Becoming Better SEO Scientists – Whiteboard Friday

Posted by MarkTraphagen

Editor’s note: Today we’re featuring back-to-back episodes of Whiteboard Friday from our friends at Stone Temple Consulting. Make sure to also check out the second episode, “UX, Content Quality, and SEO” from Eric Enge.

Like many other areas of marketing, SEO incorporates elements of science. It becomes problematic for everyone, though, when theories that haven’t been the subject of real scientific rigor are passed off as proven facts. In today’s Whiteboard Friday, Stone Temple Consulting’s Mark Traphagen is here to teach us a thing or two about the scientific method and how it can be applied to our day-to-day work.

For reference, here’s a still of this week’s whiteboard.
Click on it to open a high resolution image in a new tab!

Video transcription

Howdy, Mozzers. Mark Traphagen from Stone Temple Consulting here today to share with you how to become a better SEO scientist. We know that SEO is a science in a lot of ways, and everything I’m going to say today applies not only to SEO, but testing things like your AdWords, how does that work, quality scores. There’s a lot of different applications you can make in marketing, but we’ll focus on the SEO world because that’s where we do a lot of testing. What I want to talk to you about today is how that really is a science and how we need to bring better science in it to get better results.

The reason is in astrophysics, things like that we know there’s something that they’re talking about these days called dark matter, and dark matter is something that we know it’s there. It’s pretty much accepted that it’s there. We can’t see it. We can’t measure it directly. We don’t even know what it is. We can’t even imagine what it is yet, and yet we know it’s there because we see its effect on things like gravity and mass. Its effects are everywhere. And that’s a lot like search engines, isn’t it? It’s like Google or Bing. We see the effects, but we don’t see inside the machine. We don’t know exactly what’s happening in there.

An artist’s depiction of how search engines work.

So what do we do? We do experiments. We do tests to try to figure that out, to see the effects, and from the effects outside we can make better guesses about what’s going on inside and do a better job of giving those search engines what they need to connect us with our customers and prospects. That’s the goal in the end.

Now, the problem is there’s a lot of testing going on out there, a lot of experiments that maybe aren’t being run very well. They’re not being run according to scientific principles that have been proven over centuries to get the best possible results.

Basic data science in 10 steps

So today I want to give you just very quickly 10 basic things that a real scientist goes through on their way to trying to give you better data. Let’s see what we can do with those in our SEO testing in the future.

So let’s start with number one. You’ve got to start with a hypothesis. Your hypothesis is the question that you want to solve. You always start with that, a good question in mind, and it’s got to be relatively narrow. You’ve got to narrow it down to something very specific. Something like how does time on page effect rankings, that’s pretty narrow. That’s very specific. That’s a good question. Might be able to test that. But something like how do social signals effect rankings, that’s too broad. You’ve got to narrow it down. Get it down to one simple question.

Then you choose a variable that you’re going to test. Out of all the things that you could do, that you could play with or you could tweak, you should choose one thing or at least a very few things that you’re going to tweak and say, “When we tweak this, when we change this, when we do this one thing, what happens? Does it change anything out there in the world that we are looking at?” That’s the variable.

The next step is to set a sample group. Where are you going to gather the data from? Where is it going to come from? That’s the world that you’re working in here. Out of all the possible data that’s out there, where are you going to gather your data and how much? That’s the small circle within the big circle. Now even though it’s smaller, you’re probably not going to get all the data in the world. You’re not going to scrape every search ranking that’s possible or visit every URL.

You’ve got to ask yourself, “Is it large enough that we’re at least going to get some validity?” If I wanted to find out what is the typical person in Seattle and I might walk through just one part of the Moz offices here, I’d get some kind of view. But is that a typical, average person from Seattle? I’ve been around here at Moz. Probably not. But this was large enough.

Also, it should be randomized as much as possible. Again, going back to that example, if I just stayed here within the walls of Moz and do research about Mozzers, I’d learn a lot about what Mozzers do, what Mozzers think, how they behave. But that may or may not be applicable to the larger world outside, so you randomized.

We want to control. So we’ve got our sample group. If possible, it’s always good to have another sample group that you don’t do anything to. You do not manipulate the variable in that group. Now, why do you have that? You have that so that you can say, to some extent, if we saw a change when we manipulated our variable and we did not see it in the control group, the same thing didn’t happen, more likely it’s not just part of the natural things that happen in the world or in the search engine.

If possible, even better you want to make that what scientists call double blind, which means that even you the experimenter don’t know who that control group is out of all the SERPs that you’re looking at or whatever it is. As careful as you might be and honest as you might be, you can end up manipulating the results if you know who is who within the test group? It’s not going to apply to every test that we do in SEO, but a good thing to have in mind as you work on that.

Next, very quickly, duration. How long does it have to be? Is there sufficient time? If you’re just testing like if I share a URL to Google +, how quickly does it get indexed in the SERPs, you might only need a day on that because typically it takes less than a day in that case. But if you’re looking at seasonality effects, you might need to go over several years to get a good test on that.

Let’s move to the second group here. The sixth thing keep a clean lab. Now what that means is try as much as possible to keep anything that might be dirtying your results, any kind of variables creeping in that you didn’t want to have in the test. Hard to do, especially in what we’re testing, but do the best you can to keep out the dirt.

Manipulate only one variable. Out of all the things that you could tweak or change choose one thing or a very small set of things. That will give more accuracy to your test. The more variables that you change, the more other effects and inner effects that are going to happen that you may not be accounting for and are going to muddy your results.

Make sure you have statistical validity when you go to analyze those results. Now that’s beyond the scope of this little talk, but you can read up on that. Or even better, if you are able to, hire somebody or work with somebody who is a trained data scientist or has training in statistics so they can look at your evaluation and say the correlations or whatever you’re seeing, “Does it have a statistical significance?” Very important.

Transparency. As much as possible, share with the world your data set, your full results, your methodology. What did you do? How did you set up the study? That’s going to be important to our last step here, which is replication and falsification, one of the most important parts of any scientific process.

So what you want to invite is, hey we did this study. We did this test. Here’s what we found. Here’s how we did it. Here’s the data. If other people ask the same question again and run the same kind of test, do they get the same results? Somebody runs it again, do they get the same results? Even better, if you have some people out there who say, “I don’t think you’re right about that because I think you missed this, and I’m going to throw this in and see what happens,” aha they falsify. That might make you feel like you failed, but it’s success because in the end what are we after? We’re after the truth about what really works.

Think about your next test, your next experiment that you do. How can you apply these 10 principles to do better testing, get better results, and have better marketing? Thanks.

Video transcription by Speechpad.com

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Big Data, Big Problems: 4 Major Link Indexes Compared

Posted by russangular

Given this blog’s readership, chances are good you will spend some time this week looking at backlinks in one of the growing number of link data tools. We know backlinks continue to be one of, if not the most important
parts of Google’s ranking algorithm. We tend to take these link data sets at face value, though, in part because they are all we have. But when your rankings are on the line, is there a better way to get at which data set is the best? How should we go
about assessing these different link indexes like
Moz,
Majestic, Ahrefs and SEMrush for quality? Historically, there have been 4 common approaches to this question of index quality…

  • Breadth: We might choose to look at the number of linking root domains any given service reports. We know
    that referring domains correlates strongly with search rankings, so it makes sense to judge a link index by how many unique domains it has
    discovered and indexed.
  • Depth: We also might choose to look at how deep the web has been crawled, looking more at the total number of URLs
    in the index, rather than the diversity of referring domains.
  • Link Overlap: A more sophisticated approach might count the number of links an index has in common with Google Webmaster
    Tools.
  • Freshness: Finally, we might choose to look at the freshness of the index. What percentage of links in the index are
    still live?

There are a number of really good studies (some newer than others) using these techniques that are worth checking out when you get a chance:

  • BuiltVisible analysis of Moz, Majestic, GWT, Ahrefs and Search Metrics
  • SEOBook comparison of Moz, Majestic, Ahrefs, and Ayima
  • MatthewWoodward
    study of Ahrefs, Majestic, Moz, Raven and SEO Spyglass
  • Marketing Signals analysis of Moz, Majestic, Ahrefs, and GWT
  • RankAbove comparison of Moz, Majestic, Ahrefs and Link Research Tools
  • StoneTemple study of Moz and Majestic

While these are all excellent at addressing the methodologies above, there is a particular limitation with all of them. They miss one of the
most important metrics we need to determine the value of a link index: proportional representation to Google’s link graph
. So here at Angular Marketing, we decided to take a closer look.

Proportional representation to Google Search Console data

So, why is it important to determine proportional representation? Many of the most important and valued metrics we use are built on proportional
models. PageRank, MozRank, CitationFlow and Ahrefs Rank are proportional in nature. The score of any one URL in the data set is relative to the
other URLs in the data set. If the data set is biased, the results are biased.

A Visualization

Link graphs are biased by their crawl prioritization. Because there is no full representation of the Internet, every link graph, even Google’s,
is a biased sample of the web. Imagine for a second that the picture below is of the web. Each dot represents a page on the Internet,
and the dots surrounded by green represent a fictitious index by Google of certain sections of the web.

Of course, Google isn’t the only organization that crawls the web. Other organizations like Moz,
Majestic, Ahrefs, and SEMrush
have their own crawl prioritizations which result in different link indexes.

In the example above, you can see different link providers trying to index the web like Google. Link data provider 1 (purple) does a good job
of building a model that is similar to Google. It isn’t very big, but it is proportional. Link data provider 2 (blue) has a much larger index,
and likely has more links in common with Google that link data provider 1, but it is highly disproportional. So, how would we go about measuring
this proportionality? And which data set is the most proportional to Google?

Methodology

The first step is to determine a measurement of relativity for analysis. Google doesn’t give us very much information about their link graph.
All we have is what is in Google Search Console. The best source we can use is referring domain counts. In particular, we want to look at
what we call
referring domain link pairs. A referring domain link pair would be something like ask.com->mlb.com: 9,444 which means
that ask.com links to mlb.com 9,444 times.

Steps

  1. Determine the root linking domain pairs and values to 100+ sites in Google Search Console
  2. Determine the same for Ahrefs, Moz, Majestic Fresh, Majestic Historic, SEMrush
  3. Compare the referring domain link pairs of each data set to Google, assuming a
    Poisson Distribution
  4. Run simulations of each data set’s performance against each other (ie: Moz vs Maj, Ahrefs vs SEMrush, Moz vs SEMrush, et al.)
  5. Analyze the results

Results

When placed head-to-head, there seem to be some clear winners at first glance. In head-to-head, Moz edges out Ahrefs, but across the board, Moz and Ahrefs fare quite evenly. Moz, Ahrefs and SEMrush seem to be far better than Majestic Fresh and Majestic Historic. Is that really the case? And why?

It turns out there is an inversely proportional relationship between index size and proportional relevancy. This might seem counterintuitive,
shouldn’t the bigger indexes be closer to Google? Not Exactly.

What does this mean?

Each organization has to create a crawl prioritization strategy. When you discover millions of links, you have to prioritize which ones you
might crawl next. Google has a crawl prioritization, so does Moz, Majestic, Ahrefs and SEMrush. There are lots of different things you might
choose to prioritize…

  • You might prioritize link discovery. If you want to build a very large index, you could prioritize crawling pages on sites that
    have historically provided new links.
  • You might prioritize content uniqueness. If you want to build a search engine, you might prioritize finding pages that are unlike
    any you have seen before. You could choose to crawl domains that historically provide unique data and little duplicate content.
  • You might prioritize content freshness. If you want to keep your search engine recent, you might prioritize crawling pages that
    change frequently.
  • You might prioritize content value, crawling the most important URLs first based on the number of inbound links to that page.

Chances are, an organization’s crawl priority will blend some of these features, but it’s difficult to design one exactly like Google. Imagine
for a moment that instead of crawling the web, you want to climb a tree. You have to come up with a tree climbing strategy.

  • You decide to climb the longest branch you see at each intersection.
  • One friend of yours decides to climb the first new branch he reaches, regardless of how long it is.
  • Your other friend decides to climb the first new branch she reaches only if she sees another branch coming off of it.

Despite having different climb strategies, everyone chooses the same first branch, and everyone chooses the same second branch. There are only
so many different options early on.

But as the climbers go further and further along, their choices eventually produce differing results. This is exactly the same for web crawlers
like Google, Moz, Majestic, Ahrefs and SEMrush. The bigger the crawl, the more the crawl prioritization will cause disparities. This is not a
deficiency; this is just the nature of the beast. However, we aren’t completely lost. Once we know how index size is related to disparity, we
can make some inferences about how similar a crawl priority may be to Google.

Unfortunately, we have to be careful in our conclusions. We only have a few data points with which to work, so it is very difficult to be
certain regarding this part of the analysis. In particular, it seems strange that Majestic would get better relative to its index size as it grows,
unless Google holds on to old data (which might be an important discovery in and of itself). It is most likely that at this point we can’t make
this level of conclusion.

So what do we do?

Let’s say you have a list of domains or URLs for which you would like to know their relative values. Your process might look something like
this…

  • Check Open Site Explorer to see if all URLs are in their index. If so, you are looking metrics most likely to be proportional to Google’s link graph.
  • If any of the links do not occur in the index, move to Ahrefs and use their Ahrefs ranking if all you need is a single PageRank-like metric.
  • If any of the links are missing from Ahrefs’s index, or you need something related to trust, move on to Majestic Fresh.
  • Finally, use Majestic Historic for (by leaps and bounds) the largest coverage available.

It is important to point out that the likelihood that all the URLs you want to check are in a single index increases as the accuracy of the metric
decreases. Considering the size of Majestic’s data, you can’t ignore them because you are less likely to get null value answers from their data than
the others. If anything rings true, it is that once again it makes sense to get data
from as many sources as possible. You won’t
get the most proportional data without Moz, the broadest data without Majestic, or everything in-between without Ahrefs.

What about SEMrush? They are making progress, but they don’t publish any relative statistics that would be useful in this particular
case. Maybe we can hope to see more from them soon given their already promising index!

Recommendations for the link graphing industry

All we hear about these days is big data; we almost never hear about good data. I know that the teams at Moz,
Majestic, Ahrefs, SEMrush and others are interested in mimicking Google, but I would love to see some organization stand up against the
allure of
more data in favor of better data—data more like Google’s. It could begin with testing various crawl strategies to see if they produce
a result more similar to that of data shared in Google Search Console. Having the most Google-like data is certainly a crown worth winning.

Credits

Thanks to Diana Carter at Angular for assistance with data acquisition and Andrew Cron with statistical analysis. Thanks also to the representatives from Moz, Majestic, Ahrefs, and SEMrush for answering questions about their indices.

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Why We Can’t Do Keyword Research Like It’s 2010 – Whiteboard Friday

Posted by randfish

Keyword Research is a very different field than it was just five years ago, and if we don’t keep up with the times we might end up doing more harm than good. From the research itself to the selection and targeting process, in today’s Whiteboard Friday Rand explains what has changed and what we all need to do to conduct effective keyword research today.

For reference, here’s a still of this week’s whiteboard. Click on it to open a high resolution image in a new tab!

What do we need to change to keep up with the changing world of keyword research?

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we’re going to chat a little bit about keyword research, why it’s changed from the last five, six years and what we need to do differently now that things have changed. So I want to talk about changing up not just the research but also the selection and targeting process.

There are three big areas that I’ll cover here. There’s lots more in-depth stuff, but I think we should start with these three.

1) The Adwords keyword tool hides data!

This is where almost all of us in the SEO world start and oftentimes end with our keyword research. We go to AdWords Keyword Tool, what used to be the external keyword tool and now is inside AdWords Ad Planner. We go inside that tool, and we look at the volume that’s reported and we sort of record that as, well, it’s not good, but it’s the best we’re going to do.

However, I think there are a few things to consider here. First off, that tool is hiding data. What I mean by that is not that they’re not telling the truth, but they’re not telling the whole truth. They’re not telling nothing but the truth, because those rounded off numbers that you always see, you know that those are inaccurate. Anytime you’ve bought keywords, you’ve seen that the impression count never matches the count that you see in the AdWords tool. It’s not usually massively off, but it’s often off by a good degree, and the only thing it’s great for is telling relative volume from one from another.

But because AdWords hides data essentially by saying like, “Hey, you’re going to type in . . .” Let’s say I’m going to type in “college tuition,” and Google knows that a lot of people search for how to reduce college tuition, but that doesn’t come up in the suggestions because it’s not a commercial term, or they don’t think that an advertiser who bids on that is going to do particularly well and so they don’t show it in there. I’m giving an example. They might indeed show that one.

But because that data is hidden, we need to go deeper. We need to go beyond and look at things like Google Suggest and related searches, which are down at the bottom. We need to start conducting customer interviews and staff interviews, which hopefully has always been part of your brainstorming process but really needs to be now. Then you can apply that to AdWords. You can apply that to suggest and related.

The beautiful thing is once you get these tools from places like visiting forums or communities, discussion boards and seeing what terms and phrases people are using, you can collect all this stuff up, plug it back into AdWords, and now they will tell you how much volume they’ve got. So you take that how to lower college tuition term, you plug it into AdWords, they will show you a number, a non-zero number. They were just hiding it in the suggestions because they thought, “Hey, you probably don’t want to bid on that. That won’t bring you a good ROI.” So you’ve got to be careful with that, especially when it comes to SEO kinds of keyword research.

2) Building separate pages for each term or phrase doesn’t make sense

It used to be the case that we built separate pages for every single term and phrase that was in there, because we wanted to have the maximum keyword targeting that we could. So it didn’t matter to us that college scholarship and university scholarships were essentially people looking for exactly the same thing, just using different terminology. We would make one page for one and one page for the other. That’s not the case anymore.

Today, we need to group by the same searcher intent. If two searchers are searching for two different terms or phrases but both of them have exactly the same intent, they want the same information, they’re looking for the same answers, their query is going to be resolved by the same content, we want one page to serve those, and that’s changed up a little bit of how we’ve done keyword research and how we do selection and targeting as well.

3) Build your keyword consideration and prioritization spreadsheet with the right metrics

Everybody’s got an Excel version of this, because I think there’s just no awesome tool out there that everyone loves yet that kind of solves this problem for us, and Excel is very, very flexible. So we go into Excel, we put in our keyword, the volume, and then a lot of times we almost stop there. We did keyword volume and then like value to the business and then we prioritize.

What are all these new columns you’re showing me, Rand? Well, here I think is how sophisticated, modern SEOs that I’m seeing in the more advanced agencies, the more advanced in-house practitioners, this is what I’m seeing them add to the keyword process.

Difficulty

A lot of folks have done this, but difficulty helps us say, “Hey, this has a lot of volume, but it’s going to be tremendously hard to rank.”

The difficulty score that Moz uses and attempts to calculate is a weighted average of the top 10 domain authorities. It also uses page authority, so it’s kind of a weighted stack out of the two. If you’re seeing very, very challenging pages, very challenging domains to get in there, it’s going to be super hard to rank against them. The difficulty is high. For all of these ones it’s going to be high because college and university terms are just incredibly lucrative.

That difficulty can help bias you against chasing after terms and phrases for which you are very unlikely to rank for at least early on. If you feel like, “Hey, I already have a powerful domain. I can rank for everything I want. I am the thousand pound gorilla in my space,” great. Go after the difficulty of your choice, but this helps prioritize.

Opportunity

This is actually very rarely used, but I think sophisticated marketers are using it extremely intelligently. Essentially what they’re saying is, “Hey, if you look at a set of search results, sometimes there are two or three ads at the top instead of just the ones on the sidebar, and that’s biasing some of the click-through rate curve.” Sometimes there’s an instant answer or a Knowledge Graph or a news box or images or video, or all these kinds of things that search results can be marked up with, that are not just the classic 10 web results. Unfortunately, if you’re building a spreadsheet like this and treating every single search result like it’s just 10 blue links, well you’re going to lose out. You’re missing the potential opportunity and the opportunity cost that comes with ads at the top or all of these kinds of features that will bias the click-through rate curve.

So what I’ve seen some really smart marketers do is essentially build some kind of a framework to say, “Hey, you know what? When we see that there’s a top ad and an instant answer, we’re saying the opportunity if I was ranking number 1 is not 10 out of 10. I don’t expect to get whatever the average traffic for the number 1 position is. I expect to get something considerably less than that. Maybe something around 60% of that, because of this instant answer and these top ads.” So I’m going to mark this opportunity as a 6 out of 10.

There are 2 top ads here, so I’m giving this a 7 out of 10. This has two top ads and then it has a news block below the first position. So again, I’m going to reduce that click-through rate. I think that’s going down to a 6 out of 10.

You can get more and less scientific and specific with this. Click-through rate curves are imperfect by nature because we truly can’t measure exactly how those things change. However, I think smart marketers can make some good assumptions from general click-through rate data, which there are several resources out there on that to build a model like this and then include it in their keyword research.

This does mean that you have to run a query for every keyword you’re thinking about, but you should be doing that anyway. You want to get a good look at who’s ranking in those search results and what kind of content they’re building . If you’re running a keyword difficulty tool, you are already getting something like that.

Business value

This is a classic one. Business value is essentially saying, “What’s it worth to us if visitors come through with this search term?” You can get that from bidding through AdWords. That’s the most sort of scientific, mathematically sound way to get it. Then, of course, you can also get it through your own intuition. It’s better to start with your intuition than nothing if you don’t already have AdWords data or you haven’t started bidding, and then you can refine your sort of estimate over time as you see search visitors visit the pages that are ranking, as you potentially buy those ads, and those kinds of things.

You can get more sophisticated around this. I think a 10 point scale is just fine. You could also use a one, two, or three there, that’s also fine.

Requirements or Options

Then I don’t exactly know what to call this column. I can’t remember the person who’ve showed me theirs that had it in there. I think they called it Optional Data or Additional SERPs Data, but I’m going to call it Requirements or Options. Requirements because this is essentially saying, “Hey, if I want to rank in these search results, am I seeing that the top two or three are all video? Oh, they’re all video. They’re all coming from YouTube. If I want to be in there, I’ve got to be video.”

Or something like, “Hey, I’m seeing that most of the top results have been produced or updated in the last six months. Google appears to be biasing to very fresh information here.” So, for example, if I were searching for “university scholarships Cambridge 2015,” well, guess what? Google probably wants to bias to show results that have been either from the official page on Cambridge’s website or articles from this year about getting into that university and the scholarships that are available or offered. I saw those in two of these search results, both the college and university scholarships had a significant number of the SERPs where a fresh bump appeared to be required. You can see that a lot because the date will be shown ahead of the description, and the date will be very fresh, sometime in the last six months or a year.

Prioritization

Then finally I can build my prioritization. So based on all the data I had here, I essentially said, “Hey, you know what? These are not 1 and 2. This is actually 1A and 1B, because these are the same concepts. I’m going to build a single page to target both of those keyword phrases.” I think that makes good sense. Someone who is looking for college scholarships, university scholarships, same intent.

I am giving it a slight prioritization, 1A versus 1B, and the reason I do this is because I always have one keyword phrase that I’m leaning on a little more heavily. Because Google isn’t perfect around this, the search results will be a little different. I want to bias to one versus the other. In this case, my title tag, since I more targeting university over college, I might say something like college and university scholarships so that university and scholarships are nicely together, near the front of the title, that kind of thing. Then 1B, 2, 3.

This is kind of the way that modern SEOs are building a more sophisticated process with better data, more inclusive data that helps them select the right kinds of keywords and prioritize to the right ones. I’m sure you guys have built some awesome stuff. The Moz community is filled with very advanced marketers, probably plenty of you who’ve done even more than this.

I look forward to hearing from you in the comments. I would love to chat more about this topic, and we’ll see you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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