Analyze campaign performance with multi-touch attribution, funnel conversion analysis, and ROI calculation across channels.
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Campaign performance analysis with multi-touch attribution, funnel analysis, and ROI calculation. Three Python CLI tools — standard library only, no external dependencies.
# Step 1 — Attribution: which channels drive conversions
python scripts/attribution_analyzer.py campaign_data.json --model time-decay
# Step 2 — Funnel: where prospects drop off
python scripts/funnel_analyzer.py funnel_data.json
# Step 3 — ROI: profitability and benchmarks
python scripts/campaign_roi_calculator.py campaign_data.json
All scripts support --format text (default) and --format json for integrations.
Five attribution models:
| Model | Description | Best For |
|---|---|---|
| First-Touch | 100% to first interaction | Brand awareness |
| Last-Touch | 100% to last interaction | Direct response |
| Linear | Equal credit all touchpoints | Balanced evaluation |
| Time-Decay | More credit to recent | Short sales cycles |
| Position-Based | 40/20/40 split | Full-funnel marketing |
Stage-to-stage conversion rates, bottleneck identification, segment comparison.
ROI, ROAS, CPA, CPL, CAC, CTR, CVR — flags underperforming campaigns against industry benchmarks.
All scripts accept a JSON file. Use python -m json.tool your_file.json to validate syntax.
For complete input schemas (journey, funnel, campaign), sample data, and validation rules, see references/attribution-models-guide.md.
| Guide | Location | Purpose |
|---|---|---|
| Attribution Models | references/attribution-models-guide.md | Models with formulas, selection criteria, input schemas |
| Campaign Benchmarks | references/campaign-metrics-benchmarks.md | Benchmarks by channel and vertical |
| Funnel Optimization | references/funnel-optimization-framework.md | Stage-by-stage strategies, bottlenecks |
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