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.

Table of Contents

• Capabilities
• Input Requirements
• Output Formats
• How to Use
• Scripts
• Reference Guides
• Best Practices
• Limitations

Capabilities

• Multi-Touch Attribution: Five attribution models (first-touch, last-touch, linear, time-decay, position-based) with configurable parameters
• Funnel Conversion Analysis: Stage-by-stage conversion rates, drop-off identification, bottleneck detection, and segment comparison
• Campaign ROI Calculation: ROI, ROAS, CPA, CPL, CAC metrics with industry benchmarking and underperformance flagging
• A/B Test Support: Templates for structured A/B test documentation and analysis
• Channel Comparison: Cross-channel performance comparison with normalized metrics
• Executive Reporting: Ready-to-use templates for campaign performance reports

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
    }
  ]
}

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

How to Use

Attribution Analysis

# 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

Funnel Analysis

# Basic funnel analysis
python scripts/funnel_analyzer.py funnel_data.json

# JSON output
python scripts/funnel_analyzer.py funnel_data.json --format json

Campaign ROI Calculation

# 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

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 -- No single model tells the full story. Compare at least 3 models to triangulate channel value.
2. Set appropriate lookback windows -- Match your time-decay half-life to your average sales cycle length.
3. Segment your funnels -- Always compare segments (channel, cohort, geography) to identify what drives best performance.
4. Benchmark against your own history first -- Industry benchmarks provide context, but your own 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 -- A/B test analysis requires external tools for p-value calculations. Scripts provide descriptive metrics only.
• Standard library only -- No advanced statistical or data processing 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 -- Uses 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.

Proactive Triggers

• Attribution model not set → Last-click attribution misses 60%+ of the journey. Use multi-touch.
• No baseline metrics documented → Can't measure improvement without baselines.
• Data discrepancy between tools → GA4 and ad platform numbers rarely match. Document the gap.
• Vanity metrics dominating reports → Pageviews don't matter. Focus on conversion metrics.

Output Artifacts

Communication

All output passes quality verification:

• Self-verify: source attribution, assumption audit, confidence scoring
• Output format: Bottom Line → What (with confidence) → Why → How to Act
• Results only. Every finding tagged: 🟢 verified, 🟡 medium, 🔴 assumed.

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.