Campaign Analytics

---
name: campaign-analytics
description: 'Analyzes campaign performance with multi-touch attribution, funnel conversion, and ROI calculation for marketing optimization'
zh_description: "分析营销活动的渠道表现、转化、归因和投入产出。"
version: "1.0.1"
author: Alireza Rezvani
source: "in-house"
source_url: ""
license: MIT
tags: '["analytics", "campaign", "growth", "marketing"]'
created_at: "2026-03-04"
updated_at: "2026-09-06"
quality: 5
complexity: "intermediate"
metadata:
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.

---

## 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.

### 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
```

### 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** -- 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.