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Google uses a multitude of factors to determine how to rank search engine results. Typically, these factors are either related to the content of a webpage itself (the text, its URL, the titles and headers, etc.) or were measurements of the authenticity of the website itself (age of the domain name, number and quality of inbound links, etc.). However, in 2010, Google did something very different. Google announced website speed would begin having an impact on search ranking. Now, the speed at which someone could view the content from a search result would be a factor.
Unfortunately, the exact definition of "site speed" remained open to speculation. The mystery widened further in June, when Google's Matt Cutts announced that slow-performing mobile sites would soon be penalized in search rankings as well.
Clearly Google is increasingly acting upon what is intuitively obvious: A poor performing website results in a poor user experience, and sites with poor user experiences deserve less promotion in search results. But what is Google measuring? And how does that play into search engine rankings? Matt Peters, data scientist at Moz, asked Zoompf to help find the answers.

Disclaimer

While Google has been intentionally unclear in which particular aspect of page speed impacts search ranking, they have been quite clear in stating that content relevancy remains king. So, in other words, while we can demonstrate a correlation (or lack thereof) between particular speed metrics and search ranking, we can never outright prove a causality relationship, since other unmeasurable factors are still at play. Still, in large enough scale, we make the assumption that any discovered correlations are a "probable influence" on search ranking and thus worthy of consideration.

Methodology

To begin our research, we worked with Matt to create a list of of 2,000 random search queries from the 2013 Ranking Factors study. We selected a representative sample of queries, some with as few as one search term ("hdtv"), others as long as five ("oklahoma city outlet mall stores") and everything in between. We then extracted the top 50 ranked search result URLs for each query, assembling a list of 100,000 total pages to evaluate.
Next, we launched 30 Amazon "small" EC2 instances running in the Northern Virginia cloud, each loaded with an identical private instance of the open source tool WebPageTest. This tool uses the same web browser versions used by consumers at large to collect over 40 different performance measurements about how a webpage loads. We selected Chrome for our test, and ran each tested page with an empty cache to guarantee consistent results.
While we'll summarize the results below, if you want to check out the data for yourself you can download the entire result set here.

Results

While we captured over 40 different page metrics for each URL examined, most did not show any significant influence on search ranking. This was largely expected, as (for example) the number of connections a web browser uses to load a page should likely not impact search ranking position. For the purposes of brevity, in this section we will just highlight the particularly noteworthy results. Again, please consult the raw performance data if you wish to examine it for additional factors.

Page load time

When people say"page load time" for a website, they usually mean one of two measurements: "document complete" time or "fully rendered" time. Think of document complete time as the time it takes a page to load before you can start clicking or entering data. All the content might not be there yet, but you can interact with the page. Think of fully rendered time as the time it takes to download and display all images, advertisements, and analytic trackers. This is all the "background stuff" you see fill in as you're scrolling through a page.
Since Google was not clear on what page load time means, we examined both the effects of both document complete and fully rendered on search rankings. However our biggest surprise came from the lack of correlation of two key metrics! We expected, if anything, these 2 metrics would clearly have an impact on search ranking. However, our data shows no clear correlation between document complete or fully rendered times with search engine rank, as you can see in the graph below:
The horizontal axis measures the position of a page in the search results, while the vertical axis is the median time captured across all 2,000 different search terms used in the study. So in other words, if you plugged all 2,000 search terms into Google one by one and then clicked the first result for each, we'd measure the page load time of each of those pages, then calculate the median and plot at position 1. Then repeat for the second result, and third, and on and on until you hit 50.
We would expect this graph to have a clear "up and to the right" trend, as highly ranked pages should have a lower document complete or fully rendered time. Indeed, page rendering has a proven link to user satisfaction and sales conversions (we'll get into that later), but surprisingly we could not find a clear correlation to ranking in this case.

Time to first byte

With no correlation between search ranking and what is traditionally thought of a "page load time" we expanded our search to the Time to First Byte (TTFB). This metric captures how long it takes your browser to receive the first byte of a response from a web server when you request a particular URL. In other words, this metric encompasses the network latency of sending your request to the web server, the amount of time the web server spent processing and generating a response, and amount of time it took to send the first byte of that response back from the server to your browser. The graph of median TTFB for each search rank position is shown below:
The TTFB result was surprising in a clear correlation was identified between decreasing search rank and increasing time to first byte. Sites that have a lower TTFB respond faster and have higher search result rankings than slower sites with a higher TTFB. Of all the data we captured, the TTFB metric had the strongest correlation effect, implying a high likelihood of some level of influence on search ranking.

Page size

The surprising result here was with the the median size of each web page, in bytes, relative to the search ranking position. By "page size," we mean all of the bytes that were downloaded to fully render the page, including all the images, ads, third party widgets, and fonts. When we graphed the median page size for each search rank position, we found a counterintuitive correlation of decreasing page size to decreasing page rank, with an anomalous dip in the top 3 ranks.
This result confounded us at first, as we didn't anticipate any real relationship here. Upon further speculation, though, we had a theory: lower ranking sites often belong to smaller companies with fewer resources, and consequently may have less content and complexity in their sites. As rankings increase, so does the complexity, with the exception of the "big boys" at the top who have extra budget to highly optimize their offerings. Think Amazon.com vs. an SMB electronics retailer vs. a mom-and-pop shop. We really have no proof of this theory, but it fits both the data and our own intuition.

Total image content

Since our analysis of the total page size surprised us, we decided to examine the median size, in bytes, of all images loaded for each page, relative to the search rank position. Other then a sharp spike in the first two rankings, the results are flat and uninteresting across all remaining rankings.
While we didn't expect a strong level of correlation here we did expected some level of correlation, as sites with more images do load more slowly. Since this metric is tied closely to the fully rendered time mentioned above, the fact that this is equally flat supports the findings that page load time is likely not currently impacting search ranking.


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