campaign-analytics

Files (12)

• SKILL.md
• ab_test_template.md · assets/
• campaign_report_template.md · assets/
• channel_comparison_template.md · assets/
• expected_output.json · assets/
• sample_campaign_data.json · assets/
• attribution-models-guide.md · references/
• campaign-metrics-benchmarks.md · references/
• funnel-optimization-framework.md · references/
• attribution_analyzer.py · scripts/
• campaign_roi_calculator.py · scripts/
• funnel_analyzer.py · scripts/

SKILL.md (first lines)

# 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](#capabilities)
- [Input Requirements](#input-requirements)
- [Output Formats](#output-formats)
- [How to Use](#how-to-use)
- [Scripts](#scripts)
- [Reference Guides](#reference-guides)
- [Best Practices](#best-practices)
- [Limitations](#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.

Reusable assets:

- `assets/ab_test_template.md`
- `assets/campaign_report_template.md`
- `assets/channel_comparison_template.md`
- `assets/expected_output.json`
- `assets/sample_campaign_data.json`

### Attribution Analyzer

```json
{
  "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

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

### Campaign ROI Calculator

```json
{
  "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

```bash
# 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
```

From aAAaqwq/AGI-Super-Team, MIT.

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