Google Analytics Analysis
Analyze website performance using Google Analytics data to provide actionable insights and improvement recommendations.
Quick Start 1. Setup Authentication
This Skill requires Google Analytics API credentials. Set up environment variables:
export GOOGLE_ANALYTICS_PROPERTY_ID="your-property-id" export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account-key.json"
Or create a .env file in your project root:
GOOGLE_ANALYTICS_PROPERTY_ID=123456789 GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account-key.json
Never commit credentials to version control. The service account JSON file should be stored securely outside your repository.
- Install Required Packages
Option 1: Install from requirements file (recommended)
pip install -r cli-tool/components/skills/analytics/google-analytics/requirements.txt
Option 2: Install individually
pip install google-analytics-data python-dotenv pandas
- Analyze Your Project
Once configured, I can:
Review current traffic and user behavior metrics Identify top-performing and underperforming pages Analyze traffic sources and conversion funnels Compare performance across time periods Suggest data-driven improvements How to Use
Ask me questions like:
"Review our Google Analytics performance for the last 30 days" "What are our top traffic sources?" "Which pages have the highest bounce rates?" "Analyze user engagement and suggest improvements" "Compare this month's performance to last month" Analysis Workflow
When you ask me to analyze Google Analytics data, I will:
Connect to the API using the helper script Fetch relevant metrics based on your question Analyze the data looking for: Traffic trends and patterns User behavior insights Performance bottlenecks Conversion opportunities Provide recommendations with: Specific improvement suggestions Priority level (high/medium/low) Expected impact Implementation guidance Common Metrics
For detailed metric definitions and dimensions, see REFERENCE.md.
Traffic Metrics Sessions, Users, New Users Page views, Screens per Session Average Session Duration Engagement Metrics Bounce Rate, Engagement Rate Event Count, Conversions Scroll Depth, Click-through Rate Acquisition Metrics Traffic Source/Medium Campaign Performance Channel Grouping Conversion Metrics Goal Completions E-commerce Transactions Conversion Rate by Source Analysis Examples
For complete analysis patterns and use cases, see EXAMPLES.md.
Scripts
The Skill includes utility scripts for API interaction:
Fetch Current Performance python scripts/ga_client.py --days 30 --metrics sessions,users,bounceRate
Analyze and Generate Report python scripts/analyze.py --period last-30-days --compare previous-period
The scripts handle API authentication, data fetching, and basic analysis. I'll interpret the results and provide actionable recommendations.
Troubleshooting
Authentication Error: Verify that:
GOOGLE_APPLICATION_CREDENTIALS points to a valid service account JSON file The service account has "Viewer" access to your GA4 property GOOGLE_ANALYTICS_PROPERTY_ID matches your GA4 property ID (not the measurement ID)
No Data Returned: Check that:
The property ID is correct (find it in GA4 Admin > Property Settings) The date range contains data The service account has been granted access in GA4
Import Errors: Install required packages:
pip install google-analytics-data python-dotenv pandas
Security Notes Never hardcode API credentials or property IDs in code Store service account JSON files outside version control Use environment variables or .env files for configuration Add .env and credential files to .gitignore Rotate service account keys periodically Use least-privilege access (Viewer role only) Data Privacy
This Skill accesses aggregated analytics data only. It does not:
Access personally identifiable information (PII) Store analytics data persistently Share data with external services Modify your Google Analytics configuration
All data is processed locally and used only to generate recommendations during the conversation.