Google Analytics Analysis

Analyze website performance using Google Analytics data to provide actionable insights and improvement recommendations.

Install

git clone https://github.com/thatrebeccarae/claude-marketing.git && cp -r claude-marketing/skills/google-analytics ~/.claude/skills/

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:

1. Connect to the API using the helper script
2. Fetch relevant metrics based on your question
3. Analyze the data looking for:

• Traffic trends and patterns
• User behavior insights
• Performance bottlenecks
• Conversion opportunities

1. 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](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](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.