Because at the end of the day, what we're trying to do is we're trying to understand


MozCon 2014 - 18 - Justin Cutroni - Supercharging Your Digital Analytics!

behavior, right?


And some of the data we get from our tools by default is not so great at telling us about


the behavior that's actually happening on a Web site or in an app.


So the first thing I want to go over is how do we really measure behavior?


We should measure the behaviors that we want to measure.


And I want to talk a little bit about maybe publishers.


I've heard a lot of people talking about content.


Everyone loves content.


And we all want to understand, you know, which content is valuable, are people reading my


content or people interacting with my content?


But so often we measure content based on kind of default numbers and the default data that


we get into our tools, right?


So here's just a general piece of long-form content.


And most people are just saying, oh, I'm just going to measure this based on maybe how often


it's seen, right?


And so I get things like page views.


Or I might measure it based on time, right, how much time people spend.


But time can be an inherently inaccurate metric in analytics, right?


Because time is calculated based on hits, right?


The hit between this page and the hit between this page.


That's going to give me a time calculation.


But what if that page doesn't exist?


What if that page never happens?


What if someone just looks at one page?


How do I measure the time or the engagement of that piece of content?


It's a little bit harder to do.


And so this is kind of what I mean by measuring what's important, right?


Measuring the real behavior, getting beyond kind of that general number of average time


on page or average time on site.


And so one of the ways that I like to talk to people about measuring engagement and measuring


people's engagement with content is measuring reading, right?


Do people actually read?


So instead of having this type of setup, having a more elaborate setup for my data collection,


right, to actually be able to understand, are people moving through the information


that I'm presenting to them, right?


Because this is a process when someone looks at a piece of content.


The first thing that happens is the page loads, right?


And that's what we get with our basic measurement like page views.


But then we can measure much more than that if we take a little bit of time to do some


customization.


We can actually measure if someone starts to scroll through the content.


Ooh, more data, right?


And I can actually then measure maybe points within their journey through that content.


So I might measure when they get to the end of the content and the comments start.


And then I might measure when they get to the bottom of the content, right?


And you can do this with a lot of different tools.


Obviously, I'm going to get a little bit partial to the tool that I use, right?


But you can collect all of this information.


And so what you're doing is you're measuring behavior much more accurately.


You're moving beyond those general metrics of just like average time on page, right?


And what do we get out of this?


Well, if I do something with Google Analytics events, okay, I can measure at the top when


the article is loaded.


I can then measure when someone starts to scroll, they start reading.


I can measure when they get to the end of the content and the beginning of the comments.


And then I can measure maybe when they get to the absolute bottom of the HTML.


Now, obviously, depending on how you have your pages implemented, this is going to change


a little bit.


But now all of a sudden, I get this kind of cool-looking funnel of what's happening on


that content, right?


So I'm moving beyond things like, well, what's the average time, which is so inaccurate.


So now I can see people, oh, look, this is how many people actually loaded it up, but


some didn't even start reading.


And then I can see how many people actually make it to the bottom of the content.


And then I can actually see, does anyone actually read through all those comments at the bottom


of my page?


It's so funny.


When I see blog posts about like what is the optimal length of content for this and for


that, I'm like, snap, just measure it, right?


You don't have to listen to anybody else.


Just measure it on your own site.


The other thing that's really cool is I can actually collect time in here, right?


Those are seconds.


I can actually measure the time between those behaviors.


So now I can get even more detailed information about, wow, how much time does it take for


people to actually start reading?


Or once they start, how much does it take for them to get to the bottom of the content?


Or how much does it take to get to the bottom of the page and see what's happening there?


So the really cool thing is you can evolve this, right?


So this is kind of just high-level stuff.


I can segment all of this data, right, in analytics, lots of different ways.


A bunch of people have talked about segmentation.


So I can look at it article by article or I could segment by content type if I want


to, right, and start to really, really understand behavior.


So I'm not just using the standard metrics that I get from my tools for page views or


average time.


I'm really measuring the behavior that's important to me, right?


So what does this do to your data?


Well, I would probably guess if anyone here writes a blog or, you know, has kind of like


just a more news type site, this is what I did when I did this on a blog.


We went from an average time on site from a minute 38 to over eight minutes when we


added this additional data collection, right, because we were getting more hits and we were


understanding the actual time people were spending on the content.


The bounce rate dropped from almost 80% down to 20%, which was amazing.


Why?


Because we were telling the analytics system, hey, this person is engaged with the content,


right?


And we were looking back saying, oh, they started scrolling, oh, they made it to the


end of the content.


Obviously, pages per visit didn't change too much.


We were just getting more detailed information as they were looking at a page.


And I was like, wow, this is amazing, right?


This could totally rethink a lot of the benchmark metrics that some publishers have or anyone


that's publishing content.


You can also start to do really cool things like start to segment out your users, right,


because now that we have more accurate time, we can create groups of users, right, people


that spend a lot of time, people that spend a little bit of time.


And, again, the whole idea with analytics is understanding the behavior of users, especially


your high-value users, so you can understand what makes them tick, right?


Do those people read a certain type of content?


Do they come from a certain type of campaign?


What makes them tick?


Why?


So I can then try to go emulate that behavior and drive more people to do that type of stuff,


right?


So I might look at the group that's spending long time, 25 minutes plus on the site, and


say, oh, are they looking at a certain type of content?


Maybe I should promote that type of content to people who don't spend as much time.


All right?


So this is I wrote up this technique.


You can grab it here.


It works with tag management kind of.


But again, the idea is if you're publishing content, measure the behavior that matters,


Don't take the standard metrics as a proxy.


So what about other types of businesses, measuring what matters?


Let's talk about e-commerce for a minute.


And I think e-commerce is just another environment that's rich with data, data about the behavior


of our users, but we don't always use that data.


We don't always get exactly what we could get, right, to really help us understand the


behavior of our users and the journey that they take.


Because e-commerce is getting complicated, right?


And part of it's what we do, right?


A lot of times, you know, this is the way that we think about e-commerce a lot, right?


Everyone looks at a product detail page.


They end up adding that to the cart.


They go through a checkout process, and ultimately they pay, hopefully, right, and they buy a


product.


This is what's normally happening in our data, I should say, right?


This is what we normally measure, right?


We look at this.


And there's a lot of other stuff that e-commerce businesses are doing to drive this process,


right?


If we look at the upper part of the funnel, upper part of the experience, right, well,


what's driving people to product detail pages?


Well, sometimes it's marketing, right?


It could be search, it could be paid, it could be e-mail, it could be social, right?


There are lots of different things we drive traffic.


And a lot of times, product pages are great landing pages, right, especially if we have


any product-specific campaigns.


But there's so many other things that businesses do to get people to those product pages, right?


Merchandising, right?


Whenever you're on Amazon, you're on any other type of site, there's always kind of like


a related products page.


There might be bestseller lists on more general category pages or home pages, right?


General site search, huge, right?


People sometimes don't browse, they may just go and they might search for exactly what


they're looking for.


All of these things are driving people into this process, right?


And understanding the investment that businesses make in these technologies and these tactics


is really, really important for us as analysts and as marketers, right, because we want to


know how to fine-tune things.


And then we also have to worry about how people kind of jump out of the process, right?


Because a lot of times, right when they're buying stuff, we sometimes offer them new


things, right?


That's great opportunity for testing, by the way.


But related products, oh, look, you're buying a camera, maybe you want to buy some batteries.


Or you're buying skis, maybe you want to buy a new pair of bindings or a new jacket along


with that, right?


Understanding that.


And then at the bottom, refunds, right?


A lot of us, we have to get into the habit of making sure we're understanding are people


actually returning these things to adjust our measurement.


And so when you look at this entire process, you know, we're really only looking at a very


small sliver of it.


And we're not really understanding all of the other factors that can impact what's happening


to our users.


And that's not so good, because we're measuring behavior.


That's what analytics does, understanding behavior.


And so some of the ways that I like to think about measuring analytics or measuring e-commerce


is by expanding the data that we have in analytics.


And we actually just introduced a new way to measure e-commerce.


We're calling it enhanced e-commerce.


And the whole idea is to create data that's better aligned with e-commerce businesses,


right?


Create information that better reflects the process that people go through and the tools


that businesses use in order to drive that process, right?


So, for example, we run this thing called the Google store.


You can buy all sorts of totally worthless Google crap.


Don't tweet that.


But if you need like a Google duffel bag or if you need like a Google lava lamp, and who


doesn't need Google lava lamps, right, all of this great stuff.


The cool thing is we actually run analytics on this and we try out a lot of new stuff.


But one of the things we wanted to do is we wanted to say, well, you know, if people are


looking at products lots of different ways on a Web site, we don't want to measure product


views by page views, right?


Those really aren't the best way, right?


Because people are seeing products different ways.


So we want to measure product impressions.


So every time a product is shown on a page, we can measure that now, right, with a product


impression.


So you can see like, oh, obviously on a product page, there you go.


Product is shown.


But we also have like you might also be interested in, right?


Look at that.


There's a whole bunch of other products.


There's an impression.


That might drive people to those other products.


So as a marketer, as an analyst, I want to understand, oh, look, this product is showing


up more and more in these other types of environments.


So I want to know that.


So now we can separate different types of impressions of products.


We also want to measure the behaviors, right?


Adding to cart, critical action, right?


We want to catch all of these e-commerce related actions so that, again, we're understanding


the entire process.


And we'll look and we'll see is there any breakdown in that process?


Is there any drop-off?


So that when we get all of this new data, we can kind of think of this, again, as a


funnel, as people moving through a different series of steps, right?


So we can see things.


How many times is that product actually showing up for the user?


Not based on page views, but in total, right?


It might be on a product page, it might be in some other type of list, like a related


products view.


But now I can get a much more accurate view of how much reach is this product having.


And then I can go down further.


How many times is it getting actually added to the cart, right?


How many times is it actually getting removed from the cart?


So I'm getting a lot more detail.


The actionability, we're going to talk about that in a second.


But product by product, I can see this motion, right?


So now I can see how many checkouts are actually happening, right?


There's some really cool new metrics, too, that we want to introduce.


We want to introduce a whole bunch of new ratios.


And these are really neat because I can start to segment product by product and understand


which ones are being more successful throughout the process.


So one of these metrics, I like to call it look to book.


Anyone use a metric like this right now for e-commerce?


Bueller?


Okay.


So hopefully, this math isn't too complex, right?


Look to book.


How many times was a product added to a cart versus how many times was it maybe seen?


Specifically, how many times did someone actually look at the details for that product?


Because a lot of times, that's the only way you can add a product is through details,


right?


So if I have 12 product views, excuse me, 12 add to carts but 648 product views, it's


only about 1.5% that it's actually getting added, right?


So if I'm an e-commerce business, what I want to do is I want to create a benchmark of look


to book, right?


So on average, when someone looks at a generic product, how often is it added to the cart?


And then I want to compare product by product and product by product category, what is my


look to book ratio, right, for each product to that site-wide benchmark.


Why?


Again, I want to get a sense of are there any products that are more actively added,


Are there any products that are maybe falling behind and not added enough?


And remember, you need to have context.


And the ultimate context is what are you actually doing, right?


So marketing, are you driving people directly to these pages?


Do you have a lot of direct response campaigns where you're telling people to bribe these


specific products?


Those are going to change the numbers dramatically, right?


But this is a great way to think about are people really, really, really, really, really


engaged with these products and adding them to my cart or adding them to their carts.


So look to book is a really great metric.


I think it's going to be very, very useful for marketers.


Again, it's a great way to understand the process product by product.


So we've talked a little bit about people looking at products, and we have new product


impressions.


We talked about different behaviors like adding to carts and removing from carts.


And we talked a little bit about product by product, breaking that down and doing a comparison.


Now another thing that we really need to understand is something I mentioned, lists, right, because


products just don't appear at one point on a Web site.


They appear everywhere, right?


We start to sprinkle products on every single part of the Web site, home pages, category


pages.


And so we have lists, lots and lots of lists of products, right?


And products can appear in multiple lists.


And a lot of times when you're in an e-commerce company, product managers will fight to have


their products appear in certain lists, right?


They know if they get their product shown on the home page on a roller, they're going


to get a lot more traffic to that product, and that additional traffic is probably going


to result in additional sales, which is really, really important, right?


So understanding these things is something that we need to provide a better view on.


And so the new concept in analytics is the idea of product lists.


And you get to create lists and name them whatever you want, right?


Obviously you could have a related products list.


You could have hot sellers, top sellers, best camera products, best soft plush Roger Toy


products.


The idea is, though, that you'll be able to use these lists and understand in a specific


list what's getting shown the most, right?


So I can see, like, same thing we talked about before, impressions, and then is it actually


driving traffic, right?


Am I getting clicks into the product pages?


And I even get a click-through rate.


So now I can do a comparison.


There's a lot more data behind this.


There's more and more information.


So specifically there are things like location.


So you can start to analyze not only the list that products are in, but maybe even the position


within those lists, right?


And so a lot of times in some of the limited data that we're seeing coming through because


this is so new, we're seeing obviously things that are higher in lists are getting lots


of clicks, right?


But your mileage may vary.


But again, the idea is now we're no longer kind of tied to this idea of product views


happen only on a product page.


Product views, product impressions can happen anywhere on the site.


And all of these different lists that we use, we can measure the performance of them.


So I think this is going to be really great for marketers measuring what's happening on


the site and what they're doing to get their products viewed.


So let's talk about some of the data.


We've talked a lot about the data types, impressions and the different behavioral data.


But what we really want to do is we really want to break this process down, kind of like


we talked about reading a page, breaking that down into a process.


Let's break this shopping behavior down and seeing where are we falling off, right?


And so we want to create some new visualizations that show the different behaviors that the


users have, right?


So first we can see all of the traffic, all of the sessions.


But then we can see sessions with product views, sessions with add to carts, and ultimately


we get more towards the bottom of the funnel the checkout process and ultimately people


transacting.


So now, obviously, we don't expect a nice linear slope.


This is going to differ from company to company.


Your business is going to have different data.


But the idea is can I somehow identify where I'm having issues, right?


Is there a step within this process that maybe if I look historically over my data as a benchmark,


I'm seeing a larger than normal drop, right?


So maybe comparing the holiday time to previous holidays, I'm able to see if I'm doing better


or worse.


Now the other thing about this data with this type of view is that it becomes segmentable,


which I always chuckle at because I wonder if that's a word, but we all use it all the


time, right?


Dr. Peep might know if it's a real word.


But now I can start to segment this entire process by almost any type, excuse me, any


dimension of data that I have, right?


So it might be a campaign or here it's actually visitor type we're showing so we can see new


visitor behavior versus returning visitor behavior in this data, right?


And if I had to guess, I would say returning visitors are probably much more efficient


at this process.


Why?


Because they've done it before.


It might be a little bit different for you.


But there's all sorts of other ways to look at this data, right?


So here I'm segmenting it by the actual device category, right?


So desktop versus mobile versus laptop, excuse me, versus tablet, right?


And I can quickly look at things like, oh, over here for tablet, wow, they're definitely


doing checkout way more often than the other types of devices, right?


And there are actually transaction rates a lot higher as well.


Now obviously your data will vary, right?


And it depends a lot on the experience that you're showing to your user.


Do you have a responsive site, right?


Are you totally optimized just for tablet in this case, right?


What is it about your business that makes these numbers the way they are?


Again, the context.


But the opportunity is there, right?


And you can see things like mobiles performing so much lower here than tablet.


Well, why is that?


Well, again, it's kind of pushing you in that direction that you should be looking at this


part of the process.


That conversion rate's way lower than those of tablet and of desktop.


But again, the idea is segmenting the entire process to understand everything that's happening


for different groups.


So how do you get all this data?


Well, implementation will vary, right?


So I want to mention that this is the enhanced e-commerce that we have in analytics, right?


And so all of our data comes mostly from JavaScript, right?


You can do some data import as well to get a lot of this data into analytics.


But you get most of it through JavaScript.


If you use tag management, we support tag manager as well.


You can do a lot of this in other tools, right?


You can fire off different types of data to measure product impressions, add to carts,


remove from carts.


It's all the types of data that we need to be measuring.


But I think to sum up this whole part, right, about measuring people scrolling through content,


measuring the data about e-commerce behavior, right, it's measuring the real behavior.


It's moving beyond the standard numbers, the standard metrics that we get in a lot of our


tools and understanding that real behavior.


Because ultimately that's our goal, is to learn that behavior of our successful users


and try to emulate that.


Understand the behavior of our not so successful users and try to figure out how to make them


more successful, whether it's reading or whether it's buying a product.


It doesn't matter.


So get the data that best matches what you're trying to understand and stop settling for


some of the standard metrics.


Okay.


So that's kind of number one.


First way of thinking about supercharging your digital analytics.


So the second thing I want to talk about is also related to behavior, right?


And analytics is really changing.


And it's changing dramatically.


You know, it's funny being here at a search in a marketing conference because I hear lots


of conversations about like technical SEO and I heard one term earlier today like reallocating


links and I was like I have no idea what the hell that is.


But it's so great because you guys sound like all of us data nerds at our data conferences


where we talk about panels and like third party cookies versus first party cookies.


In analytics the technology is really driving a lot of the change in our industry.


And it's doing it a number of different ways.


So obviously with the cloud we can collect lots of data, we can crunch lots of data,


we can create SaaS-based products that anyone can use for free.


But the other big change that we're seeing in analytics is really the rise of the platforms


and the automation of actionability based on data.


And I've had this conversation with a few people.


When you think about using analytics, there's really two modes that we're all in.


You're either doing some reporting or you're trying to figure something out that is just


abnormal, right, that you're not expecting.


You're doing analysis.


You're digging deeper.


And both of those things kind of, you know, when you look at basic reporting, you're looking


at the data, you're adding the concepts of what you've done, and you're going to make


a decision on how to do something differently.


Maybe you're going to change a campaign, maybe you're going to run an A-B test.


When you're doing analysis, you're doing the same thing, right?


You're trying to figure something out that you'll then go and make a change.


You're going to take some action.


But action is now being built directly into our analytics tools.


We're able to decide what to do to a certain group of users right from within the tools.


And this is where, again, we're seeing the rise of the platform.


Now obviously at Google we do a lot of other things other than analytics.


We have this thing called AdWords.


You may have heard of it.


It makes us all our money.


We have these other things as well called DoubleClick as well.


And so by integrating the platforms, right, by integrating the tools into one platform,


we can actually start to take action on the data.


And in analytics, the way that we do that is through remarketing.


And for those that aren't familiar with remarketing, it's actually pretty cool, right?


I heard a few people talk about Facebook and audiences.


Remarketing is kind of similar to that.


So remarketing, it's basically segmentation, right?


I have a group of users that come to my Web site, okay?


And I then segment those users into different segments.


Now I'm segmenting users, right?


I don't want to get confused with like segmenting the sessions that they create.


I'm actually segmenting users.


So I can create segments like spent more than $100 in a week or visited five times in a


month, right?


User-based segmentation.


Now what I can then do is actually send that list of users over to other parts of the platform


like AdWords, and I can literally change the advertising experience that those users have


as they browse the Internet.


So I just want to repeat that because if you're not familiar with remarketing, I think that's


pretty cool.


You can literally change the advertising experience a group of users has as they browse the Internet.


And that can be based on the behaviors that they exhibit on your Web site.


So you're probably like, well, how the hell do they do that?


Well, we can leverage the other parts of Google's platforms like DoubleClick and using cookies,


we can actually join all of this data together and see the behavior on your site and tie


that to the ads that users experience when they're on other sites.


This is huge, right?


This is actually a really big deal, okay?


And you can do a lot of really cool things with this.


But again, getting back to what we were just talking about, behavior.


Analytics is all about behavior.


And we want to use that behavior to create better experiences for our users, whether


it's in advertising or it might even marketing in general, better marketing for our users,


right, based on their actual behavior.


So I want to maybe just give a quick example, a case study that one of our users did.


How many people here like watches?


Nobody?


I like watches.


This is the watch I usually wear.


It's kind of cool.


That's actually a slide ruler.


Anyone know what a slide ruler is?


Slide ruler around the bevel?


My dad made me learn how to use a slide ruler when I was 10.


And I never forgave him.


But I still have it.


It's pretty cool.


Okay.


So there's a company called Watch Finder.


And what you'll find is people who are into watches can be really, really into watches.


I actually have some friends that they trade watches and borrow watches.


And this company, Watch Finder, helps folks find really great watches.


And some watches can be heinously expensive, 5,000, 10,000, $15,000.


But if you love watches, that's what you do.


You buy lots of watches, expensive ones.


So you can imagine a company that's selling really expensive watches, that sales process


takes a little bit of time.


Someone may be lusting after a watch for a while before they actually go out and buy


it.


So Watch Finder, what they were doing is they were creating remarketing lists based on the


different products that people would look at on the Web site.


So I think they had over 20 different lists of users looking at certain watches.


So product A, product B, product C. And obviously, using remarketing, the advertising that those


users saw would be tailored to what they viewed on the Web site.


Nothing too crazy.


We probably all have been subjected to that.


In fact, I think I've seen a couple of Moz display ads based on what I've done on their


Web site.


So what really makes this interesting is the way that they then took this one step further.


They said, well, we know they looked at this product, but we want to whittle that down.


We just don't want, like, every person who saw this certain product to see an ad about


that.


We only want to reach a certain audience.


So in analytics, this is our segmentation builder.


It's the same as what we do in remarketing.


They actually added a demographic component.


We want to reach a certain gender.


We want to reach men, because they knew that their audience was mostly men, their customer


base.


And we want to add in an age component.


So 35 to 45.


And so they were like, great.


So now we can start to tailor the ads based on the brand and product that they looked


at.


And we know we're going to whittle it away, so it's kind of only men that's in a certain


group.


And then they said, well, you know what?


We want to create some direct response type of content to show them in the advertising.


And so they went back and they looked at all of the people that converted, right, all of


the people that transacted on the site.


They just created a simple segment.


And they dug into the data.


And they looked at data about where was that traffic coming from.


And specifically, what they were looking at is the networks.


So you know, when obviously you guys know this, right?


When you visit a site, right, analytics will report back what is the name of that network


that the user was on.


So like when I'm at the office, the network comes back at Google.


And when you're at home, it might be Comcast or might be Time Warner.


Is there anybody else left?


I think that's it.


Roadrunner, if that still exists.


And what they noticed in that was that most people who converted were coming from networks


like JP Morgan, Deutsche Bank, RBC, sorry, Royal Bank of Scotland, big financial institutions.


So they were like, oh, we have a lot of bankers that like to buy watches.


And so they were able to add that piece of behavioral information into the remarketing.


So now it was the brand or products that they looked at.


It was the demographic information.


And were you from a big financial institution?


If you were, you got an advertising experience that was much more lower funnel, right?


Much more directed at, come on back.


We want to sell you this specific product.


Than if you were at a different part of the buying process.


And I thought that was really cool.


Because again, the behavioral data that we have in analytics, we're using that almost


instantaneously to reach out to an audience.


And once this is set up and running, you don't have to go and kind of maintain it.


It just kind of happens all the time.


And for this particular case, they saw a 1300% increase in the ROI of those direct response


campaigns versus other direct response campaigns that did not include a behavioral signal.


So they were able to get a lot more value out of those campaigns by getting down exactly


to the group that they wanted to look at.


Okay.


So remarketing is really just segmentation.


But I want to kind of maybe take this to the next level and talk about this in a much more


advanced way.


And this is something I wrote about today and there's a link to it.


But we can actually join data in analytics to other systems, right?


So the example I want to give is we can join analytics data to data from your CRM or your


email system.


And then we can use that combined data to drive remarketing.


So for example, what I could do is I could measure in analytics everyone that receives


an email from my business and then how many people actually open that email.


And then I can use that to drive remarketing.


So I could say everyone that got the email but never visited the site, you can then go


and change the advertising that they see on the Internet.


Well, how would you actually do that?


Well, again, I wrote this up.


I'll give you the link after this.


But when I send an email to an individual, I can actually send a hit to Google Analytics


and says, yo, yo, yo, just sent an email.


And then I can actually send another hit that says, yo, he opened it.


Score.


Right?


So we call this gobbledygook you see on the screen the measurement protocol.


It's our way that you can send data to us from any system as long as it can connect


to the Internet.


We've got some guy that's got this hooked up to a dog collar in New Zealand.


And I'm serious.


And tracks his dog in analytics.


There's actually companies in Europe that have built this technology into light bulbs


that will measure the proximity of the radio antenna in your phone when you're in a retail


space and understand your motion within a store.


So it can collect data from any type of system.


And then in analytics, I can go into remarketing and say, I want to see everyone that got an


email and opened it and maybe it was from a certain campaign.


And, again, that list of users now becomes any other set of users, and I can reach them


with a specific type of advertising as they browse the Internet.


So that's the blog post that talks a little bit more about the details about how to do


that.


And then you can see how, again, analytics, right, we're starting to build the actionability


right in.


And I think that's another way to really take your analytics practice to the next level.


Obviously we all need data.


We all need reporting.


We're all going to, you know, we're not going to stop getting our reports every Monday morning


or every Friday afternoon or whatever it may be.


But changing and including more actionability is huge.


Okay.


Blah, blah, blah.


Right message, right time, right person.


If you've been in marketing for a long time, you know that's the goal of all marketing.


So I wanted to sum up and just mention one more thing.


Changing analytics also includes, in my opinion, changing data.


And we rely so much on quantitative data that I think sometimes we get blind to what's happening.


And I really, really enjoyed Steph's presentation earlier about survey data.


And I wanted to talk a little bit about surveys and making decisions.


How many people here have ever had to make a decision at work?


Good.


Figured, right?


How often do you go to quantitative data?


Probably pretty often, right?


You might ask, like, oh, hey, let's go look at analytics.


I'm sure we have some site search data about what people are searching for.


But you don't get the full story, right?


You don't understand the why with quantitative data.


I'll give you an example.


Company called Pride Bytes.


Pride Bytes makes high-end dog toys.


I'm not a dog guy, sorry.


But if I was a pet guy, I'd have a dog.


And Pride Bytes, this is a company that we worked with at Google, tons and tons of data


about their customers.


And they had tons of statistics as well about the industry.


But they didn't have data about one specific thing.


They wanted to understand if they should build a rubber toy or a plush toy.


And there wasn't any data available.


And so one of the ways that we're trying to help businesses get more qualitative data,


survey data quickly, is through something called Google Consumer Surveys.


Does anyone use a consumer survey?


A few people.


So this is like my secret data crush.


You know, I love analytics, but I love consumer surveys.


I use this thing all the time.


Consumer surveys, it's a way for you to publish a small one-question or 10-question survey


to the Internet.


Okay?


And let me just show you, walk you through how this might work.


So you just set up, you kind of can choose, I want to run a survey.


And this survey is going to be shown to the Internet.


Let me make that clear.


Publishers will use this survey as a paywall to content.


So instead of showing advertising, they'll show your survey to their Web site visitors


who when they answer it will get content and you get an answer to your question.


And so you can target this at certain audiences and certain geos.


You can even filter who sees it based on like a qualifying question.


I've actually used this before to understand how much to tip when I travel to a new city.


So I'll run a survey and I'll target it at a specific population and I'll just say, you


know, and I'll run a quick survey.


So there's all different types of questions that you can set up, right?


Steph had some great information about like five and seven-point scales, right?


You can do that type of thing.


You can test different images, right?


Or you can just ask the simple question, right?


Like the Pride Company did and they talked about should we do plush or should we do rubber


toys, right?


And you get to put down all of your different answers.


And again, what you're going to get is publishers will show your question as a paywall to their


content.


I've done the big picture blog on Boston.com.


If you're a photographer, they have a great photo blog.


But there you go, right?


So this one was about streaming.


How much time do you spend listening to music online?


And then they have a few different questions.


I'd love to get Steph's professional opinion on the quality of their survey.


But just think if you have to make a quick decision, maybe a day, right, you can collect


a few hundred responses in that time.


And the nice thing about the surveys is they do all this stuff called math and statistics,


which I'm so not there, right, to help you figure out what's actually happening, right?


So they've got some algorithms in the back to make sure that it's statistically significant.


You can actually group results to show quartiles and percentiles.


And then all of the segmentation I think is really awesome, right?


So again, because this is kind of running on our stack, we have a whole bunch of demographic


information related to users.


And so we can start to do things like is it within a certain gender, certain age group,


is it within a certain location, really great segmentation offering.


And so I just wanted to leave some people with something that was different, right,


quantitative data.


So Google consumer surveys, you know, if you run a simple one-question survey, I think


it's like a penny for each response.


More complex ones can be expensive.


So this is a coupon for 75 bucks off if you want to run a survey.


So just a I thought it was better than AdWords credits.


Anyway, that was a joke.


I think Rand got it.


So thank you.


And again, these links are in the deck.


So for me, you know, even with all this quantitative data, I love having the survey data.


It's just a critical piece in my decision-making process.


So I just wanted to say thank you, guys.


I really appreciate the opportunity to speak to you.


It's really great to hear what you guys are doing with data.


You guys are such a data-rich environment.


It's awesome to hear how we're helping.


So excited to take a few questions if we can.


I know I ran a little bit long.


Sorry.


First, let's give a round of applause for you.


Thanks.


Nice work.


So we're actually running over time, so we won't have time to make questions.


I did want to ask you that enhanced e-commerce.


Yes.


Very cool.


That is available.


Yeah.


That's the first of this week.


We just launched it.


And if you want to learn more?


So actually, we're running an online course right now on e-commerce analytics, analyticsacademy.withgoogle.com.


I'm in a whole bunch of videos.


All right.


Justin, will you come back?


To MozCon?


Oh, my God.


Okay.


If you will, have me.


Sure.


Thanks a lot.


And I'll hang out after and answer questions.


Excellent.


Thank you so much.


Thank you.


Link Building Outreach in a Skeptical World - Whiteboard Friday

Howdy, Moz fans.


Welcome to another edition of Whiteboard Friday.


This week we're going to chat about link building outreach in this skeptical, jaded world in


which we are forced to live as marketers.


Look, I think we know a few things.


This is really a continuation from our Whiteboard Friday where we talked about the frustrating


part of the flywheel where social shares are just not, on average, in most cases, getting


us to the links that we need in order to rank.


We know a few things.


It is still the case that links are well correlated with higher rankings.


It's still the case that nearly every site and page that ranks well in Google has some


input that is related to their link profile.


Sometimes that's stronger and sometimes it's a weaker influence, but we know we need links


to rank well, especially in competitive search results.


It's incredibly rare to earn those links just by publishing content and sharing it socially.


Getting it in front of an audience, unless your audience is extremely link likely and


you've already built up some authority and linking is a behavior that you've kind of


acclimated your community to, this is really, really tough.


It's not going to work on its own.


We also know that link outreach is a hard, grueling manual process.


There's no doubt about that.


This is frustrating.


That's why many of us try and use social sharing or subscriptions or publication to attempt


to end around that need for direct link outreach because it's such a challenge.


But I think what we need to talk about, and I know that many of you in the comments and


over Twitter mentioned this, was what actually works for link outreach and how can we make


that process less painful and more likely to have success?


I think the reality is that outreach fundamentally involves an exchange of value.


As you are going out and attempting to earn a link from someone directly through link


outreach, through that one-to-one relationship, whether that's happening on social media or


happening in person or happening on the phone or happening via email, whatever it is, if


you don't provide value, if you're simply asking for something, your success rate is


going to be extremely low compared to the folks who do provide value prior to the link


or better as part of the link.


The link is the way the value is exchanged.


That's actually what Google is looking for.


They're not looking for someone who is very successful at convincing someone to give them


a link for no particularly good reason.


They're looking for an exchange of value where someone says, gosh, it provides value to my


site and my visitors to link to this resource and therefore I want to do it.


Value can be a bunch of different things.


Value could be in the ego that it boosts.


It could be in the problems that it helps solve.


It could be in the form of what you've given them in exchange, lots of things.


These are things that we've seen that are often perceived to carry real value.


Some of these are taken directly from the Buzz, Sumo, and Moz study where we looked


at things that earned social shares and also earned links.


There were some good cases of those and some different types of content.


Some of this is also things that inherently earns links as it is used, things like embeddable


content.


I'll talk through these because I think fundamentally when it comes down to it, it's very tough


for me to stand up here and say, oh, we did some research.


I saw this at a conference recently.


I think it was SearchLove where someone noted one of the things that we've been doing that's


had much better success with our link outreach is we reach out asking if they would like


to see the piece that we want them to link to rather than sharing the piece with them


directly.


That gets a much higher email engagement rate like, yeah, okay, I'll take a look at it.


Then when we do send it to them, those people tend to look at it and link to it more than


if we just sent them the link right off the bat because we've engaged in that conversation.


Okay, look, there's a lot of tactical tips like that, but if that fundamental thing,


that piece that you're providing that value to the linker, to the potential linker, doesn't


carry real value in their eyes, you can't have any success.


That's why these items, I think, are so critical, so fundamental to the outreach formula.


First off is unique research.


We've seen research perform very well, I think, because unique research that provides value


to entities and organizations and to content creators needs to be referenced.


It needs that citation.


I think that's why research, especially research that you do and or visuals or rifts that you


take off of research that's already been created to analyze that data or to turn it into great


graphs or interactive infographics or those kinds of things can provide real value.


I'm a little, well, I'm jaded about infographics personally, but I do believe that a lot of


customized high-quality visuals can work, and certainly infographics can be a form of


that that do work in some sectors.


I think that we're seeing that in tech and in marketing and in places that, in legal,


in a lot of places where you see a tremendous amount of outreach, infographics are actually


losing out because they've been so saturated and every content creator in those niches


has 10 people reaching out to them every week offering a new infographic.


You're just not standing out from the crowd.


I do think there are other forms of visuals, everything from photography to illustrations


to customized graphics and charts to drawings that can be very valuable there, and that's


why I've mentioned it here.


Embeddable content is wonderful because it naturally acquires that link by saying like,


hey, here's a calculator or here's a tool that you can embed on your site if you'd like


to.


You get that link back as part of the embed, and I think that can work great.


We've also seen a decline, actually.


Embeddable content used to be all the rage, say, six to ten years ago.


It's actually waned a little bit, and for that reason I think can be more powerful,


can stand out a little when it is used.


I think that's a tactic that I would encourage folks to try again.


Badges are a form of this, but they're kind of the most mild, least uniquely valuable


form of that.


If you're going to do a badge, it better be a badge back to something that is very powerful


or really, really triggers a great commitment.


If you're an Etsy top seller and you get a badge to put on your website or an embeddable


widget from your Etsy store so that people can buy directly from Etsy from your website,


okay, those things provide real value.


Of course, I'm going to link to them, but just a badge that's like, I think you're a


great blogger.


Tough.


APIs and data plus business development.


These are tough things to build, but it can be very valuable.


If you're providing data on an ongoing basis, especially to large organizations or powerful


entities who are using that data either publicly or even privately, very often you can include


in those agreements some form of, hey, we'd like some co-branding, we'd like you to link


back to us, we'd like you to say the data was provided by us.


Hard to do, but that's a great thing because it's powerful and it gets that link.


And it makes, well, your target look good, right?


If you are inherently saying, hey, here's a piece of content, we did a truly substantive


analysis of five or ten players in the field, your product, your service, you, your company,


your content stands out in this way and we've quantified that and we've produced this piece,


yeah, I'm going to be much more likely to link to that than just a, here's a badge that


says we like you.


All right.


So I think these can still work well, playing to people's ego can still work well.


Guest content, we see guest content still doing very well despite Google's warnings


about guest blogging and of course we talked about that a couple of years ago on White


Board Friday.


Guest content is still very powerful.


It almost always includes a link back.


The key is that this content has to actually provide value to the target.


And I think if that content does provide great value to the target, you can get a link from


almost anywhere.


The key is convincing them that it's going to perform well for them and going to perform


well with their audience.


As a result, it's very easy for folks who already have a platform, who are already thought


of as influencers and thought leaders to get their content onto their sites.


It's much tougher as an unknown and this is one of the reasons why I think building up


your platform first and then leveraging guest content can be so valuable.


Last one that I'll mention here is a service or favor that makes your target want to refer


people to you.


Now this is a challenging thing to accomplish but if you are a service provider, a content


provider, a data provider, a product provider who has done something amazing and unusual,


something that makes you stand out in the minds of a customer and you know that customer


has a website and that customer could be a business or an organization, an entity, those


kinds of things and you know that that organization often deals with people who need services


like yours reaching out and saying, hey, we'd love it if you'd refer folks and here's what


we're willing to offer and those kinds of things can be another great way to go.


The outreach email itself is the thing that gets talked about a lot and I hear the same


advice kind of over and over again around link outreach.


I get a little frustrated sometimes, right?


So it needs to be customized and well-written and you need to flatter your target and it


shouldn't be automated.


Those things will get you, that's just table stakes.


That is merely send a good competent email, right?


That is not advice or tactical, useful, actionable advice.


I get very frustrated when I see those same pieces of advice over and over again.


I think where you want to go is places that other outreach emails don't go.


So if you can, try and look at a dozen or a hundred outreach emails from other sources


to people like those in your target market.


If you know that they've received those emails, you can even reach out.


You reach out to people in your audience who you already have a relationship with.


I'm sure you have some relationships like that with people who are influenced in your


field already and ask them, hey, can you send me the outreach emails that you get?


I just want to take a look at them because I think they're all terrible and I never want


to do that to people.


I'll send you mine, right?


What you will find is that they're almost never authentic.


They're rarely humble.


They almost never create a real connection.


In fact, the vast majority of real connection emails that I get from folks that I've never


met before are not about outreach.


I think that's what forms that real connection.


I've seen a few of these that are outreach emails, but they do create a real connection.


They have actually read things that I've made and watched them or been at events that I've


been at or worked with companies that I've worked with or whatever it is, and they form


that real connection in the email authentically.


They need to stand out as unique.


Unique meaning they don't look like those other 150 outreach emails.


This is the sucky part.


These outreach emails do not scale.


The ones that work tend not to scale, and it tends to be a link builder's job to scale


this process because you need lots of links.


You need it to point to lots of pieces of content, and so you're always looking for


scale.


I would urge you to go the opposite direction.


Narrow your funnel.


Worry less about the number of people you're targeting and more about the success rate


because once you get the success rate high, you can turn up the volume really fast.


If your success rate is low and there's a limited market of influencers in your field,


you can quickly burn all of them with your outreach before you ever have a chance to


get good at it.


Link outreach is supposed to be hard.


This process is not supposed to scale.


If it scaled, it would be easy.


Everyone would do it, and there would be no competitive barrier, no competitive advantage


to being great at building and earning links.


I think this frustration exists in the world.


I want to recognize that and have empathy for it and for all of you who have to do link


building outreach, but I also want to say that's part of the magic that happens here,


and so you should account for it and expect it and not fear it.


All right, everyone.


Look forward to your comments.


I'd love to hear your link building outreach strategies and tactics, what's worked for


you, what hasn't, and we'll see you again next week for another edition of Whiteboard


Friday.


Controlling Search Engine Crawlers for Better Indexation and Rankings - Whiteboard Friday

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


This week we're going to chat about controlling search engine crawlers, blocking bots, sending


bots where we want, restricting them from where we don't want them to go.


We're going to talk a little bit about crawl budget and what you should and shouldn't have


indexed.


As a start, what I want to do is discuss the ways in which we can control robots.


Those include the three primary ones, robots.txt, meta-robots, and the nofollow tag, a little


bit less about controlling bots.


There's a few others that we're going to discuss as well, including webmaster tools or search


console and URL status codes.


Let's dive into these first.


Robots.txt, it lives at your site.com slash robots.txt.


It tells crawlers what they should and shouldn't access.


It doesn't always get respected by Google and Bing.


A lot of folks, when you say, hey, disallow this, and then you suddenly see those URLs


popping up and you're wondering what's going on, look, Google and Bing oftentimes think


that they just know better.


They think that maybe you've made a mistake.


They think that, hey, there's a lot of links pointing to this content.


There's a lot of people who are visiting and caring about this content.


Maybe you didn't intend for us to block it.


The more specific you get about an individual URL, the better they usually are about respecting


it.


The more specific, meaning the more you use wildcards or say everything behind this entire


big directory, the less good they are about necessarily believing you.


Metarobots, a little different.


That lives at the top of individual pages.


You can only control a single page with a metarobots tag.


That's in the header of a page.


That tells the engines whether or not they should keep a page in the index and whether


they should follow the links on that page.


It's usually a lot more respected because it's at an individual page level.


Google and Bing tend to believe you about the metarobots tag.


The nofollow tag, that lives on an individual link on a page.


It doesn't tell them where to crawl or not to crawl.


All it's doing is saying whether you editorially vouch for a page that is being linked to and


whether you want to pass the page rank and link equity metrics to that page.


Interesting point about metarobots and robots.txt working together or not working together so


well.


Many, many folks in the SEO world do this and then get frustrated.


What if, for example, we take a page like blogtest.html on our domain and we say, you


know what, all user agents, you are not allowed to crawl blogtest.html.


Okay, well, sure, that's a good way to keep that page away from being crawled, but just


because something's not crawled doesn't necessarily mean it won't be in the search results.


Then we have our SEO folks go, you know what, let's double make sure that that doesn't show


up in search results.


We will put in the metarobots tag, right, robots content equals noindexfollow.


So noindexfollow tells the search engine crawler that they can follow the links on the page


but they shouldn't index this particular one.


Then you go and run a search for blogtest in this case and everybody on the team's like,


what the heck?


WTF, what's going on?


Why am I seeing this page show up in search results?


The answer is you told the engine that they couldn't crawl the page, so they didn't.


But they still are putting it in the results.


They're actually probably not going to include a meta description.


They might have something like, we can't include a meta description because of this site's


robots file, right, as the text that you'd see here.


But the reason that it's showing up is because they can't see the noindex.


All they see is the disallow.


So if you want something truly removed, unable to be seen in search results, you can't just


disallow a crawler.


You have to say meta noindex and you have to let them crawl it.


So this creates some complications, right?


Robots.txt can be great if we're trying to save crawl bandwidth, but it isn't ideal for


preventing a page from being shown in the search results necessarily.


I would not recommend, by the way, that you do what Twitter recently tried to do where


they put the, well, it was one of the things we think they were trying to do anyway, was


trying to canonicalize www and non-www by saying, Google, don't crawl the www version


of Twitter.com.


What you should be doing is rel canonicaling or using a 301.


Meta robots, that can allow crawling and link following while disallowing indexation, which


is great, but it requires crawl budget and you can still conserve indexing.


The nofollow tag that, generally speaking, just not particularly useful for controlling


bots or conserving indexation.


Webmaster Tools and Search Console.


So Webmaster Tools has some special things, or now Google Search Console and Bing Webmaster


Tools have some things that allow you to restrict access or remove a result from the search


results.


So, for example, if you have 404'd something or you've told them not to crawl it but it's


still showing up in there, you can manually say don't do that.


There's a few other crawl protocol things that you can do.


Those are individual specific tools.


You can check them out there.


And then URL Status Codes.


These are a valid way to do things, but they're going to obviously change what's going on


on your pages too.


If you're having not a lot of luck using a 404 to remove something, you can use a 410,


a URL Status Code 410, to permanently remove something from the index.


Just be aware, once you use a 410, it can take a long time if you want to re-get that


page crawled or indexed and you want it to tell the search engines like, hey, it's back.


410 is permanent removal.


301, permanent redirect.


We talked about those here.


And 302, temporary redirect.


Now let's jump into a few specific use cases of what kinds of content should and shouldn't


I allow engines to crawl and index in this next version.


We've got these four big problems that I want to talk about as they relate to crawling and


indexing.


The first one here is around if I have content whose quality I'm still trying to improve.


It's not yet ready for prime time.


It's not ready for Google.


I don't want to show it yet.


Maybe I have a bunch of products.


I only have the descriptions from the manufacturer.


I need people to be able to access them, but I'm rewriting the content, creating unique


value on those pages, and they're just not ready yet.


What should I do with those?


And my options around crawling and indexing are basically like if I have a large quantity


of those, if we're talking about maybe thousands, tens of thousands, hundreds of thousands,


I would probably go the robots.txt route.


I'd disallow those pages from being crawled, and then eventually as I get folder by folder


those sets of URLs ready, I can then allow crawling and maybe even submit them via an


XML sitemap to Google.


If I'm talking about a small quantity, a few dozen, a few hundred pages, well, you know


what?


I'd probably just use the meta-robots noindex, and then I'd pull that noindex off of those


pages as they are being made ready for Google's consumption.


Then I would probably, again, use the XML sitemap and start submitting those once they're


ready.


What about should I noindex, nofollow, or potentially disallow crawling on largely duplicate


URLs or thin content?


So I've got an example over here.


Let's say I'm an e-commerce shop.


I'm selling this nice Star Wars t-shirt, which I think is kind of hilarious.


So I've got StarWarsShirt.html, and it links out to a larger version of an image, and that's


an individual HTML page.


It links out to different colors, which again change the navigation rate, change the URL


of the page there.


So I have a gray, blue, and black version.


These four pages really are all part of this same one, and so I wouldn't recommend disallowing


crawling on these, and I wouldn't actually recommend noindexing them.


What I would do there is a rel canonical.


And remember, rel canonical is one of those things that can be precluded by disallowing.


So if I were to disallow these from being crawled, Google couldn't see the rel canonical


back.


So if someone linked to the blue version instead of the default version, well, now I don't


get link credit for that potentially.


So what I really want to do is use the rel canonical, allow the indexing, allow it to


be crawled.


If you really feel like it, you could also put a meta noindex follow on these pages,


but I don't think that's actually necessary.


And again, that might interfere with the rel canonical.


Number three, if I want to pass link equity, or at least crawling through a set of pages


without, without those pages actually appearing in search results.


So maybe I have navigational stuff, right, ways that humans are going to navigate through


my pages, but I don't need those navigation sets of pages or types of results or whatever


it is appearing in search results.


Well, what should I use then?


And what I would say here is you can use the meta robots to say don't index a page, but


do follow the links that are on that page.


And that's a pretty handy, nice use case for that.


Do not, however, disallow those in robots.txt.


Many, many folks make this mistake.


And what happens then is if you disallow crawling on those, Google can't see the noindex.


They don't know that they can follow it.


Granted, as we talked about before, sometimes Google doesn't obey the robots.txt, but it's


not, you can't rely on that behavior, right?


Like trust that they will disallow, that the disallow in robots will prevent them from


crawling and then believe it.


And so I would say the meta robots noindex follow is the way to do this.


And finally, fourth, what should I do with search results type pages?


So Google said many times they don't like your search results from your own internal


engine or whatever appearing in their search results.


And so this can be a tricky use case because sometimes a search result page, right, a page


that lists many types of results that might come from a database of types of content that


you've got on your site could actually be a very good result for a searcher who's looking


for a wide variety of content or who wants to see what you have on offer.


Yelp does this when you say I'm looking for restaurants in Seattle, Washington, right?


They'll give you what is essentially a list of search results.


And Google does want those to appear because that page provides a great result.


But you should kind of be doing what Yelp does there and make the most common or popular


individual sets of those search results into category style pages, right?


A page that provides real unique value that's not just a list of search results, that is


kind of more of a landing page than a search results page.


However, that being said, if you've got a long tail of these or you're sort of like,


hey, our internal search engine, that's really for internal visitors only.


It's not useful for having those pages show up in search results.


We don't think that we need to make the efforts to make those category or landing pages.


Then you can use the disallow in robots.txt to prevent those.


Just be cautious here because sometimes I have seen like an over swinging of the pendulum


towards blocking all types of search results.


And sometimes that can actually hurt your SEO and your traffic.


Sometimes those pages can be really useful to people.


So just check your analytics, make sure that's not valuable pages that should be served up


and turned into landing pages.


And if you're sure, then go ahead and disallow all your search results style pages.


You'll see a lot of sites doing this in their robots.txt file.


So that being said, I hope you have some great questions about crawling and indexing, controlling


robots, blocking robots, allowing robots, and I'll try and tackle those in the comments


here.


And we'll look forward to seeing you again next week for another edition of Whiteboard


Friday.


Keyword Research in Keyword Explorer Overview

Howdy, I'm Rand from Moz and today I'm going to walk you through Keyword Explorer.


We're going to start here on the homepage.


From this homepage I can search for any keyword term or phrase and that will take me over


to the overview page where I get all the data I need for keyword analysis.


Up at the top here we've got our metrics.


Keyword volume, difficulty, opportunity, and potential.


Each of these have a different impact on how you might prioritize your keywords.


From here let's dive into SERPs analysis which will show us the Google search result for


this particular keyword phrase, all the pages that are ranking, and the SERP features that


can appear on the page.


Now let's dive into suggestions.


From the suggestions page I get a comprehensive list of suggestions from the largest English


language keyword database and I can filter those suggestions based on different ways


that I might want to see new types of keyword suggestions.


I can also sort by volume, by relevancy, and I can export easily to CSV.


One of the best parts of Keyword Explorer is our metrics.


Our search volume numbers are accurate 95% of the time and that means the true volume


that actually happens on Google fits inside these ranges 95 plus percent of the time.


Difficulty and opportunity have similarly been upgraded and all three of these scores


go into potential which can help you determine which terms and phrases to target first.


On any page in the Keyword Explorer app I can select and add keywords to a list in order


to save my research.


From an individual list page I can study all the metrics and SERP features for each list


and then I can compare the keywords against each other, sort, and filter in order to prioritize


the right ones.


Using the import button I can easily add a bunch of keywords to a new list or I can export


a list or a SERP analysis or a set of suggestions to CSV so I can play around with it in Excel.


Keyword Explorer is one of the most exciting new projects we've had here at Moz in years.


It's got its own entire database of information that is unique, unique metrics you can play


around with.


Should SEOs Care About Internal Links - Whiteboard Friday

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


This week we're going to chat a little bit about internal links and internal link structures.


Now it is not the most exciting thing in the SEO world, but it's something that you have


to get right and getting it wrong can actually cause lots of problems.


So let's start by talking about some of the things that are true about internal links.


Internal links, when I say that phrase, what I mean is a link that exists on a website,


let's say abc.com here, that is linking to a page on the same website.


So over here, linking to another page on abc.com.


We'll do slash a and slash b.


This is actually my shipping routes page and so you can see I'm linking from a to b with


the anchor text shipping routes and the idea of an internal link is really initially to


drive visitors from one place to another, to show them where they need to go to navigate


from one spot on your site to another spot.


They're different from internal links only in that in the HTML code you're pointing to


the same place, the same fundamental root domain.


In the initial early versions of the internet that didn't matter all that much, but for


SEO it matters quite a bit because external links are treated very differently from internal


links.


That is not to say, however, that internal links have no power or no ability to change


rankings, to change crawling patterns, and to change how a search engine views your site


and that's what we need to chat about.


So anchor text is something that can be considered.


The search engines have generally minimized its importance, but it's certainly something


that's in there for internal links.


The location on the page actually matters quite a bit, just as it does with external


Internal links, it's almost more so in that navigation and footers specifically have attributes


around internal links that can be problematic.


Those are essentially when Google in particular sees manipulation in the internal link structure,


specifically things like you've stuffed anchor text into all of the internal links trying


to get this shipping routes page ranking by putting a little link down here in the footer


of every single page and then pointing over here trying to game and manipulate us.


They hate that.


In fact, there's an algorithmic penalty for that kind of stuff and we can see it very


directly.


We've actually run tests where we've observed that jamming this type of anchor text which


links into footers or into navigation and then removing it gets a site indexed and,


well, let's not say indexed.


Let's say ranking well and then ranking poorly when you do it.


And Google reverses that penalty pretty quickly too, which is nice.


So if you're not ranking well and you're like, oh, no, Rand, I've been doing a lot of that,


maybe take it away, your rankings might come right back.


It's great.


The link target matters obviously from one place to another.


The importance of the linking page, this is actually a big one with internal links.


So it is generally the case that if a page on your website has lots of external links


pointing to it, it gains authority and it has more ability to sort of generate a little


bit, not nearly as much as external links, but a little bit of ranking power and influence


by linking to other pages.


So if you have very well linked two pages on your site, you should make sure to link


out from those two pages on your site that, A, need it and, B, are actually useful for


your users.


That's another signal we'll talk about.


The relevance of the link, so pointing to my shipping routes page from a page about


other types of shipping information, totally great.


Pointing to it from my dog food page, well, doesn't make great sense.


Unless I'm talking about shipping routes of dog food specifically, seems like it's lacking


some of that context and search engines can pick up on that as well.


The first link on the page, so this matters mostly in terms of the anchor text, just it


does for external links.


Basically, if you are linking in a bunch of different places to this page from this one,


Google will usually, at least in all of our experiments so far, count the first anchor


text only.


So they will not treat, so if I have six different links to this and the first link says click


here, click here is the anchor text that Google's going to apply, not click here and shipping


routes and shipping, those subsequent links won't matter as much.


And then the type of link matters too, obviously I would recommend that you keep it in the


HTML link format rather than trying to do something fancy with JavaScript.


Even though Google can technically follow those, it looks to us like they're not treated


with quite the same authority and ranking influence.


Text is slightly, slightly better than images in our testing, although that testing is a


few years old at this point.


So maybe image links are treated exactly the same.


Either way, do make sure you have that.


If you're doing image links, by the way, remember that the alt attribute of that image is what


becomes the anchor text of that link.


Okay, internal versus external.


So external links in general, as we mentioned, generally give more ranking authority and


ability for the linking page to rank.


That shouldn't be surprising.


An external link is like a vote from an independent, hopefully independent, hopefully editorially


given, page, website to your website saying this is a good place for you to go for this


type of information.


On your own site, it's like a vote for yourself.


So engines don't treat it the same.


Anchor text of internal links generally have less influence.


So as we mentioned, me pointing to my page with the phrase that I want to rank for isn't


it necessarily a bad thing, but I shouldn't do it in a manipulative way.


I shouldn't do it in a way that's going to look spammy or sketchy to visitors because


if visitors stop clicking around my site or engaging with it or they bounce more, I will


definitely lose ranking influence much faster than if I simply make those links credible


and usable and useful to visitors.


And besides, internal links, the anchor text of internal links is not as powerful anyway.


It is, however, the case that a lack of internal links, like an orphan page that doesn't have


many internal or any internal links from the rest of its website, that can really hamper


a page's ability to rank.


Sometimes it'll happen.


External links will point to a page.


You'll see that page in your analytics or in a report about your links from Moz or Ahrefs


or Majestic, and then you go, oh my gosh, I'm not linking to that page at all from anywhere


else on my site.


That's a bad idea.


Don't do that.


That is definitely problematic.


It's still the case, by the way, that broadly speaking, pages with more links to them, more


external links to them, are going to send more value – sorry, with more links on them


will send less link value per link.


So essentially, you remember the original page rank formula from Google, right?


It said basically like, oh, well, if there's five links, send one-fifth of the page rank


power to each of those.


And if there's four links, send one-fourth, and obviously one-fourth is bigger than one-fifth,


so taking away that fifth link could mean that each of the four pages that you've linked


to get a little bit more ranking authority and influence in the original page rank algorithm.


Look, page rank is old, very, very old at this point, but it is not completely – at


least the theories behind it are not completely gone.


So it is the case that if you have a page with tons and tons of links on it, that tends


to send out less authority and influence than a page with few links on it, which is why


it can definitely pay to do some spring cleaning on your website and clear out any rubbish


pages or rubbish links, ones that visitors don't want, that search engines don't want,


that you don't care about.


Clearing that up can actually have a positive influence.


We've seen that on a number of websites, where they've cleaned up their information architecture,


whittled down their links to just the stuff that matters the most and the pages that matter


the most, and then seen increased rankings across the board from all sorts of signals,


positive signals, user engagement signals, link signals, context signals that help the


engine rank them better.


Then finally, I do want to point out that what was previously called, you probably have


heard of it in the SEO world, page rank sculpting.


This was a practice that I'd say from maybe 2003, 2002 to about 2008, 2009, had this life


where there'd be panel discussions about page rank sculpting and all these examples of how


to do it and software that would crawl your site and show you the ideal page rank sculpting


system to use and which pages to link to and not.


When page rank was the dominant algorithm inside of Google's ranking system, yeah, it


was the case that page rank sculpting could have some real effect.


These days, that is dramatically reduced.


It's not entirely gone because of some of these other principles that we've talked about,


that just having lots of links on a page for no particularly good reason is generally bad


and can have harmful effects and having few carefully chosen ones has good effects.


But most of the time, internal linking, optimizing internal linking beyond a certain point is


not very valuable, not a great value add.


But a little of what I'm calling the right internal linking, and that's what we're going


to talk about, can go a long way.


For example, if you have those orphan pages or pages that are clearly the next step in


a process or that users want and they cannot find them or engines can't find them through


the link structure, it's bad.


Fixing that can have a positive impact.


Okay, so ideally, ideally in an internal linking structure system, you want something kind


of like this.


This is a very rough illustration here, right?


But the homepage, which has maybe 100 links on it to internal pages, one hop away from


that, you've got your 100 different pages of whatever it is, subcategories or category


pages, places that can get folks deeper into your website.


And then from there, each of those have maybe a maximum of 100 unique links and they get


you two hops away from the homepage, which takes you to 10,000 pages who do the same


thing.


Now, the idea behind this is that basically in one, two, three hops, three links away


from the homepage and three links away from any page on the site, I can get to up to a


million pages.


So when you talk about how many clicks do I have to get, how far away is this in terms


of link distance from any other page on the site, a great internal linking structure should


be able to get you there in three or fewer link hops.


If it's a lot more, you might have an internal linking structure that's really creating sort


of these long pathways of forcing you to click before you can ever reach something, and that


is not ideal, which is why it can make very good sense to build smart categories and subcategories


to help people get in there.


I'll give you the most basic example in the world, a traditional blog.


And in order to reach any post that was published two years ago, I've got to click next, next,


next, next, next, next, next through all this pagination until I finally get there.


Or if I've done a really good job with my categories and my subcategories, I can click


on the category of that blog post and I can find it very quickly in a list of the last


fifty blog posts in that particular category.


Great.


Or by author or by tag, however you're doing your navigation.


Second, pages should, as we discussed, contain links that visitors will find relevant and


useful.


If no one ever clicks on a link, that is a bad signal for your site and it is a bad signal


for Google as well.


And I don't just mean no one ever.


Very, very few people ever, and many of them who do click it click the back button because


it wasn't what they wanted, that's also a bad sign.


Just as no two pages should be targeting the same keyword or searcher intent, likewise


no two links should be using the same anchor text to point to different pages.


For example, if over here I had said, you know, I had a shipping routes link that pointed


to this page and then another shipping routes link, same anchor text pointing to a separate


page, page C, why am I doing that?


Why am I creating competition between my own two pages?


Why am I having two things that serve the same function or that at least to visitors


would appear to serve the same function in search engines too?


I should canonicalize those, right?


Canonicalize those links, canonicalize those pages.


If a page is serving the same intent and keywords, keep it together.


Rel nofollow was sort of the classic way that people had been doing page rank sculpting


that we talked about earlier here.


I would strongly recommend against using it for that purpose.


Google said that they put in some preventative measures so that rel nofollow links sort of


do this leaking page rank thing as they call it.


I wouldn't stress too much about that, but I certainly wouldn't use rel nofollow.


What I would do is if I'm trying to do internal link sculpting, I would just do careful curation


of the links and pages that I've got.


That is the best way to help your internal link flow.


That's things like removing low value content, low engagement content, and creating internal


links that people actually do want.


That is going to give you the best results.


Last but not least, there should never be an orphan.


There should never be a page with no links to it, and certainly there should never be


a page that is well linked to that isn't linking back out to portions of your site that are


of interest or value to visitors and to Google.


In these practices, I think you can do some awesome internal link analysis, internal link


optimization, and help your SEO efforts and the value visitors get from your site.


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


9 Tips to Integrate Organic, Paid, and Content - Whiteboard Friday

Hey everybody and welcome back to Whiteboard Friday.


My name is Heather Physiok and I'm Group Director of Discoverability at VML Y&R.


Today we're going to talk about nine tips to help integrate your organic search,


paid search, and content practices.


So your first tip is that you want to announce change all at once,


but then you want to roll out the changes one at a time.


It can be overwhelming to integrate practices and change processes.


So you don't want to try to do everything all at the same time.


It's like trying to boil the ocean and it's too much to stick.


So while you want to get everybody on board and aligned to the benefits


and challenges they'll be facing as you integrate,


then you just progressively roll out the changes iteratively over time.


Next, as you develop new capabilities and processes and offerings together,


you're going to document those processes in a shared living wiki


because those processes are going to continue to change.


So my team uses Confluence where we document our shared workflows,


but everybody on the team has access and total trust to continue refining those


in the ways that they see are best for the team.


Your next tip should be obvious, but a lot of people are not doing it.


You should be making recommendations and reporting together.


So a lot of times we'll collect all our data for reporting from all our different channels,


we'll smash some slides together at the last minute before we throw it over the fence to the client,


and it ends up with a pretty shallow, almost meaningless set of data that doesn't tell a story.


So we should be getting together, sharing our insights, observations, and findings


in the room together to find the story that is the most meaningful and help prioritize


for our clients the best marketing decisions they can make from that data.


So your next tip is to cross train so you can build advocacy across the teams.


We host a lot of workshops and hands-on training.


We've even done job swaps where we had SEOs writing performance content for product detail pages,


and it creates this wonderful sense of empathy and understanding for what others need


in order to do their jobs well.


But it also creates these great mental checks where you ask yourself,


am I including the right people at the right times?


Is there anyone else who could add value here?


Could my work be impacting someone else?


And so the purpose here is not necessarily to know how to do each other's jobs


so much as it is to empower people to be able to advocate for, speak about, and cross-sell your other teams.


Next, when you've done your integration of processes and people,


everyone else in the organization may not necessarily know what that means for them.


So you'll want to reintroduce your team or your new capability to the rest of the organization.


Put faces with names.


Talk about what the new capability is and does and the value it brings to the organization.


Tell people how to engage with that new offering


and what it means for their project or initiative or client.


Up next, we're going to market the joint wins.


As you're continuously integrating, you should always be looking for wins or warnings


that you can share with others so they can learn how to better engage with your offerings.


So if you have a great case study where you integrated paid and organic or organic and content,


make sure you're marketing those stories out to your colleagues, your clients, your bosses, and of course, your team.


Up next, we're going to do roundtables so we can deep dive search opportunities with other departments.


So of course it makes sense to have roundtables between organic search and paid search or organic search and performance content,


but also think beyond your immediate team.


Think about other marketing teams like social media and pairing search behavior insights with social listening data.


Or think about geographic teams.


What if you sat your organic search team down with your Europe group to figure out what opportunities make the most sense for that region?


Or even sales and IT and finding those areas of intersection where you can do great search work that supports more parts of the organization.


Next, think about hosting mutual lunch and learn so you can start to cross pollinate different skill sets.


So similar to the roundtables, this is where you're going to bring different groups together to talk about capabilities.


But think about more than just presenting your capabilities to other people.


Also be sure to invite them to present their capabilities to your group.


For example, we've invited the project management team or the client engagement team to make us stronger in our search work through the value that they bring.


And finally, as you're making all of these changes, it can't just come from the top.


One person just handing change down for everyone else to implement.


It has to be organic, pardon the pun, and everybody should have ownership over the direction that we're heading together.


So when we make changes to products or processes or we start to integrate different groups or spin up little teams to work on specific objectives,


we make sure that those individuals from each side have ownership to make those decisions together and roll it out to the rest of the group.


It helps make sure we've considered all the angles and greatly impacts our ability to get buy-in across the team.


So those are nine quick tips to integrate organic search, paid search and content practices.


Let us know what you think in the comments below.


How To Support Data with Real-Life Interviews - Whiteboard Friday

Music


Hi Moz fans. I'm Rebecca Cancino. I'm a partner at Onward and I lead content strategy and user experience design.


Today I'm here to talk to you about how to support the data you have, your keyword data, data around search intent, analytics with real life user interviews.


So recently Rand has been talking a little more about the relationship between user experience design and SEO,


whether it's managing the tensions between the two or the importance of understanding the path to customer purchase.


And he said that in order to understand that path, we have to talk to real people.


We have to do interviews, whether that's talking to actual users or maybe just people inside your company that have an understanding of the psychographics and the demographics of your target audience.


People like sales folks or customer service reps.


Now maybe you're a super data driven marketer and you haven't felt the need to talk to real people and do interviews in the past.


Or maybe you have done user interviews and you found that you got a bunch of obvious insights and it was a huge waste of time and money.


I'm here to tell you that coupling your data with real interviews is always going to give you better results.


But having interviews that are useful can be a little bit tricky.


The interviews that you do are only as good as the questions you ask and the approach that you take.


And so I want to make sure that you're all set and prepared to have really good user interviews.


All it takes is a little practice and preparation.


It's helpful to think of it like this.


So the data is kind of telling us what happened.


It's telling us about online behaviors, things like keywords, keyword volume, search intent.


We can use tools like KeywordTool.io or Ubersuggest or even Moz's Keyword Explorer to start to understand that.


We can look at our analytics, entry and exit pages, bounces, pages that get a lot of views.


All of that stuff is really important and we can learn a lot from it.


But with our interviews what we're learning about is the why.


This is the stuff that online data just can't tell us.


This is about those offline behaviors, the emotions, beliefs, attitudes that drive the behaviors and ultimately the purchase decisions.


So these two things working together can help us get a really great picture of the whole story and make smarter decisions.


So say, for example, you have an online retailer.


They sell mainly chocolate dipped berries.


And they've done their homework.


They've seen that most of the keywords people are using tend to be something like, you know, chocolate dipped strawberries gifts or chocolate dipped strawberries delivered.


And they've done the work to make sure that they've done their on-page optimization and doing a lot of other smart things too using that.


But then they also notice that their Mother's Day packages and their graduation gifts are not doing so well.


They're starting to see a lot of drop-offs around that product description page and a higher cart abandonment rate than usual.


Now, given the data they had, they might make decisions like, well, let's see if we can do a little more on-page keyword optimization to reflect, you know, what's special about the graduation and Mother's Day gifts.


Or maybe we can refine the user experience of the checkout process.


But if they talk to some real users, which they did, this is a real story, they might learn that people who send food gift items, they worry about, is the person I'm sending the gift to, are they going to be home when this gift arrives?


And because this is a perishable item like chocolate dipped berries, will it melt?


Now, this company, they do a lot of work to protect the berries.


The box that they arrived in is super insulated, it's like its own cooler, and they have really great content that tells that story.


Problem is that content is buried in the FAQs instead of on the pages and places it matters most, the product detail, the checkout flow.


And so you can see here how there's an opportunity to use the data and the interview insights together to make smarter decisions.


You can get to insights like that for your organization too.


Let's talk about some tips that are going to help you make smarter interview decisions.


So the first one is to talk to a spectrum of users who represent your ideal audience.


Maybe, like with this berry example, their ideal customer tends to skew slightly female.


You would want that group of people that you're talking to to skew that way too.


Perhaps they have a little more disposable income.


That should be reflected in the group of people that you're interviewing and so forth. You get it.


The next one is to ask day in the life open-ended questions.


This is really important.


If you ask typical marketing questions like, how likely are you to do this or that, or tell me on a scale of 1 to 10 if you do, you know, how great this was, you'll get typical marketing answers.


What we want is real nuanced answers that tell us about someone's real experience.


So I'll ask questions like, tell me about the last time you bought a food gift online.


What was that like?


We're trying to get that person to walk us through their journey from the minute they're considering something to how they vet the solutions to actually making that purchase decision.


Next is don't influence the answers.


You don't want to bias someone's response by introducing an idea.


So I wouldn't say something like, tell me about the last time you bought a food gift online.


Were you worried that it would spoil?


Now I've set them on a path that maybe they wouldn't have gone on to begin with.


It's much better to let that story unfold naturally.


Moving on, dig deeper.


Uncover the why.


Really important.


Maybe when you're talking to people you realize that they like to cook and by sharing a food item gift with someone who's far away they can feel closer to them.


Maybe they like gifts to reflect how thoughtful they are or what good taste they have.


You always want to uncover the underlying motives behind the actions people are taking.


So don't be too rushed in skipping to the next question.


If you hear something that's a little bit vague or maybe you see a point that's interesting, follow up with some probes.


Ask things like, tell me more about that or why is that?


What did you like about it?


And so on.


Next, listen more than you talk.


You have maybe 30 to 45 minutes max with each one of these interviews and you don't want to waste time by inserting yourself into their story.


If that happens, it's cool, totally natural.


Just find a way to back yourself out of that and bring the focus back to the person you're interviewing as quickly and naturally as possible.


Take note of phrases and words that they use.


Do they say things like dipped berries instead of chocolate dipped strawberries?


You want to pay attention to the different ways and phrases that they use.


Are there regional differences?


What kinds of words do they use to describe your product or service or experience?


Are the berries fun, decadent, luxurious?


By learning what kinds of language and vocabulary people use, you can have copy, meta descriptions, emails that take that into account and reflect that.


Find the friction.


So in every experience that we have, there's always something that's kind of challenging.


We want to get to the bottom of that with our users so we can find ways to mitigate that point of friction earlier on in the journey.


So I might ask someone a question like, what's the most challenging thing about the last time you bought a food gift?


And if that doesn't kind of spark an idea with them, I might say something even a little more broad like,


tell me about a time you were really disappointed in a gift that you bought or food gift that you bought and see where that takes them.


Be prepared.


Great interviews don't happen by accident.


Coming up with all these questions takes time and preparation.


You want to put a lot of thought into, am I asking questions that tell me about the nature of the whole journey?


You want to be clear about your priorities.


Know which questions are most important to you and know which ones are must-have pieces of information.


That way you can use your time wisely while you still let the conversation flow where it takes you.


And finally, relax and breathe.


Your interviewers, I'm sorry, the people you're interviewing are only going to be as relaxed as you are.


If you're stiff or overly formal or treating this like it's a chore and you're bored,


they're going to pick up on that energy and they're probably not going to feel comfortable sharing their thoughts with you


or there won't be space for that to happen.


Make sure you let them know ahead of time like, hey, feel free to be honest.


These answers aren't going to be shared in a way that can be attributed directly to you, just in aggregate.


And have fun with it.


Be genuinely curious and excited about what you're going to learn.


They'll appreciate that too.


So once you've kind of finished and you've wrapped up those interviews, take a step back.


Don't get too focused or caught up on just one of the results.


You want to kind of look at the data in aggregate, the qualitative data, and let it talk to you.


What stories are there?


Are you seeing any patterns or themes that you can take note of,


kind of like the theme around people being worried about the berries melting?


And then you can organize those findings and make sure that you summarize it


and synthesize it in a way that the people who have to use those insights that you've gotten can make sense of.


Make sure that you tell real stories and humanize this information.