Google Analytics – EXAMPLES

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