name: marketing-analyst description: > Expert marketing analytics covering campaign analysis, attribution modeling, marketing mix modeling, ROI measurement, and performance reporting. Use when analyzing campaign ROI, comparing attribution models, optimizing marketing budget allocation, building executive dashboards, or running A/B test statistical analysis. license: MIT + Commons Clause metadata: version: 1.0.0 author: borghei category: marketing-growth updated: 2026-03-31 tags: [analytics, attribution, roi, campaigns, reporting]

Marketing Analyst

The agent operates as a senior marketing analyst, delivering campaign performance analysis, multi-touch attribution, marketing mix modeling, ROI measurement, and data-driven budget optimization.

Workflow

1. Define measurement objectives - Identify which campaigns, channels, or initiatives require analysis. Confirm KPIs (CPL, CAC, ROAS, pipeline, revenue). Checkpoint: every KPI has a target and a data source.
2. Collect and validate data - Pull campaign data from ad platforms, CRM, and analytics tools. Validate completeness and consistency. Checkpoint: no channel has >5% missing data.
3. Run attribution analysis - Apply multiple attribution models (first-touch, last-touch, linear, time-decay, position-based) and compare channel credit allocation. Checkpoint: results are compared across at least 3 models.
4. Analyze campaign performance - Calculate ROI, ROAS, CPL, CAC, and conversion rates per campaign. Identify top and bottom performers. Checkpoint: performance table includes target vs. actual for every metric.
5. Optimize budget allocation - Use marketing mix modeling or ROI data to recommend budget shifts. Checkpoint: reallocation recommendations are backed by expected ROI per channel.
6. Build executive report - Summarize headline metrics, wins, challenges, and next-period focus. Checkpoint: report passes the "so what" test (every data point has an actionable insight).

Marketing Metrics Reference

Acquisition Metrics

Engagement Metrics

Retention Metrics

Attribution Modeling

Model Comparison

The agent should apply multiple models and compare results to identify channel over/under-valuation:

Attribution Calculator

def calculate_attribution(touchpoints, model='position'):
    """Calculate attribution credit for a conversion journey.

    Args:
        touchpoints: List of channel names in order of interaction
        model: One of 'first', 'last', 'linear', 'time_decay', 'position'

    Returns:
        Dict mapping channel -> credit (sums to 1.0)

    Example:
        >>> calculate_attribution(['paid_search', 'email', 'organic', 'direct'], 'position')
        {'paid_search': 0.4, 'email': 0.1, 'organic': 0.1, 'direct': 0.4}
    """
    n = len(touchpoints)
    credits = {}

    if model == 'first':
        credits[touchpoints[0]] = 1.0
    elif model == 'last':
        credits[touchpoints[-1]] = 1.0
    elif model == 'linear':
        for tp in touchpoints:
            credits[tp] = credits.get(tp, 0) + 1.0 / n
    elif model == 'time_decay':
        decay = 0.7
        total = sum(decay ** i for i in range(n))
        for i, tp in enumerate(reversed(touchpoints)):
            credits[tp] = credits.get(tp, 0) + (decay ** i) / total
    elif model == 'position':
        if n == 1:
            credits[touchpoints[0]] = 1.0
        elif n == 2:
            credits[touchpoints[0]] = 0.5
            credits[touchpoints[-1]] = credits.get(touchpoints[-1], 0) + 0.5
        else:
            credits[touchpoints[0]] = 0.4
            credits[touchpoints[-1]] = credits.get(touchpoints[-1], 0) + 0.4
            for tp in touchpoints[1:-1]:
                credits[tp] = credits.get(tp, 0) + 0.2 / (n - 2)

    return credits

Example: Campaign Analysis Report

# Campaign Analysis: Q1 2026 Product Launch

## Performance Summary
| Metric       | Target  | Actual  | vs Target |
|--------------|---------|---------|-----------|
| Impressions  | 500K    | 612K    | +22%      |
| Clicks       | 25K     | 28.4K   | +14%      |
| Leads        | 1,200   | 1,350   | +13%      |
| MQLs         | 360     | 410     | +14%      |
| Pipeline     | $1.2M   | $1.45M  | +21%      |
| Revenue      | $380K   | $425K   | +12%      |

## Channel Breakdown
| Channel      | Spend   | Leads | CPL   | Pipeline |
|--------------|---------|-------|-------|----------|
| Paid Search  | $45K    | 520   | $87   | $580K    |
| LinkedIn Ads | $30K    | 310   | $97   | $420K    |
| Email        | $5K     | 380   | $13   | $350K    |
| Content/SEO  | $8K     | 140   | $57   | $100K    |

## Key Insight
Email delivers lowest CPL ($13) and strong pipeline. Recommend shifting
10% of LinkedIn budget to email nurture sequences for Q2.

Budget Optimization Framework

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

A/B Test Statistical Analysis

from scipy import stats
import numpy as np

def analyze_ab_test(control_conv, control_total, treatment_conv, treatment_total, alpha=0.05):
    """Analyze A/B test for statistical significance.

    Example:
        >>> result = analyze_ab_test(150, 5000, 195, 5000)
        >>> result['significant']
        True
        >>> f"{result['lift_pct']:.1f}%"
        '30.0%'
    """
    p_c = control_conv / control_total
    p_t = treatment_conv / treatment_total
    p_pool = (control_conv + treatment_conv) / (control_total + treatment_total)
    se = np.sqrt(p_pool * (1 - p_pool) * (1/control_total + 1/treatment_total))
    z = (p_t - p_c) / se
    p_value = 2 * (1 - stats.norm.cdf(abs(z)))

    return {
        'control_rate': p_c,
        'treatment_rate': p_t,
        'lift_pct': ((p_t - p_c) / p_c) * 100,
        'p_value': p_value,
        'significant': p_value < alpha,
    }

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

Reference Materials

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

Troubleshooting

Success Criteria

• Multi-touch attribution model deployed comparing 3+ models with documented channel credit differences
• Monthly marketing report delivered within 3 business days of month close
• Budget reallocation recommendations backed by per-channel ROI data and implemented quarterly
• Forecast accuracy within 15% of actual for 3-month projections
• Campaign performance reports include target vs actual for every KPI
• Every data point in executive reports has an actionable insight (passes "so what" test)
• Channel data completeness above 95% (no channel has >5% missing data)

Scope & Limitations

In Scope: Campaign performance analysis, multi-touch attribution modeling, marketing mix optimization, ROI/ROAS calculation, budget allocation recommendations, executive reporting, cohort retention analysis, marketing forecasting.

Out of Scope: Analytics implementation and tracking setup (see analytics-tracking skill), product analytics (see product-team skills), financial modeling beyond marketing metrics (see finance skill), data engineering and warehouse management.

Limitations: Attribution models are approximations — no model perfectly captures the buyer journey, especially for high-touch B2B sales. Forecasting uses historical extrapolation with dampening; it does not account for market disruptions or competitive moves. Budget optimization assumes linear channel scaling; most channels have diminishing returns at scale.

Scripts