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What Deep Learning and Machine Learning Mean For the Future of SEO – Whiteboard Friday

Whiteboard Friday Image of Board

Posted by randfish

Imagine a world where even the high-up Google engineers don't know what's in the ranking algorithm. We may be moving in that direction. In today's Whiteboard Friday, Rand explores and explains the concepts of deep learning and machine learning, drawing us a picture of how they could impact our work as SEOs.

For reference, here's a still of this week's whiteboard!

Video transcription

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we are going to take a peek into Google's future and look at what it could mean as Google advances their machine learning and deep learning capabilities. I know these sound like big, fancy, important words. They're not actually that tough of topics to understand. In fact, they're simplistic enough that even a lot of technology firms like Moz do some level of machine learning. We don't do anything with deep learning and a lot of neural networks. We might be going that direction.

But I found an article that was published in January, absolutely fascinating and I think really worth reading, and I wanted to extract some of the contents here for Whiteboard Friday because I do think this is tactically and strategically important to understand for SEOs and really important for us to understand so that we can explain to our bosses, our teams, our clients how SEO works and will work in the future.

The article is called "Google Search Will Be Your Next Brain." It's by Steve Levy. It's over on Medium. I do encourage you to read it. It's a relatively lengthy read, but just a fascinating one if you're interested in search. It starts with a profile of Geoff Hinton, who was a professor in Canada and worked on neural networks for a long time and then came over to Google and is now a distinguished engineer there. As the article says, a quote from the article: "He is versed in the black art of organizing several layers of artificial neurons so that the entire system, the system of neurons, could be trained or even train itself to divine coherence from random inputs."

This sounds complex, but basically what we're saying is we're trying to get machines to come up with outcomes on their own rather than us having to tell them all the inputs to consider and how to process those incomes and the outcome to spit out. So this is essentially machine learning. Google has used this, for example, to figure out when you give it a bunch of photos and it can say, "Oh, this is a landscape photo. Oh, this is an outdoor photo. Oh, this is a photo of a person." Have you ever had that creepy experience where you upload a photo to Facebook or to Google+ and they say, "Is this your friend so and so?" And you're like, "God, that's a terrible shot of my friend. You can barely see most of his face, and he's wearing glasses which he usually never wears. How in the world could Google+ or Facebook figure out that this is this person?"

That's what they use, these neural networks, these deep machine learning processes for. So I'll give you a simple example. Here at MOZ, we do machine learning very simplistically for page authority and domain authority. We take all the inputs -- numbers of links, number of linking root domains, every single metric that you could get from MOZ on the page level, on the sub-domain level, on the root-domain level, all these metrics -- and then we combine them together and we say, "Hey machine, we want you to build us the algorithm that best correlates with how Google ranks pages, and here's a bunch of pages that Google has ranked." I think we use a base set of 10,000, and we do it about quarterly or every 6 months, feed that back into the system and the system pumps out the little algorithm that says, "Here you go. This will give you the best correlating metric with how Google ranks pages." That's how you get page authority domain authority.

Cool, really useful, helpful for us to say like, "Okay, this page is probably considered a little more important than this page by Google, and this one a lot more important." Very cool. But it's not a particularly advanced system. The more advanced system is to have these kinds of neural nets in layers. So you have a set of networks, and these neural networks, by the way, they're designed to replicate nodes in the human brain, which is in my opinion a little creepy, but don't worry. The article does talk about how there's a board of scientists who make sure Terminator 2 doesn't happen, or Terminator 1 for that matter. Apparently, no one's stopping Terminator 4 from happening? That's the new one that's coming out.

So one layer of the neural net will identify features. Another layer of the neural net might classify the types of features that are coming in. Imagine this for search results. Search results are coming in, and Google's looking at the features of all the websites and web pages, your websites and pages, to try and consider like, "What are the elements I could pull out from there?"

Well, there's the link data about it, and there are things that happen on the page. There are user interactions and all sorts of stuff. Then we're going to classify types of pages, types of searches, and then we're going to extract the features or metrics that predict the desired result, that a user gets a search result they really like. We have an algorithm that can consistently produce those, and then neural networks are hopefully designed -- that's what Geoff Hinton has been working on -- to train themselves to get better. So it's not like with PA and DA, our data scientist Matt Peters and his team looking at it and going, "I bet we could make this better by doing this."

This is standing back and the guys at Google just going, "All right machine, you learn." They figure it out. It's kind of creepy, right?

In the original system, you needed those people, these individuals here to feed the inputs, to say like, "This is what you can consider, system, and the features that we want you to extract from it."

Then unsupervised learning, which is kind of this next step, the system figures it out. So this takes us to some interesting places. Imagine the Google algorithm, circa 2005. You had basically a bunch of things in here. Maybe you'd have anchor text, PageRank and you'd have some measure of authority on a domain level. Maybe there are people who are tossing new stuff in there like, "Hey algorithm, let's consider the location of the searcher. Hey algorithm, let's consider some user and usage data." They're tossing new things into the bucket that the algorithm might consider, and then they're measuring it, seeing if it improves.

But you get to the algorithm today, and gosh there are going to be a lot of things in there that are driven by machine learning, if not deep learning yet. So there are derivatives of all of these metrics. There are conglomerations of them. There are extracted pieces like, "Hey, we only ant to look and measure anchor text on these types of results when we also see that the anchor text matches up to the search queries that have previously been performed by people who also search for this." What does that even mean? But that's what the algorithm is designed to do. The machine learning system figures out things that humans would never extract, metrics that we would never even create from the inputs that they can see.

Then, over time, the idea is that in the future even the inputs aren't given by human beings. The machine is getting to figure this stuff out itself. That's weird. That means that if you were to ask a Google engineer in a world where deep learning controls the ranking algorithm, if you were to ask the people who designed the ranking system, "Hey, does it matter if I get more links," they might be like, "Well, maybe." But they don't know, because they don't know what's in this algorithm. Only the machine knows, and the machine can't even really explain it. You could go take a snapshot and look at it, but (a) it's constantly evolving, and (b) a lot of these metrics are going to be weird conglomerations and derivatives of a bunch of metrics mashed together and torn apart and considered only when certain criteria are fulfilled. Yikes.

So what does that mean for SEOs. Like what do we have to care about from all of these systems and this evolution and this move towards deep learning, which by the way that's what Jeff Dean, who is, I think, a senior fellow over at Google, he's the dude that everyone mocks for being the world's smartest computer scientist over there, and Jeff Dean has basically said, "Hey, we want to put this into search. It's not there yet, but we want to take these models, these things that Hinton has built, and we want to put them into search." That for SEOs in the future is going to mean much less distinct universal ranking inputs, ranking factors. We won't really have ranking factors in the way that we know them today. It won't be like, "Well, they have more anchor text and so they rank higher." That might be something we'd still look at and we'd say, "Hey, they have this anchor text. Maybe that's correlated with what the machine is finding, the system is finding to be useful, and that's still something I want to care about to a certain extent."

But we're going to have to consider those things a lot more seriously. We're going to have to take another look at them and decide and determine whether the things that we thought were ranking factors still are when the neural network system takes over. It also is going to mean something that I think many, many SEOs have been predicting for a long time and have been working towards, which is more success for websites that satisfy searchers. If the output is successful searches, and that' s what the system is looking for, and that's what it's trying to correlate all its metrics to, if you produce something that means more successful searches for Google searchers when they get to your site, and you ranking in the top means Google searchers are happier, well you know what? The algorithm will catch up to you. That's kind of a nice thing. It does mean a lot less info from Google about how they rank results.

So today you might hear from someone at Google, "Well, page speed is a very small ranking factor." In the future they might be, "Well, page speed is like all ranking factors, totally unknown to us." Because the machine might say, "Well yeah, page speed as a distinct metric, one that a Google engineer could actually look at, looks very small." But derivatives of things that are connected to page speed may be huge inputs. Maybe page speed is something, that across all of these, is very well connected with happier searchers and successful search results. Weird things that we never thought of before might be connected with them as the machine learning system tries to build all those correlations, and that means potentially many more inputs into the ranking algorithm, things that we would never consider today, things we might consider wholly illogical, like, "What servers do you run on?" Well, that seems ridiculous. Why would Google ever grade you on that?

If human beings are putting factors into the algorithm, they never would. But the neural network doesn't care. It doesn't care. It's a honey badger. It doesn't care what inputs it collects. It only cares about successful searches, and so if it turns out that Ubuntu is poorly correlated with successful search results, too bad.

This world is not here yet today, but certainly there are elements of it. Google has talked about how Panda and Penguin are based off of machine learning systems like this. I think, given what Geoff Hinton and Jeff Dean are working on at Google, it sounds like this will be making its way more seriously into search and therefore it's something that we're really going to have to consider as search marketers.

All right everyone, I hope you'll join me again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com


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By |March 6th, 2015|MOZ|0 Comments

The Most Important Link Penalty Removal Tool: Your Mindset

Posted by Eric Enge

Let's face it. Getting slapped by a manual link penalty, or by the Penguin algorithm, really stinks. Once this has happened to you, your business is in a world of hurt. Worse still is the fact that you can't get clear information from Google on which of your links are the bad ones. In today's post, I am going to focus on the number one reason why people fail to get out from under these types of problems, and how to improve your chances of success.

The mindset

Success begins, continues, and ends with the right mindset. A large percentage of people I see who go through a link cleanup process are not aggressive enough about cleaning up their links. They worry about preserving some of that hard-won link juice they obtained over the years.

You have to start by understanding what a link cleanup process looks like, and just how long it can take. Some of the people I have spoken with have gone through a process like this one:

link removal timeline

In this fictitious timeline example, we see someone who spends four months working on trying to recover, and at the end of it all, they have not been successful. A lot of time and money have been spent, and they have nothing to show for it. Then, the people at Google get frustrated and send them a message that basically tells them they are not getting it. At this point, they have no idea when they will be able to recover. The result is that the complete process might end up taking six months or more.

In contrast, imagine someone who is far more aggressive in removing and disavowing links. They are so aggressive that 20 percent of the links they cut out are actually ones that Google has not currently judged as being bad. They also start on March 9, and by April 30, the penalty has been lifted on their site.

Now they can begin rebuilding their business, five or months sooner than the person who does not take as aggressive an approach. Yes, they cut out some links that Google was not currently penalizing, but this is a small price to pay for getting your penalty cleared five months sooner. In addition, using our mindset-based approach, the 20 percent of links we cut out were probably not links that were helping much anyway, and that Google might also take action on them in the future.

Now that you understand the approach, it's time to make the commitment. You have to make the decision that you are going to do whatever it takes to get this done, and that getting it done means cutting hard and deep, because that's what will get you through it the fastest. Once you've got your head on straight about what it will take and have summoned the courage to go through with it, then and only then, you're ready to do the work. Now let's look at what that work entails.

Obtaining link data

We use four sources of data for links:

  1. Google Webmaster Tools
  2. Open Site Explorer
  3. Majestic SEO
  4. ahrefs

You will want to pull in data from all four of these sources, get them into one list, and then dedupe them to create a master list. Focus only on followed links as well, as nofollowed links are not an issue. The overall process is shown here:

pulling a link set

One other simplification is also possible at this stage. Once you have obtained a list of the followed links, there is another thing you can do to dramatically simplify your life. You don't need to look at every single link.

You do need to look at a small sampling of links from every domain that links to you. Chances are that this is a significantly smaller quantity of links to look at than all links. If a domain has 12 links to you, and you look at three of them, and any of those are bad, you will need to disavow the entire domain anyway.

I take the time to emphasize this because I've seen people with more than 1 million inbound links from 10,000 linking domains. Evaluating 1 million individual links could take a lifetime. Looking at 10,000 domains is not small, but it's 100 times smaller than 1 million. But here is where the mindset comes in. Do examine every domain.

This may be a grinding and brutal process, but there is no shortcut available here. What you don't look at will hurt you. The sooner you start on the entire list, the sooner you will get the job done.

How to evaluate links

Now that you have a list, you can get to work. This is a key part where having the right mindset is critical. The first part of the process is really quite simple. You need to eliminate each and every one of these types of links:

  1. Article directory links
  2. Links in forum comments, or their related profiles
  3. Links in blog comments, or their related profiles
  4. Links from countries where you don't operate/sell your products
  5. Links from link sharing schemes such as Link Wheels
  6. Any links you know were paid for

Here is an example of a foreign language link that looks somewhat out of place:

foreign language link

For the most part, you should also remove any links you have from web directories. Sure, if you have a link from DMOZ, Business.com, or BestofTheWeb.com, and the most important one or two directories dedicated to your market space, you can probably keep those.

For a decade I have offered people a rule for these types of directories, which is "no more than seven links from directories." Even the good ones carry little to no value, and the bad ones can definitely hurt you. So there is absolutely no win to be had running around getting links from a bunch of directories, and there is no win in trying to keep them during a link cleanup process.

Note that I am NOT talking about local business directories such as Yelp, CityPages, YellowPages, SuperPages, etc. Those are a different class of directory that you don't need to worry about. But general purpose web directories are, generally speaking, a poison.

Rich anchor text

Rich anchor text has been the downfall of many a publisher. Here is one of my favorite examples ever of rich anchor text:

The author wanted the link to say "buy cars," but was too lazy to fit the two words into the same sentence! Of course, you may have many guest posts that you have written that are not nearly as obvious as this one. One great way to deal with that is to take your list of links that you built and sort them by URL and look at the overall mix of anchor text. You know it's a problem if it looks anything like this:

overly optimized anchor text

The problem with the distribution in the above image is that the percentage of links that are non "rich" in nature is way too small. In the real world, most people don't conveniently link to you using one of your key money phrases. Some do, but it's normally a small percentage.

Other types of bad links

There is no way for me to cover every type of bad link in this post, but here are other types of links, or link scenarios, to be concerned about:

  1. If a large percentage of your links are coming from over on the right rail of sites, or in the footers of sites
  2. If there are sites that give you a site-wide link, or a very large number of links from one domain
  3. Links that come from sites whose IP address is identical in the A block, B block, and C block (read more about what these are here)
  4. Links from crappy sites

The definition of a crappy site may seem subjective, but if a site has not been updated in a while, or its information is of poor quality, or it just seems to have no one who cares about it, you can probably consider it a crappy site. Remember our discussion on mindset. Your objective is to be harsh in cleaning up your links.

In fact, the most important principle in evaluating links is this: If you can argue that it's a good link, it's NOT. You don't have to argue for good quality links. To put it another way, if they are not obviously good, then out they go!

Quick case study anecdote: I know of someone who really took a major knife to their backlinks. They removed and/or disavowed every link they had that was below a Moz Domain Authority of 70. They did not even try to justify or keep any links with lower DA than that. It worked like a champ. The penalty was lifted. If you are willing to try a hyper-aggressive approach like this one, you can avoid all the work evaluating links I just outlined above. Just get the Domain Authority data for all the links pointing to your site and bring out the hatchet.

No doubt that they ended up cutting out a large number of links that were perfectly fine, but their approach was way faster than doing the complete domain by domain analysis.

Requesting link removals

Why is it that we request link removals? Can't we just build a disavow file and submit that to Google? In my experience, for manual link penalties, the answer to this question is no, you can't. (Note: if you have been hit by Penguin, and not a manual link penalty, you may not need to request link removals.)

Yes, disavowing a link is supposed to tell Google that you don't want to receive any PageRank, or benefit, from it. However, there is a human element at play here. Google likes to see that you put some effort into cleaning up the bad links that you have gotten that led to your penalty. The more bad links you have, the more important this becomes.

This does make the process a lot more expensive to get through, but if you approach this with the "whatever it takes" mindset, you dive into the requesting link removal process and go ahead and get it done.

I usually have people go through three rounds of requests asking people to remove links. This can be a very annoying process for those receiving your request, so you need to be aware of that. Don't start your email with a line like "Your site is causing mine to be penalized ...", as that's just plain offensive.

I'd be honest, and tell them "Hey, we've been hit by a penalty, and as part of our effort to recover we are trying to get many of the links we have gotten to our site removed. We don't know which sites are causing the problem, but we'd appreciate your help ..."

Note that some people will come back to you and ask for money to remove the link. Just ignore them, and put their domains in your disavow file.

Once you are done with the overall removal requests, and had whatever success you have had, take the rest of the domains and disavow them. There is a complete guide to creating a disavow file here. The one incremental tip I would add is that you should nearly always disavow entire domains, not just the individual links you see.

This is important because even with the four tools we used to get information on as many links as we could, we still only have a subset of the total links. For example, the tools may have only seen one link from a domain, but in fact you have five. If you disavow only the one link, you still have four problem links, and that will torpedo your reconsideration request.

Disavowing the domain is a better-safe-than-sorry step you should take almost every time. As I illustrated at the beginning of this post, adding extra cleanup/reconsideration request loops is very expensive for your business.

The overall process

When all is said and done, the process looks something like this:

link removal process

If you run this process efficiently, and you don't try to cut corners, you might be able to get out from your penalty in a single pass through the process. If so, congratulations!

What about tools?

There are some fairly well-known tools that are designed to help you with the link cleanup process. These include Link Detox and Remove'em. In addition, at STC we have developed our own internal tool that we use with our clients.

These tools can be useful in flagging some of your links, but they are not comprehensive—they will help identify some really obvious offenders, but the great majority of links you need to deal with and remove/disavow are not identified. Plan on investing substantial manual time and effort to do the heavy lifting of a comprehensive review of all your links. Remember the "mindset."

Summary

As I write this post, I have this sense of being heartless because I outline an approach that is often grueling to execute. But consider it tough love. Recovering from link penalties is indeed brutal. In my experience, the winners are the ones who come with meat cleaver in hand, don't try to cut corners, and take on the full task from the very start, no matter how extensive an effort it may be.

Does this type of process succeed? You bet. Here is an example of a traffic chart from a successful recovery:

manual penalty recovery graph


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By |March 5th, 2015|MOZ|0 Comments

Announcing the 2015 Online Marketing Industry Survey

Posted by Cyrus-Shepard

We're very excited to announce the 2015 Online Marketing Industry Survey is ready. This is the fifth edition of the survey, which started in 2008 as the SEO Industry Survey, and has also been known as the Moz Industry Survey. Some of what we hope to learn and share:

  • Demographics: Who is practicing inbound marketing and SEO today? Where do we work and live?
  • Agencies vs. in-house vs. other: How are agencies growing? What's the average size? Who is doing inbound marketing on their own?
  • Tactics and strategies: What's working for people today? How have strategies and tactics evolved?
  • Tools and technology: What are marketers using to discover opportunities, promote themselves, and measure the results?
  • Budget and spending: What tools and platforms are marketers investing in?

This year's survey was redesigned to be easier and only take less than 10 minutes. When the results are in we'll share the data freely with you and the rest of the world, along with the insights we've gleaned from it.


If you're on a mobile device, you might find it easier to complete the survey on its own page:

Survey importance

By comparing answers and predictions from one year to the next, we can spot trends and gain insight not easily reported through any other source. This is our best chance to understand exactly where the future of our industry is headed.

Every year the Industry Survey delivers new insights and surprises. For example, the chart below (from the 2014 survey) lists average reported salary by role.

One of the data points we hope to discover is if these numbers go up or down for 2015.

Prizes. Oh, fabulous prizes.

It wouldn't be the Industry Survey without a few excellent prizes thrown in as an added incentive.

This year we've upped the game with prizes we feel are both exciting and perfect for the busy inbound marketer. To see the full sweepstakes terms and rules, go to our sweepstakes rules page. The winners will be announced by June 15th. Follow us on Twitter to stay up to date.

Grand Prize: Attend MozCon 2015 in Seattle

Come see us Mozzers in Seattle! The Grand Prize includes one ticket to MozCon 2015 plus airfare and accommodations.

2 First Prizes: Apple Watch

Shhhhhh! Because we're giving away two Apple Watches.

10 Second Prizes: $50 Amazon.com gift cards

Yep, 10 lucky people will win $50 Amazon.com gift cards. Why not buy yourself a nice book? Maybe this one?

We could use your help with sharing

The number of people who take the survey is very important! The more people who take the survey, the better and more accurate the data will be, and the more insight we can share with the industry.

So please share with your co-workers. Share on social media. Share with your email lists. You can use the buttons below this post to get you started, but remember to take the survey first!


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By |March 3rd, 2015|MOZ|0 Comments

How Google Pulls Structured Snippets from Websites’ Tables

54ee2f4a4fa809.31701150.jpg

Posted by billslawski

An article that came out at the beginning of 2015 was intended to (quietly) let people know about what Google had been doing to offer a new form of search results called Table Search. The article was titled Applying WebTables in Practice (pdf).

It tells us about an initiative that Google's structured data team embarked upon, when they started the WebTables project in the second half of the 2000s, which involved them releasing the following paper:

WebTables: Exploring the Power of Tables on the Web (pdf)

It got some nice press in the paper Structured Data on the Web (pdf).

What is Table Search?

There are many pages on the Web that are filled with data in the form of tables. It's possible that if you weren't paying attention you may have missed Google Table Search entirely—it hasn't gotten a lot of press as far as I can tell. If you include tabular data on the pages of your site, though, you may be able to find tables from your site included in the results from a query in Google Table Search.

Imagine that I am looking to buy a new camera lens, except I'm not sure which one to purchase. I've heard good things about Nikon lenses, so I go to Table Search and look for [ single lens dslr nikon]. The first table returned gives me some choices to compare different lenses:

Table Search and structured snippets

One of the interesting things to grow out of Table Search capability from Google is the structured snippet, a search result that is a combination of query results and tabular data results, as described by Google in their blog post Introducing Structured Snippets, now a part of Google Web Search.

For example, this result involving a search for [superman] includes facts from a Wikipedia table about the character:

54f0c3048173c5.27232995.jpg

Those extra facts come from the table associated with a query on Superman that shows tabular data about the character:

54f0c33940aba2.23746399.jpg

We can see Google working in structured snippets elsewhere, e.g., in presenting snippets from Twitter, like from the following profile:

54f0c979c14ae7.95271653.jpg

A search for Rand shows the following (h/t to Barbara Starr for this example of a structured snippet):

54f0c5345b4942.37671661.jpg

Note how Google is taking structured data (highlighted in yellow) from the Twitter profile and including it in the Google search result from the Twitter profile "about Rand". That data may also be from Twitter's API of data that they feed to Google. I have noticed that when there are multiple Twitter accounts for the same name, this kind of table data doesn't appear in the Google snippet.

Getting your structured snippets

The Applying Webtables in Practice paper has some suggestions on how to create tables that might be sources of structured data that Google might use:

  1. Limit the amount of boilerplate content that appears in a table
  2. Use table headings to add labels to the columns they head—this tells Google that they are filled with important data
  3. Use meaningful attribute names in table headings that make it more likely the tables might appear and rank for a relevant query
  4. Use meaningful titles, captions and semantically related text surrounding the table. These can help the search engine better understand what the table is about.
  5. The ranking of tables in Table Search can be influenced by Web ranking features such as The PageRank of a page a table is on and links pointed to that page.

If you decide to use tables on your pages, following these hints from the "Applying WebTables in Practice" paper may help lead to structured snippets showing up in your search results. The inclusion of that data may convince searchers to click through to your pages. A data-rich search result that addresses their informational and situational needs may be persuasive enough to get them to visit you. And the snippet is attached to a link to your page, so your page gets credit for the data.


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By |March 3rd, 2015|MOZ|0 Comments

Mastering Serving the User as Centroid to Reach Local Searchers

local result north beach pizza san francisco

Posted by MiriamEllis

"Google is getting better at detecting location at a more granular level—even on the desktop. The user is the new centroid." - David Mihm

The history of the centroid

The above quote succinctly summarizes the current state of affairs for local business owners and their customers. The concept of a centroid— a central point of relevance—is almost as old as local search. In 2008, people like Mike Blumenthal and Google Maps Manager Carter Maslan were sharing statistics like this:

"...research indicates that up to 80% of the variation in rank can be explained by distance from the centroid on certain searches."

At that time, businesses located near town hall or a similar central hub appeared to be experiencing a ranking advantage.

Fast forward to 2013, and Mike weighed in again with an updated definition of "industry centroids",

"If you read their (Google's) patents, they actually deal with the center of the industries … as defining the center of the search. So if all the lawyers are on the corner of Main and State, that typically defines the center of the search, rather than the center of the city… it isn't even the centroid of the city that matters. It matters that you are near where the other people in your industry are."

In other words, Google's perception of a centralized location for auto dealerships could be completely different than that for medical practices, and that neither might be located anywhere near the city center.

While the concepts of city and industry centroids may still play a part in some searches, local search results in 2015 clearly indicate Google's shift toward deeming the physical location of the desktop or mobile user a powerful factor in determining relevance. The relationship between where your customer is when he performs a search and where your business is physically located has never been more important.

Moreover, in this new, user-centric environment, Google has moved beyond simply detecting cities to detecting neighborhoods and even streets. What this means for local business owners is that your hyperlocal information has become a powerful component of your business data. This post will teach you how to better serve your most local customers.

Seeing the centroid in action

If you do business in a small town with few competitors, ranking for your product/service + city terms is likely to cover most of your bases. The user-as-centroid phenomenon is most applicable in mid-to-large sized towns and cities with reasonable competition. I'll be using two districts in San Francisco—Bernal Heights and North Beach—in these illustrations and we'll be going on a hunt for pizza.

On a desktop, searching for "pizza north beach san francisco" or setting my location to this neighborhood and city while searching for the product, Google will show me something like this:

Performing this same search, but with "bernal heights" substituted, Google shows me pizzerias in a completely different part of the city:

local result bernal heights pizza san francisco

And, when I move over to my mobile device, Google narrows the initial results down to just three enviable players in each district. These simple illustrations demonstrate Google's increasing sensitivity to serving me nearby businesses offering what I want.

The physical address of your business is the most important factor in serving the user as centroid. This isn't something you can control, but there are things you can do to market your business as being highly relevant to your hyperlocal geography.

Specialized content for the user-centroid

We'll break this down into four common business models to help get you thinking about planning content that serves your most local customers.

1. Single-location business

Make the shift toward viewing your business not just as "Tony's Pizza in San Francisco", but as "Tony's Pizza in North Beach, San Francisco". Consider:

  • Improving core pages of your website or creating new pages to include references to the proud part you play in the neighborhood scene. Talk about the history of your area and where you fit into that.
  • Interview locals and ask them to share their memories about the neighborhood and what they like about living there.
  • Showcase your participation in local events.
  • Plan an event, contest or special for customers in your district.
  • Take pictures, label them with hyperlocal terms, post them on your site and share them socially.
  • Blog about local happenings that are relevant to you and your customers, such as a street market where you buy the tomatoes that top your pizzas or a local award you've won.
  • Depending on your industry, there will be opportunities for hyperlocal content specific to your business. For example, a restaurant can make sure its menu is in crawlable text and can name some favorite dishes after the neighborhood—The Bernal Heights Special. Meanwhile, a spa in North Beach can create a hyperlocal name for a service—The North Beach Organic Spa Package. Not only does this show district pride, but customers may mention these products and services by name in their reviews, reinforcing your local connection.

2. Multi-location business within a single city

All that applies to the single location applies to you, too, but you've got to find a way to scale building out content for each neighborhood.

  • If your resources are strong, build a local landing page for each of your locations, including basic optimization for the neighborhood name. Meanwhile, create blog categories for each neighborhood and rotate your efforts on a week by week basis. First week, blog about neighborhood A, next week, find something interesting to write about concerning neighborhood B. Over time, you'll have developed a nice body of content proving your involvement in each district.
  • If you're short on resources, you'll still want to build out a basic landing page for each of your stores in your city and make the very best effort you can to showcase your neighborhood pride on these pages.

3. Multiple businesses, multiple cities

Again, scaling this is going to be key and how much you can do will depend upon your resources.

  • The minimum requirement will be a landing page on the site for each physical location, with basic optimization for your neighborhood terms.
  • Beyond this, you'll be making a decision about how much hyperlocal content you can add to the site/blog for each district, or whether time can be utilized more effectively via off-site social outreach. If you've got lots of neighborhoods to cover in lots of different cities, designating a social representative for each store and giving him the keys to your profiles (after a training session in company policies) may make the most sense.

4. Service area businesses (SABs)

Very often, service area businesses are left out in the cold with various local developments, but in my own limited testing, Google is applying at least some hyperlocal care to these business models. I can search for a neighborhood plumber, just as I would a pizza:

local results plumber bernal heights san francisco

To be painstakingly honest, plumbers are going to have to be pretty ingenious to come up with a ton of engaging industry/neighborhood content and may be confined mainly to creating some decent service area landing pages that share a bit about their work in various neighborhoods. Other business models, like contractors, home staging firms and caterers should find it quite easy to talk about district architecture, curb appeal and events on a hyperlocal front.

While your SAB is still unlikely to beat out a competitor with a physical location in a given neighborhood, you still have a chance to associate your business with that area of your town with well-planned content.

Need creative inspiration for the writing projects ahead? Don't miss this awesome wildcard search tip Mary Bowling shared at LocalUp. Add an underscore or asterisk to your search terms and just look at the good stuff Google will suggest to you:

wildcard search content ideas

Does Tony's patio make his business one of Bernal Heights' dog-friendly restaurants or does his rooftop view make his restaurant the most picturesque lunch spot in the district? If so, he's got two new topics to write about, either on his basic landing pages or his blog.

Hop over to Whitespark's favorite takeaways from Mike Ramsey's LocalUp presentation, too.

Citations and reviews with the user centroid in mind

Here are the basics about citations, broken into the same four business models:

1. Single-location business

You get just one citation on each platform, unless you have multiple departments or practitioners. That means one Google+ Local page, one Yelp profile, one Best of the Web listing. etc. You do not get one citation for your city and another for your neighborhood. Very simple.

2. Multi-location business within a single city

As with the single location business, you are entitled to just one set of citations per physical location. That means one Google+ Local listing for your North Beach pizza place and another for your restaurant in Bernal Heights.

A regular FAQ here in the Moz Q&A Forum relates to how Google will differentiate between two businesses located in the same city. Here are some tips:

  • Google no longer supports the use of modifiers in the business name field, so you can no longer be Tony's Pizza - Bernal Heights, unless your restaurant is actually named this. You can only be Tony's Pizza.
  • Facebook's policies are different than Google's. To my understanding, Facebook won't permit you to build more than one Facebook Place for the identical brand name. Thus, to comply with their guidelines, you must differentiate by using those neighborhood names or other modifiers. Given that this same rule applies to all of your competitors, this should not be seen as a danger to your NAP consistency, because apparently, no multi-location business creating Facebook Places will have 100% consistent NAP. The playing field is, then, even.
  • The correct place to differentiate your businesses on all other platforms is in the address field. Google will understand that one of your branches is on A St. and the other is on B St. and will choose which one they feel is most relevant to the user.
  • Google is not a fan of call centers. Unless it's absolutely impossible to do so, use a unique local phone number for each physical location to prevent mix-ups on Google's part, and use this number consistently across all web-based mentions of the business.
  • Though you can't put your neighborhood name in the title, you can definitely include it in the business description field most citation platforms provide.
  • Link your citations to their respective local landing pages on your website, not to your homepage.

3. Multiple businesses, multiple cities

Everything in business model #2 applies to you as well. You are allowed one set of citations for each of your physical locations, and while you can't modify your Google+ Local business name, you can mention your neighborhood in the description. Promote each location equally in all you do and then rely on Google to separate your locations for various users based on your addresses and phone numbers.

4. SABs

You are exactly like business model #1 when it comes to citations, with the exception of needing to abide by Google's rules about hiding your address if you don't serve customers at your place of business. Don't build out additional citations for neighborhoods you serve, other cities you serve or various service offerings. Just create one citation set. You should be fine mentioning some neighborhoods in your citation descriptions, but don't go overboard on this.

When it comes to review management, you'll be managing unique sets of reviews for each of your physical locations. One method for preventing business owner burnout is to manage each location in rotation. One week, tend to owner responses for Business A. Do Business B the following week. In week three, ask for some reviews for Business A and do the same for B in week four. Vary the tasks and take your time unless faced with a sudden reputation crisis.

You can take some additional steps to "hyperlocalize" your review profiles:

  • Write about your neighborhood in the business description on your profile.
  • You can't compel random customers to mention your neighborhood, but you can certainly do so from time to time when your write responses. "We've just installed the first soda fountain Bernal Heights has seen since 1959. Come have a cool drink on us this summer."
  • Offer a neighborhood special to people who bring in a piece of mail with their address on it. Prepare a little handout for all-comers, highlighting a couple of review profiles where you'd love to hear how they liked the Bernal Heights special. Or, gather email addresses if possible and follow up via email shortly after the time of service.
  • If your business model is one that permits you to name your goods or service packages, don't forget the tip mentioned earlier about thinking hyperlocal when brainstorming names. Pretty cool if you can get your customers talking about how your "North Beach Artichoke Pizza" is the best pie in town!

Investigate your social-hyperlocal opportunties

I still consider website-based content publication to be more than half the battle in ranking locally, but sometimes, real-time social outreach can accomplish things static articles or scheduled blog posts can't. The amount of effort you invest in social outreach should be based on your resources and an assessment of how naturally your industry lends itself to socialization. Fire insurance salesmen are going to find it harder to light up their neighborhood community than yoga studios will. Consider your options:

      Remember that you are investigating each opportunity to see how it stacks up not just to promoting your location in your city, but in your neighborhood.

      Who are the people in your neighborhood?

      Remember that Sesame Street jingle? It hails from a time when urban dwellers strongly identified with a certain district of hometown. People were "from the neighborhood." If my grandfather was a Mission District fella, maybe yours was from Chinatown. Now, we're shifting in fascinating directions. Even as we've settled into telecommuting to jobs in distant states or countries, Amazon is offering one hour home delivery to our neighbors in Manhattan. Doctors are making house calls again! Any day now, I'm expecting a milkman to start making his rounds around here. Commerce has stretched to span the globe and now it's zooming in to meet the needs of the family next door.

      If the big guys are setting their sights on near-instant services within your community, take note. You live in that community. You talk, face-to-face, with your neighbors every day and know the flavor of the local scene better than any remote competitor can right now.

      Now is the time to reinvigorate that old neighborhood pride in the way you're visualizing your business, marketing it and personally communicating to customers that you're right there for them.


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      By |March 2nd, 2015|MOZ|0 Comments