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Introduction

This guide outlines best practices for transitioning from Google Analytics 4 (GA4) to Gainsight PX. While both platforms offer valuable insights, Gainsight PX is specifically designed for product analytics and user engagement, making it a powerful tool for product-led growth strategies.

 

1. Understand the Key Differences

Before migrating, it's crucial to understand how Gainsight PX differs from GA4:

  • Focus: PX is product-centric, while GA4 is web/app-centric
  • User Identification: PX ties data to actual users, GA4 uses anonymous client IDs
  • Engagement Tools: PX offers native in-app engagement features
  • Data Freshness: PX provides real-time data, GA4 has some processing delay
  • Integration: PX integrates with customer success and CRM tools

2. Audit Your Current GA4 Setup

  • List all custom events and conversions you're tracking in GA4
  • Identify key metrics and KPIs you're currently monitoring
  • Review any custom dimensions or user properties you've set up

3. Map GA4 Events to Gainsight PX Events

  • Gainsight PX tracks the following by default:
    • Session/Visits
    • Page Loads
    • Link Clicks
    • Button Clicks
  • Any events that are not equivalent to these would have to be added as a custom event
  • Create a spreadsheet mapping GA4 events to equivalent PX events. For example: 
GA4 Event Gainsight PX Event
page_view Page Viewed
file_download File Downloaded
form_submit Form Submitted
  • Identify any GA4 events that don't have direct equivalents in PX.

4. Set Up Gainsight PX Tracking

  • Implement the Gainsight PX tracking code on your product
  • Use the PX SDK to track custom events that match your GA4 setup
  • Set up user identification to tie events to specific users
  • Configure any additional product-specific events not tracked in GA4

5. Recreate Key Reports and Dashboards

  • List essential reports and dashboards from GA4
  • Create a list of reporting functionality
  • Explore re-creating these in Gainsight PX, utilizing PX's product-centric features

Example transitions:

  • GA4 Conversion Funnel → PX Funnels
  • GA4 Cohort Analysis → PX Retention Analysis
  • GA4 User Explorer → PX Audience Explorer

6. Leverage Gainsight PX-Specific Features

Implement PX features not available in GA4:

  • Set up in-app guides and tooltips for user onboarding
  • Create targeted in-app surveys for user feedback
  • Utilize PX's native email campaigns for user activation
  • Implement feature adoption tracking

7. Update Integrations and Data Flows

  • Review all tools integrated with GA4 (e.g., CRM, marketing automation)
  • Set up equivalent integrations with Gainsight PX
  • Update any automated reports or data pipelines

8. Train Your Team

Conduct training sessions on Gainsight PX for different teams:

  • Product Managers: Feature adoption, user journeys
  • Customer Success: User health scores, engagement metrics
  • Marketing: User acquisition and activation metrics

Create a quick reference guide for transitioning from GA4 to PX terminology.

9. Gradual Rollout and Validation

  • Run both GA4 and Gainsight PX in parallel initially
  • Validate that key metrics match between the two systems
  • Gradually shift team members to using PX as their primary analytics tool

10. Leverage Advanced PX Capabilities

Once the basic migration is complete, explore advanced PX features:

  • Implement product analytics for feature prioritization
  • Set up advanced segmentation for personalized user experiences

Conclusion

Migrating from GA4 to Gainsight PX represents a shift towards more product-centric analytics and engagement. By following these best practices, you can ensure a smooth transition that leverages the full power of Gainsight PX for driving product-led growth.

Google Analytics 4 vs Gainsight PX Comparison

 
Feature/Aspect Google Analytics 4 Both Gainsight PX
Primary Focus Web and app analytics   Product experience analytics
Target Users Marketers, analysts   Product managers, customer success teams
Data Collection   Event-based tracking Feature usage, In-app user behavior
User Identification Anonymous client IDs   Ties data to actual user accounts
Real-time Reporting Limited real-time data   Up-to-the-second data freshness
Custom Events   Configurable custom events  
Funnel Analysis   User journey/funnel analysis  
Segmentation   Advanced user segmentation  
Integrations Google Ads, Google Marketing Platform   CRM, Customer Success platforms
In-app Engagement     Native in-app guides and surveys
Email Engagement     Email campaigns for user activation
Feedback Collection     In-app NPS, CSAT, CES surveys
User Journey Visualization Cross-platform user journeys   Product-specific user flows
E-commerce Tracking Built-in e-commerce reporting   Some e-commerce on dashboards
Attribution Modeling Advanced attribution models    
Audience Building   Audience/segment creation  
Data Export BigQuery export   CSV, S3, CS Integration
Privacy Controls   Data retention controls, consent mode  
Implementation JavaScript tag   JavaScript SDK
Mobile App Tracking   Native SDKs for iOS and Android  
Customizable Dashboards   Customizable dashboards and reports  
API Access   Data access via API
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