Archive for the ‘SEO’ Category

Well, I’m back after the holidays. Here I thought the holidays would be slow and I’d get a chance to write every day. Turns out that was a bad assumption. I was busy as heck with clients who needed it “yesterday.” That’s different than the beginning of the year – so maybe that’s a sign the economy is really improving.

Today’s post is about whether the long-tail is useful for companies with small keyword universes. This became of interest to me when a colleague of mine, Steven Ebin, suggested that this strategy was useful even for small search markets, which had not been my experience.

I define a small keyword universe as one that is less than 1,000 core keywords. The short-tail is defined as keywords that represent 60% of traffic (in my experience usually about 10-15 keywords for small universes, although that can vary substantially). The mid-tail is defined as keywords that represent the next 25% and the long-tail is defined as keywords that represent the balance. I find that many small customers have core keyword universes of this size and distribution, although size of the keyword universe really ties to the specific market, not the size of customer. Be that as it may, my experience is that there is a correlation between size of company and size of keyword market.

Let’s assume that the overall number of monthly clicks for a keyword market, including the long-tail search terms, is 2,200,000 visits.

Next, we know that not everyone clicks – so we need to ignore that traffic. Some aged results reported for AOL in the Webmaster World forums suggest that 46% of searchers don’t click through.

We then have to make some assumptions about where we will rank in each of the positional categories. For purposes of this analysis, I’ll assume that the above average SEO can get an average position of 5 for the short-tail terms, a position of 3 on average of the mid-tail terms, and an average position of 2 for the long-tail terms.

We also have to estimate the clickthrough rates for results in each of these positions in the SERPs. This data varies widely. One study from Cornell University suggests that 56% of searchers who click click on the first result. Some calculations by Jay Geiger suggest that 42.3% of searchers who click click on the first result, and some reported statistics for AOL from the prior-mentioned source show the same number as 23%.

Clickthrough Rates Based on Position in Search Results

We also have to make some assumptions about conversion rates. Estimating conversion rates is hard because it can vary so much by industry, but let’s assume we have a 0.5% conversion rate for our short-tail terms, 1% for our mid-tail terms, and 3% for our long-tail terms. This gives us a weighted-average conversion rate (based on the percentages in each part of the tail) of 1.0%, which is not unrealistic for organic traffic and may even be low. If you play with these numbers, you will see that a reasonable variation in conversion rate assumption on the long-tail doesn’t change the results of the analysis.

Why increase the conversion rate so much for the long tail? Long-tail searches are more “perfected.” The fact that people type in more specific keyword strings indicates that they are further down the decision-making cycle, and thus the conversion rates are significantly higher. The “spread” that I have assumed for the model is actually conservative. We have often seen conversion rate differentials between the short- and long-tail that are even greater.

When we run out the numbers, using all three sets of clickthrough rates we get the following results:

Results of Analysis on Long-Tail Keywords

This analysis shows that the long-tail can potentially double the business for a small company.

We can also look at this same analysis using the length of the keyword string. How does keyword string length relate to location in the tail? Are long keyword strings (3+ words) equivalent to the “long tail”? The answer is “no” – in fact the ratios of searches for length of tail versus length of keyword string are almost the inverse (60/25/15 vs 20/24/56). However, since we could also segment our keyword universe based on length of keyword string and develop a traffic strategy based on that, let’s look at the analysis that way.

Hitwise completed research on the percentage of searches based on number of terms in the keyword string in January 2009 with these results:

Searches By Number of Terms from Hitwise January 2009

It you add the searches with 3+ terms in the keyword, you get 56.06% – so a pretty substantial amount of traffic, but not as high as the 70% I have heard from others.

We also need to adapt the conversion rates to maintain an average 1% across all three categories, so that the analysis between the two approaches is “apples-to-apples.” When you run the analysis based on length of keyword string with a conversion rate to get the same number of conversions as in the prior case, you get the following results:

Results of Analysis for Small Keyword Markets, By String Length

In this case, the results are even more dramatic, with the number of conversions for keywords with three+ terms dwarfing the one- and two-keyword strings. Even if you take the conversion rate for the three+ keywords down to 0.8% (the same as for two keyword search strings), the conversions are still almost double what is in the one-and two-keyword string categories combined.

So the answer to the question is an absolute “Yes” – the long-tail can be a very valuable source of business even for small keyword universes.

Well, it’s after Thanksgiving and I finally get back to the blog. Feels good. This is the next installment about site performance analysis and how to deal with a site with worrisomely slow page loading times. It turns out I had a case study right under my nose. This site, the OnlineMatters blog. Recently, I showed a client my site and watched as it loaded, and loaded and….loaded. I was embarassed but also frustrated. I had just finished my pieces on site performance and knew that this behavior was going to cause my rankings in the SERPs to drop, even before Google releases Caffeine. While I am not trying to publish this blog to a mass audience – to do that I would need to write every day – I still wanted to rank well on keywords I care about. Given what I do, it’s an important proof point for customers and prospects.

So I am going to take advantage of this negative and turn it into a positive for you. You will see how to perform various elements of site analysis by watching me debug this blog in near real time. Yesterday, I spent three hours working through the issues, and I am not done yet. So this first piece will take us about halfway there. But even now you can learn a lot from my struggles.

The first step was to find out just how bad the problem was. The way to do this is to use Pingdom’s Full Page Analysis tool. This tool not only tests page loading speeds but also visualizes which parts of the page are causing problems. An explanation of how to use the tool can be found here, and you should read it before trying to interpret the results for your site. Here is what I got back when I ran the test:

A load time of 11.9 seconds? Ouch!  Since Pingdom runs this on their servers, the speed is not influenced by my sometime unpredictably slow Comcast connection.

Pingdom shows I had over 93 items loading with the home page of which the vast majority were images (a partial listing is shown below).  There were several (lines 1, 36, 39, 40, 41, 54) where a significant part of the load time was occurring during rendering (that is, after the element had been downloaded into the browser).  This is indicated by the blue part of the bar.  But in the majority of cases, the load time was mainly caused by the time it took from either the first request to the server until the content began downloading (the yellow portion of the bar), or from the time of downloading to the time rendering began (the green portion).  This suggested that

1. I had too big a page, because the download time for all the content to the browser was very long.
2. I might have a server bandwidth problem.

But rather than worrying about item 2, which would require a more extensive fix – either an upgrade in service or a new host – I decided to see how far I could get with some simple site fixes.

The first obvious thing to fix was the size of the home page, which was 485 KB – very heavy.  I tend to write long (no kidding?) and add several images to posts, so it seemed only natural to reduce the number of entries on my home page below the 10 I had it set for.  I set the allowable number in WordPress to 5 entries, saved the changes, and ran the test again. 

Miracle of miracles: My page now weighed 133 KB (respectable), had 72 total objects, and downloaded in six seconds.  That was a reduction in load time by almost 50% for one simple switch.  

Good, but not great.  My page loading time was still 6 seconds – it needed to be below 2.  So more work was needed.

If you look at the picture above, you can just make out that some of the slowest loading files – between 4 and 6 of them  – were .css or Javascript files.  Since these are files that are part of WordPress, I chose to let them go for the moment and move onto the next obvious class of files – images.  Since images usually represent 80% of page loading times, this was the next obvious place to look.   There were between 6 and 10 files –  mainly .png files – that were adding substantially to download times.  Most of these were a core portion of the template I was using (e.g. header.png).  So they effected the whole site and, more importantly, they had been part of the blog before I ever made one entry.  The others were the icons in my Add-to-Any toolbar, which also showed on every post on the site.

Since I developed the template myself using Artisteer when I was relatively new to WordPress, I hypothesized that an image compression tool might make a substantial improvement for little effort. 

Fortunately, the ySlow Firefox plugin, which is a site performance analyzer we will examine in my next entry, contains smushit, an image compression tool created by Yahoo! that is easy to use, identifies and shows just how much bandwidth it saves, produces all compressed files at the push of a single button, and produces excellent output quality.

So I ran the tool (I sadly did not keep a screenshot of the first run, but a sample output is below), and Smushit reduced image sizes overall by about 8%, and significantly compressed the size of the template elements.  So I downloaded the smushed images and uploaded them to the site

As you can see below – my home page was now 89.8 KB, but my load time had increased to 8.8 seconds! – and note on the right of the image that several prior runs confirmed the earlier 6 second load time. So either compression did not help or some other factor was at play. 

The fact is the actual rendering times had basically reduced from measurable amounts (e.g. 0.5 seconds) to milliseconds – so the actual file sizes had improved rendering performance.  Download times had increased – once again pointing to my host.  But before  going there, i wanted to see if there were any other elements on the site I could manipulate to improve peformance.

More in next post.  BTW, as I go to press this am, my site speed was 5.1 seconds – a new, positive record.  Nothing has changed – so more and more I’m suspecting my ISP and feeling I need a virtual private server.

NOTE: Even more important: as I go to press Google has just announced that it is adding a site performance tool to Google Webmaster Tools in anticipation of site performance becoming a ranking factor.

In my previous discussions of social media, channel architectures, and branding, I discussed the fact that I am manic about locking down my online brand (onlinematters) because there seems to be some relationship in the universal search engines between the number of posts/the number of sites that I post from under a specific username and how my posts rank.  It is as if there is some measure of trust given to an author the more he publishes from different sites and the more people see/read/link to what he has written.  I am not talking about authority given to the actual content written by the author – that is the core of search.  I am talking instead about using the author's behavior and success as a content producer to change where his content ranks for any given search result on a specific search term.  It is similar, in many ways, to what happened in the Vincent release where brand became a more important ranking factor.  In this case, the author and the brand are synonymous and when the brand is highly valued, then those results would, under my hypothesis, be given an extra boost in the rankings.

This was an instinct call, and while I believed I had data to support the theory, I had no research to prove that perhaps an underlying algorithm had been considered/created to measure this phenomenon in universal search. 

I thus considered myself twice lucky while doing my weekly reading on the latest patents to find one that indicates someone is thinking about the issue of "author rank."  On October 29th, Jaya Kawale and Aditya Pal of Yahoo!  applied for a patent with the name "Method and Apparatus for Rating User Generated Content in Search Results."  The abstract reads as follows:

Generally, a method and apparatus provides for rating user generated content (UGC) with respect to search engine results. The method and apparatus includes recognizing a UGC data field collected from a web document located at a web location. The method and apparatus calculates: a document goodness factor for the web document; an author rank for an author of the UGC data field; and a location rank for web location. The method and apparatus thereby generates a rating factor for the UGC field based on the document goodness factor, the author rank and the location rank. The method and apparatus also outputs a search result that includes the UGC data field positioned in the search results based on the rating factor.

Let's see if we can't put this into English comprehensible to the common search geek.  Kawale and Pal want to collect data on three specific ranking factors and to combine these into a single, weighted ranking factor, that is then used to influence rank ordering based on  what they term "User Generated Content" or UGC.  The authors note that typical ranking factors in search engines today are not suitable foir ranking UGC.  UGC are fairly short, they generally do not have links to or from them (rendering the back-link based analysis unhelpful) and spelling mistakes are quite common.  Thus a new set of factors is needed to adequately index and rank content from UGC.

The first issue the patent/algorithm has to deal with is defining what the term UGC includes.  The patent specifically mentions "blogs, groups, public mailing lists, Q & A services, product reviews, message boards, forums and podcasts, among other types of content." The patent does not specifically mention social media sites, but those are clearly implied. 

The second issue is to determine what sites should be scoured for UGC.  UGC sites are not always easy to identify.  An example would be a directory in which people rank references based on 5-star rating, where that is the only user input.  Is this site easy to identify as a site with UGC?  Not really, but somehow the search engine must make a decision whether this site is within its valid universe.  Clearly, some mechanism for categorizing sites with UGC needs to exist and while Kawale and Pal use the example of blog search as covering a limited universe of sites, their patent does not give any indication of how sites are to be chosen for inclusion in the crawl process.

Now we come to the ranking factors.  The three specific ranking factors proposed by Kawale and Pal are:

• Document Goodness.  The Document Goodness Factor is based on at least one (and possibly more) of the following attributes of the document itself: a user rating; a frequency of posts before and after the document is posted; a document's contextual affinity with a parent document; a page click/view number for the document; assets in the document; document length; length of a thread in which the document lies; and goodness of a child document. 
• Author Rank.  The Author Rank is a measure of the author's authority in the social media realm on a subject, and is based on on or more of the following attributes:  a number of relevant posted messages; a number of irrelevant posted messages; a total number of root documents posted by the author within a prescribed time period; a total number of replies or comments made by the author; and a number of groups to which the author is a member.
• Location Rank.  Location Rank is a measure of the authority of the site in the social media realm.  It can be based on one or more of the following attributes: an activity rate in the web location; a number of unique users in the web location; an average document goodness factor of documents in the web location; an average author rank of users in the web location; and an external rank of the web location.

These ranking factors are not used directly as calculated.  They are "normalized" for elements like document length and then combined in some mechanism to create a single UGC ranking factor. 

The main thing to note – and the item that caught my attention, obviously – is Author Rank.  Note that is has ranking factors that correspond with what I have been hypothesizing exist in the universal search engines.  That is to say, search results are not ranked only by the content on the page, but by the authority of the author who has written them, as determined by how many posts that author has made, how many sites he has made them on, how many groups he or she belongs to, and so on.

Can I say for certain that any algorithm like this has been implemented?  Absolutely not.  But my next task has to be to design an experiment to see if we can detect a whiff of it in the ether.  I'll keep you informed.

In my last post, I discussed the underlying issues regarding site loading times and SEO rankings.  What I tried to do was help the reader understand why site loading times are important from the perspective of someone designing a search engine that has to crawl billions of pages.  The post also outlines a few of the structures that they would have to put in place to accurately and effectively crawl all the pages they need in a limited time with limited processing power.  I also tried to show that a search engine like Google has a political and economic agenda in ensuring fast sites, not just a technical agenda.  Google wants as many people/eyeballs on the web as possible, so it is to their advantage to ensure that web sites provide a good user experience.  As a result, they feel quite justified in penalizing sites that do not have good speed/performance characteristics.

As you would expect, the conclusion is that if your site is hugely slow you will not get indexed and will not rank in the SERPs.  What is “hugely slow”?  Google has indicated that slow is a relative notion and is determined based on the loading times typical of sites in your geographical region.  Having said that, relative or not, from an SEO perspective I wouldn’t want to have a site where pages are taking more than 10 seconds on average to load.  We have found from the sites we have tested and built that average load times higher than approximately 10 seconds to completely load a page will have a significant impact on being indexed.  From a UE perspective, there is some interesting data that the limit on visitors patience is about 6-8 seconds.  Google has studied this data, so it would probably prefer to set its threshhold in that region.  But I doubt it can.   Many small sites are not that sophisticated, do not know these kinds of rules, and do not know how to check or evaluate their site loading times.  Besides this, there are often problems with hosts that cause servers to run slowly at times.  Google has to take that into account, as well.  So I believe that the timeout has to be substantially higher than 6-8 seconds, but 10 seconds as a crawl limit is a guess, 

I have yet to see a definitive statement by anyone as to what the absolute limit is for site speed before indexing ceases altogether (if you have a reference, please post it in the comments).  I’m sure that if a bot comes to a first page and it exceeds the bot’s timeout threshold in the algorithm, your site won’t get spidered at all.  But once the bot gets by the first page, it has to do an on-going computation of average page loading times for the site to determine if the average exceeds the built-in threshold, so at least a few pages would have to be crawled in that case. 

Now here’s where it gets interesting.  What happens between fast (let’s say < 1-2 second loading times, although this is actually pretty slow but a number Matt Cutts in the video below indicates is ok) and the timeout limit?  And how important is site speed as a ranking signal?  Let’s answer one question at a time.

When a site is slow but not slow enough to hit any built-in timeout limits (not tied to the number of pages), a couple of things can happen.   We do know that Google allocates bot time by the number of pages on the site and the number of pages it has to index/re-index.  So for a small site that performs poorly, it is likely that most of the pages will get indexed.  Likely, but not a guarantee.  It all depends on the cumulative time lag versus the average that a site creates. If a site is large, then you can almost guarantee that some pages will not be indexed, as the cumulative time lag will ultimately hit the threshold set by the bots for a site of that number of pages. By definition, some of your content will not get ranked and you will not get the benefit of that content in your rankings.

As an aside, by the way, there has been a lot of confusion around the <meta name=”revisit-after”> tag.  The revisit-after meta tag takes this form <meta name=”revisit-after” content=”5 days”>. 
This tag supposedly tells the bots how often to come back to the site to reindex this specific page (in this case 5 days).  The idea is that you can improve the crawlability of your site by telling the bots not to index certain pages all the time, but only some of the time.  I became aware of this tag at SMX East, when one of the “authorities” on SEO mentioned it as usable for this purpose.  The trouble is that, from everything I have read, the tag is completely unsupported by any of the major engines, and was only supported by one tiny search engine (SearchBC)  many years ago. 

But let’s say you are one of the lucky sites where the site runs slowly but all the pages do get indexed.  Do Google or any of the other major search engines use the site’s performance as a ranking signal?  In other words, all my pages are in the index.  So you would expect that they would be ranked based on the quality of their content and their authority derived from inbound links, site visits, time-on-site, and other typical ranking signals.  Performance is not a likely candidate for a ranking signal and isn’t important. 

If you thought that, then you were wrong. Historically, Google has said, and Matt Cutts reiterates this in the video below, that site load times do not influence search rankings.  But while that may be true now, it may not be in the near future.  And this is where Maile’s comments took me by surprise.  In a small group session at SMX East 2009, Maile was asked about site performance and rankings.  She indicated that for the “middle ground” sites that are indexing but loading slowly, site performance may already be used to influence rankings.  Who is right, I can’t say.  These are both highly respected professionals who choose their words carefully. 

Whatever is true, Google is sending us signals that this change is coming.  Senior experts like Matt and Maile don’t say these things lightly.  They are well considered and probably approved positions that they are asked to take.  This is Google’s way of preventing us from getting mad when the change occurs.  Google has the fallback of saying “we warned you this could happen.”  Which from today’s viewpoiint means it will happen.

Conclusion: Start working on your site performance now, as it will be important for SEO rankings later. 

Oh and, by the way, your user experience will just happen to be better, which is clearly the real reason to fix site performance. 

And it isn’t only Google that may make this change.  Engineers from Yahoo! recently filed a patent with the title “Web Document User Experience Characterization Methods and Systems” which bears on this topic.  Let me quote paragraph 21:

With so many websites and web pages being available and with varying hardware and software configurations, it may be beneficial to identify which web documents may lead to a desired user experience and which may not lead to a desired user experience. By way of example but not limitation, in certain situations it may be beneficial to determine (e.g., classify, rank, characterize) which web documents may not meet performance or other user experience expectations if selected by the user. Such performance may, for example, be affected by server, network, client, file, and/or like processes and/or the software, firmware, and/or hardware resources associated therewith. Once web documents are identified in this manner the resulting user experience information may, for example, be considered when generating the search results.

In does not appear Yahoo! has implemented any aspect of this patent yet, and who knows what the Bing agreement will mean for site performance and search.  But clearly this is a “problem” that the search engine muftis have set their eyes on and I would expect that if Google does implement it, others will follow.

It is one of those nights.  Those pesky technicolor dreams woke me up at 2:30 and wouldn’t let me go back to sleep.  But under the heading “turning lemons into lemonade,” at least I have some extra time to write my blog even as I am piled high with the end of month deadlines. 

Today’s topic is part of my Technical SEO series (I just named it that – now I have to go back and change all my titles and meta tags…sigh) – site load times and whether or not they effect how you rank in the SERPs.  It is another one of those topics that came out of SMX East.  In this case it was Maile Ohye, Senior Support Engineer at Google, who spoke to this issue.  Maile is a wonderfully knowledgable evangelist for Google.  I have seen her speak at many shows. Her presentations are always clear and contain good, actionable techniques for improving your rankings in Google’s SERPs. I am not alone in thinking her knowledgable.   Stephan Spencer, one of the guys I most look up to in SEO,  thought enough of Maile to interview her in August of 2007, and she was also recently interviewed by SEOMoz, another leading light in the industry (and if you haven’t used their pro tools, then you are one arrow short of a full quiver for your SEO work).   

So when Maile says “stuff,” I listen.  In her talk at SMX East, she made note that poor site load times (we are talking something between good and absolutely horrible) could harm your rankings in Google search results. Let me define the problem, then try to explain what Maile was referring to, and finally my take on all this.

Basic Concepts of Site Loading Times for Getting Indexed

One the one hand, that site loading times effect search rankings isn’t news.  Let’s take some time to lay a bit of foundation, because the how of site speeds effecting search rankings didn’t really hit me until Maile’s talk.  It’s one of those things that is obvious once you think about it, but it doesn’t really come top of mind when you are focused on specific tasks in an SEO project.  It’s a “given” in the background of your work.  Unless the site is so horribly slow that it is obviously impacting the user experience, you really don’t think about load times when you are focusing on keywords and meta tags.  The site works, move on. 

But that’s not really true from the perspective of the search bots.   Google and the other engines have to crawl billions of pages on the web on a regular basis, bring that information back, and then index it.  Some pages can be crawled infrequently, but as more of the web moves to more real-time information due to social media, the bots have to crawl more sites in real time in order to provide good results.  But there are only so many bots and so much time to crawl these billions of pages.  So if you are Google, you write your bots with algorithms that allocate this scarce resource most efficiently and, hopefully, fairly. 

How would you or I do this?  Well, if I were writing a bot, the first thing I would give it is a time limit based on the size of the site.  That’s only fair.  If you have the ability to create more content, bravo.  I want to encourage that, because it is beneficial to the community of searchers.  So all other factors being equal (e.g. site loading time), I want to allocate time to ensure all your pages get into the index.  There is also the issue of search precision and relevance: I want all that content indexed so I can present the best results to searchers.   

Of course, I can’t just set a time limit based on the number of pages.  What if one site has long pages and another one short, pithy pages (clearly not mine!)?  What if one site has lots of images or other embedded content while another does not?  My algorithm has to be pretty sophisticated to determine these factors on the fly and adapt its baseline timeout settings to new information about a site as it crawls it.

The next algorithm I would include would have to do with the frequency at which you update your data.  The more often you update, the more often I need to have my bot come back and crawl the changed pages on your site. 

Another set of algorithms would have to do with spam.  From the perspective of my limited resource and search precision, I don’t want to include pages in my index that are clearly designed only for the search engines, that are link spammers, or that may only contain PPC ads and have no relevant information for the searcher. 

You get the picture.  I only have a limited window of time to capture continually changing data from the web in order for the data in my index to be reasonably fresh.  Therefore I’ve got to move mountains (of data) in a very short period of time but only so many processing cycles to apply.  And the number of variables I have to control for in my algorithms are numerous and, in many cases, not black and white.

This is where site load times come in.  If a site is large but slow, should it be allocated as much time as it needs to be indexed?  Do I have enough processing cycles to put up with the fact it takes three times as long as a similar site to be crawled?  Is it fair given a scarce resource to allocate time to slow site if it means I can’t index five other better performing sites in my current window of opportunity?  Does it optimize search precision and the relevance of results I can show to searchers?  And last but not least, as one of the guardians of the Web, is poor site performance something I want to encourage from the perspective of user experience and making the Web useful for as many people as possible?  Let’s face it, if the web is really slow, people won’t use it, and the eyeballs that will be available to view an ad from which I stand to make money will be less. 

Hello?  Are you there?  Can you say “zero tolerance?”  And from the perspective of the universal search engines, there is also my favorite radio station – “WIFM.”  What’s In it For Me?  Answer: nothing good.  That is why Google has made page load times a factor in Adwords Quality Score, as an example.

So, in the extreme case (let’s say a page takes 30 seconds to load), the bots won’t crawl most, if any, of the site.  The engines can’t afford the time and don’t want to encourage a poor user experience.  So you are ignored – which means you never get into the indexes.

When Is a Page’s or Site’s Loading Time Considered Slow?

What is an “extreme case?”  I have looked that up and the answer is not a fixed number.  Instead, for Google, the concept of “slow loading” is relative. 

The threshold for a ‘slow-loading’ landing page is the regional average plus three seconds.

The regional average is based on the location of the server hosting your website. If your website is hosted on a server in India, for example, your landing page’s load time will be compared to the average load time in that region of India. This is true even if your website is intended for an audience in the United States.

Two things to note about how we determined the threshold: 

• We currently calculate load time as the time it takes to download the HTML content of your landing page. HTML load time is typically 10% to 30% of a page’s total load time. A three-second difference from the regional average, therefore, likely indicates a much larger disparity.
• We measure load time from a very fast internet connection, so most users will experience a slower load time than we do.

Moreover, Google has a sliding scale with which is grades a site.  The following quote applies to Adwords and landing pages, but my guess is similar algorithms and grading are used in determining how often and long a site is crawled:

A keyword’s load time grade is based on the average load time of the landing pages in the ad group and of any landing pages in the rest of the account with the same domain. If multiple ad groups have landing pages with the same domain, therefore, the keywords in all these ad groups will have identical load time grades.

Two things to note:

• When determining load time grade, the AdWords system follows destination URLs at both the ad and keyword level and evaluates the final landing page.
• If your ad group contains landing pages with different domains, the keywords’ load time grades will be based on the domain with the slowest load time. All the keywords in an ad group will always have the same load time grade.

We’ll stop here for today.  Next time, we’ll talk about happens in the nether regions between fast and clearly slow.