Data Analysis in PageSpeed Insights

Lab Data vs. Field Data

When using PageSpeed Insights, it's essential to understand the distinction between lab diagnostic data and field data to assess website performance. Lab data relies exclusively on simulated conditions using tools like Google Lighthouse. While this helps developers identify specific performance bottlenecks, it may not fully capture everyday scenarios.

For instance, while testing a new online store, a developer might find that, in lab conditions, the site's load time is excellent. However, this doesn't always account for variability in user internet speeds or device types.

|In contrast, field data captures the real-world experience of users interacting with the site across various devices and networks. This data, powered by the Chrome User Experience Report (CrUX), offers valuable insights over a rolling, 28-day period. Field data showcases metrics such as First Contentful Paint (FCP) and Largest Contentful Paint (LCP) as actual experiences rather than simulated scores.

Real-User Experience Metrics

Real-user experience metrics are the heartbeat of PageSpeed Insights, providing actionable assessments of how users perceive page speed. These metrics are not merely theoretical estimates; they reflect genuine challenges website visitors face.

Key metrics include:

• First Contentful Paint (FCP): Measures the time required for the first visible element to appear on the screen.
• Largest Contentful Paint (LCP): Indicates how quickly the 'largest' visible content (like images or text) is loaded.
• Cumulative Layout Shift (CLS): Quantifies how much the layout shifts during loading, affecting user experience. Adverse practices: Picture a strong case of divitis or the nested tables aficionado.

Having analyzed our share of client websites, as with many other developers, we often find that focusing on the 75th percentile of these metrics helps address the most frustrating experiences users might encounter. By bridging the insights provided by the lab and field data, one can effectively target areas for refinement, optimizing for real users while ensuring the site performs well in simulated tests. Ultimately, good data analysis leads to productive fixes for those common factors affecting page load time, improving overall user satisfaction.

For those afield of the canon, focusing on the 75th percentile is a common practice in web performance optimization. Imagine you have 100 marbles, all lined up from smallest to largest. The marble at the 75th spot is bulkier than all the marbles that preceded it but smaller than the last 25. That's the 75th percentile. It helps show where most marbles (or people's experiences) fall in a group. When developers focus on the 75th percentile, they examine the experiences of users who are greater than 75% of all users but less than the top 25%. It helps developers figure out where most people might be facing slowdowns or problems on a site and fix them so the website works better for everyone.

Quality Assessment and Core Web Vitals

Classifying User Experiences

Classifying user experiences is pivotal in optimizing website performance through PageSpeed Insights. PSI employs a straightforward classification system, categorizing experiences into three buckets: Good, Needs Improvement, and Poor. Each reporting category is purposefully architected to help developers prioritize their optimization efforts effectively.

For example, consider a newly established blog. Initially, the site may find a significant portion of the audience experiencing longer loading times. Drilling down on performance data, they might classify their user experience as 'Needs Improvement.' This assessment helps the developers to focus on essential changes rather than overlooking critical issues.

Here's how the classifications break down based on specific metrics:

• Good: Represents optimal performance (e.g., FCP under 1800ms).
• Needs Improvement: Indicates potential issues (e.g., LCP between 2500ms and 4000ms).
• Poor: Signals significant problems needing attention (e.g., CLS over 0.25).

Adopting this classification method clarifies which metrics require immediate action and instills a culture of continuous improvement.

Core Web Vitals Assessment Criteria

At the heart of PSI reports are the Core Web Vitals—critical metrics that significantly influence engagement and contribute to search engine rankings. Core Web Vitals have emerged as essential performance indicators for user experience, with three key metrics at their core: Largest Contentful Paint (LCP), Cumulative Layout Shift (CLS), and Interaction to Next Paint (INP). PageSpeed Insights evaluates these metrics to determine whether a page meets the set criteria.

The assessment includes:

• Largest Contentful Paint (LCP): Good experiences are classified when LCP is 2.5 seconds or less.
• Cumulative Layout Shift (CLS): A CLS score of 0.1 or lower indicates a stable visual experience.
• Interaction to Next Paint (INP): A good score for INP is 200ms or lower.

Meeting Core Web Vitals standards requires a page to score at or above the 75th percentile in all three metrics. This level of scrutiny ensures that page speed optimizations are not merely superficial but effectively enhance user satisfaction.

In practice, applying these criteria has led (this) developer(s) to regularly revisit and enhance site performance, ultimately fostering a more engaging user experience. By consistently maintaining these standards, website enthusiasts like yourself can ensure they provide a fast and reliable service, significantly impacting user retention and satisfaction.