campaign-analytics

SKILL.md

Campaign Analytics

Production-grade campaign performance analysis with multi-touch attribution modeling, funnel conversion analysis, and ROI calculation. Three Python CLI tools provide deterministic, repeatable analytics using standard library only -- no external dependencies, no API calls, no ML models.

Input Requirements

All scripts accept a JSON file as positional input argument. See assets/sample_campaign_data.json for complete examples.

Attribution Analyzer
{
  "journeys": [
    {
      "journey_id": "j1",
      "touchpoints": [
        {"channel": "organic_search", "timestamp": "2025-10-01T10:00:00", "interaction": "click"},
        {"channel": "email", "timestamp": "2025-10-05T14:30:00", "interaction": "open"},
        {"channel": "paid_search", "timestamp": "2025-10-08T09:15:00", "interaction": "click"}
      ],
      "converted": true,
      "revenue": 500.00
    }
  ]
}

Funnel Analyzer

{
  "funnel": {
    "stages": ["Awareness", "Interest", "Consideration", "Intent", "Purchase"],
    "counts": [10000, 5200, 2800, 1400, 420]
  }
}

Campaign ROI Calculator

{
  "campaigns": [
    {
      "name": "Spring Email Campaign",
      "channel": "email",
      "spend": 5000.00,
      "revenue": 25000.00,
      "impressions": 50000,
      "clicks": 2500,
      "leads": 300,
      "customers": 45
    }
  ]
}

Input Validation

Before running scripts, verify your JSON is valid and matches the expected schema. Common errors:

Missing required keys (e.g., journeys, funnel.stages, campaigns) → script exits with a descriptive KeyError

Mismatched array lengths in funnel data (stages and counts must be the same length) → raises ValueError

Non-numeric monetary values in ROI data → raises TypeError

Use python -m json.tool your_file.json to validate JSON syntax before passing it to any script.

Output Formats

All scripts support two output formats via the --format flag:

--format text (default): Human-readable tables and summaries for review

--format json: Machine-readable JSON for integrations and pipelines

Typical Analysis Workflow

For a complete campaign review, run the three scripts in sequence:

# Step 1 — Attribution: understand which channels drive conversions
python scripts/attribution_analyzer.py campaign_data.json --model time-decay

# Step 2 — Funnel: identify where prospects drop off on the path to conversion
python scripts/funnel_analyzer.py funnel_data.json

# Step 3 — ROI: calculate profitability and benchmark against industry standards
python scripts/campaign_roi_calculator.py campaign_data.json

Use attribution results to identify top-performing channels, then focus funnel analysis on those channels' segments, and finally validate ROI metrics to prioritize budget reallocation.

How to Use

Attribution Analysis

# Run all 5 attribution models
python scripts/attribution_analyzer.py campaign_data.json

# Run a specific model
python scripts/attribution_analyzer.py campaign_data.json --model time-decay

# JSON output for pipeline integration
python scripts/attribution_analyzer.py campaign_data.json --format json

# Custom time-decay half-life (default: 7 days)
python scripts/attribution_analyzer.py campaign_data.json --model time-decay --half-life 14

Funnel Analysis

# Basic funnel analysis
python scripts/funnel_analyzer.py funnel_data.json

# JSON output
python scripts/funnel_analyzer.py funnel_data.json --format json

Campaign ROI Calculation

# Calculate ROI metrics for all campaigns
python scripts/campaign_roi_calculator.py campaign_data.json

# JSON output
python scripts/campaign_roi_calculator.py campaign_data.json --format json

Scripts

1. attribution_analyzer.py

Implements five industry-standard attribution models to allocate conversion credit across marketing channels:

Model
Description
Best For

First-Touch
100% credit to first interaction
Brand awareness campaigns

Last-Touch
100% credit to last interaction
Direct response campaigns

Linear
Equal credit to all touchpoints
Balanced multi-channel evaluation

Time-Decay
More credit to recent touchpoints
Short sales cycles

Position-Based
40/20/40 split (first/middle/last)
Full-funnel marketing

2. funnel_analyzer.py

Analyzes conversion funnels to identify bottlenecks and optimization opportunities:

Stage-to-stage conversion rates and drop-off percentages

Automatic bottleneck identification (largest absolute and relative drops)

Overall funnel conversion rate

Segment comparison when multiple segments are provided

3. campaign_roi_calculator.py

Calculates comprehensive ROI metrics with industry benchmarking:

ROI: Return on investment percentage

ROAS: Return on ad spend ratio

CPA: Cost per acquisition

CPL: Cost per lead

CAC: Customer acquisition cost

CTR: Click-through rate

CVR: Conversion rate (leads to customers)

Flags underperforming campaigns against industry benchmarks

Reference Guides

Guide
Location
Purpose

Attribution Models Guide
references/attribution-models-guide.md
Deep dive into 5 models with formulas, pros/cons, selection criteria

Campaign Metrics Benchmarks
references/campaign-metrics-benchmarks.md
Industry benchmarks by channel and vertical for CTR, CPC, CPM, CPA, ROAS

Funnel Optimization Framework
references/funnel-optimization-framework.md
Stage-by-stage optimization strategies, common bottlenecks, best practices

Best Practices

Use multiple attribution models -- Compare at least 3 models to triangulate channel value; no single model tells the full story.

Set appropriate lookback windows -- Match your time-decay half-life to your average sales cycle length.

Segment your funnels -- Compare segments (channel, cohort, geography) to identify performance drivers.

Benchmark against your own history first -- Industry benchmarks provide context, but historical data is the most relevant comparison.

Run ROI analysis at regular intervals -- Weekly for active campaigns, monthly for strategic review.

Include all costs -- Factor in creative, tooling, and labor costs alongside media spend for accurate ROI.

Document A/B tests rigorously -- Use the provided template to ensure statistical validity and clear decision criteria.

Limitations

No statistical significance testing -- Scripts provide descriptive metrics only; p-value calculations require external tools.

Standard library only -- No advanced statistical libraries. Suitable for most campaign sizes but not optimized for datasets exceeding 100K journeys.

Offline analysis -- Scripts analyze static JSON snapshots; no real-time data connections or API integrations.

Single-currency -- All monetary values assumed to be in the same currency; no currency conversion support.

Simplified time-decay -- Exponential decay based on configurable half-life; does not account for weekday/weekend or seasonal patterns.

No cross-device tracking -- Attribution operates on provided journey data as-is; cross-device identity resolution must be handled upstream.

Related Skills

analytics-tracking: For setting up tracking. NOT for analyzing data (that's this skill).

ab-test-setup: For designing experiments to test what analytics reveals.

marketing-ops: For routing insights to the right execution skill.

paid-ads: For optimizing ad spend based on analytics findings.