Analytics Review Skill

Analyze website performance with Pirsch using the Overview-Drill Down-Insights pattern.

Phase 1: OVERVIEW (High-Level Metrics)

List Available Domains

mcp__pirsch__pirsch_list_domains({})

Get Cached Overview

Quick snapshot of key metrics:

mcp__pirsch__pirsch_overview({
  domainId: "<domain-id>"
})

Get Detailed Totals

mcp__pirsch__pirsch_total({
  domainId: "<domain-id>",
  from: "2025-01-01",
  to: "2025-01-31"
})

Returns:

• Visitors
• Views
• Sessions
• Bounces
• Bounce rate
• Conversion rate

Get Growth Metrics

mcp__pirsch__pirsch_growth({
  domainId: "<domain-id>",
  from: "2025-01-01",
  to: "2025-01-31"
})

Check Real-Time Activity

mcp__pirsch__pirsch_active({
  domainId: "<domain-id>",
  seconds: 600  // Last 10 minutes
})

Phase 2: DRILL DOWN (Detailed Analysis)

Visitor Time Series

mcp__pirsch__pirsch_visitors({
  domainId: "<domain-id>",
  from: "2025-01-01",
  to: "2025-01-31",
  scale: "day"  // day, week, month
})

Top Pages

mcp__pirsch__pirsch_pages({
  domainId: "<domain-id>",
  from: "2025-01-01",
  to: "2025-01-31",
  limit: 20,
  includeAvgTimeOnPage: true
})

Traffic Sources (Referrers)

mcp__pirsch__pirsch_referrers({
  domainId: "<domain-id>",
  from: "2025-01-01",
  to: "2025-01-31",
  limit: 20
})

UTM Campaign Analysis

// By source
mcp__pirsch__pirsch_utm({
  domainId: "<domain-id>",
  from: "2025-01-01",
  to: "2025-01-31",
  dimension: "source"
})

// By medium
mcp__pirsch__pirsch_utm({
  domainId: "<domain-id>",
  dimension: "medium"
})

// By campaign
mcp__pirsch__pirsch_utm({
  domainId: "<domain-id>",
  dimension: "campaign"
})

Phase 3: INSIGHTS (Comparative Analysis)

Period Comparison

Compare two time periods:

mcp__pirsch__pirsch_compare({
  domainId: "<domain-id>",
  from: "2025-01-01",
  to: "2025-01-31",
  compareFrom: "2024-12-01",
  compareTo: "2024-12-31",
  scale: "day"
})

Returns:

• Visitor trends for both periods
• Delta values (absolute change)
• Growth percentages

Report Formatting

Dashboard Summary

## Website Analytics Dashboard
verygoodplugins.com | January 2025

### Key Metrics
| Metric | Value | vs Last Month |
|--------|-------|---------------|
| Visitors | 12,450 | +21.7% |
| Pageviews | 45,230 | +17.6% |
| Bounce Rate | 42.3% | -2.8pp |
| Avg Duration | 2m 34s | +16.7% |

### Top Pages
1. /pricing - 4,230 views
2. /features - 3,890 views
3. /blog/getting-started - 2,450 views
4. /docs/api - 1,890 views
5. /about - 1,230 views

### Top Referrers
1. google.com - 5,230 visitors
2. twitter.com - 1,890 visitors
3. github.com - 1,450 visitors
4. reddit.com - 890 visitors

### UTM Sources
1. newsletter - 2,340 visitors
2. twitter-organic - 1,230 visitors
3. google-ads - 890 visitors

Trend Analysis

## Weekly Trend Analysis

Week 1: 2,890 visitors
Week 2: 3,120 visitors (+8%)
Week 3: 3,450 visitors (+11%)
Week 4: 3,780 visitors (+10%)

Overall trend: Steady growth, averaging +9.7% week-over-week

Common Analysis Scenarios

Traffic Spike Investigation

1. Get daily visitors to identify the spike date
2. Check referrers for that date range
3. Check UTM sources for campaign attribution
4. Review top pages to see what content drove traffic

Campaign Performance

1. Filter by UTM campaign name
2. Compare visitors, bounce rate, and time on site
3. Check which pages campaign traffic landed on
4. Compare to organic traffic for baseline

Content Performance

1. Get top pages by views
2. Check average time on page
3. Identify high-bounce pages for optimization
4. Find underperforming content

Best Practices

Do

• Start with overview before drilling down
• Use appropriate date ranges for context
• Compare periods of equal length
• Look at trends, not just absolute numbers
• Consider seasonality in comparisons

Don't

• Draw conclusions from single data points
• Ignore context (holidays, launches, etc.)
• Compare weekends to weekdays
• Forget to check mobile vs desktop
• Overlook bounce rate on landing pages