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Comprehensive Guide: Measuring Convert.com Split Tests in Google Analytics 4

Uncover the Benefits, Challenges, and Step-by-Step Setup for GA4 Integration

🚀 IN THIS ARTICLE YOU WILL:

📖 Overview

Using Google Analytics 4 (GA4) to measure Convert.com split tests offers powerful analytics capabilities, but it also comes with certain trade-offs. GA4 provides advanced segmentation, user-centric metrics, and seamless integration with Google services, making it a valuable tool for analyzing your experiments. However, its complexity and the need for manual setup of test reporting can introduce challenges. Let's explore the benefits and drawbacks before diving into a step-by-step guide to streamline your integration and maximize the insights from your A/B tests.

🎯 Benefits:

  • Advanced Analytics: GA4 provides robust analytics capabilities, including user-centric measurement, cross-device tracking, and advanced segmentation.
  • Flexible Reporting: Create custom reports and explorations tailored to your specific needs.
  • Integration: GA4 natively integrates with other Google products, such as Google Ads and BigQuery.
  • Scalability: GA4 can handle large volumes of data, making it suitable for enterprise-level testing.
  • Using Google Analytics, enables marketers to analyze test data more fluently using advanced segmentation, allowing them to integrate the data with media spend, SEO, and other analytics.
  • Cost savings related to Google Analytics 4 being a free tool

⚠️ Drawbacks:

  • Complexity: GA4 has a steeper learning curve compared to other analytics tools.
  • No Out-of-the-Box Test Reporting: Unlike Convert's native reporting, GA4 requires manual setup for test analysis.
  • Data Discrepancies: Minor differences in tracking methods may lead to slight discrepancies between Convert and GA4 data.
  • Limited Historical Data: GA4 does not support retroactive data collection, so test data is only available from the time of integration.
  • Having the evaluation on a different tool than the testing tool, increases the chances of errors, latency, and complexity.

Now that we've considered the pros and cons let's dive into the setup process.

Step 1: Initial Setup

Prerequisites:

  • Convert.com account with tracking code
  • GA4 property and tracking code
  • Both codes properly placed (Convert before GA4)

Step 2: Enable Convert-GA4 Integration

Project Level Integration:

  • Navigate to Project Configuration > Integrations
  • Select "Google Analytics" and "Google Analytics 4 Integration"
  • Authenticate with Google
  • Select your GA4 property and data stream
  • Alternative: Experience Level Integration
  • Set up GA4 integration per individual experience

Step 3: Create GA4 Audiences

After activating your experience:

  • Use Convert's "Create GA4 Audiences" feature
  • Verify audience creation in GA4
  • Note: Keep experience names under 255 characters

Step 4: Analyze Results in GA4

Two methods to analyze data:


Method 1: Using GA4 Audiences

  • Reports > Acquisition > Traffic Acquisition
  • Add comparison using Audience name
  • Select variation audiences
  • Add relevant metrics

Method 2: Using Experience Impression Event

  • Create custom dimension for experience_impression:
    • Use format: CONV-[Experience ID]-[Variation ID]
  • Create exploration report:
    • Example Data Analysis:

    • Let's say you're testing a new checkout page:

      Variation Users Conversions Conv. Rate
      Control 10,000 500 5.00%
      Variation 1 10,000 600 6.00%

Step 5: Statistical Analysis


Use an A/B test calculator (like abtestguide.com/calc/):

Input:

  • Control: 10,000 visitors, 500 conversions (5.00%)
  • Variant: 10,000 visitors, 600 conversions (6.00%)
  • Confidence Level: 95%
  • Two-tailed test

The calculator would show:

  • Statistical significance achieved
  • Relative improvement: 20%
  • P-value: 0.003
  • Confidence interval: 0.5% to 1.5%

Important Metrics to Track in GA4:

  • Conversion events
  • Engagement metrics
  • User behavior flows
  • Custom events
  • Session duration

💡 Pro Tips:

Data Quality:

  • Use GA4 DebugView for verification
  • Monitor audience creation
  • Check real-time reports initially
  • Best Practices:
  • Wait for statistical significance
  • Use consistent naming
  • Document test configurations
  • Consider seasonal effects

Advanced Analysis:

  • Create custom explorations
  • Use path analysis
  • Compare segments
  • Analyze user behavior patterns

Troubleshooting:


If data isn't appearing:

  • Check tracking code placement
  • Verify GA4 property settings
  • Confirm audience creation
  • Review browser cookie settings

Remember:

  • Always wait for statistical significance
  • Consider sample size requirements
  • Document all test configurations
  • Monitor data quality regularly
  • This method provides clean, reliable data analysis while leveraging GA4's powerful segmentation and reporting capabilities. However, it also comes with certain trade-offs, such as increased complexity and potential data discrepancies. Need help? Convert's support team is available 24/7.