# Campaign Analytics

> > **Note:** Bundled scripts ship as Markdown reference (`.md`) — copy the code out of the `.md` file to run it.

- Skill: `neekware/campaign-analytics-2` (Agent Skill, multi-file: 12 files)
- Install (CLI): `npx skillmds@latest add neekware/campaign-analytics-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/neekware/campaign-analytics-2/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: neekware (https://skillmd.com/u/neekware)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/neekware/campaign-analytics-2

---

> **Note:** Bundled scripts ship as Markdown reference (`.md`) — copy the code out of the `.md` file to run it.

# 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

```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": 500.0
    }
  ]
}
```

### 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": 5000.0,
      "revenue": 25000.0,
      "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:

```bash
# 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

```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
```

### Funnel Analysis

```bash
# 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

```bash
# 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

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

- **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.

> **Creator:** Alireza Rezvani
> **License:** MIT
> **Source Repo:** `neekware/dojo-skills`
> **Source Bucket:** `marketing-skill`
> **Original Path:** `marketing-skill/campaign-analytics`