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. 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

## 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

# 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