wehub-resource-sync
442 lines
14 KiB
Markdown
442 lines
14 KiB
Markdown
# Google Analytics Analysis Examples
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Practical examples of common analytics tasks and analysis patterns.
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## Example 1: Traffic Overview Analysis
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**User Request**: "Review our Google Analytics performance for the last 30 days"
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**Analysis Steps**:
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1. Fetch core metrics for the period
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2. Compare with previous 30 days
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3. Identify trends and anomalies
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4. Generate insights and recommendations
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**Script Command**:
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```bash
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python scripts/analyze.py \
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--period last-30-days \
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--compare previous-period \
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--metrics sessions,activeUsers,newUsers,bounceRate,engagementRate
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```
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**Sample Output Analysis**:
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```
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Traffic Overview (Last 30 Days vs Previous 30 Days)
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Sessions: 45,230 (+12.5%)
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Active Users: 32,150 (+8.3%)
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New Users: 18,920 (+15.2%)
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Bounce Rate: 42.3% (-3.1pp)
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Engagement Rate: 68.5% (+4.2pp)
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Key Insights:
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✓ Strong growth in new user acquisition (+15.2%)
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✓ Improving engagement (bounce rate down, engagement rate up)
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⚠ Sessions per user declining slightly (1.41 → 1.38)
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Recommendations:
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1. HIGH: Investigate new user source - identify which channels driving growth
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2. MEDIUM: Implement retention campaign for existing users
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3. LOW: A/B test homepage to improve session depth
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```
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## Example 2: Traffic Source Analysis
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**User Request**: "What are our top traffic sources and which perform best?"
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**Analysis Steps**:
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1. Group traffic by source/medium
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2. Calculate engagement metrics per source
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3. Identify high-value vs high-volume sources
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4. Recommend optimization strategies
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**Script Command**:
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```bash
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python scripts/ga_client.py \
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--days 30 \
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--metrics sessions,engagementRate,conversions,bounceRate \
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--dimensions sessionSource,sessionMedium \
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--order-by sessions \
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--limit 20
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```
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**Sample Insights**:
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```
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Top Traffic Sources (Last 30 Days)
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Source/Medium Sessions Eng.Rate Conv. Bounce
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--------------------------------------------------------------
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google/organic 18,240 72.3% 245 38.2%
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direct/(none) 12,150 65.1% 189 45.6%
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facebook/social 5,430 58.2% 67 52.3%
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newsletter/email 3,210 81.5% 124 28.1%
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google/cpc 2,890 68.9% 98 41.2%
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Key Insights:
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✓ Newsletter traffic has highest engagement (81.5%) and lowest bounce (28.1%)
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✓ Organic search is largest volume but mid-tier engagement
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⚠ Social traffic underperforming (high bounce, low conversion)
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Recommendations:
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1. HIGH: Invest in email list growth - highest quality traffic
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2. HIGH: Optimize Facebook campaigns - high spend, poor performance
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3. MEDIUM: Improve organic landing pages to boost engagement
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4. LOW: Test paid search expansion - good ROI potential
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```
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## Example 3: Content Performance Analysis
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**User Request**: "Which pages have the highest bounce rates and need improvement?"
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**Analysis Steps**:
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1. Get page-level metrics
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2. Filter for high-traffic pages
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3. Identify performance issues
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4. Prioritize improvements
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**Script Command**:
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```bash
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python scripts/ga_client.py \
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--days 30 \
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--metrics screenPageViews,bounceRate,averageSessionDuration,conversions \
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--dimensions pagePath,pageTitle \
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--order-by screenPageViews \
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--limit 50
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```
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**Sample Analysis**:
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```
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High-Bounce Pages Needing Attention
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Page Views Bounce Avg.Time Conv.
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-------------------------------------------------------------------
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/blog/getting-started 8,430 67.2% 0:45 12
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/products/pricing 6,210 71.8% 1:12 23
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/features/comparison 4,890 64.5% 2:03 18
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/resources/guides 3,650 69.3% 1:28 8
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Issues Identified:
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❌ Pricing page: High bounce despite good time-on-page (missing CTA?)
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❌ Getting started: Short visit time suggests content mismatch
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⚠ Comparison page: Long time but high bounce (decision paralysis?)
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Recommendations:
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1. HIGH: Add clear CTAs to pricing page (demo request, free trial)
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2. HIGH: Review "getting started" keywords - traffic may be mismatched
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3. MEDIUM: Simplify comparison page - reduce options or add guidance
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4. MEDIUM: Add exit-intent popups on high-bounce pages
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5. LOW: Internal linking to reduce single-page sessions
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```
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## Example 4: Conversion Funnel Analysis
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**User Request**: "Analyze our conversion funnel and identify drop-off points"
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**Analysis Steps**:
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1. Define funnel steps (landing → product → cart → checkout → purchase)
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2. Measure completion rates at each step
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3. Identify biggest drop-offs
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4. Suggest optimizations
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**Script Command**:
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```bash
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python scripts/analyze.py \
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--analysis-type funnel \
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--steps "homepage,/products,/cart,/checkout,/confirmation" \
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--period last-30-days
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```
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**Sample Funnel Analysis**:
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```
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Conversion Funnel Analysis (Last 30 Days)
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Step Sessions Drop-off Conversion
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------------------------------------------------------
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1. Homepage 45,230 - 100.0%
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2. Product Page 18,920 58.2% 41.8%
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3. Cart 5,430 71.3% 12.0%
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4. Checkout 2,150 60.4% 4.8%
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5. Purchase 1,290 40.0% 2.9%
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Critical Issues:
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❌ CRITICAL: 71.3% drop-off from product to cart (industry avg: 45%)
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❌ HIGH: 60.4% drop-off from cart to checkout (industry avg: 30%)
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⚠ MEDIUM: Homepage to product 58.2% (could be better)
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Recommendations:
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1. CRITICAL: Cart page optimization
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- Add trust badges (secure checkout, money-back guarantee)
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- Show shipping costs earlier
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- Implement abandoned cart email
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- A/B test one-click checkout
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2. HIGH: Checkout flow improvement
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- Reduce form fields
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- Add guest checkout option
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- Display progress indicator
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- Test express payment (Apple Pay, Google Pay)
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3. MEDIUM: Product page enhancements
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- Better product images and videos
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- Customer reviews and ratings
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- Clear shipping and return policy
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- Recommended products
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Expected Impact: +15-25% overall conversion rate
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```
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## Example 5: Mobile vs Desktop Performance
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**User Request**: "Compare mobile and desktop performance"
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**Analysis Steps**:
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1. Segment metrics by device category
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2. Identify device-specific issues
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3. Recommend mobile optimizations
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**Script Command**:
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```bash
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python scripts/ga_client.py \
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--days 30 \
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--metrics sessions,bounceRate,averageSessionDuration,conversions,engagementRate \
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--dimensions deviceCategory \
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--order-by sessions
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```
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**Sample Device Comparison**:
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```
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Device Performance (Last 30 Days)
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Device Sessions Bounce Avg.Time Conv.Rate Eng.Rate
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--------------------------------------------------------------------
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mobile 26,140 48.5% 2:15 2.1% 64.2%
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desktop 17,890 35.2% 4:32 4.8% 76.8%
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tablet 1,200 42.1% 3:18 3.2% 68.5%
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Key Findings:
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❌ Mobile conversion rate 56% lower than desktop (2.1% vs 4.8%)
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❌ Mobile bounce rate 38% higher (48.5% vs 35.2%)
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✓ Mobile represents 57.8% of traffic (good mobile reach)
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⚠ Mobile engagement duration 50% shorter
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Root Causes:
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- Slower mobile page load times
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- Complex checkout on small screens
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- Poor mobile navigation
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- Touch target sizing issues
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Recommendations:
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1. CRITICAL: Mobile checkout optimization
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- Implement single-page checkout for mobile
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- Add autofill for forms
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- Larger buttons and form fields
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- Mobile-specific payment options
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2. HIGH: Mobile performance improvements
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- Optimize images for mobile (WebP, lazy loading)
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- Implement AMP for key landing pages
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- Reduce JavaScript bundle size
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- Enable browser caching
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3. MEDIUM: Mobile UX enhancements
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- Sticky navigation for easy access
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- Improve mobile search functionality
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- Add click-to-call buttons
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- Optimize forms for mobile keyboards
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4. LOW: Mobile-specific features
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- Geolocation for store finder
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- Mobile app promotion
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- Progressive Web App (PWA) features
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Expected Impact: +30-50% mobile conversion rate
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```
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## Example 6: Geographic Performance Analysis
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**User Request**: "Which countries should we focus our marketing efforts on?"
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**Analysis Steps**:
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1. Segment by country/region
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2. Calculate ROI metrics per market
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3. Identify growth opportunities
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4. Recommend market prioritization
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**Script Command**:
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```bash
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python scripts/ga_client.py \
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--days 90 \
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--metrics sessions,activeUsers,conversions,totalRevenue,engagementRate \
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--dimensions country \
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--order-by totalRevenue \
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--limit 20
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```
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**Sample Geographic Analysis**:
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```
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Top Markets by Revenue (Last 90 Days)
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Country Sessions Users Revenue Conv.Rate Eng.Rate
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----------------------------------------------------------------------
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United States 125,340 89,230 $245,680 3.8% 72.3%
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United Kingdom 28,450 19,840 $52,340 3.2% 68.9%
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Canada 18,920 13,210 $31,450 2.9% 65.4%
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Australia 12,680 8,940 $24,120 3.1% 70.1%
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Germany 15,430 10,230 $18,920 1.9% 64.2%
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Emerging Markets (High Engagement, Lower Revenue):
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India 22,140 16,780 $4,230 1.2% 71.5%
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Brazil 8,930 6,540 $2,840 1.5% 68.2%
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Mexico 6,420 4,890 $2,120 1.6% 66.8%
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Market Insights:
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✓ US is strongest market (high volume + high conversion)
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✓ UK/Canada/Australia: Good performance, expansion ready
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⚠ Germany: High traffic but low conversion (pricing/localization?)
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✓ India/Brazil: High engagement, untapped revenue potential
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Recommendations:
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1. HIGH: Germany localization project
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- Translate product pages and checkout
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- Add local payment methods (SEPA, Sofort)
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- Currency conversion (EUR pricing)
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- Local customer support
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Expected: +50-80% German conversion rate
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2. MEDIUM: Emerging market strategy
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- India: Lower-priced product tier, UPI payment
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- Brazil: Installment payment options
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- Mexico: Spanish localization
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Expected: 3x revenue from emerging markets
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3. MEDIUM: UK/Canada expansion
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- Increase ad spend (+20%)
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- Localized campaigns
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- Region-specific promotions
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Expected: +25% revenue from these markets
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4. LOW: New market exploration
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- Test France, Spain, Italy (EU proximity)
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- Test Singapore, Japan (APAC)
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- Small budget pilots before full launch
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```
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## Example 7: Campaign Performance Analysis
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**User Request**: "Which marketing campaigns are delivering the best ROI?"
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**Analysis Steps**:
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1. Group by campaign name and source
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2. Calculate cost per acquisition (if ad spend data available)
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3. Measure campaign contribution to revenue
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4. Recommend budget reallocation
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**Script Command**:
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```bash
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python scripts/ga_client.py \
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--days 30 \
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--metrics sessions,conversions,totalRevenue,engagementRate \
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--dimensions sessionCampaignName,sessionSource,sessionMedium \
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--filter "sessionCampaignName!=(not set)" \
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--order-by conversions
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```
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**Sample Campaign Analysis**:
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```
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Campaign Performance (Last 30 Days)
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Campaign Source Medium Sessions Conv. Revenue Eng.Rate
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------------------------------------------------------------------------------
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spring-sale-2026 google cpc 12,340 234 $28,450 71.2%
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email-product-launch email email 4,890 189 $24,120 82.5%
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retargeting-q1 facebook cpc 8,920 145 $18,920 64.3%
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brand-awareness google display 18,450 89 $12,340 52.1%
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social-organic facebook social 6,540 67 $8,450 58.7%
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Campaign Efficiency:
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✓ Email campaign: Best engagement (82.5%) and conversion rate (3.9%)
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✓ Spring sale: High volume and good ROI
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⚠ Brand awareness: High spend, low conversion
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❌ Social organic: Underperforming vs paid
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Recommendations:
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1. HIGH: Scale email marketing
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- Increase send frequency (weekly → 2x/week)
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- Expand email list acquisition
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- Segment for personalization
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Expected: +40% email revenue
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2. MEDIUM: Optimize brand awareness campaign
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- Narrow targeting to high-intent audiences
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- Add retargeting pixel for display viewers
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- Test different creatives
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- Consider reducing budget if no improvement
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3. MEDIUM: Retargeting expansion
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- Increase budget (+30%)
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- Add cart abandonment flow
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- Segment by product category
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Expected: +25% retargeting conversions
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4. LOW: Social media strategy review
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- Focus on paid over organic
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- Test Instagram vs Facebook
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- Video content experiments
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```
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## Common Analysis Patterns
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### Trend Analysis Template
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```python
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# Compare week-over-week or month-over-month
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# Identify seasonal patterns
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# Detect anomalies and investigate causes
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```
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### Segmentation Template
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```python
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# Break down by key dimensions (source, device, location)
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# Compare segment performance
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# Identify best and worst performers
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# Allocate resources accordingly
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```
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### Cohort Analysis Template
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```python
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# Group users by acquisition date
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# Track retention over time
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# Measure lifetime value
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# Optimize onboarding
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```
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### Attribution Analysis Template
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```python
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# Multi-touch attribution modeling
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# Identify assist channels
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# Understand customer journey
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# Budget allocation
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```
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## Pro Tips
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### Ask Better Questions
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Instead of: "Show me analytics data"
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Ask: "What are the top 3 issues hurting our conversion rate?"
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### Request Actionable Insights
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Instead of: "What's our bounce rate?"
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Ask: "Which pages have high bounce rates and how can we fix them?"
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### Compare Time Periods
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Instead of: "How many sessions last month?"
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Ask: "How does last month compare to the previous month?"
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### Focus on Business Goals
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Instead of: "Show me all metrics"
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Ask: "Which marketing channels drive the most revenue?"
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### Request Prioritization
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Instead of: "List all problems"
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Ask: "What are the top 3 improvements we should make this quarter?"
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