<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT -->
---
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.
tags: [marketing, attribution, campaign, channel-roi]
---

# 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

| Model | How Credit is Assigned | Best For | Limitation |
|-------|----------------------|----------|------------|
| **First-Touch** | 100% to first interaction | Understanding awareness drivers | Ignores nurture and conversion touches |
| **Last-Touch** | 100% to last interaction before conversion | Understanding closing channels | Ignores awareness and nurture |
| **Linear** | Equal credit to all touchpoints | Simple, unbiased baseline | No signal on which touches matter most |
| **Time-Decay** | More credit to recent touchpoints | Long sales cycles with clear momentum | Undervalues awareness investments |
| **Position-Based (U-Shaped)** | 40% first, 40% last, 20% middle | Balanced view of full funnel | Still arbitrary weight assignment |
| **Data-Driven (Algorithmic)** | ML-based credit assignment | Large datasets, sophisticated teams | Requires significant data volume; black box |

### 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

| Channel | Spend ($) | Impressions | Clicks | CTR | Conversions | CPA ($) | Revenue ($) | ROAS |
|---------|-----------|-------------|--------|-----|-------------|---------|-------------|------|
| Paid Search | | | | % | | | | X.Xx |
| Paid Social | | | | % | | | | X.Xx |
| Display / Programmatic | | | | % | | | | X.Xx |
| Email | | | | % | | | | X.Xx |
| Organic Search | | N/A | | N/A | | | | N/A |
| Content / SEO | | N/A | | N/A | | | | N/A |
| Referral / Partner | | | | % | | | | X.Xx |
| Events / Webinars | | N/A | N/A | N/A | | | | X.Xx |
| **Total** | **$** | | | | | **$** | **$** | **X.Xx** |

### Key Formulas

| Metric | Formula | Interpretation |
|--------|---------|---------------|
| **ROAS** | Revenue / Ad Spend | >3x = healthy for most B2B; >4x for e-commerce |
| **CPA** | Total Spend / Conversions | Must be < Customer LTV for sustainability |
| **CAC** | Total S&M Cost / New Customers | Include all costs (headcount, tools, agency fees) |
| **iROAS** | Incremental Revenue / Incremental Spend | Measures true causal impact (not just correlation) |
| **Marginal ROI** | Change in Revenue / Change in Spend | Use for budget reallocation — invest where marginal ROI is highest |

### Incrementality Testing

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

| Method | How It Works | When to Use |
|--------|-------------|-------------|
| **Geo-lift test** | Run campaign in test geos, hold back control geos | Measuring offline + online impact |
| **Holdout test** | Randomly exclude % of audience from ads | Measuring online display/social lift |
| **PSA (Ghost Ads)** | Show public service ad instead of brand ad to control | Measuring brand lift without full holdout |
| **Pre/Post with control** | Compare before/after with a control group | Quick directional read (less rigorous) |

---

## Campaign Funnel Analysis

### Funnel Stage Metrics

| Stage | Metric | Benchmark (B2B SaaS) | Benchmark (E-Commerce) |
|-------|--------|---------------------|----------------------|
| **Awareness** | Impressions, Reach, CPM | CPM: $5-15 | CPM: $2-8 |
| **Consideration** | Clicks, CTR, CPC | CTR: 1-3%, CPC: $2-10 | CTR: 2-5%, CPC: $0.50-3 |
| **Conversion** | Sign-ups, Purchases, CVR | CVR: 2-5% (trial), 1-3% (paid) | CVR: 1-4% |
| **Retention** | Repeat rate, LTV | 30-day retention: 20-40% | Repeat purchase: 20-30% |

### Funnel Diagnostic Template

```markdown
## 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:

| Input | Source | Example |
|-------|--------|---------|
| Winning variant lift | A/B test analysis | +12% conversion rate |
| Campaign conversion volume | Campaign data | 5,000 conversions / month |
| Average conversion value | Revenue data | $50 per conversion |

**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

| Confidence | Sample Size | Recommendation |
|-----------|-------------|----------------|
| >95% significance, >1000 conversions | Adequate | Scale to full traffic |
| >90% significance, 500-1000 conversions | Borderline | Extend test 1 more week |
| <90% significance | Insufficient | Do not scale — inconclusive |

---

## Marketing Mix Modeling (MMM) — Overview

When to use MMM vs. Attribution:

| Dimension | Attribution | MMM |
|-----------|-------------|-----|
| **Granularity** | User-level | Channel-level aggregate |
| **Scope** | Digital touchpoints | All channels (including offline, TV, OOH) |
| **Causation** | Correlation-based | Regression-based (closer to causal) |
| **Latency** | Real-time | Quarterly refresh |
| **Best for** | Tactical optimization | Strategic budget allocation |

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

```markdown
# 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

<!-- Source: .faos/custom/skills/business/campaign-analytics/SKILL.md -->
