🇺🇸English

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.

Table of Contents

• Capabilities
• Input Requirements
• Output Formats
• How to Use
• Scripts
• Reference Guides
• Best Practices
• Limitations

Capabilities

• Multi-Touch Attribution : Five attribution models (first-touch, last-touch, linear, time-decay, position-based) with configurable parameters
• Funnel Conversion Analysis : Stage-by-stage conversion rates, drop-off identification, bottleneck detection, and segment comparison
• Campaign ROI Calculation : ROI, ROAS, CPA, CPL, CAC metrics with industry benchmarking and underperformance flagging
• A/B Test Support : Templates for structured A/B test documentation and analysis
• Channel Comparison : Cross-channel performance comparison with normalized metrics
• Executive Reporting : Ready-to-use templates for campaign performance reports

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
    }
  ]
}

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

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:

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

Best Practices

1. Use multiple attribution models -- No single model tells the full story. Compare at least 3 models to triangulate channel value.
2. Set appropriate lookback windows -- Match your time-decay half-life to your average sales cycle length.
3. Segment your funnels -- Always compare segments (channel, cohort, geography) to identify what drives best performance.
4. Benchmark against your own history first -- Industry benchmarks provide context, but your own historical data is the most relevant comparison.
5. Run ROI analysis at regular intervals -- Weekly for active campaigns, monthly for strategic review.
6. Include all costs -- Factor in creative, tooling, and labor costs alongside media spend for accurate ROI.
7. Document A/B tests rigorously -- Use the provided template to ensure statistical validity and clear decision criteria.

Limitations

• No statistical significance testing -- A/B test analysis requires external tools for p-value calculations. Scripts provide descriptive metrics only.
• Standard library only -- No advanced statistical or data processing 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 -- Uses 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.

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.

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.

Related Skills

• marketing-demand-acquisition : For planning campaigns that analytics measures.
• social-media-analyzer : For social-specific analytics complementing cross-channel analysis.
• marketing-strategy-pmm : For strategic context behind campaign performance.
• content-creator : For optimizing content based on analytics findings.

Weekly Installs

41

Repository

borghei/claude-skills

GitHub Stars

29

First Seen

Feb 23, 2026

Security Audits

Gen Agent Trust HubPassSocketFailSnykPass

Installed on

claude-code34

opencode27

gemini-cli27

github-copilot27

cline27

codex27