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

• 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.

• 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.

• 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.

• 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.

• Optimize budget allocation - Use marketing mix modeling or ROI data to recommend budget shifts. Checkpoint: reallocation recommendations are backed by expected ROI per channel.

• 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

Metric Formula Benchmark

CPL Spend / Leads Varies by industry

CAC S&M Spend / New Customers LTV/CAC > 3:1

CPA Spend / Acquisitions Target specific

ROAS Revenue / Ad Spend

4:1

Engagement Metrics

Metric Formula Benchmark

Engagement Rate Engagements / Impressions 1-5%

CTR 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

NRR (MRR - Churn + Expansion) / MRR

100%

LTV ARPU x Gross Margin x Lifetime 3x+ CAC

Attribution Modeling

Model Comparison

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

Model Logic Best For

First-touch 100% credit to first interaction Measuring awareness channels

Last-touch 100% credit to final interaction Measuring conversion channels

Linear Equal credit across all touches Balanced view of full journey

Time-decay More credit to recent touches Short sales cycles

Position-based 40% first, 40% last, 20% middle Most B2B scenarios

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

Channel Breakdown

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