Interpret GA4, GSC, and SE Ranking data for content optimization.Provides benchmarks, status indicators, and actionable insights.
Installation
Details
Usage
After installing, this skill will be available to your AI coding assistant.
Verify installation:
skills listSkill Instructions
name: analytics-interpretation description: Interpret GA4 and GSC data with benchmarks, status indicators, and actionable insights
Analytics Interpretation
When to Use
- Analyzing content performance reports
- Understanding traffic patterns
- Interpreting search console data
- Making data-driven content decisions
- Explaining metrics to stakeholders
Metric Benchmarks
Google Analytics 4 (GA4)
| Metric | Good | Warning | Poor | Action When Poor |
|---|---|---|---|---|
| Avg Time on Page | >3 min | 1-3 min | <1 min | Improve content depth, add multimedia |
| Bounce Rate | <40% | 40-70% | >70% | Add internal links, improve intro hook |
| Engagement Rate | >60% | 30-60% | <30% | Review content quality, add CTAs |
| Scroll Depth | >75% | 50-75% | <50% | Add visual breaks, improve structure |
| Pages/Session | >2.5 | 1.5-2.5 | <1.5 | Improve internal linking |
Google Search Console (GSC)
| Metric | Good | Warning | Poor | Action When Poor |
|---|---|---|---|---|
| CTR | >5% | 2-5% | <2% | Improve title/meta description |
| Avg Position | 1-3 | 4-10 | >10 | Strengthen content, build links |
| Impressions Trend | Growing | Stable | Declining | Refresh content, target new keywords |
| Mobile Usability | PASS | - | FAIL | Fix mobile issues immediately |
| Core Web Vitals | GOOD | NEEDS_IMPROVEMENT | POOR | Optimize performance |
Interpreting Combined Signals
Traffic Quality Matrix
High Engagement
│
┌──────────────┼──────────────┐
│ HIDDEN GEM │ STAR │
│ Low traffic │ High traffic│
│ High quality│ High quality│
│ → Promote │ → Maintain │
Low ───────┼──────────────┼──────────────┼─── High
Traffic │ │ │ Traffic
│ UNDERPERFORM│ LEAKY │
│ Low traffic │ High traffic│
│ Low quality │ Low quality │
│ → Rework │ → Optimize │
└──────────────┼──────────────┘
│
Low Engagement
Search Intent Alignment
| GSC Signal | GA4 Signal | Interpretation |
|---|---|---|
| High impressions | Low clicks | Title/meta mismatch with intent |
| High CTR | High bounce | Content doesn't deliver on promise |
| Low CTR | High engagement (when clicked) | Hidden gem, improve snippet |
| Growing impressions | Stable clicks | Ranking improving, CTR opportunity |
Score Calculation Methodology
Content Health Score (0-100)
health_score = (
engagement_score × 0.30 +
seo_score × 0.30 +
ranking_score × 0.20 +
trend_score × 0.20
)
Component Calculations:
engagement_score = normalize(
time_on_page_score × 0.4 +
bounce_rate_score × 0.3 +
scroll_depth_score × 0.3
)
seo_score = normalize(
ctr_score × 0.4 +
position_score × 0.4 +
impressions_growth × 0.2
)
ranking_score = normalize(
avg_position × 0.5 +
visibility_score × 0.3 +
keyword_coverage × 0.2
)
trend_score = normalize(
traffic_trend × 0.4 +
ranking_trend × 0.3 +
engagement_trend × 0.3
)
Score Interpretation
| Score | Rating | Status | Action |
|---|---|---|---|
| 90-100 | Excellent | Performing optimally | Maintain, minor tweaks |
| 75-89 | Good | Solid performance | Optimize weak areas |
| 60-74 | Fair | Room for improvement | Address key issues |
| 40-59 | Poor | Underperforming | Major revision needed |
| 0-39 | Critical | Failing | Complete overhaul |
Trend Analysis
Week-over-Week Comparison
| Metric | This Week | Last Week | Change | Status |
|--------|-----------|-----------|--------|--------|
| Sessions | 1,245 | 1,180 | +5.5% | ↑ GROWING |
| Avg Position | 4.2 | 4.8 | +0.6 | ↑ IMPROVING |
| CTR | 2.8% | 2.6% | +0.2pp | ↑ IMPROVING |
| Bounce Rate | 42% | 38% | +4pp | ↓ DECLINING |
Interpreting Trends
| Trend Pattern | Interpretation | Recommended Action |
|---|---|---|
| ↑↑↑ All metrics up | Content gaining momentum | Double down, create related content |
| ↑↓↑ Mixed signals | Transition period | Monitor closely, identify cause |
| ↓↓↓ All metrics down | Content declining | Urgent refresh needed |
| →→→ All flat | Plateau reached | Experiment with new angles |
Anomaly Detection
Significant Change Thresholds
| Metric | Significant Change | Alert Level |
|---|---|---|
| Traffic | ±30% WoW | HIGH |
| CTR | ±1pp WoW | MEDIUM |
| Position | ±5 positions | HIGH |
| Bounce Rate | ±10pp WoW | MEDIUM |
Common Anomaly Causes
| Anomaly | Possible Causes |
|---|---|
| Sudden traffic drop | Algorithm update, technical issue, competitor |
| CTR spike | SERP feature win, seasonal interest |
| Position fluctuation | Google testing, competitor changes |
| Engagement drop | Content staleness, UX issue |
Output Templates
Metric Summary Card
## {Metric Name}
**Current Value**: {value}
**Benchmark**: {benchmark}
**Status**: {GOOD|WARNING|POOR}
**Trend**: {↑|→|↓} ({change}% vs last period)
**Interpretation**: {1-2 sentence explanation}
**Recommended Action**: {specific action if needed}
Executive Summary
## Content Performance Summary
**Overall Health**: {score}/100 ({rating})
### Key Wins
- {positive finding 1}
- {positive finding 2}
### Concerns
- {issue 1}
- {issue 2}
### Priority Actions
1. {highest priority action}
2. {second priority action}
3. {third priority action}
More by MadAppGang
View allChoose optimal external AI models for code analysis, bug investigation, and architectural decisions. Use when consulting multiple LLMs via claudish, comparing model perspectives, or investigating complex Go/LSP/transpiler issues. Provides empirically validated model rankings (91/100 for MiniMax M2, 83/100 for Grok Code Fast) and proven consultation strategies based on real-world testing.
CRITICAL - Guide for using Claudish CLI ONLY through sub-agents to run Claude Code with OpenRouter models (Grok, GPT-5, Gemini, MiniMax). NEVER run Claudish directly in main context unless user explicitly requests it. Use when user mentions external AI models, Claudish, OpenRouter, or alternative models. Includes mandatory sub-agent delegation patterns, agent selection guide, file-based instructions, and strict rules to prevent context window pollution.
MANDATORY tracking protocol for multi-model validation. Creates structured tracking tables BEFORE launching models, tracks progress during execution, and ensures complete results presentation. Use when running 2+ external AI models in parallel. Trigger keywords - "multi-model", "parallel review", "external models", "consensus", "model tracking".
XML tag structure patterns for Claude Code agents and commands. Use when designing or implementing agents to ensure proper XML structure following Anthropic best practices.