The author's views are entirely his or her own
(excluding the unlikely event of hypnosis) and may not always reflect
the views of Moz.
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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:
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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:
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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.
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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.
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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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