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Manage — Analyzes campaign performance with multi-touch attribution, funnel conversion analysis, and ROI calculation for marketing optimization. Use when analyzing marketing campaigns, ad p
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Token-aware reasoning workflow with real tools: picks an operating mode to control cost, runs a structured pipeline (decompose → validate → verify → snapshot), and gives Claude Program-of-Thought, RK4/Euler, a code gate, and a safe skill router. Use when: multi-step or high-stakes tasks, real math, precise computation, audits, or the user mentions APEX, PoT, pipeline, or scientific mode.
**v00.33.0**: Ingested from antigravity-awesome-skills community repo
run multiple local CLI agents in parallel (separate tmux sessions)
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| name | campaign-analytics |
| description | Manage — Analyzes campaign performance with multi-touch attribution, funnel conversion analysis, and ROI calculation for marketing optimization. Use when analyzing marketing campaigns, ad p |
| license | MIT |
| metadata | {"version":"1.0.0","author":"Alireza Rezvani","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"} |
| executor | HYBRID |
| skill_id | business.marketing-skill.campaign-analytics |
| status | ADOPTED |
| security | {"level":"standard","pii":false,"approval_required":false} |
| anchors | ["business","marketing","data_science","performance"] |
| tier | 2 |
| input_schema | [{"name":"code_or_task","type":"string","description":"Code snippet, script, or task description to process","required":true}] |
| output_schema | [{"name":"report","type":"string","description":"Analysis report or summary from 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.
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", "timestamp": "2025-10-01T10:00:00", "interaction": "click"},
{"channel": "email", "timestamp": "2025-10-05T14:30:00", "interaction": "open"},
{"channel": "paid_search", "timestamp":
{
"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
}
]
}
Before running scripts, verify your JSON is valid and matches the expected schema. Common errors:
journeys, funnel.stages, campaigns) → script exits with a descriptive KeyErrorstages and counts must be the same length) → raises ValueErrorTypeErrorUse python -m json.tool your_file.json to validate JSON syntax before passing it to any script.
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 pipelinesFor a complete campaign review, run the three scripts in sequence:
# 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.
# 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 |
Manage — Analyzes campaign performance with multi-touch attribution, funnel conversion analysis, and ROI calculation for marketing optimization.
Use this skill when analyzing marketing campaigns, ad p
If this skill fails to produce the expected output: (1) verify input completeness, (2) retry with more specific context, (3) fall back to the parent workflow without this skill.