Performance Analytics

---
name: performance-analytics
description: Analyze marketing performance with key metrics, trend analysis, and optimization
  recommendations
type: reference
version: 1.0.0
category: business
last_updated: 2026-02-03
source: https://github.com/anthropics/knowledge-work-plugins
related_skills:
- campaign-planning
- competitive-analysis
capabilities: []
requires: []
see_also: []
tags: []
---

# Performance Analytics Skill

Frameworks for measuring, reporting, and optimizing marketing performance across channels and campaigns.

## Key Marketing Metrics by Channel

### Email Marketing

| Metric | Definition | Benchmark Range |
|--------|-----------|----------------|
| Delivery rate | Emails delivered / emails sent | 95-99% |
| Open rate | Unique opens / emails delivered | 15-30% |
| Click-through rate (CTR) | Unique clicks / emails delivered | 2-5% |
| Click-to-open rate (CTOR) | Unique clicks / unique opens | 10-20% |
| Unsubscribe rate | Unsubscribes / emails delivered | <0.5% |
| Conversion rate | Conversions / emails delivered | 1-5% |

### Paid Advertising (Search and Social)

| Metric | Definition |
|--------|-----------|
| Click-through rate (CTR) | Clicks / impressions |
| Cost per click (CPC) | Total spend / clicks |
| Conversion rate | Conversions / clicks |
| Cost per acquisition (CPA) | Total spend / conversions |
| Return on ad spend (ROAS) | Revenue / ad spend |
| Quality Score (search) | Google's relevance rating (1-10) |

### SEO / Organic Search

| Metric | Definition |
|--------|-----------|
| Organic sessions | Visits from organic search |
| Keyword rankings | Position for target keywords |
| Organic CTR | Clicks / impressions in search results |
| Domain authority | Third-party authority score |
| Backlinks | Number of external sites linking to you |
| Organic conversion rate | Organic conversions / organic sessions |

### Overall Marketing / Pipeline

| Metric | Definition |
|--------|-----------|
| Marketing qualified leads (MQLs) | Leads meeting marketing qualification criteria |
| MQL to SQL conversion rate | SQLs / MQLs |
| Pipeline generated | Dollar value of opportunities created |
| Customer acquisition cost (CAC) | Total marketing + sales cost / new customers |
| Marketing-sourced revenue | Revenue from marketing-originated deals |

## Reporting Templates

### Weekly Marketing Report
- **Top 3 metrics** with week-over-week change
- **What worked** this week (1-2 bullet points with data)
- **What needs attention** (1-2 bullet points with data)
- **This week's priorities** (3-5 action items)

### Monthly Marketing Report
1. Executive summary (3-5 sentences)
2. Key metrics dashboard (table with MoM and target comparison)
3. Channel-by-channel performance summary
4. Campaign highlights and results
5. What worked and what did not (with hypotheses)
6. Recommendations and next month priorities
7. Budget spend vs. plan

## Trend Analysis and Forecasting

### Trend Identification
1. Directional trends over 4+ periods
2. Inflection points and what caused them
3. Seasonality patterns
4. Anomalies and their causes
5. Leading indicators

### Simple Forecasting Approaches
- **Linear projection**: extend the current trend line forward
- **Moving average**: smooth noise by averaging last 3-6 periods
- **Year-over-year comparison**: use last year's pattern as baseline
- **Funnel math**: forecast outputs from inputs
- **Scenario modeling**: best case, expected case, worst case

## Attribution Modeling Basics

| Model | How It Works | Best For |
|-------|-------------|----------|
| Last touch | 100% credit to last interaction | Understanding final conversion triggers |
| First touch | 100% credit to first interaction | Understanding top-of-funnel effectiveness |
| Linear | Equal credit to all touchpoints | Fair representation of all channels |
| Time decay | More credit closer to conversion | Balanced view favoring recent interactions |
| Position-based (U-shaped) | 40% first, 40% last, 20% middle | Valuing both discovery and conversion |
| Data-driven | Algorithmic credit | Most accurate (requires high volume) |

## Optimization Recommendations Framework

### Optimization Levers by Funnel Stage

| Funnel Stage | Problem Signal | Optimization Levers |
|-------------|---------------|---------------------|
| Awareness | Low impressions, low reach | Budget, targeting, channel mix, creative format |
| Interest | Low CTR, low engagement | Ad creative, headlines, content hooks, audience targeting |
| Consideration | High bounce rate, low time on page | Landing page content, page speed, content relevance, UX |
| Conversion | Low conversion rate | Offer, CTA, form length, trust signals, page layout |
| Retention | High churn, low repeat engagement | Onboarding, email nurture, product experience, support |

### Prioritization Framework

**Priority order**:
1. High impact, low effort (do immediately)
2. High impact, high effort (plan and resource)
3. Low impact, low effort (do if capacity allows)
4. Low impact, high effort (deprioritize)

### Testing Best Practices
- Test one variable at a time
- Define success metric before launching
- Calculate required sample size before starting
- Run tests for minimum one full business cycle
- Document all tests and results regardless of outcome

### Continuous Optimization Cadence
- **Daily**: monitor paid campaigns for budget pacing and anomalies
- **Weekly**: review channel performance, pause underperformers, scale winners
- **Bi-weekly**: refresh ad creative and test new variants
- **Monthly**: full performance review, identify new optimization opportunities
- **Quarterly**: strategic review of channel mix, budget allocation, and targeting