google-analytics
Analyze Google Analytics data, review website performance metrics, identify traffic patterns, and suggest data-driven improvements. Use when the user asks about analytics, website metrics, traffic analysis, conversion rates, user behavior, or performance optimization.
What this skill does
# 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: ```bash 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: ```env 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 ```bash # 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 4. **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 ```bash python scripts/ga_client.py --days 30 --metrics sessions,users,bounceRate ``` ### Analyze and Generate Report ```bash 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: ```bash 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.
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