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