Campaign Analytics

name: campaign-analytics description: Measure multi-touch attribution, calculate channel ROI, analyze marketing funnels, and integrate A/B test results into campaign performance. Use when evaluating campaign effectiveness, optimizing channel spend, building attribution models, or reporting on marketing performance.

Campaign Analytics

Marketing campaign measurement framework — multi-touch attribution, channel ROI analysis, funnel diagnostics, and performance reporting. Turns campaign data into actionable spend allocation decisions.

Use this skill when

• Evaluating marketing campaign performance across channels
• Building or choosing a multi-touch attribution model
• Calculating ROI and ROAS per marketing channel
• Analyzing marketing funnel conversion at each stage
• Integrating A/B test results into campaign-level decisions
• Optimizing marketing budget allocation across channels
• Preparing marketing performance reports for leadership

Do not use this skill when

• Setting up analytics instrumentation from scratch (use analytics-tracking)
• Analyzing individual A/B test results (use ab-test-analysis)
• Optimizing a single landing page for conversion (use page-cro)
• Applying behavioral psychology to messaging (use marketing-psychology)
• Designing the campaign creative or strategy (use marketing agent skills)

Instructions

1. Select attribution model appropriate to your business (see model comparison).
2. Collect channel data — spend, impressions, clicks, conversions, revenue per channel.
3. Calculate channel ROI using the ROAS and incremental lift frameworks.
4. Analyze funnel conversion stage by stage to find drop-off points.
5. Integrate experiment results to quantify revenue impact of winning variants.
6. Produce the Campaign Performance Report with allocation recommendations.

Multi-Touch Attribution Models

Model Comparison

Choosing the Right Model

Sales cycle < 7 days → Last-Touch (sufficient for short cycles)
Sales cycle 7-30 days → Position-Based (captures full journey)
Sales cycle > 30 days → Time-Decay or Data-Driven (long nurture matters)
Limited data (<1000 conversions/month) → Linear (unbiased baseline)
Abundant data (>5000 conversions/month) → Data-Driven (if tooling supports)

Attribution Implementation Checklist

• UTM parameters standardized across all channels
• Touchpoint tracking implemented (cookies, device graph, CRM matching)
• Attribution window defined (7-day click, 1-day view, 30-day click, etc.)
• Cross-device tracking configured (if applicable)
• Offline touchpoints included (events, sales calls, direct mail)
• Attribution model selected and documented
• Baseline established for comparison

Channel ROI Framework

Per-Channel Metrics Table

Key Formulas

Incrementality Testing

Attribution models show correlation, not causation. Use incrementality tests to measure true impact:

Campaign Funnel Analysis

Funnel Stage Metrics

Funnel Diagnostic Template

## Campaign Funnel — [Campaign Name] — [Period]

| Stage | Volume | Rate | Benchmark | Gap | Diagnosis |
|-------|--------|------|-----------|-----|-----------|
| Impressions | | — | — | — | |
| Clicks | | CTR: % | % | | |
| Landing Page Views | | LPV rate: % | 90%+ | | |
| Conversions | | CVR: % | % | | |
| Revenue | $ | AOV: $ | $ | | |

### Biggest Drop-off
- **Stage:** [X] → [Y]
- **Expected rate:** [X]%
- **Actual rate:** [Y]%
- **Hypothesis:** [Why the drop-off occurred]
- **Recommended action:** [Specific intervention]

A/B Test Integration

Connecting Experiment Results to Campaign Decisions

When an A/B test completes, translate the result into campaign-level impact:

Revenue Impact Calculation:

Incremental conversions = Current conversions x Lift %
                       = 5,000 x 0.12 = 600
Incremental revenue    = 600 x $50 = $30,000 / month
Annualized impact      = $30,000 x 12 = $360,000

When to Scale Winning Variants

Marketing Mix Modeling (MMM) — Overview

When to use MMM vs. Attribution:

MMM is valuable when:

• Significant offline spend (events, TV, print, billboards)
• Need to model diminishing returns (saturation curves)
• Want to factor in seasonality and macroeconomic trends
• Budget allocation decisions across 5+ channels

Output Template: Campaign Performance Report

# Campaign Performance Report — [Period]

## Executive Summary
- Total spend: $[X] across [Y] channels
- Total revenue attributed: $[X]
- Blended ROAS: [X]x
- Key insight: [1 sentence]

## Channel Performance
[Per-Channel Metrics Table from above]

## Top Performing Campaigns
| Campaign | Channel | Spend | Revenue | ROAS | Key Driver |
|----------|---------|-------|---------|------|------------|
| | | $ | $ | X.Xx | |

## Funnel Analysis
[Funnel diagnostic with biggest drop-off identified]

## Attribution Insights
- Model used: [model name]
- Top converting paths: [e.g., Paid Search → Email → Direct]
- Undervalued channels: [channels receiving less credit than expected]

## Budget Recommendation
| Channel | Current Spend | Recommended Spend | Change | Rationale |
|---------|--------------|-------------------|--------|-----------|
| | $ | $ | +/-% | |

## Next Period Plan
1. [Action] — Expected impact — Owner
2. [Action] — Expected impact — Owner

Common Mistakes

• Attributing 100% credit to last touch — over-invests in bottom-funnel at the expense of awareness
• Comparing channels without controlling for intent — branded search has high conversion because of pre-existing intent, not because the ad is effective
• Ignoring incrementality — correlation is not causation; run holdout tests before making large budget shifts
• Reporting vanity metrics — impressions and clicks don't pay the bills; report on revenue, ROAS, and CPA
• Optimizing for CPA alone — the cheapest leads are often the lowest quality; optimize for CAC payback or LTV:CAC
• No attribution window discipline — without a defined window (7-day, 30-day), you'll double-count conversions

Additional Resources

• Related skills: ab-test-analysis (experiment-level analysis), analytics-tracking (instrumentation), page-cro (landing page optimization), marketing-psychology (behavioral science for messaging)
• Google's Marketing Mix Model (Meridian) — open-source MMM framework
• Meta's Robyn — open-source MMM library