Campaign Analytics

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.

Input Requirements

All scripts accept a JSON file as positional input argument. See assets/sample_campaign_data.json for complete examples.

Attribution Analyzer

{  "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

{  "funnel": {    "stages": ["Awareness", "Interest", "Consideration", "Intent", "Purchase"],    "counts": [10000, 5200, 2800, 1400, 420]  }}

Campaign ROI Calculator

{  "campaigns": [    {      "name": "Spring Email Campaign",      "channel": "email",      "spend": 5000.00,      "revenue": 25000.00,      "impressions": 50000,      "clicks": 2500,      "leads": 300,      "customers": 45    }  ]}

Input Validation

Before running scripts, verify your JSON is valid and matches the expected schema. Common errors:

• Missing required keys (e.g., journeys, funnel.stages, campaigns) → script exits with a descriptive KeyError
• Mismatched array lengths in funnel data (stages and counts must be the same length) → raises ValueError
• Non-numeric monetary values in ROI data → raises TypeError

Use python -m json.tool your_file.json to validate JSON syntax before passing it to any script.

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

Typical Analysis Workflow

For a complete campaign review, run the three scripts in sequence:

# Step 1 — Attribution: understand which channels drive conversionspython scripts/attribution_analyzer.py campaign_data.json --model time-decay
# Step 2 — Funnel: identify where prospects drop off on the path to conversionpython scripts/funnel_analyzer.py funnel_data.json
# Step 3 — ROI: calculate profitability and benchmark against industry standardspython 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.

How to Use

Attribution Analysis

# Run all 5 attribution modelspython scripts/attribution_analyzer.py campaign_data.json
# Run a specific modelpython scripts/attribution_analyzer.py campaign_data.json --model time-decay
# JSON output for pipeline integrationpython 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

# Basic funnel analysispython scripts/funnel_analyzer.py funnel_data.json
# JSON outputpython scripts/funnel_analyzer.py funnel_data.json --format json

Campaign ROI Calculation

# Calculate ROI metrics for all campaignspython scripts/campaign_roi_calculator.py campaign_data.json
# JSON outputpython 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:

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

Best Practices

1. Use multiple attribution models -- Compare at least 3 models to triangulate channel value; no single model tells the full story.
2. Set appropriate lookback windows -- Match your time-decay half-life to your average sales cycle length.
3. Segment your funnels -- Compare segments (channel, cohort, geography) to identify performance drivers.
4. Benchmark against your own history first -- Industry benchmarks provide context, but 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 -- Scripts provide descriptive metrics only; p-value calculations require external tools.
• Standard library only -- No advanced statistical 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 -- 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.

Related Skills

• analytics-tracking: For setting up tracking. NOT for analyzing data (that's this skill).
• ab-test-setup: For designing experiments to test what analytics reveals.
• marketing-ops: For routing insights to the right execution skill.
• paid-ads: For optimizing ad spend based on analytics findings.