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Feb 5, 2025
How to Analyze A/B Test Results in LogRocket with Optimal UX Integration
Introduction
Combine the power of LogRocket's session replay and UX analytics with your A/B testing data from Optimal UX. This integration enables product teams to identify friction points, understand user behavior, and make informed decisions about which variants truly provide the best user experience.
Why Use LogRocket for A/B Test Analysis?
Watch real user sessions segmented by experiment variants
Identify UX friction points in different variants
Analyze user behavior with session replay
Share concrete evidence across teams
Make informed product decisions based on actual user interactions
Step-by-Step Integration Guide
Enable the Integration
Access your Optimal UX dashboard
Navigate to Settings > Integrations
Find the LogRocket integration card
Enable the integration toggle
Understanding Data Flow
When enabled, Optimal UX automatically sends experiment participation data to LogRocket:
Events are tracked using LogRocket's JavaScript SDK
The integration uses custom events with the format:
Security Note: Optimal UX uses experiment slugs rather than full experiment names in the tracking calls to enhance security. This prevents sensitive information about your experiments from being exposed in the tracking data.
Using Experiment Data in LogRocket
Accessing Experiment Sessions
Open your LogRocket dashboard
Click on "Add filters or use saved segments to refine your dashboard"
Choose "Custom Event"
Pick an event that starts with "Experiment "
Save this as a segment
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Analysis Capabilities
LogRocket’s product analytics suite provides several capabilities that help you compare and evaluate A/B test variants. Here’s a short list:
• Funnel Analysis & Conversion Tracking: Build funnels that let you compare conversion rates and drop-offs between different test variants.
• Custom Event Tracking: Define and retroactively apply custom events to measure key actions and KPIs for each variant.
• Session Replay & Behavioral Insights: Replay sessions segmented by variant to see exactly how users interact with each version.
• Frustration Metrics: Monitor signals like rage clicks, dead clicks, and error rates to gauge user frustration across variants.
• User Segmentation & Filtering: Segment users into cohorts (e.g., by test group) so you can analyze performance differences in a granular way.
Cross-Functional Benefits
For Product Teams
Validate design decisions with real user data
Identify opportunities for improvement
Prioritize product changes based on impact
For UX Teams
Understand user behavior in each variant
Identify usability issues
Validate design hypotheses
For Engineering Teams
Monitor technical performance
Track error rates by variant
Identify browser-specific issues
For Design Teams
View actual user interactions
Validate design implementations
Identify design inconsistencies
Conclusion
The integration between Optimal UX and LogRocket bridges the gap between A/B testing and user experience analysis. By combining these tools, teams can make data-driven decisions based on both quantitative metrics and qualitative user behavior insights.
Read more:
How to Analyze A/B Test Results in LogRocket with Optimal UX Integration
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