The Nifty Guide to Local Content Strategy and Marketing

Posted by NiftyMarketing

This is my Grandma.

She helped raised me and I love her dearly. That chunky baby with the Gerber cheeks is me. The scarlet letter "A" means nothing… I hope.

This is a rolled up newspaper. 

When I was growing up, I was the king of mischief and had a hard time following parental guidelines. To ensure the lessons she wanted me to learn "sunk in" my grandma would give me a soft whack with a rolled up newspaper and would say,

"Mike, you like to learn the hard way."

She was right. I have spent my life and career learning things the hard way.

Local content has been no different. I started out my career creating duplicate local doorway pages using "find and replace" with city names. After getting whacked by the figurative newspaper a few times, I decided there had to be a better way. To save others from the struggles I experienced, I hope that the hard lessons I have learned about local content strategy and marketing help to save you fearing a rolled newspaper the same way I do.

Lesson one: Local content doesn't just mean the written word

Content is everything around you. It all tells a story. If you don't have a plan for how that story is being told, then you might not like how it turns out. In the local world, even your brick and mortar building is a piece of content. It speaks about your brand, your values, your appreciation of customers and employees, and can be used to attract organic visitors if it is positioned well and provides a good user experience. If you just try to make the front of a building look good, but don't back up the inside inch by inch with the same quality, people will literally say, "Hey man, this place sucks… let's bounce."

I had this experience proved to me recently while conducting an interview at Nifty for our law division. Our office is a beautifully designed brick, mustache, animal on the wall, leg lamp in the center of the room, piece of work you would expect for a creative company.

Anywho, for our little town of Burley, Idaho it is a unique space, and helps to set apart our business in our community. But, the conference room has a fluorescent ballast light system that can buzz so loudly that you literally can't carry on a proper conversation at times, and in the recent interviews I literally had to conduct them in the dark because it was so bad.

I'm cheap and slow to spend money, so I haven't got it fixed yet. The problem is I have two more interviews this week and I am so embarrassed by the experience in that room, I am thinking of holding them offsite to ensure that we don't product a bad content experience. What I need to do is just fix the light but I will end up spending weeks going back and forth with the landlord on whose responsibility it is.

Meanwhile, the content experience suffers. Like I said, I like to learn the hard way.

Start thinking about everything in the frame of content and you will find that you make better decisions and less costly mistakes.

Lesson two: Scalable does not mean fast and easy growth

In every sales conversation I have had about local content, the question of scalability comes up. Usually, people want two things:

1. Extremely Fast Production 
2. Extremely Low Cost

While these two things would be great for every project, I have come to find that there are rare cases where quality can be achieved if you are optimizing for fast production and low cost. A better way to look at scale is as follows:

The rate of growth in revenue/traffic is greater than the cost of continued content creation.

A good local content strategy at scale will create a model that looks like this:

Lesson three: You need a continuous local content strategy

This is where the difference between local content marketing and content strategy kicks in. Creating a single piece of content that does well is fairly easy to achieve. Building a true scalable machine that continually puts out great local content and consistently tells your story is not. This is a graph I created outlining the process behind creating and maintaining a local content strategy:

This process is not a one-time thing. It is not a box to be checked off. It is a structure that should become the foundation of your marketing program and will need to be revisited, re-tweaked, and replicated over and over again.

1. Identify your local audience

Most of you reading this will already have a service or product and hopefully local customers. Do you have personas developed for attracting and retaining more of them? Here are some helpful tools available to give you an idea of how many people fit your personas in any given market.

Facebook Insights

Pretend for a minute that you live in the unique market of Utah and have a custom wedding dress line. You focus on selling modest wedding dresses. It is a definite niche product, but one that shows the idea of personas very well.

You have interviewed your customer base and found a few interests that your customer base share. Taking that information and putting it into Facebook insights will give you a plethora of data to help you build out your understanding of a local persona.

We are able to see from the interests of our customers there are roughly 6k-7k current engaged woman in Utah who have similar interests to our customer base.

The location tab gives us a break down of the specific cities and, understandably, Salt Lake City has the highest percentage with Provo (home of BYU) in second place. You can also see pages this group would like, activity levels on Facebook, and household income with spending habits. If you wanted to find more potential locations for future growth you can open up the search to a region or country.

From this data it's apparent that Arizona would be a great expansion opportunity after Utah.

Neilson Prizm

Neilson offers a free and extremely useful tool for local persona research called Zip Code Lookup that allows you to identify pre-determined personas in a given market.

Here is a look at my hometown and the personas they have developed are dead on.

Each persona can be expanded to learn more about the traits, income level, and areas across the country with other high concentrations of the same persona group.

You can also use the segment explorer to get a better idea of pre-determined persona lists and can work backwards to determine the locations with the highest density of a given persona.

Google Keyword Planner Tool

The keyword tool is fantastic for local research. Using our same Facebook Insight data above we can match keyword search volume against the audience size to determine how active our persona is in product research and purchasing. In the case of engaged woman looking for dresses, it is a very active group with a potential of 20-30% actively searching online for a dress.

2. Create goals and rules

I think the most important idea for creating the goals and rules around your local content is the following from the must read book Content Strategy for the Web.

You also need to ensure that everyone who will be working on things even remotely related to content has access to style and brand guides and, ultimately, understands the core purpose for what, why, and how everything is happening.

3. Audit and analyze your current local content

The point of this step is to determine how the current content you have stacks up against the goals and rules you established, and determine the value of current pages on your site. With tools like Siteliner (for finding duplicate content) and ScreamingFrog (identifying page titles, word count, error codes and many other things) you can grab a lot of information very fast. Beyond that, there are a few tools that deserve a more in-depth look.

BuzzSumo

With BuzzSumo you can see social data and incoming links behind important pages on your site. This can you a good idea which locations or areas are getting more promotion than others and identify what some of the causes could be.

Buzzsumo also can give you access to competitors' information where you might find some new ideas. In the following example you can see that one of Airbnb.com's most shared pages was a motiongraphic of its impact on Berlin.

urlProfiler

This is another great tool for scraping urls for large sites that can return about every type of measurement you could want. For sites with 1000s of pages, this tool could save hours of data gathering and can spit out a lovely formatted CSV document that will allow you to sort by things like word count, page authority, link numbers, social shares, or about anything else you could imagine.

4. Develop local content marketing tactics

This is how most of you look when marketing tactics are brought up.

Let me remind you of something with a picture. 

Do not start with tactics. Do the other things first. It will ensure your marketing tactics fall in line with a much bigger organizational movement and process. With the warning out of the way, here are a few tactics that could work for you.

Local landing page content

Our initial concept of local landing pages has stood the test of time. If you are scared to even think about local pages with the upcoming doorway page update then please read this analysis and don't be too afraid. Here are local landing pages that are done right.

Marriot's Burley local page is great. They didn't think about just ensuring they had 500 unique words. They have custom local imagery of the exterior/interior, detailed information about the area's activities, and even their own review platform that showcases both positive and negative reviews with responses from local management.

If you can't build your own platform handling reviews like that, might I recommend looking at Get Five Stars as a platform that could help you integrate reviews as part of your continuous content strategy.

Airbnb Neighborhood Guides

I not so secretly have a big crush on Airbnb's approach to local. These neighborhood guides started it. They only have roughly 21 guides thus far and handle one at a time with Seoul being the most recent addition. The idea is simple, they looked at extremely hot markets for them and built out guides not just for the city, but down to a specific neighborhood.

Here is a look at Hell's Kitchen in New York by imagery. They hire a local photographer to shoot the area, then they take some of their current popular listing data and reviews and integrate them into the page. This idea would have never flown if they only cared about creating content that could be fast and easy for every market they serve.

Reverse infographicing

Every decently sized city has had a plethora of infographics made about them. People spent the time curating information and coming up with the concept, but a majority just made the image and didn't think about the crawlability or page title from an SEO standpoint.

Here is an example of an image search for Portland infographics.

Take an infographic and repurpose it into crawlable content with a new twist or timely additions. Usually infographics share their data sources in the footer so you can easily find similar, new, or more information and create some seriously compelling data based content. You can even link to or share the infographic as part of it if you would like.

Become an Upworthy of local content

No one I know does this better than Movoto. Read the link for their own spin on how they did it and then look at these examples and share numbers from their local content.

60k shares in Boise by appealing to that hometown knowledge.

65k shares in Salt Lake following the same formula.

It seems to work with video as well.

Think like a local directory

Directories understand where content should be housed. Not every local piece should be on the blog. Look at where Trip Advisor's famous "Things to Do" page is listed. Right on the main city page.

Or look at how many timely, fresh, quality pieces of content Yelp is showcasing from their main city page.

The key point to understand is that local content isn't just about being unique on a landing page. It is about BEING local and useful.

Ideas of things that are local:

• Sports teams
• Local celebrities or heroes 
• Groups and events
• Local pride points
• Local pain points

Ideas of things that are useful:

• Directions
• Favorite local sports
• Granular details only "locals" know

The other point to realize is that in looking at our definition of scale you don't need to take shortcuts that un-localize the experience for users. Figure and test a location at a time until you have a winning formula and then move forward at a speed that ensures a quality local experience.

5. Create a content calendar

I am not going to get into telling you exactly how or what your content calendar needs to include. That will largely be based on the size and organization of your team and every situation might call for a unique approach. What I will do is explain how we do things at Nifty.

1. We follow the steps above.
2. We schedule the big projects and timelines first. These could be months out or weeks out. 
3. We determine the weekly deliverables, checkpoints, and publish times.
4. We put all of the information as tasks assigned to individuals or teams in Asana.

The information then can be viewed by individual, team, groups of team, due dates, or any other way you would wish to sort. Repeatable tasks can be scheduled and we can run our entire operation visible to as many people as need access to the information through desktop or mobile devices. That is what works for us.

6. Launch and promote content

My personal favorite way to promote local content (other than the obvious ideas of sharing with your current followers or outreaching to local influencers) is to use Facebook ads to target the specific local personas you are trying to reach. Here is an example:

I just wrapped up playing Harold Hill in our communities production of The Music Man. When you live in a small town like Burley, Idaho you get the opportunity to play a lead role without having too much talent or a glee-based upbringing. You also get the opportunity to do all of the advertising, set design, and costuming yourself and sometime even get to pay for it.

For my advertising responsibilities, I decided to write a few blog posts and drive traffic to them. As any good Harold Hill would do, I used fear tactics.

I then created Facebook ads that had the following stats: Costs of $.06 per click, 12.7% click through rate, and naturally organic sharing that led to thousands of visits in a small Idaho farming community where people still think a phone book is the only way to find local businesses.

Then we did it again.

There was a protestor in Burley for over a year that parked a red pickup with signs saying things like, "I wud not trust Da Mayor" or "Don't Bank wid Zions". Basically, you weren't working hard enough if you name didn't get on the truck during the year.

Everyone knew that ol' red pickup as it was parked on the corner of Main and Overland, which is one of the few stoplights in town. Then one day it was gone. We came up with the idea to bring the red truck back, put signs on it that said, "I wud Not Trust Pool Tables" and "Resist Sins n' Corruption" and other things that were part of The Music Man and wrote another blog complete with pictures.

Then I created another Facebook Ad.

A little under $200 in ad spend resulted in thousands more visits to the site which promoted the play and sold tickets to a generation that might not have been very familiar with the show otherwise.

All of it was local targeting and there was no other way would could have driven that much traffic in a community like Burley without paying Facebook and trying to create click bait ads in hope the promotion led to an organic sharing.

7. Measure and report

This is another very personal step where everyone will have different needs. At Nifty we put together very custom weekly or monthly reports that cover all of the plan, execution, and relevant stats such as traffic to specific content or location, share data, revenue or lead data if available, analysis of what worked and what didn't, and the plan for the following period.

There is no exact data that needs to be shared. Everyone will want something slightly different, which is why we moved away from automated reporting years ago (when we moved away from auto link building… hehe) and built our report around our clients even if it took added time.

I always said that the product of a SEO or content shop is the report. That is what people buy because it is likely that is all they will see or understand.

8. In conclusion, you must refine and repeat the process

From my point of view, this is by far the most important step and sums everything up nicely. This process model isn't perfect. There will be things that are missed, things that need tweaked, and ways that you will be able to improve on your local content strategy and marketing all the time. The idea of the cycle is that it is never done. It never sleeps. It never quits. It never surrenders. You just keep perfecting the process until you reach the point that few locally-focused companies ever achieve… where your local content reaches and grows your target audience every time you click the publish button.

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Google Told Me I'm Pregnant: From Strings to Diagnosis

Posted by CraigBradford

In the near future, I think Google Now could tell you are pregnant or diagnose you with a medical condition before your doctor ever could. Humans are great at recognising patterns but only if we know we are creating them or where to look. Remember the Target story of how they knew a young girl was pregnant before she or her father did? Increases in technology like smart watches and the trend of "the quantified self" mean messages like being told you are pregnant aren't impossible in the near future. So how do we go from weather reports and traffic updates to a medical diagnosis?

Strings-to-things, things-to-actions

When Google, Yahoo and Bing announced Schema.org in 2011, search engines were still in the strings-to-things phase. In my opinion, Google, in particular, are already moving on from that goal. The most recent addition to the Schema.org vocabulary is actions. See, the Schema site for more details or my SMX Munich deck for more details. 

In my presentation, I made the point that the future of structured data isn't about understanding what a thing is, it's about understanding what a thing can do. If search engines can understand what your website, app or other interfaces can do, and they can understand user intent, they can match queries to the best place to do that action. How does Google know what we want to do?

Actions-to-anticipation

Many people have said that Google wants to become the ultimate personal assistant. Things like Google Now and conversational search reinforce this standpoint. However, a prerequisite for that position is the concept of time. For a computerised personal assistant to be truly as useful as the real thing, they need to be aware of the past, the present and more importantly the future. 

Historically, Google and other search engines have dealt with things from the past. Webpages by their nature are in the past, or at best, live. This makes the anticipation and initiative that you would expect from great personal assistant difficult for Google. They have very little data to predict what you might want to do or are going to do in the future. Gmail and Google calendar are the two most obvious ones that come to mind (if you use them). 

Forgetting privacy or intellectual property for a second, imagine Google had access to every app on your phone and the data within it. What might they be able to know about you?

Just the apps above could give Google access to:

• What music I've listened to in the past
• What movies I've watched
• What I've been eating and drinking recently
• How much exercise I do
• What articles I might read in the near future
• Flights I have booked
• Houses I might want to buy

Google Now - An IFTTT for your life

While I was in Munich, I saw an announcement that Google had opened the Google Now API to a selection of hand-picked, third-party apps.

This got me thinking. I do not know what the relationship will be or what data Google would have access to but one of the apps that have been accepted to work with Google Now is Lyft. The example Google gave in the article was a generic prompt to order a cab. For example, you arrive at an airport and Google Now might push you a notification to get a cab:

Some more examples

See more examples here.

While the Lyft example above is interesting, it made me realise that allowing apps to talk to each other via Google Now would essentially turn your smartphone into an IFTTT for your life. So rather than a generic Lyft alert, what if they combined a few apps? They could use my British Airways app to see I have an upcoming flight, Google maps to know when I've arrived in Munich, and my Gmail account to see where I am staying. There are probably specific hotel apps they could use too. Using this, rather than getting a generic get a car card, I get one that's already personalised the quote to where I'm going.

Anticipation to diagnosis

The ultimate personal assistant would not only tell us what we expect, they would tell us things we never thought to consider. This would only be made possible by advanced pattern recognition, anticipation, and initiative beyond the possibility of a human. 

What patterns do you already create but don't currently correlate? If you feel sluggish or tired on a Thursday, we do not necessarily correlate that to something that you may be allergic to that you ate on Monday. Many people spend years with conditions such as gluten or lactose intolerance but never make the connection between what they eat and how they feel. Humans cannot easily track and analyse lots of data like that, computers can. 

So how can Google tell you are pregnant? I am not a doctor but I suspect like the Target example, there may be early signs of pregnancy that we do not think about at the moment (biological or otherwise). For a start, there could be a process of first increasing the priority that a particular pattern receives. For example, there may be lots of small things that people change before trying to get pregnant. If you're using a lot of different apps combined with hardware like heart rate monitors and blood pressure monitors, it wouldn't be too difficult for Google to take an educated guess. Just using the information in the Target article we know people do things like:

• Change their diet - This would be easy to see through apps like MyFitnessPal
• Change their buying habits - Amazon app or other store apps
• They may do more exercise - Several places they could get this

After all of the above, let's not forget Google knows everything you've searched for online and your browsing history if you use Chrome. I do not think it would take much to guess someone is thinking about having a family based on his or her search history alone. 

Let's assume that based on the above, Google lowers the "pregnancy card" trigger threshold. This means they look closer at changes that might suggest your pregnant. I am not a doctor, so bear with me while I think out loud. Other than urine or blood samples, what other quantitative data is there that you might be pregnant? 

For context, I recently learned that eating something that you have an intolerance to can show an elevated heart rate for two hours after eating. One test to check for allergies is to track your heart rate throughout the day. This was where this idea came from in the first place. Using a smartwatch with a heart rate monitor, plus My Fitness Pal, Google could make suggestions that you are allergic to foods you never thought of due to recognising patterns in elevated heart rate after your meals. This made me wonder what else could be possible. There's a ton of tech for tracking:

• Heart rate (smartwatch)
• Your weight (Withings)
• Your blood pressure (Withings)
• Sleep (Jawbone)

Could Google make a guess from this data alone? I cannot stress enough about my lack of medical qualifications, but I wonder if pregnancy impacts things like REM and deep sleep changes, significant blood pressure or heart rate changes at certain times of the day. Who knows, and maybe one of these things alone wouldn't be enough to know for sure, but combined, I think it will not be long before pregnancy prediction or similar could be done.

Enough about pregnancy, (Google probably thinks I am looking to start a family) what else? What things using heart rate alone could Google diagnose or push to us in Google Now? Could they push notifications to people who are diabetic to remember to take insulin? Could they diagnose diabetes? Could they flag heart problems before it is too late? I have no idea, but I'm excited to see where things go in the next few years.

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How Google's Evolution is Forcing Marketers to Invest in Loyal Audiences - Whiteboard Friday

Posted by randfish

Given Google's recent changes to SERPs and their April 21 mobile deadline, does SEO still come first? In today's Whiteboard Friday, Rand walks you through tactics you can use to build a loyal audience before you need to do SEO.

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

Click on it to open a high resolution image in a new tab!

Video transcription

Howdy Moz fans and welcome to another edition of Whiteboard Friday. This week we're chatting on some of the changes that Google has made that are forcing marketers to invest more and more in building loyal audiences before they do SEO. This is kind of a reverse of years past where we could use SEO as that initial channel where we attracted visits who would become our customers, our email subscribers, our social media fans and followers. All of these things have kind of switched direction.

Why move SEO later in the process?

There are some reasons why. First off, Google has for a lot of broad, head of the demand curve queries, they've taken some of the value and equity away from those with things like instant answers and Knowledge Graph, along with lots and lots of other verticals.

Knowledge Graph

I do a search for "plaid shirts" and I get this instant answer showing me what a plaid shirt looks like and a Knowledge Graph. This is a fake example. I don't think they actually do this for plaid shirts yet, but they will.

Personalization

Personalization by history, we're seeing a ton of personalization. I think history is one of the biggest influencers on personalization. Google+ still is a little bit, but your search history and what you've clicked on in the past tends to be big predictors of this. You can see this in two areas, not just in the results that Google shows, but also in what they're suggesting to you in your Search Suggest as you type.

Now, where Google is trying to predictively say, "Hey, we think you're going to want coffee right now because we see that you stepped out of your office and you live in Seattle, and you are a human being. So you must want coffee." They have these ranking signals, that are relatively new over the past few years and certainly much stronger than in years past around user and usage data, around search volume and what you searched for using quality raters and human and manual controls. Signals that are heavily correlated with brand, even if brand itself isn't necessarily a ranking factor.

Fewer results

Of course, there are fewer results now. I don't know if you guys caught this, but I thought one of the most fascinating things that Dr. Pete showed off recently in his MozCast data set was that it used to be the case that Google would show 10 results even if they had a set of images, a news result, and a local pack. Now basically these count as individual results. So you're not getting 10 results on a page. If you've got images and a couple of news things, you're getting seven results that are web results. Ten domains appear, ten big domains, powerful domains, places like Amazon and Yelp and those kinds of things, at least for U.S. search results, appear on 17% of all page one queries. There are a little fewer results to work with and more results biased to these bigger, better-known sites.

All of these things are contributing to this world in which doing SEO first and then earning loyalty through two other channels through SEO is really, really hard. It's making the value of having a loyal audience before you need to do SEO that much more valuable, which is why I figured we'd run through some of the tactics that you can use to build a loyal audience.

This is actually a question from one of our Whiteboard Friday loyal audience members. Thank you very much. Much appreciated.

How to build a loyal audience

Some tactics to build loyalty, we talked about a few of these, but creating an expectation that you can consistently deliver upon is a huge part of how loyalty is created. Humans love to form habits. Thankfully for marketers, we're terrible at breaking those habits.

Consistency

If you can form a habit, you can create a loyal member of your audience, but this is very challenging unless you deliver consistency. That consistency needs to be created through an expectation. That could be when you publish. That could be what you're going to do. That could be the format of the content that you're providing. That could be how your solution or problem or product is delivered. But it needs to create those things in order to build that loyal audience.

Reach your audience where they are

Secondly, provide your content through the channels, the apps, the accounts, the formats that your audience is already using. If I say, "Hey, in order to get Whiteboard Friday, you need to sign up for a Moz account first," the viewability of Whiteboard Friday is going to go down. If on the other hand, which we don't have this but we really should have it, there was a subscribe on iTunes and you could get each Whiteboard Friday as a podcast, gosh, that is something that many Whiteboard Friday viewers, in fact, many people in the technology and marketing worlds already have access to. Therefore it reduces the friction of subscribing to Whiteboard Friday. We might build more people into our loyal audience.

This is definitely something to think about. You need to be able to identify those channels and then be there.

Where SEO fits

I'm saying don't start with SEO as your primary web marketing tactic anymore. I think we have to build into it. These challenges are too great. Not only are they too great, I think they could be overcome today, but they are growing. All of them are growing so substantially, instant answers and Knowledge Graph are becoming a bigger and bigger part of search results. Google Now is something that Google is pushing on so incredibly hard. I think they're going to be pushing it with new devices. They're clearly pushing it with app results inside of search results. I think these ranking signals are only going to get stronger. I think there's going to be more personalization. I think every one of these you can see an up and to the right trend.

Therefore, when we do SEO, we have to think about it as, "How do I earn a loyal audience and then use their amplification to help me perform in search?" Rather than, "How do I do SEO for my website to earn visitors that I can convert into a loyal audience?" That's a new a challenge, a new paradigm for us.

Be unique and memorable

Craft a stylistically unique and memorable approach to solving your audience's problem. One of the things that I find is challenging in a lot of businesses that we talk to, that I get to interact with is that they think, "Hey, we're the best player in this field. We're the best at doing this. Therefore, we should be able to earn a great customer audience." I think this ignores why marketing exists and ignores the power that marketing has and the power of influencing human beings overall.

The best really is not necessarily enough. We are not perfectly logical creatures where we go, "Hey, I am thinking about a new social media monitoring solution. I need to watch Twitter, Facebook, Google+, LinkedIn, and Instagram for my business. Therefore I'm going to create my criteria. I'm going to evaluate all 716 providers that are in the market today that fit my price range and those criteria. Then I'm going to choose effectively the best one. No, we're biased by the ones we've heard of, the ones our friends recommend, the ones we stumble across versus don't stumble across, the ones that have a loud voice, the ones that have a credible voice. These things bias us. Therefore, being stylistically unique and memorable have outsized power to determine whether people will become part of your loyal audience.

More isn't necessarily better

I've talked about this a few times, but I'm strongly of the opinion, especially when it comes to loyalty, that more content may actually be worse than better content. Moz publishes between 7 and 10 blog posts a week. That's a lot of content. I think there are weeks where we published 12 blog posts. For me to say this is a little odd. But the challenge here is prior to building a loyal audience. Once you have a loyal audience, you can start to expand that audience by reaching out and broadening the spectrum of content that you create, and you can afford to be a little more risk taking in that. When you are trying to build loyalty early on, you need to have that consistency of quality.

People are going to return because you keep delivering great stuff again and again. When that suffers, your audience will suffer as well. If I watch my first three Whiteboard Fridays and then the fourth one is not great, I expect to lose a ton of those viewers. But if I have tens of thousands of people who are watching Whiteboard Friday and I deliver one bad one out of twenty, maybe I have a little more room to play there.

Focus your efforts

Focus. This is a big challenge because I think a lot of us think very broadly about who we want to appeal to, the types of content we want to create, the types of marketing we want to do. This is very challenging from a loyalty perspective because passionate fans tend to congregate around very, very focused causes and very focused creators of content or focused brands or focused organizations. Its much tougher to build that passion into a group of users if you're trying to appeal to a very broad set. That's just how it is.

Don't forget engagement

Lastly, but not least, this is very tactical, but I found it extremely powerful when a brand is starting out, when a project is starting out, to engage and respond as much as possible with your customers. That could be over social channels, that could be in comments, that could be in emails, that could be directly in outreach, whatever it is. But if you see someone who you can reach out to engaging with you, replying to them, talking to them, conversing with them in some way, forming a connection is extremely powerful. It especially is important for first interactions.

I'm not going to say, "You need to respond to everything all the time, always." If you can identify, "This is the first interaction that we've had with this person," if you interact and if that interaction is positive, it can create loyalty just on its own. That's a lovely way to start scaling up from a small starting point.

All right everyone, hope you've enjoyed this edition of Whiteboard Friday. We'll see you again next week. Take care.

Video transcription by Speechpad.com

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Using Term Frequency Analysis to Measure Your Content Quality

Posted by EricEnge

It's time to look at your content differently—time to start understanding just how good it really is. I am not simply talking about titles, keyword usage, and meta descriptions. I am talking about the entire page experience. In today's post, I am going to introduce the general concept of content quality analysis, why it should matter to you, and how to use term frequency (TF) analysis to gather ideas on how to improve your content.

TF analysis is usually combined with inverse document frequency analysis (collectively TF-IDF analysis). TF-IDF analysis has been a staple concept for information retrieval science for a long time. You can read more about TF-IDF and other search science concepts in Cyrus Shepard's excellent article here.

For purposes of today's post, I am going to show you how you can use TF analysis to get clues as to what Google is valuing in the content of sites that currently outrank you. But first, let's get oriented.

Conceptualizing page quality

Start by asking yourself if your page provides a quality experience to people who visit it. For example, if a search engine sends 100 people to your page, how many of them will be happy? Seventy percent? Thirty percent? Less? What if your competitor's page gets a higher percentage of happy users than yours does? Does that feel like an "uh-oh"?

Let's think about this with a specific example in mind. What if you ran a golf club site, and 100 people come to your page after searching on a phrase like "golf clubs." What are the kinds of things they may be looking for?

Here are some things they might want:

1. A way to buy golf clubs on your site (you would need to see a shopping cart of some sort).


2. The ability to select specific brands, perhaps by links to other pages about those brands of golf clubs.


3. Information on how to pick the club that is best for them.


4. The ability to select specific types of clubs (drivers, putters, irons, etc.). Again, this may be via links to other pages.


5. A site search box.


6. Pricing info.


7. Info on shipping costs.


8. Expert analysis comparing different golf club brands.


9. End user reviews of your company so they can determine if they want to do business with you.


10. How your return policy works.


11. How they can file a complaint.


12. Information about your company. Perhaps an "about us" page.


13. A link to a privacy policy page.


14. Whether or not you have been "in the news" recently.


15. Trust symbols that show that you are a reputable organization.


16. A way to access pages to buy different products, such as golf balls or tees.


17. Information about specific golf courses.


18. Tips on how to improve their golf game.

This is really only a partial list, and the specifics of your site can certainly vary for any number of reasons from what I laid out above. So how do you figure out what it is that people really want? You could pull in data from a number of sources. For example, using data from your site search box can be invaluable. You can do user testing on your site. You can conduct surveys. These are all good sources of data.

You can also look at your analytics data to see what pages get visited the most. Just be careful how you use that data. For example, if most of your traffic is from search, this data will be biased by incoming search traffic, and hence what Google chooses to rank. In addition, you may only have a small percentage of the visitors to your site going to your privacy policy, but chances are good that there are significantly more users than that who notice whether or not you have a privacy policy. Many of these will be satisfied just to see that you have one and won't actually go check it out.

Whatever you do, it's worth using many of these methods to determine what users want from the pages of your site and then using the resulting information to improve your overall site experience.

Is Google using this type of info as a ranking factor?

At some level, they clearly are. Clearly Google and Bing have evolved far beyond the initial TF-IDF concepts, but we can still use them to better understand our own content.

The first major indication we had that Google was performing content quality analysis was with the release of the Panda algorithm in February of 2011. More recently, we know that on April 21 Google will release an algorithm that makes the mobile friendliness of a web site a ranking factor. Pure and simple, this algo is about the user experience with a page.

Exactly how Google is performing these measurements is not known, but what we do know is their intent. They want to make their search engine look good, largely because it helps them make more money. Sending users to pages that make them happy will do that. Google has every incentive to improve the quality of their search results in as many ways as they can.

Ultimately, we don't actually know what Google is measuring and using. It may be that the only SEO impact of providing pages that satisfy a very high percentage of users is an indirect one. I.e., so many people like your site that it gets written about more, linked to more, has tons of social shares, gets great engagement, that Google sees other signals that it uses as ranking factors, and this is why your rankings improve.

But, do I care if the impact is a direct one or an indirect one? Well, NO.

Using TF analysis to evaluate your page

TF-IDF analysis is more about relevance than content quality, but we can still use various precepts from it to help us understand our own content quality. One way to do this is to compare the results of a TF analysis of all the keywords on your page with those pages that currently outrank you in the search results. In this section, I am going to outline the basic concepts for how you can do this. In the next section I will show you a process that you can use with publicly available tools and a spreadsheet.

The simplest form of TF analysis is to count the number of uses of each keyword on a page. However, the problem with that is that a page using a keyword 10 times will be seen as 10 times more valuable than a page that uses a keyword only once. For that reason, we dampen the calculations. I have seen two methods for doing this, as follows:

The first method relies on dividing the number of repetitions of a keyword by the count for the most popular word on the entire page. Basically, what this does is eliminate the inherent advantage that longer documents might otherwise have over shorter ones. The second method dampens the total impact in a different way, by taking the log base 10 for the actual keyword count. Both of these achieve the effect of still valuing incremental uses of a keyword, but dampening it substantially. I prefer to use method 1, but you can use either method for our purposes here.

Once you have the TF calculated for every different keyword found on your page, you can then start to do the same analysis for pages that outrank you for a given search term. If you were to do this for five competing pages, the result might look something like this:

I will show you how to set up the spreadsheet later, but for now, let's do the fun part, which is to figure out how to analyze the results. Here are some of the things to look for:

1. Are there any highly related words that all or most of your competitors are using that you don't use at all?


2. Are there any such words that you use significantly less, on average, than your competitors?


3. Also look for words that you use significantly more than competitors.

You can then tag these words for further analysis. Once you are done, your spreadsheet may now look like this:

In order to make this fit into this screen shot above and keep it legibly, I eliminated some columns you saw in my first spreadsheet. However, I did a sample analysis for the movie "Woman in Gold". You can see the full spreadsheet of calculations here. Note that we used an automated approach to marking some items at "Low Ratio," "High Ratio," or "All Competitors Have, Client Does Not."

None of these flags by themselves have meaning, so you now need to put all of this into context. In our example, the following words probably have no significance at all: "get", "you", "top", "see", "we", "all", "but", and other words of this type. These are just very basic English language words.

But, we can see other things of note relating to the target page (a.k.a. the client page):

1. It's missing any mention of actor ryan reynolds


2. It's missing any mention of actor helen mirren


3. The page has no reviews


4. Words like "family" and "story" are not mentioned


5. "Austrian" and "maria altmann" are not used at all


6. The phrase "woman in gold" and words "billing" and "info" are used proportionally more than they are with the other pages

Note that the last item is only visible if you open the spreadsheet. The issues above could well be significant, as the lead actors, reviews, and other indications that the page has in-depth content. We see that competing pages that rank have details of the story, so that's an indication that this is what Google (and users) are looking for. The fact that the main key phrase, and the word "billing", are used to a proportionally high degree also makes it seem a bit spammy.

In fact, if you look at the information closely, you can see that the target page is quite thin in overall content. So much so, that it almost looks like a doorway page. In fact, it looks like it was put together by the movie studio itself, just not very well, as it presents little in the way of a home page experience that would cause it to rank for the name of the movie!

In the many different times I have done an analysis using these methods, I've been able to make many different types of observations about pages. A few of the more interesting ones include:

1. A page that had no privacy policy, yet was taking personally identifiable info from users.


2. A major lack of important synonyms that would indicate a real depth of available content.


3. Comparatively low Domain Authority competitors ranking with in-depth content.

These types of observations are interesting and valuable, but it's important to stress that you shouldn't be overly mechanical about this. The value in this type of analysis is that it gives you a technical way to compare the content on your page with that of your competitors. This type of analysis should be used in combination with other methods that you use for evaluating that same page. I'll address this some more in the summary section of this below.

How do you execute this for yourself?

The full spreadsheet contains all the formulas so all you need to do is link in the keyword count data. I have tried this with two different keyword density tools, the one from Searchmetrics, and this one from motoricerca.info.

I am not endorsing these tools, and I have no financial interest in either one—they just seemed to work fairly well for the process I outlined above. To provide the data in the right format, please do the following:

1. Run all the URLs you are testing through the keyword density tool.


2. Copy and paste all the one word, two word, and three word results into a tab on the spreadsheet.


3. Sort them all so you get total word counts aligned by position as I have shown in the linked spreadsheet.


4. Set up the formulas as I did in the demo spreadsheet (you can just use the demo spreadsheet).


5. Then do your analysis!

This may sound a bit tedious (and it is), but it has worked very well for us at STC.

Summary

You can also use usability groups and a number of other methods to figure out what users are really looking for on your site. However, what this does is give us a look at what Google has chosen to rank the highest in its search results. Don't treat this as some sort of magic formula where you mechanically tweak the content to get better metrics in this analysis.

Instead, use this as a method for slicing into your content to better see it the way a machine might see it. It can yield some surprising (and wonderful) insights!

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Are On-Topic Links Important? - Whiteboard Friday

Posted by randfish

How much does the context of a link really matter? In today's Whiteboard Friday, Rand looks at on- and off-topic links to uncover what packs the greatest SEO punch and shares what you should be looking for when building a high-quality link.

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're going to chat a little bit about on-topic and off-topic links. One of the questions and one of the topics that you see discussed all the time in the SEO world is: Do on-topic links matter more than off-topic links? By on topic, people generally mean they come from sites and pages that are on the same or very similar subject matter to the site or page that I'm trying to get the link to.

It sort of makes intuitive sense to us that Google would care somewhat about this, that they would say, "Oh, well, here's our friend over here," we'll call him Steve. No we're going to call him Carl, because Carl is a great name.

Carl, of course, has CarlsCloset.net, CarlsCloset.net being a home organization site. Carl is going out, and he's doing some link building, which he should, and so he's got some link targets in mind. He looks at places like RealSimple.com, the magazine site, Sunset Magazine, UnderwaterHoagies.com, Carl being a great fan of all things underwater and sandwich related. So as he's looking at these sites, he's thinking to himself, well, from an SEO perspective, is it necessary the case that Real Simple, which has a lot of content on home organization and on cleaning up clutter and those kinds of things, is that going to help Carl's Closet site rank better than, say, a link from UnderwaterHoagies.com?

The answer is a little tough here. It could be the case that UnderwaterHoagies.com has a feature article all about how submariners can keep their home in order, even as they brunch under the sea. But maybe the link from RealSimple.com is coming from a less on-topic article and page. So this starts to get really messy. Is it the site that matters, or is it the page that matters? Is it the context that matters? Is it the link itself and where that's embedded in the site? What is the real understanding that Google has between relationships of on-topic and off-topic? That's where you get a lot of convoluted information.

I have seen and we have probably all heard a ton of anecdotal evidence on both sides. There are SEOs who will argue passionately from their experience that what they've seen is that on-topic links are hugely more beneficial than off-topic ones. You'll see the complete opposite from some other folks. In fact, most of my personal experiences, when I was doing more directed link building for clients way back in my SEO consulting days and even more recently as I've helped startups and advised folks, has been that off-topic links, UnderwaterHoagies.com linking to Carl's Closet, that still seems to provide quite a bit of benefit, and it's very had to gauge whether it's as much, less than, more than any of these other ones. So I think, on the anecdotal side, we're in a tough spot.

What we can say is that probably there's some additional value from on-topic sites, on-topic pages, or on-topic link connections, that Google has some idea of context. We've seen them make huge strides with algorithms like Hummingbird, certainly with their keyword matching and topic modeling algorithms. It seems very unlikely that there would be nothing in Google's algorithm that looks at the context or relationship of content between linking pages and linking websites.

However, in the real world, things are almost never equal. It's not like they're going to get exactly the same anchor text from the same importance of a page that has the same number of external links, that the content is exactly the same on all three of these websites pointing over to Carl's Closet. In the real world, Carl is going to struggle much harder to get some of these links than others. So I think that the questions we need to ask ourselves, as folks who are doing directed marketing and trying to earn links, is: Will the link actually help people? Is that link going to be clicked?

If you're on a page on Real Simple that you think very few people ever reach, you think very few people will ever click that link because it just doesn't appear to provide much value, versus you're in an article all about home organization on Underwater Hoagies, and it was featured on their home page, and you're pretty sure that a lot of the submariners who are eating their subs under the sea are very interested in this topic and they're going to click on that link, well you know what? That's a link that helps people. That probably means search engines are going to treat it with some reverence as well.

Does the link make sense in context? This is a good one to ask yourself when you are doing any kind of link building that's directed that could potentially be manipulative. If the link makes sense in context, it tends to be the case that it's going to be more useful. So if Carl contributes the article to UnderwaterHoagies.com, and the link makes sense in context, and it will help people, I think it's appropriate to put it there. If that's not the case, it could look a little manipulative. It could certainly be perceived as self-serving.

Then, can you actually acquire the link? It's wonderful when you go out and you make a list of, hey, here's the most important and relevant sites in our sector and niche, and this is how we're going to build topical authority. But if you can't get those links, hey that's tough potatoes, man. It's no better than putting a list of links and just sorting them by, God knows, a horrible metric like PageRank or Alexa rank or something like that.

I would instead ask yourself if it's realistic for you to be able to get those links and pursue those as well as pursuing or looking at the metrics, and the importance, and the topical relevance.

Let's think about this from a broad perspective. Search engines are caring about what? They're caring about matching the content relevance to the searcher's query. They care about raw link popularity. That's sort of like the old-school algorithms of PageRank and number of links and that kind of thing. They do care about topical authority and brand authority. We talked about on Whiteboard Friday previously around some topical authorities and how Google determines the authority and the subject matter of a site's authority. They care about domain authority, the raw importance of a domain on the web, and they care about things like engagement, user and usage data, and given how much they can follow all of us around the web these days, they probably know pretty well whether people are clicking on these articles using these pages or not.

Then anchor text. Not every link that you might build or acquire or earn is going to provide all of these in one single package. Each of them are going to be contributing pieces of those puzzles. When it comes to the on-topic/off-topic link debate, I'm much more about caring about the answers to these kinds of questions -- Can I acquire the link? Is it useful to people? Will they actually use it? Does the link make sense in context? -- than I am about is it on-topic or off-topic? I'm not sure that I would ever urge you to prioritize based on that.

That said, I'm certainly looking forward to your feedback this week and hearing about your experiences with on-topic and off-topic links, and hopefully we'll see you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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

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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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:

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:

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:

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:

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:

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:

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