Campaign Analytics

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:

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:

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:

3. campaign_roi_calculator.py

Calculates comprehensive ROI metrics with industry benchmarking:

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

1. Use multiple attribution models -- Compare at least 3 models to triangulate channel value; no single model tells the full story. 2. Set appropriate lookback windows -- Match your time-decay half-life to your average sales cycle length. 3. Segment your funnels -- Compare segments (channel, cohort, geography) to identify performance drivers. 4. Benchmark against your own history first -- Industry benchmarks provide context, but 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

Related Skills

1. Use multiple attribution models -- Compare at least 3 models to triangulate channel value; no single model tells the full story. 2. Set appropriate lookback windows -- Match your time-decay half-life to your average sales cycle length. 3. Segment your funnels -- Compare segments (channel, cohort, geography) to identify performance drivers. 4. Benchmark against your own history first -- Industry benchmarks provide context, but 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.