And maybe they're away at camp for the first time ever.


Reimagining Customer Retention and Evangelism Presented by Kristen Craft

And you get this video that shows what your kid is doing.


And maybe, just maybe, your kid is even featured in this video.


Can you imagine the sense of pride that you feel?


Not only are you going to go and watch that video a million times over,


but you're going to send it to everyone you know.


The grandparents and the aunt and uncles and the neighbors and everyone in your town.


And then the kid gets home from camp.


And they see the video.


And maybe they see themselves in the video.


And for a kid, I mean, this is like, you're like a movie star if you're in this video on the internet.


So those kids, they go and they watch it again and again and they share it with their friends.


Which is so cool.


How are they doing this?


Well, I think it's because they are making the customer the hero.


The customer in this case is the campers or maybe in some ways even the parents.


But they are making the customer the hero.


They're putting the kids front and center in this story.


So very tactically, how can we do this?


How can we take this to heart and implement this ourselves?


Well, first of all, we can use you language.


I know this came up earlier, I think with Justine, that using you language is very powerful.


It's putting somebody else in the center stage of your story.


And what's really cool here is that not only are they using you language in order to help, you know,


the customer see themselves as the hero of that story,


but then once those families start sharing that video with everybody else,


it allows those other viewers to imagine themselves, envision themselves as the hero of that story.


Second, they're showing a wide array of perspectives.


It would be so easy for them to put Steve and Kate, the actual founders, on camera and saying like,


our philosophy is blah, blah, blah.


But instead, they're actually putting the kids on camera.


And not just one, they're putting a lot of different kids on camera to share their perspectives.


Last, they're piggybacking on emotional moments.


And that emotion matters a lot.


They're sending this video out when parents are already feeling, you know, a sense of perhaps longing,


missing their kids who are away, a sense of pride, perhaps, in what their kids are doing and accomplishing at that camp.


And they're piggybacking on those moments, which is a great way to get people to actually take action,


evangelize, spread the word about the experience that you can offer.


All right. As we come to a close here, I want to take us back for a sec to my dad and his large car phone.


My dad was an excellent salesperson.


And it wasn't because of his car phone or any sort of newfangled technology.


It wasn't even because of the chit chat that he had going on with his clients.


It was because he gave those people a real experience of what it is truly like to know him and have a personal connection with him.


He's one of those people that can just find common ground and finds every single person curiosity inspiring.


He gave people that experience of actually knowing him and appreciating, you know, what he did and letting them know that he appreciated what they did.


So where does this leave us?


Well, I think we've we fall we fail ourselves and undermine ourselves if we're trying to use short term tactics.


Those are only going to lead us to short term relationships.


Instead, if we are able to recognize and leverage experience as our ultimate differentiator, we will be able to keep these lifelong customers.


My hope for all of you is that you will seek out ways large and small to impact the experience that you're giving to people.


Seek out ways that your clients can show that experience and give people the most authentic personal experience possible.


I love this stuff a ton. So if any of you have great examples of things that you've seen or you've done yourselves, please let's come and talk.


I'm going to be down by the Wistia booth earlier or sorry later today and then around later tonight.


Searcher Task Accomplishment - Whiteboard Friday

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday.


This week we're chatting about a new Google ranking factor.


Now I want to be clear.


This is not something that's directly in Google's algorithm for sure.


It's just that they're measuring a lot of things that lead us to this conclusion.


This is essentially what Google is optimizing toward with all of their ranking signals,


and therefore it's what SEOs nowadays have to think about optimizing for with our content,


and that is searcher task accomplishment.


So what do I mean by this?


Well, look, when someone does a search like disinfect a cut, they're trying to actually


accomplish something.


In fact, no matter what someone is searching for, it's not just that they want a set of


results, they're actually trying to solve a problem.


And for Google, the results that solve that problem fastest and best and with the most


quality are the ones that they want to rank.


In the past, they've had to do all sorts of algorithms to try and get at this from obtuse


angles, but now with a lot of the work that they're doing around measuring engagement


and with all of the data that's coming to them through Chrome and through Android, they're


able to get much, much closer to what is truly accomplishing the searcher's task.


And that's because they really want results that satisfy the query and fulfill the searcher's


task.


So pretty much every, I'm excluding navigational searches, but every informational and transactional


type of search, I mean navigational, right?


They just want to go to that website.


But informational and transactional search query is basically this, right?


I have an expression of need.


That's what I'm telling Google.


But behind that, there's a bunch of underlying goals, things that I want to do.


I want to know information.


I want to accomplish something.


I want to complete an activity, right?


And when I do that, when I perform my search, I have this sort of evaluation of results.


Is this going to help me do what I want?


And then I choose one, and then I figure out whether that result actually helps me complete


my task.


And if it does, I might have discovery of additional needs around that.


Like once you've answered my disinfect a cut, now it's, okay, now I kind of want to know


how to prevent an infection because you described using disinfectant and then you said infections


are real scary.


So let me go look up how do I prevent that from happening, right?


So there's that discovery of additional needs.


Or you decide, hey, this did not help me complete my task.


I'm going to go back to evaluation of results.


Or I'm going to go back to my expression of need in the form of a different search query.


That's what gives Google the information to say, yes, this result helped the searcher


accomplish their task or no, this result did not help them do it.


This is true for a bunch of things.


I'll walk you through some examples, right?


So if I search for how to get a book published, that's an expression of need.


But underlying that is a bunch of different goals.


Like, well, you're going to be asking about like traditional versus self-publishing and


then you're going to want to know about agents and publishers and the publishing process


and the pitch process, which is very involved.


And then you're going to get into things like covers and book marketing and tracking sales


and all this different stuff.


Because once you reach your evaluation down here and you get into discovery of additional


needs, you find all these other things that you need to know.


If I search for invest in Ethereum, right?


Well, maybe I know enough to start investing right away, but probably, especially recently


because there's been a ton of search activity around it, I probably need to understand what


the heck is the blockchain and what is cryptocurrency, this blockchain powered currency system and


what's the market for that like and what has it been doing lately and what's my purchase


process and where can I actually go to buy it and what do I have to do to complete that


transaction?


If I search for something like FHA loans, well, that might mean I'm in the mindset of


thinking about real estate, right?


I'm buying usually my first house for an FHA loan and that means that I need to know things


about conditions by region and the application process and what are the providers in my area


and how can I go apply, right?


All of these different things.


If I do a search for Seattle event venues, well, that means I'm probably looking for


a list of multiple event venues and then I need to narrow down my selection by the criteria


I care about like region or capacity, the price, the amenities and then once I have


all that, I need contact information so that I can go to them.


Google is going to reward in all of these scenarios, Google is going to reward the results


that help me accomplish the task, discover the additional needs and solve those additional


needs as well rather than the ones that maybe provide a slice of what I need and then make


me go back to the search results and choose something else or change my query to figure


out more.


Google is also going to reward and you can see this in all these results, they're going


to reward ones that give me all the information I need, that help me accomplish my task before


they ask for something in return.


The ones that are basically just a landing page that say, oh yeah, Seattle event venues,


enter your email address and all this other information and we'll be in touch with a list


of venues that are right for you.


Yeah, guess what?


It doesn't matter how many links you have, you are not ranking my friends and that is


so different from how it used to be.


It used to be that you could have that contact form, you could have that on there, you could


not solve the searcher's query, you could basically be very conversion rate focused


on your page and so long as you could get the right links and the right anchor text


and use the right keywords on the page, guess what?


You could rank.


Those days are ending.


I'm not going to say they're gone, but they are ending and this new era of searcher task


accomplishment is here.


There's a challenge.


I want to be totally upfront that there is a real challenge and a problem between this


world of optimizing for searcher task accomplishment and the classic world of we want our conversions.


The CRO in your organization, which might be your director of marketing or might be


your CEO or maybe if your team is big enough, you might have a CRO specialist conversion


rate optimization specialist on hand and they're thinking, hey, I need the highest percent


of form completions possible.


When someone lands on this page, I'm trying to get from 2% to 4%.


How do we get 4% of people visiting this page to complete the form?


That means removing distractions.


That means not providing information upfront.


That means having a great teaser that says, hey, we can give this to you and here's testimonials


that say we can provide this information, but let's not give it right upfront.


Don't give away the golden goose, my friend.


We want these conversions.


We need to get our qualified leads into the funnel versus the SEO who today has to think


about how do I get searchers to accomplish their task without friction?


This lead capture form, that's friction.


Every organization, I think, needs to decide which way they're going to go.


Are they going to go for basically long-term SEO, which is I'm going to solve the searcher's


task and then I'm going to figure out ways later to monetize and to capture value or


am I going to basically lose out in the search results to people who are willing to do this


and go this route instead and drive traffic from other sources?


Maybe I'll rank with different pages and I'll send some people here or maybe I will pay


for my traffic or I'll try and do some barnacle SEO and get links from people who do rank


up top there, but I won't do it directly myself.


This is a choice we all have.


How do you do this?


Let's say you've gone the SEO path.


You've decided, yes, Rand, I'm in.


I want to help the searcher accomplish their task.


I recognize I'm going to have to be willing to sacrifice some conversion rate optimization.


Well, there's two things here.


We want to first gain a deep understanding of what drives searchers to search.


Number two, we want to understand what makes some searchers come away unsatisfied.


Once they've performed this query, why do they click the back button?


Why do they choose a different result?


Why do they change their query to something else?


There are ways we can figure out both of these.


For number one, some of the best things that you can do are talk to people who actually


have those problems and who are actually performing those searches or have performed them through


interviews.


You can certainly do it through surveys.


I will provide you a link to a document that I did around specifically how to get a book


published.


I did a survey that I ran that looked at searcher task accomplishment and what people hope that


content would have for them.


You can see the results are quite remarkable.


I'll actually embed my presentation on searcher task accomplishment in this Whiteboard Friday


and make sure to link that as well.


You can do this with in-person conversations, and powerful things can come out of those


that you wouldn't get through remote or through email.


You can certainly look at competitors, so check out what your competitors are saying


and what they're doing that you may not have considered yet.


You can try putting yourself in your searcher's shoes.


What if I searched for disinfect a cut?


What would I want to know?


What if I searched for FHA loans?


I'm buying a house for the first time.


What am I thinking about?


I'm thinking about a bunch of things.


I'm thinking about price and neighborhood and all this.


Okay, how do I accomplish all that in my content, or at least how do I provide navigation so


that people can accomplish all that without having to go back to the search results?


To help with number two, understanding what makes those searchers come away unsatisfied,


auto-suggest and related searches are great.


In fact, related searches, which are at the very bottom of the page in a set of search


results, are usually searches people performed after they performed the initial search.


I say usually because there can be some other things in there, but usually someone who searched


for FHA loans then searches for Jumbo loans or 30-year fixed loans or mortgage rates or


those kinds of things.


That's the next step, and so you can say, you know what?


I know what you want next.


Let me go help you, and auto-suggest related searches.


Those are great for that.


Also too are your internal search analytics for people who landed on a page and performed


a site search or clicked on a next link on your site.


What did they want to do?


Where did they want to go next?


That helps tell you what those people need.


Having conversations with those who only got partway through your funnel, so if you have


a lead capture at some point or you collect email at some point, you can reach out to


people who initially came to you for a solution but didn't get all the way through that process


and talk to them.


Finally, if you track the search results, generally speaking what we see here at Moz,


what I see for almost all the results I'm tracking is that more and more people who


do a great job of this, of searcher task accomplishment, are rising in the rankings, and the folks


who are not are falling.


Over time, if you watch those in your spaces and do some rank tracking competitively, you


can see what types of content is helping people accomplish those tasks and what Google is


rewarding.


That said, look forward to your comments.


We'll see you again next week for another edition of Whiteboard Friday.


You Can't Type a Concept: Why Keywords Still Matter Presented by Dr. Pete Meyers

Thank you.


Thank you for waking up.


First of all, if you have been awake all night, welcome to stop number 7 on the pub crawl.


There's no booze here, but you're not gonna know anyway, because you're pretty much done.


So thank you in general.


This is my fifth year on the Moscon stage.


And as some of you know, it's been a rough couple of months here at Moz.


We really appreciate the support of our customers and the community, and what's amazing is that


our customers are a community in many ways.


And so we hope you'll see as we go through this refocus on Search, on Core Search, on


Moz Pro and local, that there are a lot of things in the pipe and good things to come,


and I hope you'll stick with us through that.


But thank you for being with us.


Thank you for being here.


So one of my screens looks weird.


Sorry.


You're used to me coming up here and talking about how the SERPs are changing.


And what's cool in a way is that we just launched SERP features for Moz Pro.


Rob was up here yesterday.


This topic I've talked about for the last couple of years has kind of become mainstream.


I'm glad to have other people talk about it.


I'm glad to have Rob here yesterday talking about it better than I probably would.


And so I want to take a little different approach today, and I want to talk not about how 10


blue links and the things around them are changing, but how searches themselves are


changing, how we are changing as searchers.


And the thing about that is as searches change, Google has to adapt, and as Google changes,


we have to adapt.


And so I want to talk about how searches changed from the searcher perspective and how that's


changing things like keyword research.


So the first stop on this journey is something that happened in April, around May of 2013.


Google launched conversational search on desktop.


And it seemed like a toy.


It was essentially voice search on desktop.


And still, most of us don't run voice searches on desktop.


But voice search was coming.


It was evolving on mobile.


And I think what you have to realize is that Google had to adapt to the kind of searches


people were making on voice.


And what's different about this is with mobile, you can sometimes look and say, well, you


know what, only 5% of our traffic is mobile.


Maybe we'll wait.


Maybe we're not sure.


But with voice search, Google had to change all searches.


They had to change the core engine to adapt to voice search.


So for all of us, whether we used that modality or not, things started to change about three


years ago.


And in August of the same year, we got the Hummingbird update.


And update is probably a poor choice of words.


Danny Sullivan said it's like we put a new engine in the car.


This was a core rewrite of the Google algorithm.


And like caffeine before it, the thing that we don't sometimes realize is that everybody


wanted to know, what did Hummingbird do?


What did it do that day?


What was the update?


What Hummingbird did was power changes for the next three years, even through now and


for the next couple of years to come.


And much of that power was around dealing with these more complex long-to-way voice


searches and building the infrastructure and the speed Google would need to do that.


And so ever since Hummingbird, things have been evolving because they have that new platform,


that new power.


And then October 2015, Google announced RankBrain.


A couple months ago, I want to tie in RankBrain.


A couple months ago, I made this bad decision.


I said, you know what?


I'm going to get up at Moscow and only talk about what I'm 80% sure is true about RankBrain.


And I stared at that blank page for about five minutes.


I said, all right.


Either I'm going to have to start making stuff up or this is going to be really tough.


So I did it the hard way.


So I'm going to try to stick today to what I think is mostly true about RankBrain.


I'm going to try not to speculate.


You've heard a lot of speculation.


One thing I know is probably true.


RankBrain launched in April 2015, even though it was announced in October.


Some of that reason I think that's true I can't talk about.


But thankfully, Steven Levy published a post where he leaked April 2015 and that.


And some people got a little nervous about that.


And apparently he wasn't supposed to say it.


So I'm just going to tell you that that's probably pretty true.


So RankBrain had been in play about six months before it was released, before the announcement.


And it's been over a year now.


So this is something we've been living with for a while.


So I want to talk about the kind of changes that have popped up.


So this is the kind of a classic query, the way we would have typed it pre-voiced.


We'd say something like space needle height.


And don't worry about the knowledge graph here, but what I'm trying to show is that


Google understands what this means, right?


They can return the actual height, they return relevant organic results.


But in 2016, we can also do a search like what is the height of the space needle and


get virtually the same results.


Google understands this, they can parse it, and they can return that height.


And we can even get a little stranger and say how tall is this space needle in meters?


Google knows what we're talking about, they can do that translation, they understand this


semantically, they're not just trying to match those results.


But they know what this means.


They can equate all three of these queries.


So how do we deal with this as search marketers from a keyword research perspective?


Well, we've read a lot of posts, you probably read a lot of posts recently about, well,


target concepts, okay?


And okay, let's say we have this concept of tallness, right?


And tall and height are kind of synonyms.


Size and big are sort of related.


And we've got units like feet and meters.


So let's group that whole thing into the concept of tallness.


Okay, that kind of makes sense, that's true, right?


So what do we do?


Just build great concepts.


There you go.


Any questions?


Yep, 40 minutes.


Okay, how many in the audience have ever been told or read to just build great content?


Raise your hands.


Have you ever heard that, right?


So here's the problem.


We can laugh about it, but it's true.


It's fundamentally good advice.


It's a little loaded because anything great is usually good advice.


Nobody wakes up in the morning and says, you know, I'm going to get up and I'm going to


build shit content today.


It's sort of in there.


Great is great.


But the problem is that it's just not tactical.


And the problem I have when we say to target concepts and not keywords is that this isn't


what we do as search marketers because of this.


You have to type something into the box.


And so if I tell you to target concepts, even though we know concepts are real, we know


concepts are real, we know all of these very different searches are coming together to


form a concept, you still have to type something into that box.


So what is it?


And that's what I want to try to talk about today.


I got to always do the scary stuff like you didn't know what was going on and wake you


up a little bit.


So these are those slides.


So let's start with the stuff you kind of know, right?


You know, we used to if you sold scarves, you would stuff everything into the title.


It's like buy scarf, scarf, scarves on sale cheap now, right?


We all did it.


Come on.


I'm not going to ask you to raise your hands, but you did it.


So we know Google's gotten better at this, right?


They can equate these things.


If I type a search like scarf, I'm going to try and show you what they're matching to


using their own highlighting.


The highlighting algorithm and the search algorithm are not necessarily the same, but


I think they're related enough that we can kind of see, okay, Google knows that scarves


with an FS and scarves with a VES match scarf.


Okay.


We know they can do that.


I can flip it around if I type in proxies plural, and I wasn't buying proxies.


Definitely.


Google knows that proxy with a Y is a match.


They can do stemming.


If I type in ballroom dancing, they understand that it matches dance, dances, dancers, those


are basically the same in this context.


If I type in Warner Brothers, they know that they actually go by Warner Bros, which, you


know, it's 2016, come on.


But you look at the top view results, the word brothers is not in them.


Google is directly equating this.


They didn't have to put Warner Bros slash brothers.


They can do this.


If I type in SNL skits, they know that SNL, Saturday Night Live, I don't even have to


have SNL in there.


They can flip it the other way.


This is a clothing store, not your daughter's jeans.


They actually go by NYDJ.


The results are all NYDJ.


Those are the same to Google.


So we're just going to start to get a little weirder.


Reverse directory, which can have some spammy people in it.


Almost every one of the results above the fold actually matches reverse lookup.


Doesn't use the word directory at all.


Google is equating those two synonyms.


You don't have to mention that word.


Discount airfare matches.


Discount flights, cheap flights, cheap tickets, discount flights, all of these are the same


in Google's eyes.


This one is a little weird.


So we've got two abbreviations, right?


NJDMV, the New Jersey Department of Motor Vehicles.


But here's the thing that's cool.


In New Jersey, it's not called the DMV.


It's called the Motor Vehicle Commission.


Google can match NJDMV to New Jersey Motor Vehicle Commission and New Jersey MVC.


I don't even have to have the words Department of Motor Vehicles or DMV on my site.


They understand that these two concepts are the same.


If I ask what production car holds the top speed records, I don't have to have the words


top production car.


I can just have Varon Super Sport and Bugatti Varon.


They know that the answer is a relevant match to the question.


This is a completely different world of keyword research than we're used to.


Upscale department stores matches luxury department stores.


Okay.


But it also matches Barney's, Bergdorf Goodman, Saks Fifth Avenue, Nordstrom's, Neiman Marcus.


Google knows that the brands are relevant to this query.


And here's one for horoscope dates.


They match zodiac.


They match actual dates.


May 21st through June 20th is being shown as a relevance match to the word dates.


Google gets that this is relevant.


So this has come a long way.


This is what we see now.


This is not what's coming.


This is what's here now.


So you know what to do.


You just take all these words and you jam them into meta keywords, right?


We know how to do this.


You've stopped doing this, I hope.


But what's sad is this doesn't even actually work in theory, let alone in meta keywords.


So let's say you're trying to sell blenders.


And you go, okay, I need all the versions, right?


I need blenders and blender and blended.


And I'm gonna have to put that on my page.


Well, this is a search for blenders.


Okay.


This is what we expect.


We've got shopping results.


We have an ad.


This is a SERP where Google says I think you want to buy blenders.


But this is a search for blender.


And this is the blender foundation, an open source 3D graphics platform, and this entire


SERP is around that brand and around the blender foundation.


And Google sees that as the dominant intent.


So the results for blender and blenders now are completely different.


And one is not a good match for you.


And this is blended.


The Drew Barrymore or Adam Sandler movie.


That has nothing to do with blenders.


It's not as bad as you might think it is, but I'm not gonna recommend you actually watch


it.


But this is the entire SERP.


So if you're going and saying, well, I know I need these words that are all kind of these


aren't even the right words for you.


And the right combinations.


So what do we do in this world where Google is not only equating these very disparate


keywords as concepts, but sometimes they're saying that these things we think are the


same aren't the same because they know that most people searching for blended want the


movie, not blended drinks.


All right.


To make it worse, of course, the algorithm is learning now.


We have machine learning in play.


I apologize for this slide because I thought it was really funny when I put it up there


and then I realized that I have a Ph.D. in experimental psychology and it was normal


to walk around and have people with brains in jars.


And I once got on the elevator and my friend had a brain in the jar and I was like, dude,


nice brain.


This was a normal human interaction for me in graduate school.


So then I put it up and thought maybe some people don't like the whole brain in the jar


thing.


So I left it up for two minutes and talked about it.


To me that's hilarious.


So deep learning.


We know one thing about rank brain, we think, and that's that it uses deep learning.


What's deep learning?


Wikipedia.


Deep learning is a branch of machine learning.


All right.


We can do that.


Based on a set of algorithms, we know algorithms, we're comfortable so far, that attempt to


model high-level abstractions in data by using a deep graph with multiple processing layers


composed of multiple linear and nonlinear transformations.


Yeah.


So I got ten minutes to explain deep learning.


Which took about two months of learning to condense this into ten minutes.


This is a neural network.


From actually I did work on neural networks in the mid to late 90s.


So what we're trying to do with the neural network is model the way the brain works.


So we know the brain has these neurons in a neural network, they're called nodes, and


there's connections between them.


And by the strength and the signaling of the connections between these neurons, if you


put thousands and millions and trillions of them together, we have trillions of connections,


amazing things happen and we get the way we think.


And so we tried to model that.


And the way a neural network works, a simple neural network, this is a very simple one,


is we have these inputs and we have these outputs we want to achieve.


So we want to put something into the machine and we want to get something out, right?


And so we have this input layer of nodes and we have this output layer and we're trying


to build something in between that can model that input to output.


And why we do this, you might say why don't we just use a lookup table, why don't we just


put all the things into a table and say if you put in this input, we get this output.


Because what we do with the neural network and what we do with machine learning is we


put in this data to train it, but then the machine itself can handle new inputs that's


never seen before.


So the machine has the ability to adapt to new things it hasn't seen, that's why we build


this.


And we use models like back propagation, I won't get deep into this, but I have an analogy.


So we actually start from the output and we train the machine back to the input.


If you've ever been to a restaurant with a kid or you were a kid and you have the maze


on the placemat and the maze was a little harder than you thought it would be, especially


if you're like 46 like me and you're like this is kind of embarrassing because this


maze is a little too hard for me, you may have started from the end and worked your


way back to the beginning.


That's kind of how Backpack works.


So we have this model.


And the thing about neural networks in the 90s was they were kind of cool and they did


some novel things, but they couldn't really do anything particularly interesting.


So we abandoned them for a while.


And then we got more computing power and things got faster and by magnitude, huge magnitudes


over the course of ten years.


And so I'm going to really grossly oversimplify, but deep learning is basically this times


about a million.


And I'm not playing a Dr. Evil exaggeration here.


I actually was talking to Dr. Matt in our data science department and I was like yeah,


I used to build these neural networks with like eight nodes or something and three or


four hidden layers.


How many parameters he's building in this model, I'm going to show you, and you're like


how many parameters is that?


It's like about three and a half million.


So this is in some ways quantity over quality.


There's been new math behind this.


There's new things we had to build to make this possible.


But what we basically found is after a decade of increases in computer power, suddenly deep


learning can do some amazing things.


So Google has kind of told us they use it.


I want to give you a couple clues about what they've said that you might have missed.


Google has a course online on Udacity, a deep learning course that is taught by Google.


And there's a quiz in this course where they actually ask you how might you use deep learning


to power Google search?


And then they give you what they think the answer might be.


Well, this is the answer slide.


So a classifier is just the thing that basically takes the input and says, well, how likely


is a certain output?


So the answer to the quiz is use a classifier that takes the pair, query and web page as


an input, and classifies the pair as relevant and not relevant.


And what they're showing you here is if somebody types in Udacity deep learning, machine learning


and supervised learning should be relevant.


And that you can do this with deep learning.


And strangely, this looks a little bit like the kind of queries we saw before.


And so this is Google saying, hey, let me give you this little clue.


This is kind of what we're doing.


That Steven Levy paper definitely worth reading.


This is a quote with Jeff Dean who's in the machine learning group.


That said, by early 2014, Google's machine learning masters believed that should change.


I'm going to tell you what that is.


That is the heuristic approach to search, the ranking factors approach we all know championed


by Amit Singhal since he joined the group and ran search for years.


Did amazing things.


But at some point, the machine learning people said, we think we can do something different.


So we had a series of discussions with the ranking team.


We said we should try this and see is there any gain to be had.


Let's just try it.


Because Amit was very resistant to turning some of this over to machine learning.


The experiment his team had in mind turned out to be central to search how well a document


in the ranking matches a query.


As measured by whether the user clicks on it.


I'm not going to speculate on that, but kind of interesting to see that said.


We sort of said let's just compute the score and see what happens.


Okay, fine.


So about two years ago they did this experiment.


This experiment worked extremely well.


So well that they changed their direction on search.


Amit left the head of machine learning, took his job.


No one has speculated on the politics of that and I won't because I'm not at Google and


I don't know.


But it is no coincidence that the champion of the old way was replaced by the champion


of the new way.


And the champion of the new way is the head of machine learning.


So there was a major shift about two years ago.


We also have some clues.


Google has a site called TensorFlow, which is a platform for developing machine learning


that you can use.


It's very powerful.


Deep learning specifically.


And they also released a model that they call Word2Vec.


And I want to just kind of explain generally what this does.


So we have these very crude ways of matching relevance, matching a keyword to a document


that we used to use.


And they've evolved over time to things that we may have heard of, like LSI and LDA that


we talked about in the past.


And these models can kind of understand words in context.


But they don't really understand relationships really well.


They don't really understand phrases well and how they relate to each other and kind


of the semantics.


And so there's a deep learning model called Word2Vec.


And what it does, a vector, I'm not going to give a linear algebra talk here, but a


vector is a point in 3D space with a direction.


So they try to translate words into vectors based just on huge, huge amounts of words


in context.


So the machine, this is called, this is unsupervised, the machine is kind of learning about all


these words in context.


And what they found is with this deep learning model, it begins to understand relationships.


So there's a couple examples here where if you look at where king and queen fall in space


and you look at man and woman, they have the same relationship.


If you look at walking versus walk and swimming versus swim, the machine learns that these


have the same relationship.


The machine learns this without us telling them, without us trying to build the rules.


So suddenly we have a model for relevance, a very advanced model for relevance where


we can teach Google, we can teach the algorithm how the relationships between words work.


This is not a coincidence.


We think this is what's in play.


We know for a fact that RankBrain is not query translation.


So if you typed in Justin Bieber Lake, Google rightly, rightly says you meant Justin Timberlake.


Not that other bad man who we don't speak of.


That's a very personal topic for me.


This is query translation.


This is Google saying there's no such person as Justin Bieber Lake, you're an idiot.


We think you meant this.


That's query translation.


That's not what's happening in the age of RankBrain.


If you look at NJDMV, New Jersey, NJDMV are all in there being matched and so is MVC and


motor vehicle commission.


This is happening in realtime at the same time.


Google is not saying NJDMV equals New Jersey motor vehicle commission so we're only going


to match this thing we translated to.


No, they're matching everything in realtime all at once.


This is not query translation.


This is a new way of factoring in relevance.


Is this specific query RankBrain?


I don't know.


I'm going to say two things about that.


One, it doesn't matter in a sense.


This is real.


This is what we're dealing with today.


What you call it isn't that important.


But Dr. Matt in data science built a prototype a couple months ago.


I'm going to tell you right now, this is not available and you can't use it and I'm sorry.


I'll probably get that star docked off of my ratings right now.


But I'm not going to announce that you can use it 20 minutes from now.


We are trying to build some of this into product.


And he came with an idea and he said, well, I've got this idea.


And I'm like, oh, yeah, I'd be interested.


And he's like, oh, well, I already kind of built it.


So let me show you.


And this is just kind of a piece of that.


And so what this does is take a query and it pulls up the SERPs and it uses a deep learning


model to try and figure out what's relevant.


In this model, we can type in New Jersey DMV and we can see that one of the results, state


of New Jersey Motor Vehicle Commission is considered high similarity, about 96%.


So what I'm saying is I'm not sure if that previous slide is RankBrain, but we can build


a deep learning model that basically produces the same result.


So we're converging on the idea that RankBrain very likely or deep learning, RankBrain may


be broader, deep learning very likely has a role in determining relevance in 2016.


So, all right, thank you for sending through that ten minutes.


So what do we do about it?


I do want to make this tactical.


And obviously that's the tough part of all this.


All right.


I'm going to do something I don't usually do.


This is Keyword Explorer.


I'm going to use one of our products as a context.


I'm not trying to sell it to you.


You can do this the hard way, the old way with Excel and paper and pencil, whatever


tools you want to use.


I'm going to try and give you a method you can use.


But I'm going to use Keyword Explorer as a context because in building it, we really


did build it from the standpoint of how we ourselves do keyword research and where we


think we need to be in the next year.


And so it does echo the kind of approach I'm talking about.


So let's go back to that space needle height example.


And we pull up our keyword research, our real basic research, and we get a thousand results


back.


How do we target that?


Well, we can build a thousand pages.


And we used to do this.


And it was stupid.


And we stopped.


I hope.


I hope you stopped.


If you haven't stopped, stop.


Or we could stuff a bunch of it just into one paragraph.


And we could get a paragraph like this.


Space needle height.


The space needle height in Seattle is 605 feet.


The space needle height in stories is just over 60.


It's interesting to note, it's not interesting at all, that the space needle height comparison


to the Empire State Building is about half as high.


In contrast, the Seattle space needle height comparison to Chicago's Willis Tower is only


about one-third the height.


Actually very proud of this crap SEO copy that I wrote.


But you know and I know, it's not natural.


We stuff these very similar phrases into a paragraph.


This is not how people talk.


It's not how people write.


It's what we're trying to get away from.


So we don't want to do that either.


And truthfully, that's still only about five phrases.


And we've got 995 to go.


That's going to be a pretty lousy experience.


So in keyword explorer, we have this grouping option.


And it does what you think it does.


You know, putting similar, very similar keywords into groups.


And this is something you can do.


You know that there are a whole lot of phrases that are only going to vary by a couple words.


They have basically the same concept.


We can group those.


And let's say I pick three groups.


So I've got groups that are a little bit different.


Space needle how tall, which is a little more question oriented.


Space needle height, our original.


And height in stories, which is conceptually a bit different.


So let's say I pick these three groups.


These are essentially concepts, okay?


But one way I might go about targeting them is to pick a representative phrase for each


concept.


I went through about 50 names, like exemplars and prototypes and archetypes.


I'm just going to call them ideals.


So pick an ideal phrase that represents the concept.


And so when I pick these three concepts, I end up with something like how tall is the


space needle, height of the space needle, and space needle is blank stories.


So I'm trying to pick something that fits more of a natural language profile.


And then I can write a paragraph like this.


How tall is the space needle?


The height of the space needle in Seattle, Washington is 605 feet, including the antenna.


Interestingly, the space needle is approximately 60 stories tall.


It only occupies six floors, with most of the tower being structural.


While it was once the tallest building in Seattle, the space needle now ranks only seventh.


I got all of my phrases in there and even an extra one.


It sounds much more human.


But here's the thing that you might be missing.


If I go back to that original grouping page, that space needle height category represents


300 keywords.


This paragraph in my original thousand keyword research represents about 500 keywords.


And so by taking my keywords, grouping them in the concept and bringing that concept back


into kind of an idealized phrase, I'm still targeting keywords, but I'm using those to


target much, much, much larger groups and not creating this kind of spammy chunk.


So I'm going to call this approach the three Gs.


It took like three days to come up with things that all had the same letter.


I went through the three Cs and the three Fs.


The three Gs is where we landed.


So let's go back to our blenders situation.


So the first thing we do, we gather all the keywords, get our big list.


We return a thousand at a time.


You know how it goes.


It could be 50,000 by the time you're done.


And we pick these groups.


We put them into groups.


These are kind of our concepts.


So for blenders, let's say we ended up with best for smoothies, best for frozen drinks,


best blenders money.


And we generate these ideal phrases.


So best blenders for smoothies, top rated blenders for frozen drinks, what's the best


blender for the money?


And now by using these ideal phrases, we can target the concepts and hopefully dozens or


hundred keywords all at once, but we're not dealing with the concept as some kind of generic


space.


We're dealing with actual phrases that we can do all the things with that we've typically


done.


So how do you pick those ideal phrases when they group?


All right.


I'm going to pitch keyword explorer a little bit here.


And those of you who are Moz Pro customers already use it and can already use it for


free.


We have a score called potential that I helped build that takes volume, difficulty, opportunity,


and kind of helps you find the sweet spot.


And so one thing it will let you do actually is take an entire group, turn it into a list,


and find the keywords in it that sort of have the best combination.


So we pick something like best blender for smoothies.


The other thing we could do down the road that we'd like to be able to do is use deep


learning to fight deep learning.


So what Matt's tool will let you do is enter in multiple phrases, look at all the results


that come back, and tell you what matches all the phrases best on average.


And so we can type in something like best blender for smoothies, best smoothie blender,


smoothie blender reviews, best smoothie maker reviews, and it comes back and says, you know


what, the best smoothie blender for every budget is a pretty good match to the whole


mess and you'd do all right if you had something like that.


This is where we're headed.


As Google uses deep learning more, we are headed to having to build deep learning based


tools and that's what this is.


So there may come a time where we're able to say to you, you know what, given this entire


group of 500 keywords, this is actually what we think you should target.


Maybe these five would cover it pretty well.


So we hope that's coming.


But I think you can still conceptually do this even without deep learning.


So again, gather, group, generate, scoop up all your keywords, put them into these conceptual


groups, and then from every group, pick an idealized phrase.


Now there's one more step and I'm going to call it gap, which is this.


You might be thinking, okay, so do these become our keywords?


These become our keywords that we rank track and do all our traditional SEO activities


on?


I'm going to say no.


These are keywords that you're going to build content around.


But there's going to be gaps.


You're not going to know exactly.


You're making your best guess of how Google could take this entire group of keywords and


kind of turn it into one useful thing.


But it's going to leave some stuff out.


So we're still going to try and rank track.


We're still going to follow Google results across as many of those originals as possible,


look for the gaps, and then use those gaps to refine these concepts or essentially add


a few more.


But the goal at the end of the day is that not only are we kind of representing concepts


as Google sees them, but instead of trying to craft content and pages around thousands


of key phrases, maybe we're just using dozens or hundreds that really represent that entire


group and we're going about it in a more intelligent way.


All right.


So you may look at this and say, well, why don't we just write for people?


Right?


Why can't we just write for humans?


And you can't.


And that would be a good thing.


If you have the choice between SEO, keyword spam, stuffing, and writing for people in


2016, write for people.


But the reason I'm going to suggest we not just do that is basically this.


We go back to early days of Google, say 1999.


We have that heuristic approach.


If this, then that.


More complicated than that.


But basically the set of rules that became the ranking factor.


And now we've moved on to machine learning.


But machine learning is still not people.


It doesn't understand relationships.


It doesn't understand semantics in the way we do.


And so we're not there yet.


And so when you can write for people and you'll be in pretty good shape, the best thing you


could do is understand how Google tries to emulate how we think.


And to match this process.


And so there is still a technical SEO aspect where I think, yeah, you'll do okay if you


stick to the far right.


But if you can master that middle ground and understand what Google's doing with RankBrain,


understand how they're treating relevance, you're going to be a lot better off.


So we've come this far.


We can go from these old queries like space needle height to how tall is a space needle.


And there are ways for us to represent that in our keyword research and take thousands


of phrases and reduce them.


I want to leave you with kind of one last thought.


I think it's easy to look at this and say, well, voice search is kind of a gimmick, right?


And we're going to have to learn to do voice search over the next year or two.


And some people aren't going to adapt to that.


And what I want to say is I think that's completely wrong.


I don't think we have to learn to do voice search.


I think, you know, somebody in the mid 40s has been using search since the beginning.


We unlearned voice search.


Voice search is our natural modality.


We talk in phrases.


But when early search engines came out, early search engines were dumb.


And so we became dumb.


And we typed these one and two and three word queries.


And that's how we started to do SEO and keyword research because search engines were dumb


and that's how they work.


So what we're doing now is not learning to do voice search.


We're untraining ourselves.


And if you have kids, you've seen this.


I have an Amazon Echo at home.


The kids use Siri.


They talk in sentences.


They walk up to it.


They use voice.


It's natural for them.


But they will never do these strange cryptic two and three word queries in their lives


because they'll never have to because search engines won't be that dumb again.


So we aren't learning to do this.


We're unlearning.


And Google has to adapt the entire algorithm to this.


So it doesn't matter whether your voice search is 1% or 5% or 10%.


This is affecting all of us right now.


It's been affecting us, all of us, for three years.


So again, I hope you'll try this process.


Gather, group, generate.


The whole idea to break it down is we're going from keywords to concept back to keywords


so that we still have something tactical to do and we can still pursue our keyword research.


I think that's it.


I've got time for questions.


Thank you.


Wow, that was awesome, Pete.


Thanks.


A little fast.


So we've got a couple questions here.


The first one is are related topics and related queries in Google Trends a good place to start?


A good place to learn about how machine learning, how Google understands our target concepts?


Yeah, I think so.


We don't know exactly what engine they're using for related questions, but it does tell


us what they think is relevant.


Related questions, as Rob said yesterday, is actually great for featured snippets, for


seeing from a query what kind of questions might be associated with that.


So if you're trying to target a featured snippet especially, you can say, hey, you know what,


these semi-core queries, these are the kind of questions Google matches to that.


They're not seeing that always as directly relevant, but it's taking you on this kind


of journey of what they match, two related searches, two related questions, three or


four steps down the road.


So it can really start to expand your research, yeah.


So the question I was going to ask actually came up here, too, is what's the status and


outlook of this and international?


Are the same algorithms applied in languages other than English?


As far as we know, because RankBrain was deep learning based, it doesn't care.


So it is supposedly out globally.


So I have not measured the impact in other.


What about, is there like, do you have any estimation on the percentage of queries that


RankBrain covers?


Like are there any queries that don't have?


Yeah, so this is the, Google has tried to explain this, but I think they purposely made


it confusing.


Everything.


But it doesn't always have an impact.


So RankBrain is, it's relevant, this is a new relevance engine.


So this is replacing how we used to measure relevance.


So RankBrain is always there saying, what's relevant to this, and trying to take a broader


view.


But in some cases, that's going to be very, very similar to what it used to be.


So it's always there, it's always operating, but it might only be a few percent of queries


that it really impacts in a heavy way.


It's certainly impacting very long tail queries, questions, things like that, more than the


narrow ones.


But we can see even with that kind of Blender blended, Blender's example, that's probably


not RankBrain.


But Google is looking at the context of searches, they look at what people are clicking on and


they're saying, hey, these are really very, conceptually three very different things.


And so all of this new stuff is operating on basically everything.


But whether it's actually changed any given query for you, that's what you kind of have


to dig in and see.


Okay.


How to Beat Your Competitor's Rankings with More *Comprehensive* Content - Whiteboard Friday

Howdy Moz fans and welcome to another edition of Whiteboard Friday.


This week we're going to chat about something, well, something I've noticed, something we've


noticed here at Moz, which is that there seems to be this extra weight that Google is putting


right now on what I'm going to call content comprehensiveness, the degree to which a piece


of content answers all of a searcher's potential questions.


I think this is one of the reasons that we keep seeing statistics like word length and


document length is correlated, well correlated with higher rankings and why it tends to be


the case that longer documents tend to do better in search results.


I'm going to break this down.


On the broad ranking inputs, when Googlebot is over here and sort of considering like


which URL should I rank, someone searched for best time to apply for jobs and what am


I going to put in here?


They tend to look at a bunch of stuff, right?


Okay, domain authority and page level link authority and keyword targeting for sure and


topic authority of the domain and load speed and freshness and that.


These four, all of which are sort of related, right?


Searcher engagement and satisfaction, right?


The degree to which when people land on that page they have a good experience, they don't


bounce back to the search results and click another result.


The diversity and uniqueness of that content compared to everything else in the results.


The raw content quality, which I think Google has probably lots of things they use to measure


content quality including engagement and satisfaction, so these might overlap.


Then comprehensiveness.


It's sort of this right mix of these three things, right?


The depth, the trustworthiness and the value that the content provides seems to really


speak to this.


It's something we've been seeing like Google kind of overweighting right now, especially


over the last 12, 18 months.


There seems to be this confluence of queries where this very comprehensive content comes


up in ranking positions that we wouldn't ordinarily expect.


It throws off things around link metrics and keyword targeting metrics and sometimes SEOs


go, what is going on there?


In particular, we see this happening with informational and research-focused queries,


with product and brand comparison type queries like best stereo or best noise-canceling headphones


or those types of things, and broad questions, implicit or explicit questions that have complex


or multifaceted answers to them.


Probably yes, you would see this type of very comprehensive content ranking better.


In fact, I did some of these queries.


For things like job application best practices, gender bias in hiring, resume examples.


These are like broad questions, informational, research-focused, product comparison stuff.


Then not so much, you would not see these in things like job application for Walmart,


which literally just takes you to Walmart's job application page, which is not a particularly


comprehensive format, and the comprehensive stuff ranks vastly below that.


Gender bias definition, which takes you to a short page with the definition, and resume


template Google Docs, which takes you to Google Docs' resume template.


These are almost more navigational or more short format answer in what they're doing.


I didn't actually mean to replace that.


If you want to nail this, if you identify that your queries are not in this bucket,


they are in this bucket, you probably want to try and aim for some of this content comprehensiveness.


To do that, I've got kind of a three-step methodology.


It is not easy.


It is hard.


It is going to take a lot of work.


I don't mean to oversimplify, but if you do this, you tend to be able to beat out even


much more powerful websites for the queries you're going after.


First off, you need to identify all, all the questions that are inherent in the searcher's


query.


Those could be explicit or implicit, meaning they're implied or they're obvious.


They could be dependent on the person's background, the searcher's background, which means you


need to identify who are all the types of people searching for this and what differences


do they have.


We may need different types of content to serve different folks, and there needs to


be some bifurcation or segmentation on the page to help them get there.


Same thing on their purpose.


Some people who are searching for job application best practices may be employers.


Some people may be job applicants.


Some may be employees.


Some may be people who are starting companies.


Some may be HR directors.


You need to provide that background for all of them.


One of the ways to do this, to get all the questions, truly all the questions, is to


survey.


You can do that to your users or your community, or you can do it through some sort of third-party


system like, for example, Ollie Gardner from Unbounce was very kind and did this for Moz


recently where he was asking about customer confusion and objections and issues.


He used Usability Hub's tests.


Usability Hub, you can use this there as well.


You can also use Q&A sites, things like Quora.


You can use social media sites like Twitter or LinkedIn or Facebook if you're trying to


gather some of this data informally.


Once you have all these questions, you need to assemble the information that answers all


of these types of questions, hopefully in a way that your competition cannot or would


not get.


That means things like proprietary data, competitive landscape information, which many folks are


only willing to talk about themselves, not how they relate to others.


It means industry and community opinions, which most folks are not willing to go out


and get, especially if they're bigger, aggregated or uniquely processed metrics.


Obviously, one of the most salient recent examples from the election that's just passed


is sites like FiveThirtyEight or The Upshot or Huffington Post who build these models


based on other people's data that they've aggregated and included.


It also could mean that you are putting together information in visual or audio or interactive


mediums.


Now that you have this competitive advantage in terms of the content and you have all of


the questions, you can assemble this stuff in formats that other people don't or won't


create or use.


That could be things like guides that require extraordinary amounts of work.


The Beginner's Guide to SEO is a good example from Moz, but there are many, many others


in all sorts of fields.


Highly customized formats that have these interactive or visual components that other


people are generally unwilling to invest the effort in to create.


Free to download or access reports and data that other people would charge for or they


put behind paywalls.


Non-transactional or non-call-to-action focused formats.


For example, a lot of the times when you do stuff in this job search arena, you see folks


who are trying to promote their service or their product and therefore they want to have


you input something before they give you anything back.


If you do that for free, you can often overwhelm the comprehensiveness of what anyone else


in the space is doing.


This process, like I said, not easy but can be a true competitive advantage, especially


if you're willing to take on these individual key phrases and terms in a way that your competition


just can't or won't.


I'd love to hear if you've got any examples of these, if you've tried it before.


If you do use this process, please feel free to post the results in the comments.


We'd love to check it out.


We'll see you again next week for another edition of Whiteboard Friday.


Aren't 301s, 302s, and canonicals all the same - Whiteboard Friday

Hey, Moz fans, it's Dr. Pete, your friendly neighborhood marketing scientist here at Moz.


And I want to talk today about an issue that comes up probably about every three months


since the beginning of SEO history.


And it's a question that looks something like this.


Aren't 301s, 302s, and canonicals all basically the same?


So if you're busy and you need the short answer, it's no, they're not.


But you may want the more nuanced approach.


This popped up again about a week ago because John Mueller on the webmaster team at Google


had posted about redirection for secure sites.


And in it, someone had said, oh, wait, 302s don't pass page rank.


John said, no, that's a myth.


It's incorrect that 302s don't pass PR, which is a very short answer to a very long technical


question.


And so SEOs, of course, jumped on that and it turned into 301s and 302s are the same.


Cats are dogs, cakes are pie, up is down.


And we all did our freak out that happens four times a year.


And so I want to get into why this is a difficult question, why these things are important,


why they are different, and why they're different not just from a technical SEO perspective


but from the intent and why that matters.


And I've talked to John a little bit.


I'm not going to put words in his mouth, but I think 95% of this will be approved.


And if you want to ask him, that's OK afterwards too.


So let's talk a little bit about classic 301, 302.


So a 301 redirect situation is what we call a permanent redirect.


And what we're trying to accomplish is something like this.


We have an old URL, URL A, and let's say, for example, a couple of years ago, Moz moved


our entire site from seomoz.org to moz.com.


That was a permanent change.


And so we wanted to tell Google two things in all bots and browsers.


First of all, send the people to the new URL.


And second, pass all the signals.


All these equity, PR, ranking signals, whatever you want to call them, authority, that should


go to the new page as well.


So people and bots should both end up on this new page.


A classic 302 situation is something like a one day sale.


And so what we're saying is for some reason we have this main page with the product.


We can't put the sale information on that page.


We need a new URL.


Maybe it's our CMS.


Maybe it's a political thing.


Doesn't matter.


And so we want to do a 302, a temporary redirect that says, hey, you know what?


All the signals, all the ranking signals, the PR, for Google's sake, keep the old page.


That's the main one.


But send people to this other page just for a couple of days.


And then we're going to take that away.


And so these do two different things.


One of these tells the bots, hey, this is the new home.


And the other one tells it, hey, stick around here.


This is going to come back.


But we want people to see the new thing.


And so I think sometimes Google interprets our meaning and can change things around.


And we get frustrated because we go, why are they doing that?


Why don't they just listen to our signals?


And the problem is this.


In the real world, we end up with things like this.


We have page W, that 301's to page T, that 302's to page F, and page F rel canonicals


back to page W. And Google reads this and says WTF.


What do we do?


We've sent bad signals.


We've done something that just doesn't make sense.


And Google is forced to interpret us.


And that's a very difficult thing.


And we do a lot of strange things.


We'll set up 302s because that's what's in our CMS.


That's what's easy in an Apache rewrite file.


We forget to change it to a 301.


Our devs don't know the difference.


And so we end up with a lot of ambiguous situations, a lot of mixed signals.


And Google is trying to help us.


And sometimes they don't help us very well.


But they just run into these problems a lot.


And in this case, the bots have no idea where to go.


The people are going to end up on that last page.


But the bots are going to have to choose.


And they're probably going to choose badly because our intent isn't clear.


So there's a couple situations I want to cover because I think they're fairly common.


And I want to show that this is complex.


Google can interpret.


But there are some reasons.


And there's some rhyme or reason.


So the first one is that long-term 302s are probably going to be treated as 301s.


They don't make any sense.


If you set up a 302 and you leave it for six months, Google is going to look at that and


say, you know what?


I think you meant this to be permanent.


And you made a mistake.


We're going to pass ranking signals.


And we're going to send people to page B. And I think that generally makes sense.


Some types of 302s just don't make sense at all.


So if you're migrating from non-secure to secure, from HTTP to HTTPS, and you set up


a 302, that's a signal that doesn't quite make sense.


Why would you temporarily migrate?


This is probably a permanent choice.


And so in that case, and this is actually what John was addressing in this post originally,


in that case, Google is probably going to look at that and say, you know what?


I think you meant 301s here.


And they're going to pass signals to the secure version.


And we know they prefer that anyway.


So they're going to make that choice for you.


If you're confused about where the signals are going, then look at the page that's ranking.


Because in most cases, the page that Google chooses to rank is the one that's getting


the ranking signals.


It's the one that's getting the PR and the authority.


So if you have a case like this, a 302, and you leave it up permanently, and you start


to see that page B is the one that's being indexed and ranking, then page B is probably


the one that's getting the ranking signals.


So Google has interpreted this as a 301.


If you leave a 302 up for six months, and you see that Google is still taking people


to page A, then page A is probably where the ranking signals are going.


So they can give you an indicator of what their decision is.


It's a little hard to reverse that.


But if you've left a 302 in place for six months, then I think you have to ask yourself,


what was my intent?


What am I trying to accomplish here?


Part of the problem with this is that when we ask this question, aren't 302s, 301s, canonicals


all basically the same, what we're really implying is, aren't they the same for SEO?


And I think this is a legitimate but very dangerous question.


Because yes, we need to know how the signals are passed.


And yes, Google may pass ranking signals through any of these things.


But for people, they're very different.


This is important.


I'm going to talk about real canonical briefly.


Because real canonical is a bit different.


We have page A and page B again, and we're going to canonical from page A to page B.


And what we're basically saying with this is, look, I want you, the bots, to consider


page B to be the main page.


For some reason, I have to have these near duplicates.


I have to have these other copies.


But this is the main one.


This is what I want to rank.


But I want people to stay on page A. And so this is entirely different from a 301.


Where I want people and bots to go to page B. And it's different from a 302, where I'm


going to try and keep the bots where they are, but send people over here.


So take it from a user perspective.


I have had in Q&A all the time, people say, well, I've heard that real canonical passes


ranking signals.


Which should I choose?


Should I choose that or 301?


What's better for SEO?


And that's true.


We do think it generally passes ranking signals.


But for SEO is a bad question.


Because these are completely different user experiences.


And either you're going to want people to stay on page A, or you're going to want people


to go to page B.


And so I just want you to keep in mind when you look at these three things, it's true


that 302s can pass PR.


But if you're in a situation where you want a permanent redirect, you want people to go


to page B, you want bots to go to page B, you want page B to rank, use the right signal.


Don't confuse Google.


They may make bad choices.


Some of your 302s may be treated as 301s.


It doesn't make them the same.


And they're all canonical is a very, very different situation that essentially leaves


people behind and sends bots ahead.


So keep in mind what your use case actually is.


Keep in mind what your goals are.


And don't get over focused on the ranking signals themselves or the SEO uses.


Because all of these three things have different purposes.


So I hope that makes sense.


If you have any questions or comments, or you've seen anything weird actually happen


on Google, please let us know and I'll be happy to address that.


5 Ways to Get More with Less Through Link Building — Whiteboard Friday

Hi Moz fans, welcome to What More Try Day. I'm Paddy Moogan, I'm the CEO of Aera. Today


we're going to talk about link building. I'm going to talk to you about five tactics to


get more from less with your link building. Before we go into those, I'm going to set


some context about what we mean by more from less. So at the moment in the world in the


UK and the US, lots of businesses are being squeezed because of rising costs. Customers


are struggling to cope with their living costs as well, which is affecting companies and


what products they can sell. So what happens as a result is agencies and in-house teams


come under more pressure to get more results, but maybe from less budget or they won't get


the budgets increased because companies are worried about rising costs. So what we need


to try and do as agencies and in-house teams is think of tactics to get more from less.


And what we mean by that is inputs are normally around time, resources, budget. Those are


the things that drive the work that we do. And the outputs normally links, traffic, revenue,


conversions, those kinds of things. What we need to try and do is not need more and more


of this to get more and more of that. So we can actually use the same amount of inputs


or lesser inputs to drive the same outputs in terms of the links, traffic and revenue.


That's really, really good for our stakeholders. If you work in-house, your bosses will be


really happy about that. If you're an agency, your clients will be really happy about that


as well. So here's five ways to think about doing those things, all focused around digital


PR, link building and content marketing. So number one, try to favour repeatable content


ideas. So when you're coming up with ideas in the ideation process, try and look for


ones that you can repeat over and over again. So for example, can you plug in some new data


for 2022, then for 2023? Can you put some new information in there, some new facts,


rather than having a campaign that has a fixed start and an end point? If you have a fixed


start and an end point, once that end point comes, you can't keep outreaching it. So the


results you're going to get from that content are quite limited by time. Whereas if you


can keep that content going over and over again and keep refreshing it with new ideas,


new data and new data points, you can keep refreshing it, keep getting links and keep


getting more from that content, rather than relying upon building new content every single


time. Number two, don't stop outreaching content.


We see this quite a lot. So 25% of SEOs outreach content over and over again, leaving 75% that


just leave content behind once they've gotten a few links to it. What normally happens is


you launch a campaign, get some links, then you move on to the next one. These campaigns


that you've already outreached, if you can keep going with them, if they're working,


just keep doing the outreach because whilst you're building new content and launching


more campaigns, you can keep getting links to the older content and keep getting results


for that content as well, which again means you're getting more from less from the same


outputs, you're getting more and more outputs. Number three, look for existing link worthy


content. So when you start working on link building and launching content, it's really


tempting just to launch brand new content, launch brand new campaigns. Whereas sometimes


there's content sat right there on the website that is already link worthy and may already


have some links going to it. So rather than diving off and creating new content, take


a quick look at the website itself, see which pages have got lots of links to them, see


why they've gotten links and see if you can pick them back up again. If you can, the nice


thing about this is you can keep building links to the old content whilst you're launching


brand new campaigns. So you don't need to not launch brand new campaigns, but try not


to default to launching new ones and forget about what could be right in front of you


with the existing content. Number four, I'm a big fan of simplifying execution. Again,


it's really tempting to think of an idea and think of the most complex, the most shiny,


interactive way to launch that content. And sometimes that's fine, that's the right thing


to do, but don't let your mind default to that because that is expensive in terms of


your own resources and budget. So try and simplify as much as you can. If you've got


a good idea, it can be launched inside a blog post or just a very, very simple graphic or


even a short few lines of text with some data. Just focus on that rather than worrying about


design and development resources because if the angle is good enough, it will still get


links and you don't need to worry about going viral. If you spend just a couple of hours


launching a blog post, it gets a handful of links, that's still a good result compared


to spending six or eight weeks on a big, shiny, interactive piece to get maybe 10 or 15 links.


So don't worry too much about going viral, focus on the angles, focus on keeping things


simple. Number five, this is a really overlooked area in SEO in our experience, so look for


internal linking opportunities. If you have pages that have gotten lots of external links


pointing to them, you need to try and filter the equity from those pages through to your


commercial pages because usually the homepage gets the most links, but then you've got categories


and products which aren't normally that link-worthy for most websites, so they don't get a lot


of links pointing directly to them, but they're the ones who want to rank, right? They're


the ones that drive revenue. So if you have links going to your content, your campaigns,


maybe a blog, maybe some guides, try and filter that link equity from those pages to your


most important commercial pages, your products and your categories, then track the results,


track the rankings, track the traffic and you'll see that filtering that link equity


makes those pages rank a bit better and again, you're getting more from less because that's


just internal linking. You don't need more design, more development and you can still


drive more outputs with pretty minimal inputs. So that's it. That's five ways to get more


from less with link building. As we move into this era of businesses being a bit more picky


with their budgets and being squeezed a little bit more, these are going to be really, really


important to think about and even when we come out of recessions and things are going


well again, these are still good things to be thinking about. No stakeholder will be


unhappy if you're trying to get more from less when it comes to budget. So I'm Paddy


Moogan. If you want to follow me on Twitter, I'm at Paddy Moogan. I really hope that was


Google My Business: Battling Bad Info & Safeguarding Your Search Strategy

Okay everybody, we're going to talk about local SEO.


So even if you don't do a lot of local SEO, like me, it's so important to know what's


going on in that space because so many of Google's queries are turning into local SEO,


local intent, local everything.


And our next speaker is probably one of the foremost authorities on the planet in this


space.


She owns Sterling Sky Agency in Canada, owns the Local Search Forum, she wrote the Expert's


Guide to Local Search, local SEO, she's a Google contributor, all around general expert


and amazing human being.


She spoke last year at MozCon, I learned so much.


Excited to see what she talks about today.


Let's kick it over to Joy Hawkins.


Hello, so for those of you that aren't familiar with my background, I run a fully remote team


of employees in the US and Canada and everybody's remote, they all work from home.


And although I love working from home, I do often miss the ability to do office pranks


on my coworkers.


So last year during our annual meetup, we decided to have some fun and I had someone


install a Chrome extension on one of my colleagues' computers.


What it does is it changes the word cloud on your page to the word butt for every single


instance.


And we installed it, thought it would be really fun, but kind of forgot about it for a few


months until one day my coworker sent me a frantic message on Slack.


She wanted to know why one of our clients was using this website that was talking about


butt devices and she was very confused.


She's like, what are butt devices?


So she then proceeded to Google what they were and instantly wished that she hadn't.


See, my coworker, Nikki, she was very confused and she made some very bad decisions due to


having the wrong information.


I would say this is also true of many people in the local search industry.


I often see a lot of confusion and poor decision-making being made due to having the wrong information.


So, one of the things that we see a lot in the local search industry is these studies


that are done.


And they're talking about Google My Business and they often analyze performance based on


these things called views.


So these are these charts you see in the Google My Business dashboard and at first glance,


it seems like a very good metric to track.


Why wouldn't you want to see how many people have viewed your listing on Google?


However, over the years, I've watched many agencies use this metric and then run into


issues when their graphs look like this.


Why on earth did your views quadruple in one day?


Or this, views flatlined for a few days only to go back to where they were?


Or this?


The thing that people often get confused is because these examples are not matching up


with other real-world data.


So it's not like their calls flatlined and so they're kind of wondering like, should


I even trust this data?


Is it even correct?


And the biggest reason why people are confused is because they're misunderstanding what exactly


this metric is reporting on.


Going to Google's Help Center, if you try to see what their definition of a view is,


it's not extremely helpful.


I'm going to give you a few examples of how views are actually generated.


So every time that you browse on Google Maps, if you go to Google Maps and you zoom in or


you look around, every single business that has an icon, you are contributing one view


to that business.


So if you're like me and you zoom around a lot, every time you refresh is going to add


another view.


Additionally, if you're looking up, let's say your favorite restaurant on Google, there


are competitor listings at the bottom of each knowledge panel.


And you're not looking at these, but these are still all getting a view.


Photo views are very similar.


If you're browsing a restaurant listing, for example, and clicking around like crazy, every


single photo that is on your screen will get one view.


So you can kind of start to see how it's really easy for these numbers to get inflated.


And the best way to kind of think of a view is to think about like hit counters on websites.


So you know how these used to be these like awesome metrics that we used to think are


cool back in the 90s?


Well, honestly, this is very similar to how views are calculated.


They're extremely high inflated numbers.


So a much better metric to use is searches.


Although these are not unique numbers, at least they actually represent actual searches


done on Google for terms that surf your listing.


And one of the things I like to do with these is rather than looking at them in the Google


My Business dashboard, I like to import them into Data Studio.


So you can kind of see them month over month.


And you can do this via the GMB API.


And it's a much better way of looking at the data.


There is another metric also inside Google My Business that has a ton of value.


And I rarely see marketers in the local SEO space even reference it.


So inside Google My Business Insights, there is a section called search queries.


And I believe that one of the main reasons why these kind of get neglected is because


they only exist inside the Google My Business dashboard.


So they're not yet available in the API.


And they're not included in a lot of the third party tools that people use to analyze their


clients.


So thinking about this, if you have to go manually pull them from Google My Business,


wouldn't it be just easier then to look into Search Console, which does actually export


easier?


Technically, if you add UTM codes to a listing inside Google My Business, you can look at


traffic inside Search Console.


And if you're not really sure how to do this, I've linked to a page that will give you instructions


on step by step on how this is done.


So you can track traffic in Search Console from Google My Business specifically.


And you want to make sure, kind of one tip here, is make sure that you use the Google


and organic for the medium and the source.


If you don't, what ends up happening?


You actually end up stealing from your organic traffic.


You might have a new client and you look at their organic traffic and you see a decline.


Not exactly what you want to see for someone that just started working with you.


So always make sure you get that straight and there's more details in that article there.


But I really wanted to know exactly how the data inside Search Console compared with what


you see in Google My Business Insights.


So we decided to do this really in-depth case study.


We took 78 different Google My Business listings for 29 different businesses.


And for each one, we went in and we took the top queries inside Search Console for the


last three months and we compared it to what Google My Business Insights was showing for


the last quarter.


Inside Search Console, we were specifically looking at the impressions and clicks that


were for the page containing the UTM code, so as described earlier.


So as shown here, this is kind of an example.


We went in, we only looked at the page and the activity on that page that was matching


the exact URL we were using on the Google My Business listing.


And what we found after compiling all the data and comparing the two lists was that


66% of the top 10 queries were different.


So a lot more variance than what we were expecting to see.


I'll share an example that will help illustrate this.


This was an example of a plastic surgeon.


In the last quarter, according to Google My Business, these were the top 10 keywords that


surfaced the listing on Google.


However, if I look in Search Console, this is the top 10 list.


And the only one that's matched between the two lists is the one that I put in bold there,


which is rhinoplasty.


So these lists in this example are actually 90% different.


So quite some variance there.


And if this was a new client and you were trying to kind of figure out which terms were


driving volume based on these numbers, you could easily miss a ton of valuable information.


If I had just looked in Search Console, for example, I might have concluded that lip enhancements


were the thing to go after.


And I may have missed out on lip fillers or lip injections, which were both highly searched


according to Google My Business.


So the real question that I wanted to answer was, why are they so different?


And which one is correct?


So first, unlike any other metric available to us, search queries in Google My Business


Insights are unique visits.


So technically, it might report on the same person twice if they're using different devices.


So if I'm on my computer and I'm searching and then I go on my phone, it would report


me as two different people.


But it's not going to report me 10 times if I go on my computer and search 10 times for


a client.


So this is different than Search Console and kind of explains why there was some variance.


The second reason why the data sets didn't match is because one quarter inside Google


My Business Insights is not actually representing the last three months.


And I didn't know this.


Like my entire career working in local search, I did not know this until recently.


So we started digging around, looking for examples when I was putting together this


study.


And here's an example here of a screenshot of the last quarter according to Google My


Business Insights.


And I pulled this on May 13th of this year.


It was for a client of mine that does lawn care in the summer.


And he installs Christmas lights in the winter.


Due to how much I monitor his website traffic at various different places, I am extremely


confident that massive searches for Christmas lights installation do not happen after Christmas


is over.


It's also helpful if you have common sense here.


So the last three months, given when I pulled this, would be February to April.


But this is not reflecting searches done from February to April.


And I also kind of concluded that was true because as I was going back over several weeks


and checking this report, it didn't change.


These numbers stayed the same regardless of the fact that time had passed.


So I'm not sure what date range this is looking at.


It is looking at a one quarter period, but it is not the last three months.


And this helps explain why these two lists came up different.


Google does not do this in real time when it comes to Google My Business Insights.


The third and fourth reasons require me to dive a bit deeper to further kind of understand


why the data differed so much.


We decided to break out all of the search queries into four different categories.


And I'll kind of explain what these mean.


So the first category are near me terms.


They are literally that.


They are terms that include near me.


So like plumber near me or doctor near me, for example.


The next type we targeted was branded.


So we wanted to see, okay, someone's searching specifically for a local company or a person


that works at that local company.


The third type that we did was implicit.


So this is where the user puts in a search that is local in nature, but doesn't actually


specify a location.


So for example, when you just search plumber or you just search lawyer, Google returns


local results even though you didn't actually specify a city.


And then finally, explicit queries.


This is where the person actually specifies a location.


So for example, lawyer Chicago or plumber Denver, et cetera.


That would be considered explicit query.


So here's an example just kind of comparing the two.


So I'm Canadian, first of all.


And because I'm Canadian, one of the only zip codes that I know is 90210.


So I just use that as a default for all my examples here.


So I sent my location to 90210 Beverly Hills and I did a search for Botox and I did a search


for Botox Beverly Hills.


And I wanted to see just how different these are.


And you'll see here these two results sets are very different.


The local pack results are showing different businesses.


And you'll see, of course, the fact that Google does return local results for Botox, tell


you that they do consider it to be a keyword that has local intent.


However, something that I thought was really interesting is when you scroll down and you


start looking at organic results, Google is not showing me local businesses at all for


Botox.


The only exposure these businesses are getting is in the three pack.


If you go down to the organic results, it's extremely unlikely that any local business


has the SEO budget to compete with a site like Botox.com.


However, when you search Botox Beverly Hills, for example, the organic search tells a very


different story.


And you'll kind of see here we've got the local businesses showing.


Second I put in Beverly Hills, all of a sudden the organic results change.


Another example here is comparing car accident lawyer with car accident lawyer Beverly Hills.


You'll see again the three pack, completely different results.


However, the organic results, similar to the last example, are also different.


But when I don't enter a city, Google is actually pulling in a lot of the attorneys from Los


Angeles, which is kind of interesting.


The moment I start specifying Beverly Hills, it is showing me specifically Beverly Hills


attorneys.


Even the directories that rank in both cases are ranking different pages.


So that's something to kind of keep in mind.


So I decided to try this in a town that I was a little more familiar with that was local


to me.


So Aurora is a suburb of Toronto.


Most people that live in Aurora, they work in Toronto, but it's 40 minutes away from


Toronto.


And I found the same thing happen.


When I was searching for car accident lawyer located in Aurora as my location, I was getting


all these organic results from Toronto.


So it's almost like Google is pulling in results from the next biggest city over when it comes


to organic, that they're looking at a much broader area.


Whereas the moment you put in the city name, like Aurora, they are now specifically going


to pull in businesses that are in that town.


So how this relates to our study, we found that the majority of keywords that were represented


in the top 10 inside Google My Business Insights, but missing from Search Console, were implicit


queries.


So queries exactly like Botox or a car accident lawyer, where there's no city specified.


And this is kind of interesting.


So looking at Botox as an example, according to Google My Business Insights, Botox had


49 different people, like unique users, searching for it.


But according to Search Console, it had 18 impressions.


And I get like, we've already talked about two different reasons why these are different.


But there's something else that I noticed while digging into this.


If you don't rank organically, none of the mobile searches are going to count in Search


Console as impressions.


Now let this sink in, because this is something that I feel like a lot of people don't realize.


There are no website icons on your Google My Business listing when someone searches


on mobile.


They actually have to click on the listing first, then they see a website icon.


And that's important to remember, because Search Console tracks your website.


If there is no website icon present, it is not going to count as a search.


Google My Business, however, tracks your Google My Business listing.


So regardless if it's on mobile, whether there's a website icon or whatever, it would technically


get counted there.


But looking for this plastic surgeon, you'll see the majority of the traffic that he's


getting from Google My Business in the last six months is from mobile devices.


It's getting six times the clicks that desktop is getting.


And what's more interesting is when you look at the click-through rate, the mobile and


tablet click-through rate for this business is almost 30%, whereas desktop is 1.4%.


And like we all work in SEO, we know that 30% is a very, very abnormal click-through


rate.


But it all starts to make sense when you realize that there are so many searches on mobile


that are not counting.


They're not generating impressions at all, because there is no website icon present until


the person clicks on the listing.


And if I look at the entire site, just to make sure that there's something else not


going on here, the entire site does not have this pattern at all.


It is only when I start isolating the traffic coming to the website from Google My Business.


This is what it looks like when I look at traffic to the Google My Business listing


for just Botox.


So specifically that query.


You'll see again how crazy that CTR looks.


And the thing here is you cannot use Search Console impression data to get an accurate


view of search volume in local search, because if your business gets a lot of traffic from


the three-pack, it is missing all of that information from mobile searches.


Another really interesting takeaway from our study was the difference in search volume


for implicit queries compared to explicit queries.


When we looked inside Google My Business Insights, the total volume of the implicit queries was


51% higher than the explicit queries.


I found this fascinating.


After spending the last 14 years working a local search, I feel like all I ever hear


business owners and local SEOs say are things like, Joy, I checked my rankings for personal


injury, Lori Dallas, or my client doesn't rank for auto insurance, Chicago.


I hear that way more than I ever hear people say, you know, like, oh, I'm not ranking for


auto insurance or I'm not ranking for car accident lawyer.


So why is that?


Well, what I always assumed was that implicit queries wouldn't be as great for conversions.


And it's probably my background in Google Ads that kind of shapes this in my head.


After all, someone searching for plumber, they could be researching.


Maybe they want to be a plumber one day, but someone typing in plumber Denver is more likely


to hire someone near them, right?


Well, again, I kind of questioned myself, so I wanted to kind of answer this question


with our study.


So I went back to the same set of businesses.


However, I had to kind of squish the data down a bit because not all the businesses


we looked at originally are actually doing Google Ads.


And it's important to know, like, local businesses don't generally have massive budgets.


So these are all small accounts.


They're spending less than $10,000 a month on ads.


And this is often what we're working with in local search.


We have limited budgets, and it can sometimes be hard to come to the right conclusions because


our data is so limited.


So we were able to look at a total of 894 conversions across these 15 businesses to


try and see if we could identify any trends.


We only included keywords that had at least 100 impressions for this part.


And just to clarify, I'm not saying keywords with low search volume aren't important.


They are hugely important.


But we wanted to look at the top ones to get a better sense of how they are performing


in comparison to explicit queries.


And we also counted conversions as leads.


So contact form submissions, calls using call tracking, and live chat.


And this was the breakdown of conversion rates for the different types of keywords that we


found.


So contrary to previous assumptions, it showed that implicit queries actually converted at


a higher rate than explicit queries.


However, I want to really point out that this was not consistent across all industries.


It varied quite a bit.


So for example, compare these two.


The private investigator here, his top searched implicit queries converted better, but it


definitely wasn't true for the auto dealer.


The auto dealer, on the other hand, his explicit queries converted four times better than his


implicit queries.


Either way, the concept that explicit queries are always better at converting is wrong.


And that was kind of the question I wanted to answer.


Something else to keep in mind.


It is possible that there's another reason why small businesses and local SEOs tend to


focus so heavily on explicit queries.


According to our study, 40% of the top search console, top queries that were missing from


Site Google My Business Insights were explicit queries.


And this kind of led to the last and final reason why they varied so much.


This is pretty substantial.