---
name: "campaign-analytics"
description: "Analyze campaign performance on the user's numbers: ROAS, CPA, ROI, multi-touch attribution and funnel drop-off. Use whenever the user asks which channel or ad is working, where spend is wasted, why conversions dropped, or shares campaign results to review."
license: MIT
metadata:
version: 1.0.0
author: Alireza Rezvani
category: marketing
domain: campaign-analytics
updated: 2026-02-06
python-tools: attribution_analyzer.py, funnel_analyzer.py, campaign_roi_calculator.py
tech-stack: marketing-analytics, attribution-modeling
---
# 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": 500.00
}
]
}
```
### 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
}
]
}
```
### 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.