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.

2. 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

3. 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.