Analyze Core Web Vitals results

Once you've enabled Core Web Vitals monitoring and executed your performance tests, NeoLoad provides comprehensive analysis capabilities to help you understand your application's user experience performance. Core Web Vitals results are calculated at the page level, giving you detailed insights into how each page performs across the three essential client-side metrics.

Note: As of 2025.3, Core Web Vitals analysis is only available in NeoLoad. Results cannot be analyzed in NeoLoad Web yet.

Access Core Web Vitals results

Core Web Vitals results are integrated into the standard NeoLoad results interface. To access your Core Web Vitals data, follow these steps:

  1. Open your test results in NeoLoad.

  2. Go to the Results tab.

  3. Navigate through the available result views to analyze your Core Web Vitals data.

Result views

Different result views provide varying levels of detail to match your analysis needs.

The Summary view provides high-level highlights of your Core Web Vitals results:

  • Top 5 worst performers: Displays the five worst-performing URLs for each Core Web Vitals metric (LCP, INP, CLS), helping you identify pages that need immediate attention.

Use the Summary view to quickly identify which pages require optimization.

The Values view offers granular analysis capabilities, showing detailed statistics for each monitored URL:

  • URL-level metrics: View Core Web Vitals results broken down by individual URLs.

  • Percentile analysis: Examine the 50th, 75th, and 95th percentiles for each metric.

  • User path breakdown: See how Core Web Vitals vary across different user paths in your test scenario.

The Values view is ideal for detailed performance analysis.

Graphs view

The Graphs view provides visual analysis tools to help you understand performance patterns and distributions:

  • Percentile graphs: Visualize the distribution of all percentile values for each Core Web Vitals metric.

  • Scatter plots: Examine the relationship between different metrics and identify outliers or patterns.

  • Time series analysis: Track how Core Web Vitals metrics change throughout your test run.

Use the Graphs view to identify performance trends, correlations between metrics, and variations in user experience over time.

Real-time monitoring

Real-time monitoring allows you to observe Core Web Vitals performance as your test runs.

During test runs, you can monitor Core Web Vitals performance in real-time:

  • Live scatter plots: View Core Web Vitals metrics as they're captured during the test run.

Interpreting Core Web Vitals results

Proper interpretation of your results helps you make informed optimization decisions.

Use these industry-standard performance thresholds to understand what each metric represents:

Metric Good Needs Improvement Poor
LCP (seconds) ≤ 2.5 2.5 - 4.0 > 4.0
INP (milliseconds) ≤ 200 200 - 500 > 500
CLS ≤ 0.1 0.1 - 0.25 > 0.25

The 75th percentile provides the most meaningful assessment of your user experience performance.

Industry standards recommend using the 75th percentile when evaluating Core Web Vitals performance. This means that 75% of your users should experience performance at or better than the threshold values. Focus your analysis on 75th percentile results to align with industry best practices for Core Web Vitals assessment (opens in new tab).

Export results for further analysis

Follow these steps to your export Core Web Vitals results:

  1. In the Results interface, go to the Values view.

  2. Select the Core Web Vitals data you want to export.

  3. Choose Export to CSV to save the data in a format suitable for spreadsheet analysis.

Exported data includes all percentile values, URL information, and metric calculations, allowing you to perform custom analysis or create reports for stakeholders.

Performance analysis tips

It's easy to feel overwhelmed while trying to analyze a large batch of data.

Use these strategies to get the most value from your Core Web Vitals analysis:

  • Compare across test runs: Track Core Web Vitals improvements or regressions by comparing results from different test executions.

  • Identify patterns: Look for correlations between traditional performance metrics and Core Web Vitals to understand root causes.

  • Focus on user impact: Prioritize optimization efforts on pages with poor Core Web Vitals scores that represent critical user journeys.

  • Consider page context: Different page types may have different performance expectations based on their complexity and user expectations.