So… You ran a report in Lighthouse and the lab data results were not that good. Therefore, you prepare yourself and set sail into the sea seeking the elusive treasure of seamless user experience. The sun is shining bright, the sea is smooth as a mirror and your website has never scored so high!
A month goes by and the waters begin to agitate, dark clouds begin to crowd, and your Core Web Vitals report hits like a lightning strike.
Now your ship is sinking and it’s too little, too late!
Here we will go through the two different data types for measuring web performance: Laboratory Data and Real User Data. We will cover what they are, their differences, use cases, limitations, and benefits.
Before embarking on your journey to optimize your website, join us and discover what these reports have to offer.
What is Real User (Field) Data?
Look at it as a diverse landscape, a field where explorers document their experiences in the wild.
Real User Data is our intrepid explorer, capturing the real-world interactions of visitors navigating websites. It thrives on the unpredictability of the internet, showcasing the full spectrum of devices, network conditions, and user behaviors. This data, sourced from tools like Chrome User Experience Report (CrUX) and reflecting the past 28 days, provides an unfiltered view of the web’s vibrant and ever-evolving ecosystem.
What is Lab Data?
Imagine a well-equipped laboratory where scientists observe and record experiments. Lab Data in the web realm is quite similar. It’s data meticulously collected in a controlled environment that simulates user interactions with a website. But, as we tread deeply into the forest of web analytics, we must acknowledge its limitations.
What are Lab Data Limitations?
While Lab Data is a valuable tool in our web performance exploration, it’s essential to highlight its limitations. Here, we’ll uncover the challenges of relying on this controlled yet somewhat confined data source.
Accuracy Within Controlled Environments:
Laboratory Data is exact in its controlled settings, but this precision comes at a cost. It may not accurately reflect the real-world variability of user behaviors, devices and network conditions. Similar to studying a caged animal in a zoo and assuming it behaves the same way in the wild.
Limited Real-World User Interactions:
In the controlled laboratory environment, interactions are scripted and standardized – This means, laboratory data is collected by a robot. While this approach ensures consistency, it fails to capture the full spectrum of user behaviors. Think of it as trying to understand human behavior solely by observing actors in a well-rehearsed play – it’s informative but lacks the spontaneity and full nuanced context of real life.
Potential for Optimization Bias:
Sometimes, when we tinker with things based solely on laboratory data, we end up with optimizations that work great in controlled settings but go haywire in the real world. It’s like trying to trick the bot by making quick fixes on a website – sure, it’s a common practice for quick results, but it’s not the best approach. In fact, it often backfires in the long run, causing more trouble than it’s worth.
That being said, it is possible that you won’t have enough traffic/users on your website for Google to start collecting this information and provide Real User Data. So our advice? Use Lab Data as a starting point and transition to Real User Data as soon as it’s available.
Limited Insights into User Frustrations:
In the world of Lab Data, frustration and impatience often go unnoticed. When data is collected under controlled circumstances, the authentic user experience, complete with moments of irritation or confusion, remains hidden.
What is the Difference Between Lab Data and Field Data?
It’s essential to grasp the profound distinctions between Lab Data and Field Data, akin to understanding the contrast between a controlled laboratory experiment and an untamed expedition into the wilderness.
Lab Data: The Precision of Controlled Environments
Imagine a meticulously controlled laboratory. Here, data is collected with surgical precision, similar to scientists conducting experiments under controlled conditions. In the digital realm, Lab Data mirrors this environment. It’s a well-orchestrated setting where a simulated user interacts with a website, enabling quick and systematic data collection – this interaction emulates a low CPU power cellphone, running on a throttled mobile internet connection; measuring performance in the worst scenario possible. Like a laboratory’s controlled variables, this data type offers a highly controlled environment for testing website performance metrics.
However, as we venture further into the labyrinth of Lab Data, we uncover its limitations. While it excels in precision, it often struggles to encapsulate the rich tapestry of real-world variability.
Field Data (Real User Data): The Unpredictable Wilderness
Now, picture a vast, untamed wilderness. Field Data, often called Real User Data, is our daring explorer in this metaphorical jungle. It captures the raw, unfiltered experiences of visitors navigating websites in the real world. Much like explorers encountering various terrains, creatures, and climates, Field Data thrives in the unpredictable landscape of the internet.
This data type embraces the diversity of devices, network conditions, and user behaviors with open arms. Just as CrUX, considers the superior 75% percentile of real users, Field Data accounts for the full array of devices, network connections, and user browsers. It’s the equivalent of experiencing a wildlife safari, where every moment holds the potential for surprise. From the swift clickers who breeze through web pages to the contemplative explorers who linger and ponder, Field Data accommodates the full spectrum of user interactions, painting a comprehensive portrait of how users genuinely engage with websites.
The Essence of the Difference
The essence of the difference between these data types lies in their environments. Lab Data is like controlled laboratory experiments, providing precision and consistency but lacking the wild, unpredictable nature of real-world internet use.
In contrast, Field Data, or Real User Data, embodies the chaos and diversity of the internet wilderness, capturing the genuine experiences of users as they navigate websites. Real Data, gathered over the last 28 days to provide an average, contrasts with Lighthouse’s instant analysis.
Each has its merits, but understanding their differences is pivotal to unlocking the true potential of web performance evaluation.
Is Real User Data More Accurate?
Now, the critical question arises: Is Real User Data the ultimate compass for website performance? In many ways, yes, and here’s why:
- Real-World Environment: Real User Data paints a broader picture of the unpredictable internet landscape, capturing the diversity of user experiences.
- User Behavior Variability: In the jungle of the internet, user behaviors are as diverse as the creatures in the wild. Real User Data accommodates this diversity beautifully.
- Reliability and Representativeness: Unlike Lab Data, influenced by controlled settings, Real User Data is a reliable reflection of how websites perform across different setups of devices, network conditions and user behavior.
What Are the Benefits of Using Real User Data?
Having Real User Data as your ‘trusty’ guide offers many advantages:
- Precision in Improvement Efforts: It helps you pinpoint specific elements causing delays in your website’s performance, allowing for targeted optimization.
- Tracking Progress and Iterative Enhancement: Using real user data, which is the data actually used by Google Search Console for performance analysis, can guide you toward improving your website. It allows you to track the effectiveness of changes made over time.
- Industry Benchmarking: Compare your website’s performance to others in the digital landscape, gaining valuable insights and a competitive edge.
What is Lab Data in Lighthouse?
As we continue our exploration, it’s worth mentioning Lab Data in the context of Google Lighthouse. Google Lighthouse is like your trusty explorer’s toolkit, conducting automated tests on websites to simulate real user interactions.
Core Web Vitals
It’s crucial to understand the beating heart of web performance—Core Web Vitals. These essential metrics – CLS, LCP and INP – encapsulate various facets of the user experience, shaping how we perceive and optimize website performance.
Metrics: Do They Give Different Results Between Field Data and Lab Data?
Metrics, the guiding stars of our journey… They often offer diverse readings when we compare Field Data (Real User Data) and Lab Data. This intriguing phenomenon is a testament to the complex and multifaceted nature of the web.
- Field Data – Embracing the Unpredictable: Real User Data takes the lead in the untamed wilderness of the internet, where user interactions are as varied as the ecosystems of a rainforest. It captures the unscripted, ever-changing experiences of actual users navigating websites. Picture your expedition party navigating through ever-shifting terrains and encountering the unexpected—this is the essence of Field Data.
- Lab Data – The Control Tower: Conversely, Lab Data operates within the controlled confines of a laboratory-like setting. Similar to observing creatures in a carefully constructed enclosure, offering precision and consistency. Yet, it cannot wholly replicate the dynamic, real-world conditions of the internet jungle.
Conclusion
Lab Data is a valuable guide, particularly in controlled environments. It excels in precision and consistency, offering quick insights into specific elements that impact website performance. This makes Laboratory Data indispensable in initial testing and targeted optimizations. However, its value shines brightest when complemented by Real User Data. While Lab Data provides a focused lens for controlled scenarios, Real User Data captures the wild, unpredictable nature of internet usage. The symbiotic relationship between the two is essential, ensuring a comprehensive understanding of web performance and effective optimization across diverse scenarios. Balancing the strengths of both is key to navigating the complexities of the online realm successfully.
