SEO Metrics for Estimating Ranking Difficulty Webcast | SpyFu

Host: Mike is the founder and president of
SpyFu, founder of VelocityScape and SpyFu. And Mike was awarded Arizona’s top entrepreneur
under 35 which is really, you know a kind of a cool thing. And this is a quote I got
from Mike’s LinkedIn page; success is an endless cycle of failures that you can manage to learn
from. So Mike, we do have your presentation up in the screen and you are the presenter
so, Mike please take it. Mike Roberts: Thanks. So yeah, again my name
is Mike Roberts. I’m the president and founder of SpyFu. And this is a new presentation.
Actually every presentation from me is new. But this is extremely new contents for me
and for us. It’s basically the last 2 weeks I’ve been spending researching what the ranking
factors to go into ranking difficulty. That is how hard is it going to be for me to rank
on this, on this keyword. And the results of that research are just today as of like
noon, live on the site so, I thought it would be cool to do something extremely timely.
It’s not exactly just the demo of SpyFu which is what we sort of talked about. But it’s
basically, not as cutting edge of research as you can possibly have, very, very, very
new. Real quick, let me tell you about SpyFu. We were started here in actually Scottsdale
or Cave Creek, I was actually up in Tatum Ranch, since this is a local crowd, you guys
may know where that is. And we were founded in 2005. The original product was actually
called GoogSpy. And we changed the time to SpyFu when we launched the new one. We’re
actually the very first competitive intelligence tool. Prior to SpyFu, you couldn’t… If you wanted
to know which keyword your competitors are buying or, on Google AdWords or ranking on,
on Google, you would basically do guess and check. And so when we put that first product
together, it’s the first of its kind. Soon, soon you know, we’ve been in the space, like
I said for 6 or 7 years and we continued to evolve you know, well it’s not so much about
getting those keywords anymore, is it? It’s about figuring out which ones represent the
best opportunity and you know, which one’s are actually going to be profitable on. We
want to tell you what you’re able to rank on, which ones you’re going to be profitable
on before you even start so that you’re not wasting your time and spinning your wheels.
One thing to note is that we do no consulting, we make all of our money on basically people
paying $79 a month to use our service. So from an SEO perspective, that’s the audience,
we also of course do AdWords and we specialize in that as well. But from an SEO perspective
there’s things that we’re really good at. What we really nailed are how to win SEO budget.
We have a platform of reporting called spy for recon reports that basically, we’re designed
from the ground up to make you, the SEO look like a million bucks. The idea is to explain
the value and highlight the opportunity so, compellingly the clients won’t just love the
work that you do but they’ll want more of it, right? And then at the end of our presentation
we sort of just say, well, here’s what your competitors are doing, they’re acting, now
get to work, right? So that’s how the whole spy for recon document is structured. And
when we tell them the value and we highlight the opportunity, we don’t talk to them in
terms of just ranks and clicks. We actually translate those things into dollars and we
do a really great job of it. So these are the things that we’re currently good at. We
can ask you, you know, what’s your goal and that should have something to do with money.
I’m going to breeze through these things because what I really want to get to is the meat which
is the subject of my research. We hope you find your keyword universe and
some of the ways that you can do those are just knowing what they are. You can also steal
them from your PPC campaign. This is a great win because if you look at what your PPC campaign
is, the keywords that you rank on all ready, I’m sorry, the keywords that you’re buying
but you also rank on, you can easily justify that to your clients or to your you know,
your internal… your boss or whatever. So we want to look at which keywords you already
have traction on. And then we combine those keywords into keyword groups, silos if you
will. So that you’re not constantly thinking about you know, the individual keywords telling
your client oh yeah, we lost rank on this keyword. We want to talk to them about well,
this is a portfolio of keywords. So we do these stuff, right? We calculate and you know,
like metrics. Not just on the keyword level but also on the keyword group level. Of course we give you everything but what
you really want to be talking here, you want to control that conversation with your customer,
talking to them about the value you created out of macro level. So when we calculate the
size of opportunity, the size of keyword group, we take into account the keyword search volume,
the value per click like in terms of you know, relative to PPC dollars, AdWord dollars, where
you’re currently ranked and ultimately what your goal is. When we apply the click through
curve, you should be probably familiar with it, but ultimately the idea is that you get
more clicks when you’re in the number one position than if you’re in the number 2 or
number 3 and so on. We also take into account you know, number of ads, shopping, video,
pictures. Our click through curve is pretty solid. All these things, we’re really good
at. When you combine all these things together, you can actually talk to people in terms of
dollars. So I mean, managers or entrepreneurs or anybody that you’re dealing with doesn’t
really think in terms of ranks or if they do, if you can get them to think in terms
of ranks, they’re going to discount your value because they don’t really… they’re certainly
not going to give you a premium, they’re going to discount your value. But if you can talk
to them in terms of dollars, it’s a huge win. So then we add up all that opportunity and
we group them into these reports that look like this. So that’s what we’re good at. We’re
currently very capable of figuring out what opportunity looks like and what value of SEO
looks like and we’re basically the best that there is. But the place that we suck is when
it comes to trying to calculate difficulty. This is actually, these are slides ripped
from another presentation that I’ve done. And you notice that there’s nowhere in here
where I’m talking about a single metric from SpyFu. When I’m telling you okay, your next
step after you calculate opportunity is look at the SERP, you know look at the backlinks,
look at the SEOMoz, look at the difficulty, blah, blah, blah, blah, right? None of it
has anything to do with SpyFu. But imagine what we could do if we knew difficulty. If
we not only know the size of the opportunity but also the difficulty, we could come up
with, we could tell you look, this is not just like a big opportunity but it’s easy.
You will get these ranks. You will get this traffic. It’s a low hanging fruit report that
we can produce, right? There’s all kinds of ways that I can you know,
revolutionize this stuff if I can do that. But right now, I can’t, okay? So the last
couple weeks, my mission has been to figure out how to quantify this. Okay, so where do
we start? Well my theory is the best place to look and to figure out what affects rankings
in the first place, actually that should be A-F-F-E-C-T-S but … by the way, Roy, try
me if you have any questions. I’ve got like a little studio audience here to help me out
with sort of queues as to whether or not I’m explaining things right. But if you have questions,
because some of these stuff is going to become a little bit technical as I go through you
know, experiments, correlation … Host: Well I’m glad you asked, Mike because
what I’d like to ask from our participants in today’s webinar, if you do have any questions,
please type them to the question box. Mike will answer the questions. We have a little
area during the end of this webinar for any questions you might have. And again, the person
that asks Mike the best question, Mike’s going to basically tell me who that person is and
we’re awarding them one free hour of SEO consulting with our company. So you know, Mike I don’t
have any particular questions right now but you know at the end of your presentation,
we’ll certainly address those. Mike Roberts: Yeah. I’m just about to get
into the crazy stuff. Host: All right. Here’s the crazy stuff guys. Mike Roberts: All right. So hopefully I’ve
explained it well, I’ve really tried. I’ll tell you what, when I first wrote an article
sort of a draft article for my team. I let them look at it and they’re like ‘this is
just offensive, I am overwhelmed by this data’ and so this is sort of my first step of trying
to make this palatable. I’m pretty sure it works but you know, I mean if not… All right,
so let’s see, okay yes. So basically I want to figure out what affects ranking in the
first place particularly the things that I currently measure, right? I wanted to start
out with the stuff that’s visible on the SERP page because people talked about that it’s
just… the first thing you do as an SEO is you actually scan the page and there’s a lot
of what almost seems like, it’s expert knowledge but I feel it feels like instincts. You’ve
looked at it and you’re like oh, instinctually I know that this is a more difficult keyword
to rank on but what are those cues and in my experience when you have this sort of expert
knowledge that feels like instincts, if you can put any amount of that into code and then
do it in like a billion times a second you’ve got like the makings of a great algorithm. So anyway, let me talk about my methodology
real quick. In order to compare, in order to figure out what ranking factors, what factors,
you know, what metrics affect rankings, what I do is I take each individual metric like
keyword and title and I reorder the top 50 searches as though they’re the only factor
that Google uses to search the search results. So if the keyword is in title, twice then
you know, okay then that’s the number one result and down at the bottom there’s no keyword
and title. So that’s what we pretend. What we do is we compare that to the way the Google
actually does rank it. So it kind of looks like this, right? So this is keyword hits
and title. You see this column right here. I actually don’t have the actual text here
because they’d be really wide. But ultimately this is the metric for keyword hits in the
title. For, basically what I’m saying when I say keyword, I mean it has one gallon beverage
dispenser. 1, 2, 3, 4. That means that there’s not an exact, in this case, it’s the number
of times that there’s partial hit. So then we sort them, right? And then we compare
these differences. And the way that we compare the differences is using what’s called a Spearman’s
Correlation Coefficient. And so technically if you know anything, if you know about Spearman’s,
this sort of order isn’t exactly right. But it gets the point across. You actually have
to account per ties, and we do. So when I apply this methodology, I can see a correlation
between certain metrics. And these are not super strong correlations, I should explain
real quickly what a correlation coefficient looks like. It ranges between negative one
and one. And one is basically, that is the exact same data. One means that is a perfect,
perfect match. It’s not necessarily the exact same data but we go the Google search results
in the exact order that Google put them, we would have a correlation coefficient of one,
okay? If we have our data completely backwards, then we would have a correlation coefficient
of negative one. If we have a correlation coefficient of zero, that means it’s pure
white noise, right? So these correlation coefficients are relatively small but they also have a
small margin of error. So what we’re looking at here is when I say
point one, that means it’s not like a very strong signal. Google doesn’t rank their search
results exactly based on result is homepage or number of keywords hit and title which
we know intuitively. But what I can say is with a plus or minus of this top one could
maybe range between .09 and .11. There’s actually a smaller margin of errors on that, because
I’m looking at 65,000 rows of data. So it’s pretty huge. So anyway, what we found is that
of the things that you could see on the SERP result is homepage is the biggest. Keyword
hits and title is the number 2. But what I found interesting here was that keyword hits
in the URL was very small. I’ve always heard that keyword hits in URL is a big deal, I’m
sorry, putting the keyword in the URL is a big deal and what I want to point out is that
keyword hit is… this particular metric, keyword hits in URL actually also contains
exact match domains, because I’m looking at the full URL. So if it’s an exact match domain,
that’s also in there. So this thing should be dragging in there up. So I wanted to drill
down deeper into that and see like is that true? I mean I’m double checking my work.
I’ve always heard that that’s actually kind of an important thing. So I drill in, right? And here’s the domain
only. It’s the exact match domain only. And this is the URL without the query string so
really not a big difference between the full URL. So this was, see this is .05 and this
is .05. Those are the same ones, okay? And number of keyword hits in URL. This would
be number of keyword hits in URL. But then when you drill down deeper it’s like putting
it in the path is as close to white noise or putting them into subdomain or literally
not even the first level of the path but like anywhere in the path or page name or the querystring
or anything after the domain, is nearly white noise. Because remember, this might be a plus
or minus .01. So it’s very, very close to zero correlation and… But there’s nothing
to say that it has a negative impact. It’s just that refactoring your URLs so that they
have the keyword in them probably isn’t the first order of work. You could spend a lot
more time getting the right titles, I mean or whatever. But there is some evidence that
putting the keyword in the query string could potentially, I mean it’s a negative correlation
with it, right? It’s very, very small negative correlation. But it’s a negative correlation
nonetheless. So what that means when I say negative correlation is it may actually negatively
impact your ranking. Probably not going to negatively impact your rankings, but it could,
it’s associated with low ranking keywords or low rank results. Okay, so I wanted to find out, and this is
like you know, sort of what I do, is I want to figure out whether or not I can combine
any of these factors, these on-page visible things together to make a super-metric, right?
And in my experience, I’ve done a lot of algorithm and stuff like that, usually you actually
don’t do much good by combining things together. Usually it’s, and if it is good it’s like
10% better. So it doesn’t usually pay off really well. But you know, I search for those
needles in the haystack. That’s what I do to try to make everything that we do better
and so I was actually able to figure out a way to combine. And I forgot to put this on
the slide exactly what I combined. It’s like keyword and title and results at homepage,
and… I’ll try to publish this later, I need to put that on the slide. But I ultimately
was able to, you know what, I’ll do this after the presentation. I know where it is. I have
the formula some place. If anybody wants the formula, I’ll happily disclose it. But anyways,
I was actually able to combine several of these on page signals together to create one
that actually correlates better. 30% better than the best one. So that’s cool. So what we’re doing here is not exactly what
Google does. Google may have millions of pages that it could push to the top result. And
what we’re doing is reordering the top 50, right? So we’re basically trying to reconstruct
exactly the identical tip of the iceberg without taking any of the rest of the iceberg into
account, right? And the truth is, I mean we could go a thousand results deep but like
you can’t get you know, there are certain levels past which you can’t get… you can’t
get all of Google’s results. So you know, we’re doing this top 50 which is more results
than anybody has taken into account and doing this sort of study but, I think that the growth
actually matters, right? Metrics that get better as you go from the top 10 to the top
20 to the top 30 to the top 40. The deeper you go into the SERPs, the more fundamental
that is of a ranking factor, right? Because before you get to the top position, you have
to get to the top 1000. And you know, before you get, and then you have to get to the top
100 and then you have to get to the top 10, right? But if you really want to understand
the way that Google ranks, you need to try to predict you know, deeper down the results
than you know, the top 10 or the top 30 or the top 50 or whatever. So I look at growth, okay? And you can look
at this. And this is basically, what this is the correlation coefficient plotted based
on, okay so this is the top 10, top 20, top 30, top 40. So ones that are, these guys that
are going up into the right are actually getting better at predicting Google’s rank, or Google’s
results as you consider more and more of Google’s documents. And the ones that are going down
to the right are getting worse and worse. Makes sense? Yeah? Okay. So what this means,
and this sort of should correspond how you feel, like this is like if you have an SEO
instinct, I’m saying the keyword in title is more important than exact match domain.
Because you see exact match domain here in purple becoming less and less important as
you go down in ranks, I’m sorry, as you go deeper into the results. And then you have
for example, number of keyword hits in title going up pretty rapidly. Not actually very
important at all in the top 10 or the top 20. But as you go deeper and deeper, it becomes
more and more important. So I think, my argument is that’s a more fundamental ranking factor. So here’s another way of looking at these
things. And you can sort of visually see the heat map how these each of these on page are
in-SERP metrics sort of grow and how they are relative to one another. So we are talking
about exact keyword in title, or I’m sorry, we’re talking about number of keyword hits
in title. And see how it goes from orange to green. And sort of if we are to rank this
list here, it’s ordered by top 50 rather than by top 10. But if they were ordered by top
10, number of keyword hits in title would be very near the bottom, it would be third,
I believe from the bottom whereas it sort of it makes a recovery but at top 50, it actually
is third from the top. All right, so this is, I don’t know, maybe
a little bit of in depth stuff here. Okay, but what you can do is actually plot a linear,
you can linearly regress or you could do a regression of any of these. It doesn’t have
to be linear. And essentially figure out well what’s the flow. You know, how fast is this
thing getting better or how fast is it getting worse. And if you figured that out, it’s essentially
the growth rate. It allows you then to predict into the future, predict deeper into the results.
So if we wanted to predict well, what’s it going to be in the top 100 or the top 200
or the top, well, I think these things probably aren’t linear forever. So we don’t want to
go to the top thousand probably without like maybe a better model or something. But I think
it’s fair to like project into the top 100 or top 200 or something. So when we do that, so what I ended up doing
is taking this 47X and this 39X, those are essentially, that’s the growth rate. That’s
like, if you remember like, I don’t know, 7th grade Algebra, that’s the slope. The slope
in this case is .0047X. And so I multiply that by like 100,000, to make it… like make
you annoyed every time you saw it, so I may show you, I’m not sure whether or not I’ll
show you, I’ll show you these number again in like an article that I publish. But these
are the growth rates. And so you can see faster growing versus slower growing. It’s a simple
number. Anyway, here’s this growth rate, here’s what happens when we use that growth rate
to project the top 100. Okay. So you can see keyword hits in title has a
very fast growth rate and we predict that it’s going to become more significant. So
it’s sorted out here, down here at .08 which would have put it you know, somewhere in here
in the list. And because of its growth rate. By the time you get into the top 100 it’s
a much more significant metric. Conversely, you have results in homepage and does that
makes sense, what results in homepage is? That means that the search results is actually
the homepage. On a gut level when you’re just looking for SERP and you see a bunch of homepages
there, you’re like oh this is kind of a difficult SERP. And so you know, that’s like sort of
common sense, SEO common sense or SEO instinct that sort of proved to be true here. It’s
actually a pretty strong factor. But it becomes less important as you go down… as you go
deeper and deeper into the SERP. Similarly, you got exact match domain. Exact
match domain is actually the slowest, or it’s the fastest growing… it’s the fastest, what’s
the opposite of growth? Losing? You know, negative growth. It goes down into the right
the fastest. So I wanted to contrast those and give you another way of sort of visualizing
this growth. So we’ve actually taken some of these metrics, these new things and integrated
them onto SpyFu. And this is brand new today as a coincidence in a way or why don’t I just
say that I did it for you guys? But we actually happen to launch this thing at about 11:30
today. And so you can go to This is all free stuff that I’m showing you right
now. You can go to and type in any keyword and you’d be able to see this sort
of analysis. So you can see on a roll up level we’re like okay, there’s two homepages in
results. Another thing that I sort of didn’t put in
this research is what happens with WDU domain. But that’s sort of a historically interesting
thing to look at, right? Especially if we’re like looking for medical terms. Google is
probably going to almost give a brand effect to those government and educational domains.
So we want to look for that. It gives us a sense of the difficulty. In this case, I think
I’m looking at like Lance Armstrong, lots of keywords in title meaning lots of results.
This is the number of results with the keyword in title. Lots of results in keyword in URL.
So it’s a relatively well optimized page. I see well, when you’re actually scanning
the SERP, we bolded the things that you need to be looking for. You got the homepage, you
got an exact match domain here, you got the keyword in the title. So you can sort of just
look and see how much bold there is too, it gives you a general view or it improves your
ability to scan. You got the keyword in the URL here. Actually I didn’t actually call
that one out but you see them here. In cases where you got 2 SERP, 2 results in the same
SERP or more than two, you can see that here. And we also added to this thing the position
based click through curve. I think that’s, it’s funny that we’ve never done that before
because everyone’s always like take all these keywords and put them into a spreadsheet and
do this really annoying calculation that’s not even, you know that’s based on 2006 AOL
data, which is the most annoying thing you ever have to do with keywords. And it doesn’t
take into account universal results or ads or anything like that. So we decided we’d
put that underneath keyword page. One other thing that’s sort of, of note here is this
domain diversity. And that basically shows you how many different domains are on this
individual SERP. Okay, so the next thing that I want to look
at after looking at the stuff that’s visible on SERP is domain level metrics, right? So
you know, you got like your backlinks, you got you know, you got page rank, so on that
type of stuff. So when we did these, I looked at… I pulled in a bunch of majestic SEO
stuff, I was also going to pull in SEOMoz, the same metrics from SEOMoz and the same
metrics from like AHF. But I did, just because I wanted to see how hard I wanted to work,
I actually pulled in like a sample of those and compared them against each other. And
those like metric by metric even for like trust flow versus Moz Trust there’s like a
.91 correlation coefficient or very, very high correlation coefficient between pretty
much all of those metrics. So I was like well then I will just pull in one and see how those
metrics perform and if I need to do the other ones later then I guess I will. But save me
a little bit of time. So the best metric is trust flow from majestic followed by this
interesting, just straight up domain age. That’s a metric that we pulled from the SpyFu
database. At SpyFu we have like 79 months of history.
So you can actually look and see… well actually you currently can’t but I can, you can see
every keyword that any domain ranked on 6 years ago. And anyway, this domain age thing
is actually just the first time that we saw a domain show up in the ranks, show up in
any search result even if there was an advertisement. And so that domain age was almost as good
as trust flow in predicting Google’s rank. I thought that was really… I was surprised,
almost annoyed because it was so simple. But yeah, I guess, I don’t know, you just don’t
really want to believe in whole domain age thing. And it’s not exactly the same as how
long ago it was registered, right? It’s like actually how long it’s been kind of trusted
by Google if you like, if you want to think of it that way. Okay, so we also looked at backlinks. And
then you see that domain page rank is the lowest performing. If you pay close attention
to this stuff, that’s not a surprise. But I mean everybody uses page rank, well not
every I mean, you find yourself doing it even though you don’t want to. Anyway, there’s
a lot of metrics even domain age that beats page rank, okay? So you could happily probably
replace your domain page rank dependency with damn your anything. Okay, I wanted to throw something into the
mix. I, before I even sort of thought of doing this experiment, I came up with a metric my
own and I wanted to see if I could predict ranking results based on actual performance
in the SERP without taking into account backlink data. My idea is that this would be a good
metric to combine with other metrics. So I was like it’s going to be great, I look at
a page… I’m going to look at these you know, backlink data points and actually eventually
combine a SERP performance metric at the domain level with those things to make a better super-metric.
This domain strength is not like a straightforward equation that I can just layout for you. It’s
actually algorithm based which means that there’s a whole bunch of branching, you know
statements and stuff like that to figure out what an actual domain strength becomes. But
I use a lot of the tricks that I used to calculate SEO value, the opportunity side of things
that I was talking about earlier, that click through curve that we do that takes into account
universal search and ads and stuff like that, ultimately we’re looking at how many keywords
a domain ranks on, what positions the keywords are in, how many times it ranks with the same
keyword, how many searches that keyword gets, how much the keyword costs, the competitiveness
of the keyword. A lot of different factors go into this but I’m kind of, that’s kind
of what we do and so I thought I’d compare it. And I will say that I was extraordinarily
surprised that it beats all these other metrics because I have a lot of, I basically have
not predicted that in any case but it beats them. And of course I’ll release data and
we should probably do a follow up study to make sure that I know what I’m talking about.
I’m pretty sure I know what I’m talking about but we’ll have to pull in a whole new set
of data and re-compare and so on. But at this, especially domain strength, does not suck.
It actually in my opinion a very, very solid metric. Here’s how it looks as it grows, okay. So all of these things actually have a pretty
solid growth rate. None of them, you see have a negative growth rate like we saw with the
other, with the on-page metrics. Everything here goes up into the right. Here’s how it
looks as a heat map. So you see domain strength on the top and actually what’s interesting
is that page rank has an interesting growth rate. It starts out actually as a negative
correlation and then results as falls still the lowest rank but it makes a little bit
of a comeback. The next best metric is the trust flow. I’ve seen trust flow and citation
flow be very similar. What I think is pretty interesting is that there’s not that much…
you can look at a raw number like class C blocks and know what that’s derived from.
And be really, really, basically within the margin of error of accurate. And that’s backed
up by basically SEOMoz. SEOMoz will compare their number to the class C block numbers
that they have and it’s basically the same. The other value of looking only at class C
blocks is you’re going to look at backlink data is that it is not scaled to, it’s power
curved just like search volume is power curved. So you can actually divide. If you want to
stick with using backlink data to estimate keyword difficulty or whatever you should
look at like class C blocks because you could divide search volume over class C blocks to
get basically a really solid metric versus dividing something that’s, dividing into something
that’s like called to zero to a hundred. You talked to me about dividing into a power curve
into a linear curve and that’s just, it’s just going to make the big keywords always
win. And so that sucks. So yeah, anyway, moving along. Getting like weird looks from my inside
audience. They’re like what the hell. I will not talk about that anymore. All right, so
here’s the growth rate. SpyFu domain strength actually has the fastest growth rate also.
And you’ll note that, that like 186 is scaled to the same as those, as these guys. So you
have like 47 and 109, and 186 is fast. 186, it says 155 is fast. Even 135 is a strong
growth rate. So let’s talk about how we’re using these in SpyFu. Back at that same keyword
result page when we’re analyzing the organic rankings, we have the domain strength right
there. So you can take all of these things into account. All at once in one spot. All right, moving on to page rank stuff, okay?
Because this is essentially the holy grail of estimating difficulty or predicting rank,
is how many backlinks does this page have. Not like the entire domain but this individual
page. And in fact you see that these are like the best correlation coefficients that we’ve
seen. Class C blocks linking to URL is the best metric there is. Oh you don’t know this,
I could have actually maybe skipped that. But anyway, I pushed forward. Here’s the issue,
is that I was only able to get page level metrics for about 40% of the search result.
So 60% of them basically returned N/As. So it was actually like… I was like what do
I do here? Everything else I’ve compared against has been you know, the full data sets, 65,000
rows or whatever. And now I can only get a very small amount of data like I mean, that’s
a significant loss. 60% of the URLs didn’t contain this page… I couldn’t get any of
the page metrics back. So that’s sort of the weakness of it, when it’s there it’s super
strong, very, very good. Page level metrics are great. Class C blocks, linking of URL
is outstanding. Even URL backlinks, anything’s really good
there. But if it’s not there, and you want to try and apply it universally which I do,
right? Because I ultimately want to come up with a keyword difficulty, I mean I ultimately
end up having to just take these things into account and I don’t know, come up with what
is an old value. But it makes it a little bit more difficult especially it makes it
a lot more difficult when you’re kind of trying to do this without the benefit of an algorithm
and you’re just sort of looking at a SERP and have some numbers. But anyway, here’s
how this worked out, right? The growth rate was also very different. The top tier we have
you know, this is the best case, right where we filtered out all of the rows where we didn’t
have page level metrics and down below is the full data set and you see that you’re
going from yellow to green or red to orange. Basically you’re getting more… you’re getting
colder or whatever it is. You’re getting better as you go from top 10 to top 50 whereas the
opposite is true when you’re doing this with the full data set… And it actually makes
sense, right? The further you go down the search results, the less likely there to have
to page level metrics because those are like, those pages are just not as popular probably.
I mean statistically speaking, you will end up having less data points on those. So here’s
how the whole thing looks, okay? And what I did here, this is actually the best case
scenario for the page metrics. So what I did was I tried to combine several of these data
points to come up with like really the foundation for my keyword difficulty stuff. I wanted
to say, okay, what can I actually do to really you know, do the best job I can. And you see
there’s a big jump here between the best individual metric and then the best… and then just
a set of super-metrics. So I was actually able to come up with 4 super-metrics
that all outperformed everything else. Even basically the page level metrics on their
best day, right? I can actually apply on page signal plus domain strength, right? Or basically
it’s like on page signal plus domain strength taking no page level metrics into account
or whatsoever and actually beat the page level metric. So the very best, the holy grail,
the perfect metric, right? The page level backlink data is not as good as on page signals
plus domain strength which is actually a fairly huge surprise for me. Of course we can make
it incrementally better by taking on main page signals, domain strength and then these
page level citation flows that I actually use which is right… someplace. Where’s citation
flow? Oh there it is. So I may be should try that with class C blocks. That would be kind
of cool and it occurred to me … at the time. But I should probably do that, see if I can
actually really do well. But yeah, anyway so, that’s kind of the big surprise for me
is that I was able to beat those backlink metrics. Here’s what this thing looks like as a big
old heat map. So you see that there’s pretty good growth in all of these top metrics. They’re
not like slow growths. Here’s what that growth looks like if we were to project 200 results
deep. Because this growth here for these guys is actually stronger than the growth here
for this guys, the separation becomes nearly 50% once you project further down to the SERPs.
So how are we using this? Basically, so what I did is I took these metrics and effectively
used them to calculate the keyword difficulty. This is the end of my mission, right? There’s
actually a few steps in between but I’ll save you the horribly boring math part there if
you’re not already horribly bored, so you look at the same search page I was talking
about and we got ranking difficulty right here front and center. We also have it integrated,
probably seen it before, into the organic search SERP analysis. One thing that I didn’t
mention before but this is really interesting for the Martha Stewart one, is that we have
these social domains and so surprise, surprise, Martha Stewart is on Pinterest, I would be
interested to see how this affects, I’ve actually looked to see how this affects the SERPs and
I can’t find an exact pattern yet. I’ve actually looked to see what social in SERP actually
does. But it’s not like having Pinterest. Pinterest doesn’t necessarily rank high on
the SERPs and Twitter doesn’t necessarily rank high on the SERPs. Actually I believe
there is, I did look and see, oh yeah, it’s results from Wikipedia. So this isn’t that
strong of a signal and it trends downward. I use that as a baseline. So I want to figure
out a way to algorithmically incorporate social and domain, or social and SERP but I can’t
quite. But you could, you know you got that gut level instinct as an SEO looking at this
and oh,,,, Wikipedia, Martha blog,
Martha Stewart Weddings. And then Pinterest and Twitter. It’s like okay, it’s going to
be hard for me to get above those, right? And then you look here and there’s 30
in SERPs. See your domain diversity is like 39.6%. So that 245 is really high keyword
difficulty. And we captured it well but I feel like you know, looking at these social
signals and domain diversity could potentially improve. You could use that to hone your skills.
But you know, we provided, it’s just that I haven’t captured them in algorithm yet.
But it’s sort of what I’ll be working on. Anyway, so I’ve got a little bit of time for
a quick demo of the other place we’re integrating this. And that’s in our keyword research which
is keyword smart search. So I just put in like an interesting niche here, the ultrasound
technician and what you can do here is actually filter by SEO difficulty, right? So I want
something that’s less difficult than a 100. And that’s going to filter for me. And really
I want to make sure that there’s a difficultyn so I want it to be greater than 25. And then
I also want this to let’s see, I definitely don’t want it to be zero. Let’s go for 5 or
more searches a day and see what we get. Okay, so you can see what we’re doing, I’ve actually
try and figure out like cost per click. Let’s see, maybe I could do $2 and more. I haven’t
actually done $2 and more. It’d be interesting but you can sit here and do this with your
smart search all day. Oh look at that, there’s number one results that I was looking at.
We’re still pretty good. Diagnostic ultrasound program, easy difficulty 56.9 and a high cost
per click. Not a bad search volume. I mean for the cost per click. So you can now use
this. We just now made it so that you can start refining your keyword research based
on these stuff. And we’ll be integrating keyword difficulty pretty much all over the site in
various different ways you know, going forward. But that is all I got. Did I go over? Did
I stay inbound? I don’t know. Do we have any questions?

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