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Use when analyzing marketing campaigns, ad performance, attribution models, conversion rates, or calculating marketing ROI, ROAS, CPA, and campaign metrics across channels.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Alireza Rezvani\n  category: marketing\n  domain: campaign-analytics\n  updated: 2026-02-06\n  python-tools: attribution_analyzer.py, funnel_analyzer.py, campaign_roi_calculator.py\n  tech-stack: marketing-analytics, attribution-modeling\n---\n\n# Campaign Analytics\n\nProduction-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.\n\n---\n\n## Input Requirements\n\nAll scripts accept a JSON file as positional input argument. See `assets\u002Fsample_campaign_data.json` for complete examples.\n\n### Attribution Analyzer\n\n```json\n{\n  \"journeys\": [\n    {\n      \"journey_id\": \"j1\",\n      \"touchpoints\": [\n        {\"channel\": \"organic_search\", \"timestamp\": \"2025-10-01T10:00:00\", \"interaction\": \"click\"},\n        {\"channel\": \"email\", \"timestamp\": \"2025-10-05T14:30:00\", \"interaction\": \"open\"},\n        {\"channel\": \"paid_search\", \"timestamp\": \"2025-10-08T09:15:00\", \"interaction\": \"click\"}\n      ],\n      \"converted\": true,\n      \"revenue\": 500.00\n    }\n  ]\n}\n```\n\n### Funnel Analyzer\n\n```json\n{\n  \"funnel\": {\n    \"stages\": [\"Awareness\", \"Interest\", \"Consideration\", \"Intent\", \"Purchase\"],\n    \"counts\": [10000, 5200, 2800, 1400, 420]\n  }\n}\n```\n\n### Campaign ROI Calculator\n\n```json\n{\n  \"campaigns\": [\n    {\n      \"name\": \"Spring Email Campaign\",\n      \"channel\": \"email\",\n      \"spend\": 5000.00,\n      \"revenue\": 25000.00,\n      \"impressions\": 50000,\n      \"clicks\": 2500,\n      \"leads\": 300,\n      \"customers\": 45\n    }\n  ]\n}\n```\n\n### Input Validation\n\nBefore running scripts, verify your JSON is valid and matches the expected schema. Common errors:\n\n- **Missing required keys** (e.g., `journeys`, `funnel.stages`, `campaigns`) → script exits with a descriptive `KeyError`\n- **Mismatched array lengths** in funnel data (`stages` and `counts` must be the same length) → raises `ValueError`\n- **Non-numeric monetary values** in ROI data → raises `TypeError`\n\nUse `python -m json.tool your_file.json` to validate JSON syntax before passing it to any script.\n\n---\n\n## Output Formats\n\nAll scripts support two output formats via the `--format` flag:\n\n- `--format text` (default): Human-readable tables and summaries for review\n- `--format json`: Machine-readable JSON for integrations and pipelines\n\n---\n\n## Typical Analysis Workflow\n\nFor a complete campaign review, run the three scripts in sequence:\n\n```bash\n# Step 1 — Attribution: understand which channels drive conversions\npython scripts\u002Fattribution_analyzer.py campaign_data.json --model time-decay\n\n# Step 2 — Funnel: identify where prospects drop off on the path to conversion\npython scripts\u002Ffunnel_analyzer.py funnel_data.json\n\n# Step 3 — ROI: calculate profitability and benchmark against industry standards\npython scripts\u002Fcampaign_roi_calculator.py campaign_data.json\n```\n\nUse attribution results to identify top-performing channels, then focus funnel analysis on those channels' segments, and finally validate ROI metrics to prioritize budget reallocation.\n\n---\n\n## How to Use\n\n### Attribution Analysis\n\n```bash\n# Run all 5 attribution models\npython scripts\u002Fattribution_analyzer.py campaign_data.json\n\n# Run a specific model\npython scripts\u002Fattribution_analyzer.py campaign_data.json --model time-decay\n\n# JSON output for pipeline integration\npython scripts\u002Fattribution_analyzer.py campaign_data.json --format json\n\n# Custom time-decay half-life (default: 7 days)\npython scripts\u002Fattribution_analyzer.py campaign_data.json --model time-decay --half-life 14\n```\n\n### Funnel Analysis\n\n```bash\n# Basic funnel analysis\npython scripts\u002Ffunnel_analyzer.py funnel_data.json\n\n# JSON output\npython scripts\u002Ffunnel_analyzer.py funnel_data.json --format json\n```\n\n### Campaign ROI Calculation\n\n```bash\n# Calculate ROI metrics for all campaigns\npython scripts\u002Fcampaign_roi_calculator.py campaign_data.json\n\n# JSON output\npython scripts\u002Fcampaign_roi_calculator.py campaign_data.json --format json\n```\n\n---\n\n## Scripts\n\n### 1. attribution_analyzer.py\n\nImplements five industry-standard attribution models to allocate conversion credit across marketing channels:\n\n| Model | Description | Best For |\n|-------|-------------|----------|\n| First-Touch | 100% credit to first interaction | Brand awareness campaigns |\n| Last-Touch | 100% credit to last interaction | Direct response campaigns |\n| Linear | Equal credit to all touchpoints | Balanced multi-channel evaluation |\n| Time-Decay | More credit to recent touchpoints | Short sales cycles |\n| Position-Based | 40\u002F20\u002F40 split (first\u002Fmiddle\u002Flast) | Full-funnel marketing |\n\n### 2. funnel_analyzer.py\n\nAnalyzes conversion funnels to identify bottlenecks and optimization opportunities:\n\n- Stage-to-stage conversion rates and drop-off percentages\n- Automatic bottleneck identification (largest absolute and relative drops)\n- Overall funnel conversion rate\n- Segment comparison when multiple segments are provided\n\n### 3. campaign_roi_calculator.py\n\nCalculates comprehensive ROI metrics with industry benchmarking:\n\n- **ROI**: Return on investment percentage\n- **ROAS**: Return on ad spend ratio\n- **CPA**: Cost per acquisition\n- **CPL**: Cost per lead\n- **CAC**: Customer acquisition cost\n- **CTR**: Click-through rate\n- **CVR**: Conversion rate (leads to customers)\n- Flags underperforming campaigns against industry benchmarks\n\n---\n\n## Reference Guides\n\n| Guide | Location | Purpose |\n|-------|----------|---------|\n| Attribution Models Guide | `references\u002Fattribution-models-guide.md` | Deep dive into 5 models with formulas, pros\u002Fcons, selection criteria |\n| Campaign Metrics Benchmarks | `references\u002Fcampaign-metrics-benchmarks.md` | Industry benchmarks by channel and vertical for CTR, CPC, CPM, CPA, ROAS |\n| Funnel Optimization Framework | `references\u002Ffunnel-optimization-framework.md` | Stage-by-stage optimization strategies, common bottlenecks, best practices |\n\n---\n\n## Best Practices\n\n1. **Use multiple attribution models** -- Compare at least 3 models to triangulate channel value; no single model tells the full story.\n2. **Set appropriate lookback windows** -- Match your time-decay half-life to your average sales cycle length.\n3. **Segment your funnels** -- Compare segments (channel, cohort, geography) to identify performance drivers.\n4. **Benchmark against your own history first** -- Industry benchmarks provide context, but historical data is the most relevant comparison.\n5. **Run ROI analysis at regular intervals** -- Weekly for active campaigns, monthly for strategic review.\n6. **Include all costs** -- Factor in creative, tooling, and labor costs alongside media spend for accurate ROI.\n7. **Document A\u002FB tests rigorously** -- Use the provided template to ensure statistical validity and clear decision criteria.\n\n---\n\n## Limitations\n\n- **No statistical significance testing** -- Scripts provide descriptive metrics only; p-value calculations require external tools.\n- **Standard library only** -- No advanced statistical libraries. Suitable for most campaign sizes but not optimized for datasets exceeding 100K journeys.\n- **Offline analysis** -- Scripts analyze static JSON snapshots; no real-time data connections or API integrations.\n- **Single-currency** -- All monetary values assumed to be in the same currency; no currency conversion support.\n- **Simplified time-decay** -- Exponential decay based on configurable half-life; does not account for weekday\u002Fweekend or seasonal patterns.\n- **No cross-device tracking** -- Attribution operates on provided journey data as-is; cross-device identity resolution must be handled upstream.\n\n## Related Skills\n\n- **analytics-tracking**: For setting up tracking. NOT for analyzing data (that's this skill).\n- **ab-test-setup**: For designing experiments to test what analytics reveals.\n- **marketing-ops**: For routing insights to the right execution skill.\n- **paid-ads**: For optimizing ad spend based on analytics findings.\n","","imported","https:\u002F\u002Fgithub.com\u002Falirezarezvani\u002Fclaude-skills","user_system_seed","SkillOPIC",true,67,184,"2026-05-16 13:59:25",{"id":8,"name":21,"slug":22,"icon":23,"description":24,"sort":25,"createdAt":26},"写作研究","writing","mdi-pencil-outline","从学术写作到创意文案，让 AI 成为你的专属写作助手",1,"2026-05-16 12:53:40",{"id":7,"name":28,"slug":29,"icon":30,"description":31,"moduleId":8,"sort":32,"skillCount":33,"createdAt":26},"文案策划","copywriting","mdi-comment-text-outline","广告文案、品牌故事、Slogan",4,72,[35],{"id":36,"skillId":4,"version":37,"fileName":38,"fileSize":39,"filePath":40,"fileHash":41,"manifest":42,"createdAt":19},"156fae18-1c72-4310-8b90-88747674245e","1.0.0","campaign-analytics.zip",33103,"uploads\u002Fskills\u002F8fce3421-db00-4cc8-baaa-4e82fb90283a\u002Fcampaign-analytics.zip","ed6340af1efd334e8ee99017c91e4750facdff65bc96be88357ae52a74da1d1d","[{\"path\":\"SKILL.md\",\"isDirectory\":false,\"size\":8406},{\"path\":\"assets\u002Fab_test_template.md\",\"isDirectory\":false,\"size\":3504},{\"path\":\"assets\u002Fcampaign_report_template.md\",\"isDirectory\":false,\"size\":4024},{\"path\":\"assets\u002Fchannel_comparison_template.md\",\"isDirectory\":false,\"size\":4974},{\"path\":\"assets\u002Fexpected_output.json\",\"isDirectory\":false,\"size\":3865},{\"path\":\"assets\u002Fsample_campaign_data.json\",\"isDirectory\":false,\"size\":4730},{\"path\":\"references\u002Fattribution-models-guide.md\",\"isDirectory\":false,\"size\":9598},{\"path\":\"references\u002Fcampaign-metrics-benchmarks.md\",\"isDirectory\":false,\"size\":8991},{\"path\":\"references\u002Ffunnel-optimization-framework.md\",\"isDirectory\":false,\"size\":10949},{\"path\":\"scripts\u002Fattribution_analyzer.py\",\"isDirectory\":false,\"size\":12499},{\"path\":\"scripts\u002Fcampaign_roi_calculator.py\",\"isDirectory\":false,\"size\":18172},{\"path\":\"scripts\u002Ffunnel_analyzer.py\",\"isDirectory\":false,\"size\":10902}]",{"code":44,"message":45,"data":46},200,"success",{"items":47,"stats":48,"page":51},[],{"averageRating":49,"totalRatings":49,"ratingCounts":50},0,[49,49,49,49,49],{"limit":52,"offset":49,"hasMore":53,"nextOffset":52,"ratedOnly":16},15,false]