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Analyzes campaign performance with multi-touch attribution, funnel conversion, and ROI calculation for marketing optimization
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Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.
Design production-grade multi-agent orchestration systems. Covers five core patterns (sequential pipeline, parallel fan-out/fan-in, hierarchical delegation, event-driven, consensus), platform-specific implementations, handoff protocols, state management, error recovery, context window budgeting, and cost optimization.
App Store Optimization toolkit for researching keywords, optimizing metadata, and tracking mobile app performance on Apple App Store and Google Play Store.
SKILL.md を表示中
SOC 職業分類に基づく
| name | campaign-analytics |
| description | Analyzes campaign performance with multi-touch attribution, funnel conversion, and ROI calculation for marketing optimization |
| zh_description | 用于活动、分析，支持内容、营销、渠道和数据分析。 |
| version | 1.0.0 |
| author | Alireza Rezvani |
| source | in-house |
| source_url | |
| license | MIT |
| tags | ["analytics", "campaign", "growth", "marketing"] |
| created_at | 2026-03-04 |
| updated_at | 2026-03-20 |
| quality | 5 |
| complexity | intermediate |
| metadata | null |
| category | marketing |
| domain | campaign-analytics |
| updated | "2026-02-06T00:00:00.000Z" |
| python-tools | attribution_analyzer.py, funnel_analyzer.py, campaign_roi_calculator.py |
| tech-stack | marketing-analytics, attribution-modeling |
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.
All scripts accept a JSON file as positional input argument. See assets/sample_campaign_data.json for complete examples.
{
"journeys": [
{
"journey_id": "j1",
"touchpoints": [
{"channel": "organic_search",
{
"funnel": {
"stages": ["Awareness", "Interest", "Consideration", "Intent", "Purchase"],
"counts": [10000, 5200, 2800, 1400, 420]
}
}
{
"campaigns": [
{
"name": "Spring Email Campaign",
"channel": "email",
"spend": 5000.00,
"revenue": 25000.00,
"impressions": 50000,
"clicks": 2500,
"leads": 300,
"customers": 45
}
]
}
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# 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
# Basic funnel analysis
python scripts/funnel_analyzer.py funnel_data.json
# JSON output
python scripts/funnel_analyzer.py funnel_data.json --format json
# 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
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 |
Analyzes conversion funnels to identify bottlenecks and optimization opportunities:
Calculates comprehensive ROI metrics with industry benchmarking:
| 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 |