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
|--------------|---------|---------|-----------|
| 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
|--------------|---------|-------|-------|----------|
| 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%
