: Print advertising impact
- Seasonality: Time-based patterns
- Carryover: Delayed effects (adstock)

Outputs:
- Channel contribution to sales
- ROI by channel
- Optimal budget allocation

Budget Optimization

┌─────────────────────────────────────────────────────────────┐
│            Budget Allocation Recommendation                  │
├─────────────────────────────────────────────────────────────┤
│  Channel        Current    Optimal    Change    Expected ROI │
│  Paid Search    30%        35%        +5%       4.2x         │
│  Social Paid    25%        20%        -5%       2.8x         │
│  Display        15%        10%        -5%       1.5x         │
│  Email          10%        15%        +5%       8.5x         │
│  Content        10%        12%        +2%       5.2x         │
│  Events         10%        8%         -2%       2.2x         │
├─────────────────────────────────────────────────────────────┤
│  Projected Impact: +15% pipeline with same budget            │
└─────────────────────────────────────────────────────────────┘

Reporting

Marketing Dashboard

┌─────────────────────────────────────────────────────────────┐
│              Marketing Performance - [Month]                 │
├─────────────────────────────────────────────────────────────┤
│  ACQUISITION                                                 │
│  Visitors: 125K (+12%)   Leads: 5.2K (+8%)   CAC: $145 (-5%)│
├─────────────────────────────────────────────────────────────┤
│  PIPELINE                                                    │
│  MQLs: 823    SALs: 495    SQLs: 198    Pipeline: $2.4M     │
│  Conv: 15.8%  Conv: 60.1%  Conv: 40.0%  vs Goal: +108%      │
├─────────────────────────────────────────────────────────────┤
│  REVENUE                                                     │
│  Marketing Revenue: $580K    ROI: 5.2x    vs Goal: +115%    │
├─────────────────────────────────────────────────────────────┤
│  CHANNEL PERFORMANCE                                         │
│  [Bar chart by channel showing leads and pipeline]          │
├─────────────────────────────────────────────────────────────┤
│  TRENDS                                                      │
│  [Line chart showing key metrics over time]                 │
└─────────────────────────────────────────────────────────────┘

Executive Summary Template

# Marketing Performance: [Period]

## Headline
[One sentence summary of performance]

## Key Metrics
| Metric | Actual | Goal | Status |
|--------|--------|------|--------|
| Leads | X | Y | 🟢/🟡/🔴 |
| MQLs | X | Y | 🟢/🟡/🔴 |
| Pipeline | $X | $Y | 🟢/🟡/🔴 |
| Revenue | $X | $Y | 🟢/🟡/🔴 |

## Wins
- [Win 1]
- [Win 2]

## Challenges
- [Challenge 1]
- [Challenge 2]

## Next Period Focus
- [Focus area 1]
- [Focus area 2]

Reference Materials

references/metrics.md - Marketing metrics guide
references/attribution.md - Attribution modeling
references/reporting.md - Reporting best practices
references/forecasting.md - Forecasting methods

Scripts

# Campaign analyzer
python scripts/campaign_analyzer.py --data campaigns.csv --output report.html

# Attribution calculator
python scripts/attribution.py --touchpoints journeys.csv --model position

# ROI calculator
python scripts/roi_calculator.py --spend spend.csv --revenue revenue.csv

# Forecast generator
python scripts/forecast.py --historical data.csv --periods 6

Marketing Analyst

Skill Details

Repository Files

Marketing Analyst

Core Competencies

Marketing Metrics Framework

Acquisition Metrics

Engagement Metrics

Retention Metrics

Attribution Modeling

Model Comparison

Attribution Report

Campaign Analysis

Campaign Performance Template

A/B Test Analysis

Marketing Mix Modeling

MMM Framework

Budget Optimization

Reporting

Marketing Dashboard

Executive Summary Template

Reference Materials

Scripts

Related Skills

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Matplotlib

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Pydeseq2

Query Writing

Pydeseq2

Scientific Visualization

Skill Information