The Inbound Marketing Economy

Posted by KelseyLibert

When it comes to job availability and security, the future looks bright for inbound marketers.

The Bureau of Labor Statistics (BLS) projects that employment for marketing managers will grow by 13% between 2012 and 2022. Job security for marketing managers also looks positive according to the BLS, which cites that marketing employees are less likely to be laid off since marketing drives revenue for most businesses.

While the BLS provides growth estimates for managerial-level marketing roles, these projections don’t give much insight into the growth of digital marketing, specifically the disciplines within digital marketing. As we know, “marketing” can refer to a variety of different specializations and methodologies. Since digital marketing is still relatively new compared to other fields, there is not much comprehensive research on job growth and trends in our industry.

To gain a better understanding of the current state of digital marketing careers, Fractl teamed up with Moz to identify which skills and roles are the most in demand and which states have the greatest concentration of jobs.

Methodology

We analyzed 75,315 job listings posted on Indeed.com during June 2015 based on data gathered from job ads containing the following terms:

  • “content marketing” or “content strategy”
  • “SEO” or “search engine marketing”
  • “social media marketing” or “social media management”
  • “inbound marketing” or “digital marketing”
  • “PPC” (pay-per-click)
  • “Google Analytics”

We chose the above keywords based on their likelihood to return results that were marketing-focused roles (for example, just searching for “social media” may return a lot of jobs that are not primarily marketing focused, such as customer service). The occurrence of each of these terms in job listings was quantified and segmented by state. We then combined the job listing data with U.S. Census Bureau population estimates to calculate the jobs per capita for each keyword, giving us the states with the greatest concentration of jobs for a given search query.

Using the same data, we identified which job titles appeared most frequently. We used existing data from Indeed to determine job trends and average salaries. LinkedIn search results were also used to identify keyword growth in user profiles.

Marketing skills are in high demand, but talent is hard to find

As the marketing industry continues to evolve due to emerging technology and marketing platforms, marketers are expected to pick up new skills and broaden their knowledge more quickly than ever before. Many believe this rapid rate of change has caused a marketing skills gap, making it difficult to find candidates with the technical, creative, and business proficiencies needed to succeed in digital marketing.

The ability to combine analytical thinking with creative execution is highly desirable and necessary in today’s marketing landscape. According to an article in The Guardian, “Companies will increasingly look for rounded individuals who can combine analytical rigor with the ability to apply this knowledge in a practical and creative context.” Being both detail-oriented and a big picture thinker is also a sought-after combination of attributes. A report by The Economist and Marketo found that “CMOs want people with the ability to grasp and manage the details (in data, technology, and marketing operations) combined with a view of the strategic big picture.”

But well-rounded marketers are hard to come by. In a study conducted by Bullhorn, 64% of recruiters reported a shortage of skilled candidates for available marketing roles. Wanted Analytics recently found that one of the biggest national talent shortages is for marketing manager roles, with only two available candidates per job opening.

Increase in marketers listing skills in content marketing, inbound marketing, and social media on LinkedIn profiles

While recruiter frustrations may indicate a shallow talent pool, LinkedIn tells a different story—the number of U.S.-based marketers who identify themselves as having digital marketing skills is on the rise. Using data tracked by Rand and LinkedIn, we found the following increases of marketing keywords within user profiles.

growth of marketing keywords in linkedin profiles

The number of profiles containing “content marketing” has seen the largest growth, with a 168% increase since 2013. “Social media” has also seen significant growth with a 137% increase. “Social media” appears on a significantly higher volume of profiles than the other keywords, with more than 2.2 million profiles containing some mention of social media. Although “SEO” has not seen as much growth as the other keywords, it still has the second-highest volume with it appearing in 630,717 profiles.

Why is there a growing number of people self-identifying as having the marketing skills recruiters want, yet recruiters think there is a lack of talent?

While there may be a lot of specialists out there, perhaps recruiters are struggling to fill marketing roles due to a lack of generalists or even a lack of specialists with surface-level knowledge of other areas of digital marketing (also known as a T-shaped marketer).

Popular job listings show a need for marketers to diversify their skill set

The data we gathered from LinkedIn confirm this, as the 20 most common digital marketing-related job titles being advertised call for a broad mix of skills.

20 most common marketing job titles

It’s no wonder that marketing manager roles are hard to fill, considering the job ads are looking for proficiency in a wide range of marketing disciplines including social media marketing, SEO, PPC, content marketing, Google Analytics, and digital marketing. Even job descriptions for specialist roles tend to call for skills in other disciplines. A particular role such as SEO Specialist may call for several skills other than SEO, such as PPC, content marketing, and Google Analytics.

Taking a more granular look at job titles, the chart below shows the five most common titles for each search query. One might expect mostly specialist roles to appear here, but there is a high occurrence of generalist positions, such as Digital Marketing Manager and Marketing Manager.

5 most common job titles by search query

Only one job title containing “SEO” cracked the top five. This indicates that SEO knowledge is a desirable skill within other roles, such as general digital marketing and development.

Recruiter was the third most common job title among job listings containing social media keywords, which suggests a need for social media skills in non-marketing roles.

Similar to what we saw with SEO job titles, only one job title specific to PPC (Paid Search Specialist) made it into the top job titles. PPC skills are becoming necessary for more general marketing roles, such as Marketing Manager and Digital Marketing Specialist.

Across all search queries, the most common jobs advertised call for a broad mix of skills. This tells us hiring managers are on the hunt for well-rounded candidates with a diverse range of marketing skills, as opposed to candidates with expertise in one area.

Marketers who cultivate diverse skill sets are better poised to gain an advantage over other job seekers, excel in their job role, and accelerate career growth. Jason Miller says it best in his piece about the new breed hybrid marketer:

future of marketing quote linkedin

Inbound job demand and growth: Most-wanted skills and fastest-growing jobs

Using data from Indeed, we identified which inbound skills have the highest demand and which jobs are seeing the most growth. Social media keywords claim the largest volume of results out of the terms we searched for during June 2015.

number of marketing job listings by keyword

“Social media marketing” or “social media management” appeared the most frequently in the job postings we analyzed, with 46.7% containing these keywords. “PPC” returned the smallest number of results, with only 3.8% of listings containing this term.

Perhaps this is due to social media becoming a more necessary skill across many industries and not only a necessity for marketers (for example, social media’s role in customer service and recruitment). On the other hand, job roles calling for PPC or SEO skills are most likely marketing-focused. The prevalence of social media jobs also may indicate that social media has gained wide acceptance as a necessary part of a marketing strategy. Additionally, social media skills are less valuable compared to other marketing skills, making it cheaper to hire for these positions (we will explore this further in the average salaries section below).

Our search results also included a high volume of jobs containing “digital marketing” and “SEO” keywords, which made up 19.5% and 15.5% respectively. At 5.8%, “content marketing” had the lowest search volume after “PPC.”

Digital marketing, social media, and content marketing experienced the most job growth

While the number of job listings tells us which skills are most in demand today, looking at which jobs are seeing the most growth can give insight into shifting demands.

digital marketing growth on  indeed.com

Digital marketing job listings have seen substantial growth since 2009, when it accounted for less than 0.1% of Indeed.com search results. In January 2015, this number had climbed to nearly 0.3%.

social media job growth on indeed.com

While social media marketing jobs have seen some uneven growth, as of January 2015 more than 0.1% of all job listings on Indeed.com contained the term “social media marketing” or “social media management.” This shows a significant upward trend considering this number was around 0.05% for most of 2014. It’s also worth noting that “social media” is currently ranked No. 10 on Indeed’s list of top job trends.

content marketing job growth on indeed.com

Despite its growth from 0.02% to nearly 0.09% of search volume in the last four years, “content marketing” does not make up a large volume of job postings compared to “digital marketing” or “social media.” In fact, “SEO” has seen a decrease in growth but still constitutes a higher percentage of job listings than content marketing.

SEO, PPC, and Google Analytics job growth has slowed down

On the other hand, search volume on Indeed has either decreased or plateaued for “SEO,” “PPC,” and “Google Analytics.”

seo job growth on indeed.com

As we see in the graph, the volume of “SEO job” listings peaked between 2011 and 2012. This is also around the time content marketing began gaining popularity, thanks to the Panda and Penguin updates. The decrease may be explained by companies moving their marketing budgets away from SEO and toward content or social media positions. However, “SEO” still has a significant amount of job listings, with it appearing in more than 0.2% of job listings on Indeed as of 2015.

ppc job growth on indeed.com

“PPC” has seen the most staggered growth among all the search terms we analyzed, with its peak of nearly 0.1% happening between 2012 and 2013. As of January of this year, search volume was below 0.05% for “PPC.”

google analytics job growth on indeed.com

Despite a lack of growth, the need for this skill remains steady. Between 2008 and 2009, “Google Analytics” job ads saw a huge spike on Indeed. Since then, the search volume has tapered off and plateaued through January 2015.

Most valuable skills are SEO, digital marketing, and Google Analytics

So we know the number of social media, digital marketing, and content marketing jobs are on the rise. But which skills are worth the most? We looked at the average salaries based on keywords and estimates from Indeed and salaries listed in job ads.

national average marketing salaries

Job titles containing “SEO” had an average salary of $102,000. Meanwhile, job titles containing “social media marketing” had an average salary of $51,000. Considering such a large percentage of the job listings we analyzed contained “social media” keywords, there is a much larger pool of jobs; therefore, a lot of entry level social media jobs or internships are probably bringing down the average salary.

Job titles containing “Google Analytics” had the second-highest average salary at $82,000, but this should be taken with a grain of salt considering “Google Analytics” will rarely appear as part of a job title. The chart below, which shows average salaries for jobs containing keywords anywhere in the listing as opposed to only in the title, gives a more accurate idea of how much “Google Analytics” job roles earn on average.national salary averages marketing keywords

Looking at the average salaries based on keywords that appeared anywhere within the job listing (job title, job description, etc.) shows a slightly different picture. Based on this, jobs containing “digital marketing” or “inbound marketing” had the highest average salary of $84,000. “SEO” and “Google Analytics” are tied for second with $76,000 as the average salary.

“Social media marketing” takes the bottom spot with an average salary of $57,000. However, notice that there is a higher average salary for jobs that contain “social media” within the job listing as opposed to jobs that contain “social media” within the title. This suggests that social media skills may be more valuable when combined with other responsibilities and skills, whereas a strictly social media job, such as Social Media Manager or Social Media Specialist, does not earn as much.

Massachusetts, New York, and California have the most career opportunities for inbound marketers

Looking for a new job? Maybe it’s time to pack your bags for Boston.

Massachusetts led the U.S. with the most jobs per capita for digital marketing, content marketing, SEO, and Google Analytics. New York took the top spot for social media jobs per capita, while Utah had the highest concentration of PPC jobs. California ranked in the top three for digital marketing, content marketing, social media, and Google Analytics. Illinois appeared in the top 10 for every term and usually ranked within the top five. Most of the states with the highest job concentrations are in the Northeast, West, and East Coast, with a few exceptions such as Illinois and Minnesota.

But you don’t necessarily have to move to a new state to increase the odds of landing an inbound marketing job. Some unexpected states also made the cut, with Connecticut and Vermont ranking within the top 10 for several keywords.

concentration of digital marketing jobs

marketing jobs per capita

Job listings containing “digital marketing” or “inbound marketing” were most prevalent in Massachusetts, New York, Illinois, and California, which is most likely due to these states being home to major cities where marketing agencies and large brands are headquartered or have a presence. You will notice these four states make an appearance in the top 10 for every other search query and usually rank close to the top of the list.

More surprising to find in the top 10 were smaller states such as Connecticut and Vermont. Many major organizations are headquartered in Connecticut, which may be driving the state’s need for digital marketing talent. Vermont’s high-tech industry growth may explain its high concentration of digital marketing jobs.

content marketing job concentration

per capita content marketing jobs

Although content marketing jobs are growing, there are still a low volume overall of available jobs, as shown by the low jobs per capita compared to most of the other search queries. With more than three jobs per capita, Massachusetts and New York topped the list for the highest concentration of job listings containing “content marketing” or “content strategy.” California and Illinois rank in third and fourth with 2.8 and 2.1 jobs per capita respectively.

seo job concentration

seo jobs per capita

Again, Massachusetts and New York took the top spots, each with more than eight SEO jobs per capita. Utah took third place for the highest concentration of SEO jobs. Surprised to see Utah rank in the top 10? Its inclusion on this list and others may be due to its booming tech startup scene, which has earned the metropolitan areas of Salt Lake City, Provo, and Park City the nickname Silicon Slopes.

social media job concentration

social media jobs per capita

Compared to the other keywords, “social media” sees a much higher concentration of jobs. New York dominates the rankings with nearly 24 social media jobs per capita. The other top contenders of California, Massachusetts, and Illinois all have more than 15 social media jobs per capita.

The numbers at the bottom of this list can give you an idea of how prevalent social media jobs were compared to any other keyword we analyzed. Minnesota’s 12.1 jobs per capita, the lowest ranking state in the top 10 for social media, trumps even the highest ranking state for any other keyword (11.5 digital marketing jobs per capita in Massachusetts).

ppc job concentration

ppc jobs per capita

Due to its low overall number of available jobs, “PPC” sees the lowest jobs per capita out of all the search queries. Utah has the highest concentration of jobs with just two PPC jobs per 100,000 residents. It is also the only state in the top 10 to crack two jobs per capita.

google analytics job concentration

google analytics jobs per capita

Regionally, the Northeast and West dominate the rankings, with the exception of Illinois. Massachusetts and New York are tied for the most Google Analytics job postings, each with nearly five jobs per capita. At more than three jobs per 100,000 residents, California, Illinois, and Colorado round out the top five.

Overall, our findings indicate that none of the marketing disciplines we analyzed are dying career choices, but there is a need to become more than a one-trick pony—or else you’ll risk getting passed up for job opportunities. As the marketing industry evolves, there is a greater need for marketers who “wear many hats” and have competencies across different marketing disciplines. Marketers who develop diverse skill sets can gain a competitive advantage in the job market and achieve greater career growth.

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Your Daily SEO Fix: Week 2

Posted by Trevor-Klein

Last week, we began posting short (< 2-minute) video tutorials that help you all get the most out of Moz’s tools. Each tutorial is designed to solve a use case that we regularly hear about from Moz community members—a need or problem for which you all could use a solution.

Today, we’ve got a brand-new roundup of the most recent videos:

  • How to Examine and Analyze SERPs Using New MozBar Features
  • How to Boost Your Rankings through On-Page Optimization
  • How to Check Your Anchor Text Using Open Site Explorer
  • How to Do Keyword Research with OSE and the Keyword Difficulty Tool
  • How to Discover Keyword Opportunities in Moz Analytics

Let’s get right down to business!

Fix 1: How to Examine and Analyze SERPs Using New MozBar Features

The MozBar is a handy tool that helps you access important SEO metrics while you surf the web. In this Daily SEO Fix, Abe shows you how to use this toolbar to examine and analyze SERPs and access keyword difficulty scores for a given page—in a single click.

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Fix 2: How to Boost Your Rankings through On-Page Optimization

There are several on-page factors that influence your search engine rankings. In this Daily SEO Fix, Holly shows you how to use Moz’s On-Page Optimization tool to identify pages on your website that could use some love and what you can do to improve them.

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Fix 3: How to Check Your Anchor Text Using Open Site Explorer

Dive into OSE with Tori in this Daily SEO Fix to check out the anchor text opportunities for Moz.com. By highlighting all your anchor text you can discover other potential keyword ranking opportunities you might not have thought of before.

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Fix 4: How to Do Keyword Research with OSE and the Keyword Difficulty Tool

Studying your competitors can help identify keyword opportunities for your own site. In this Daily SEO Fix, Jacki walks through how to use OSE to research the anchor text for competitors websites and how to use the Keyword Difficulty Tool to identify potential expansion opportunities for your site.

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Fix 5: How to Discover Keyword Opportunities in Moz Analytics

Digesting organic traffic that is coming to your site is an easy way to surface potential keyword opportunities. In this Daily SEO Fix, Chiaryn walks through the keyword opportunity tab in Moz Analytics and highlights a quick tip for leveraging that tool.

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Looking for more?

We’ve got more videos in last week’s round-up! Check it out here.


Don’t have a Pro subscription? No problem. Everything we cover in these Daily SEO Fix videos is available with a free 30-day trial.

Sounds good. Sign me up!

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

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Developing Innovative Content: What You Need to Know

Posted by richardbaxterseo

A few weeks ago, I attended a breakfast meeting with a bunch of entrepreneurs in the technology, space (yes, space travel), software and engineering industry. I felt so blown away by the incredible talent of the speakers. You know, there are people out there building things, like private satellite networks, bio printing facilities, quantum computers and self-driving cars. I was completely transfixed by the incredibly future facing, innovative and exceptionally inventive group in front of me. I also immediately wished I’d worked a little harder in my twenties.

After the presentations, one of the questions that came up during the Q&A session was: “what’s the next big thing?”

Wow. Have you ever thought about “the next big thing”?

Part of the magic of predicting innovation is that it’s really, really hard to get right. Those that can accurately predict the future (in my humble opinion) are those that tend to understand how people will respond to an idea once they’re exposed to it. I think predicting this is a very special skill indeed.

Then again, we’re expected to be able to predict the outcome of our marketing, all the time. While predicting it is one thing, making it happen it is a whole different ball game.

Competition for the attention of our customers is getting tougher

In our industry, when you really boil down what it is we do, we’re fixing things, making things, or we’re communicating things.

Most of the time, we’re building content that communicates: ideas, stories, news and guidance–you get the idea. The problem is, no matter which vertical you work in, we’re all competing for something: the attention of our customers.

As our customers get smarter, that competition is getting tougher and tougher.

The most successful marketers in our industry all have a special trait in common. They are good at finding new ways to communicate ideas. Take a look at classic presentations
like this from Ross Hudgens to see just how powerful it can be to observe, imitate and develop an idea with astounding viral reach.

I particularly enjoy the idea of taking a piece of content and making improvements, be it through design, layout or simply updating what’s there. I like it because it’s actually pretty easy to do, and there’s growing evidence of it happening all over the Internet. Brands are taking a second look at how they’re developing their content to appeal to a wider audience, or to appeal to a viral audience (or both!).

For example; take a look at this beautiful
travel guide to Vietnam (credit: travelindochina.com) or this long form guide to commercial property insurance (credit: Towergate Insurance / Builtvisible.com) for examples of brands in competitive verticals developing their existing content. In verticals where ordinary article content has been done to death, redeveloping the medium itself feels like an important next step.

Innovative isn’t the same thing as technical

I’ve felt for a long time that there’s a conflict between our interpretation of “innovative” and “technical”. As I’ve written before, those that really understand how the web works are at a huge advantage.
Learn how it’s built, and you’ll find yourself able to make great things happen on your own, simply by learning and experimenting.

In my opinion though, you don’t have to be able to learn how to build your own site or be a developer. All you have to do is learn the vocabulary and build a broad understanding of how things work in a browser. I actually think we all need to be doing this, right now. Why?

We need more innovation in content marketing

I think our future depends on our industry’s ability to innovate. Of course, you still need to have your basics in place. We’ll always be
T-Shaped marketers, executing a bit of technical SEO here, a bit of content strategy there. But, we’re all SEOs and we know we need to acquire links, build audiences and generally think big about our ambitions. When your goal is to attract new followers, fans, links, and garner shares in their thousands, you need to do something pretty exciting to attract attention to yourself.

The vocabulary of content development

I’ve designed this post to be a primer on more advanced features found in innovative content development. My original MozCon 2014 presentation was designed to educate on some of the technologies we should be aware of in our content development projects and the process we follow to build things. We’ll save process for another post (shout in the comments if you think that would be useful!) and focus on the “what” for now.

At Builtvisible, we’re working hard on extending our in-house content development capabilities. We learn through sharing amazing examples with each other. Our policy is to always attempt to deconstruct how something might have been developed, that way, we’re learning. Some of the things we see on the web are amazing–they deserve so much respect for the talent and the skills that surface the content.

Here are some examples that I think demonstrate some of the most useful types of approach for content marketers. I hope that these help as much as they’ve helped us, and I hope you can form a perspective of what innovative features look like in more advanced content development. Of course, do feel welcome to share your own examples in the comments, too! The more, the merrier!

The story of EBoy

eBoy: the graphic design firm whose three co-founders and sole members are widely regarded as the “godfathers” of pixel art.

The consistent styling (as well as the beautifully written content) is excellent. Technically speaking, perhaps the most clever and elegant feature is the zoom of the image positioned on the Z axis in a <canvas> container (more on this in a moment).

An event listener (jQuery) helps size the canvas appropriate to the browser window size and the z axis position shifts on scroll to create an elegant zoom effect.


View the example here:

http://www.theverge.com/2014/6/17/5803850/pixel-perfect-the-story-of-eboy.

<canvas> is an HTML element which can be used to draw graphics using scripting (usually JavaScript). This can, for instance, be used to draw graphs, make photo composition or simple animations.

Colorizing the past

Take a look at
Pixart Printing’s Guide to Colourizing the Past (credit: Pixartprinting / Builtvisible.com) for a clever example of <canvas> in use. Here’s one of the images (tip, mouse-over and click the image):

The colorization feature takes advantage of the power of the canvas element. In this case, the color version of the image is applied to the canvas as a background image, with the black and white version on a layer above. Clicking (or touching, on mobile) erases portions of the top image, revealing the color version underneath.

Chrome Experiments: Globe

Globe is “simple” global data visualization of the Earth’s population growth over a set range of dates. The 3d visualization based in
webGL: a JavaScript API for rendering interactive 3D graphics and 2D graphics within any compatible web browser without the use of plug-ins.


View the example here:

http://globe.chromeexperiments.com/.

WebGL is a really exciting, emerging option available to content marketers who might want to experiment with immersive experiences or highly interactive, simulated environments.

Some of my
favourite WebGL examples include Hello Racer and Tweetopia, a 3d Twitter Hastag visualizer.

If you’d like to see more examples of webGL in action, take a look at
Chrome Experiments. Don’t worry, this stuff works in the latest versions of Firefox and IE, too.

Polygon’s PS4 Review

You might have seen me cover this long form concept over at Builtvisible. Polygon’s Playstation 4 review is a fully featured “long form” review of Sony’s much loved gaming machine. The bit that I love is the SVG visualizations:

“What’s SVG?”, I hear you ask!

SVG is super-fast, sharp rendering of vector images inside the browser. Unlike image files (like .jpg, .gif, .png), SVG is XML based, light on file size, loads quickly and adjusts to responsive browser widths perfectly. SVG’s XML based schema lends itself to some interesting manipulation for stunning, easy to implement effects.

View Polygon’s example here: http://www.polygon.com/a/ps4-review

That line tracing animation you see is known as
path animation. Essentially the path attribute in the SVG’s XML can be manipulated in the DOM with a little jQuery. What you’ll get is a pretty snazzy animation to keep your users eyes fixated on your content and yet another nice little effect to keep eyeballs engaged.

My favourite example of SVG execution is Lewis Lehe’s
Gridlocks and Bottlenecks. Gridlocks is a AngularJS, d3.js based visualization of the surprisingly technical and oft-misunderstood “gridlock” and “bottleneck” events in road traffic management.

It’s also very cool:

View the example here:http://setosa.io/blog/2014/09/02/gridlock/.

I have a short vocabulary list that I expect our team to be able to explain (certainly these questions come up in an interview with us!). I think that if you can explain what these things are, as a developing content marketer you’re way ahead of the curve:

  • HTML5
  • Responsive CSS (& libraries)
  • CSS3 (& frameworks)
  • JavaScript (& frameworks: jQuery, MooTools, Jade, Handlebars)
  • JSON (api post and response data)
  • webGL
  • HTML5 audio & video
  • SVG
  • HTML5 History API manipulation with pushState
  • Infinite Scroll

Want to learn more?

I’ve
amassed a series of videos on web development that I think marketers should watch. Not necessarily to learn web development, but definitely to be able to describe what it is you’d like your own content to do. My favourite: I really loved Wes Bos’s JS + HTML5 Video + Canvas tutorial. Amazing.

Innovation in content is such a huge topic but I realize I’ve run out of space (this is already a 1,400 word post) for now.

In my follow up, I’d like to talk about how to plan your content when it’s a little more extensive than just an article, give you some tips on how to work with (or find!) a developer, and how to make the most of every component in your content to get the most from your marketing efforts.

Until then, I’d love to see your own examples of great content and questions in the comments!

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

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What SEOs Need to Know About Topic Modeling &amp; Semantic Connectivity – Whiteboard Friday

Posted by randfish

Search engines, especially Google, have gotten remarkably good at understanding searchers’ intent—what we
mean to search for, even if that’s not exactly what we search for. How in the world do they do this? It’s incredibly complex, but in today’s Whiteboard Friday, Rand covers the basics—what we all need to know about how entities are connected in search.

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 talking topic modeling and semantic connectivity. Those words might sound big and confusing, but, in fact, they are important to understanding the operations of search engines, and they have some direct influence on things that we might do as SEOs, hence our need to understand them.

Now, I’m going to make a caveat here. I am not an expert in this topic. I have not taken the required math classes, stats classes, programming classes to truly understand this topic in a way that I would feel extremely comfortable explaining. However, even at the surface level of understanding, I feel like I can give some compelling information that hopefully you all and myself included can go research some more about. We’re certainly investigating a lot of topic modeling opportunities and possibilities here at Moz. We’ve done so in the past, and we’re revisiting that again for some future tools, so the topic is fresh on my mind.

So here’s the basic concept. The idea is that search engines are smarter than just knowing that a word, a phrase that someone searches for, like “Super Mario Brothers,” is only supposed to bring back results that have exactly the words “Super Mario Brothers,” that perfect phrase in the title and in the headline and in the document itself. That’s still an SEO best practice because you’re trying to serve visitors who have that search query. But search engines are actually a lot smarter than this.

One of my favorite examples is how intelligent Google has gotten around movie topics. So try, for example, searching for “That movie where the guy is called The Dude,” and you will see that Google properly returns “The Big Lebowski” in the first ranking position. How do they know that? Well, they’ve essentially connected up “movie,” “The Dude,” and said, “Aha, those things are most closely related to ‘The Big Lebowski. That’s what the intent of the searcher is. That’s the document that we’re going to return, not a document that happens to have ‘That movie about the guy named ‘The Dude’ in the title, exactly those words.'”

Here’s another example. So this is Super Mario Brothers, and Super Mario Brothers might be connected to a lot of other terms and phrases. So a search engine might understand that Super Mario Brothers is a little bit more semantically connected to Mario than it is to Luigi, then to Nintendo and then Bowser, the jumping dragon guy, turtle with spikes on his back — I’m not sure exactly what he is — and Princess Peach.

As you go down here, the search engine might actually have a topic modeling algorithm, something like latent semantic indexing, which was an early model, or a later model like latent Dirichlet allocation, which is a somewhat later model, or even predictive latent Dirichlet allocation, which is an even later model. Model’s not particularly important, especially for our purposes.

What is important is to know that there’s probably some scoring going on. A search engine — Google, Bing — can understand that some of these words are more connected to Super Mario Brothers than others, and it can do the reverse. They can say Super Mario Brothers is somewhat connected to video games and very not connected to cat food. So if we find a page that happens to have the title element of Super Mario Brothers, but most of the on-page content seems to be about cat food, well, maybe we shouldn’t rank that even if it has lots of incoming links with anchor text saying “Super Mario Brothers” or a very high page rank or domain authority or those kinds of things.

So search engines, Google, in particular, has gotten very, very smart about this connectivity stuff and this topic modeling post-Hummingbird. Hummingbird, of course, being the algorithm update from last fall that changed a lot of how they can interpret words and phrases.

So knowing that Google and Bing can calculate this relative connectivity, connectivity between the words and phrases and topics, we want to know how are they doing this. That answer is actually extremely broad. So that could come from co-occurrence in web documents. Sorry for turning my back on the camera. I know I’m supposed to move like this, but I just had to do a little twirl for you.

Distance between the keywords. I mean distance on the actual page itself. Does Google find “Super Mario Brothers” near the word “Mario” on a lot of the documents where the two occur, or are they relatively far away? Maybe Super Mario Brothers does appear with cat food a lot, but they’re quite far away. They might look at citations and links between documents in terms of, boy, there’s a lot pages on the web, when they talk about Super Mario Brothers, they also link to pages about Mario, Luigi, Nintendo, etc.

They can look at the anchor text connections of those links. They could look at co-occurrence of those words biased by a given corpi, a set of corpuses, or from certain domains. So they might say, “Hey, we only want to pay attention to what’s on the fresh web right now or in the blogosphere or on news sites or on trusted domains, these kinds of things as opposed to looking at all of the documents on the web.” They might choose to do this in multiple different sets of corpi.

They can look at queries from searchers, which is a really powerful thing that we unfortunately don’t have access to. So they might see searcher behavior saying that a lot of people who search for Mario, Luigi, Nintendo are also searching for Super Mario Brothers.

They might look at searcher clicks, visits, history, all of that browser data that they’ve got from Chrome and from Android and, of course, from Google itself, and they might say those are corpi that they use to connect up words and phrases.

Probably there’s a whole list of other places that they’re getting this from. So they can build a very robust data set to connect words and phrases. For us, as SEOs, this means a few things.

If you’re targeting a keyword for rankings, say “Super Mario Brothers,” those semantically connected and related terms and phrases can help with a number of things. So if you could know that these were the right words and phrases that search engines connected to Super Mario Brothers, you can do all sorts of stuff. Things like inclusion on the page itself, helping to tell the search engine my page is more relevant for Super Mario Brothers because I include words like Mario, Luigi, Princess Peach, Bowser, Nintendo, etc. as opposed to things like cat food, dog food, T-shirts, glasses, what have you.

You can think about it in the links that you earn, the documents that are linking to you and whether they contain those words and phrases and are on those topics, the anchor text that points to you potentially. You can certainly be thinking about this from a naming convention and branding standpoint. So if you’re going to call a product something or call a page something or your unique version of it, you might think about including more of these words or biasing to have those words in the description of the product itself, the formal product description.

For an About page, you might think about the formal bio for a person or a company, including those kinds of words, so that as you’re getting cited around the web or on your book cover jacket or in the presentation that you give at a conference, those words are included. They don’t necessarily have to be links. This is a potentially powerful thing to say a lot of people who mention Super Mario Brothers tend to point to this page Nintendo8.com, which I think actually you can play the original “Super Mario Brothers” live on the web. It’s kind of fun. Sorry to waste your afternoon with that.

Of course, these can also be additional keywords that you might consider targeting. This can be part of your keyword research in addition to your on-page and link building optimization.

What’s unfortunate is right now there are not a lot of tools out there to help you with this process. There is a tool from Virante. Russ Jones, I think did some funding internally to put this together, and it’s quite cool. It’s 
nTopic.org. Hopefully, this Whiteboard Friday won’t bring that tool to its knees by sending tons of traffic over there. But if it does, maybe give it a few days and come back. It gives you a broad score with a little more data if you register and log in. It’s got a plugin for Chrome and for WordPress. It’s fairly simplistic right now, but it might help you say, “Is this page on the topic of the term or phrase that I’m targeting?”

There are many, many downloadable tools and libraries. In fact, Code.google.com has an LDA topic modeling tool specifically, and that might have been something that Google used back in the day. We don’t know.

If you do a search for topic modeling tools, you can find these. Unfortunately, almost all of them are going to require some web development background at the very least. Many of them rely on a Python library or an API. Almost all of them also require a training corpus in order to model things on. So you can think about, “Well, maybe I can download Wikipedia’s content and use that as a training model or use the top 10 search results from Google as some sort of training model.”

This is tough stuff. This is one of the reasons why at Moz I’m particularly passionate about trying to make this something that we can help with in our on-page optimization and keyword difficulty tools, because I think this can be very powerful stuff.

What is true is that you can spot check this yourself right now. It is very possible to go look at things like related searches, look at the keyword terms and phrases that also appear on the pages that are ranking in the top 10 and extract these things out and use your own mental intelligence to say, “Are these terms and phrases relevant? Should they be included? Are these things that people would be looking for? Are they topically relevant?” Consider including them and using them for all of these things. Hopefully, over time, we’ll get more sophisticated in the SEO world with tools that can help with this.

All right, everyone, hope you’ve enjoyed this addition of Whiteboard Friday. Look forward to some great comments, and we’ll see you again next week. Take care.

Video transcription by Speechpad.com

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Introducing Followerwonk Profile Pages

Posted by petebray

Followerwonk has always been primarily about social graph analysis and exploration: from tracking follower growth, comparing relationships, and so on.

Followerwonk now adds content analysis and user profiling, too

In the Analyze tab, you’ll find a new option to examine any Twitter user’s tweets. (Note that this is a Pro-only feature, so you’ll need to be a subscriber to use it.)

You can also access these profile pages by simply clicking on a Twitter username anywhere else in Followerwonk.

For us, this feature is really exciting, because we let you analyze not just yourself, but other people too. In fact, Pro users can analyze as many other Twitter accounts as they want!

Now, you’ll doubtlessly learn lots by analyzing your own tweets. But you already probably have a pretty good sense of what content works well for you (and who you engage with frequently).

We feel that Profile Pages really move the needle by letting you surface the relationships and content strategies of competitors, customers, and prospects.

Let’s take a closer look.

Find the people any Twitter user engages with most frequently

Yep, just plug in a Twitter name and we’ll analyze their most recent 2000 tweets. We’ll extract out all of the mentions and determine which folks they talk to the most.

Here, we see that 
@dr_pete talks most frequently with (or about) Moz, Rand, Elisa, and Melissa. In fact, close to 10% of his tweets are talking to these four! (Note the percentage above each listed name.)

This analysis is helpful as it lets you quickly get a sense for the relationships that are important for this person. That provides possible inroads to that person in terms of engagement strategies.

Chart when and what conversations happen with an analyzed user’s most important relationships

We don’t just stop there. By clicking on the little “see engagement” link below each listed user, you can see the history of the relationship.

Here, we can see when the engagements happened in the little chart. And we actually show you the underlying tweets, too.

This is a great way to quickly understand the context of that relationship: is it a friendly back and forth, a heated exchange, or the last gasp of a bad customer experience? Perhaps the tweets from a competitor to one his top customers occurred weeks back? Maybe there’s a chance for you to make inroads to that customer?

There’s all sorts of productive tea-reading that can happen with this feature. And, by the way, don’t forget that you already have the ability to track all the relationships a competitor forms (or breaks), too.

Rank any Twitter user’s tweets by importance to surface their best content

This is my favorite feature—by far—in Followerwonk.

Sure, there are other tools that tell you your most popular tweets, but there are few that let you turn that feature around and examine other Twitter users. This is important because (let’s face it) few of us have the volume of RTs and favorites to make self-analysis that useful. But when we examine top Twitter accounts, we come away with hints about what content strategies they’re using that work well.

Here we see that Obama’s top tweets include a tribute, an irreverent bit of humor, and an image that creatively criticizes a recent Supreme Court ruling. What lessons might you draw from the content that works best for Obama? What content works best for other people? Their image tweets? Tweets with humor? Shorter tweets? Tweets with links? Go do some analyzing!

Uncover top source domains of any Twitter users

Yep, we dissect all the URLs for any analyzed user to assemble a list of their top domains.

This feature offers a great way to quickly snapshot the types of content and sources that users draw material from. Moreover, we can click on “see mentions” to see a timeline of when those mentions occurred for each domain, as well as what particular tweets accounted for them.

In sum…

These features offer exciting ways to quickly profile users. Such analysis should be at the heart of any engagement strategy: understand who your target most frequently engages with, what content makes them successful, and what domains they pull from.

At the same time, this approach reveals content strategies—what, precisely, works well for you, but also for other thought leaders in your category. Not only can you draw inspiration from this approach, but you can find content that might deserve a retweet (or reformulation in your own words).

I don’t want to go too Freudian on you, but consider this: What’s the value of self-analysis? I mean that to say that unless you have a lot of data, any analytics product isn’t going to be totally useful. That’s why this addition in Followerwonk is so powerful. Now you can analyze others, including thought leaders in your particular industry, to find the secrets of their social success.

Start analyzing!

Finally, this is a bittersweet blog post for me. It’s my last one as a Mozzer. I’m off to try my hand at another bootstrapping startup: this time, software that lets you build feature tours and elicit visitor insights. I’m leaving Followerwonk in great hands, and I look forward to seeing awesome new features down the line. Of course, you can always stay in touch with me on Twitter. Keep on wonkin’!

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Panda 4.1: The Devil Is in the Aggregate

Posted by russvirante

I wish I didn’t have to say this. I wish I could look in the eyes of every victim of the last Panda 4.1 update and tell them it was something new, something unforeseeable, something out of their control. I wish I could tell them that Google pulled a fast one that no one saw coming. But I can’t.

Like many in the industry, I have been studying Panda closely since its inception. Google gave us a rare glimpse behind the curtain by providing us with the very guidelines they set in place to build their massive machine-learned algorithm which came to be known as Panda. Three and a half years later, Panda is still with us and seems to still catch us off guard.
Enough is enough.

What I intend to show you throughout this piece is that the original Panda questionnaire still remains a powerful predictive tool to wield in defense of what can be a painful organic traffic loss. By analyzing the winner/loser reports of Panda 4.1 using standard Panda surveys, we can determine whether Google’s choices are still in line with their original vision. So let’s dive in.

The process

The first thing we need to do is acquire a winners and losers list. I picked this excellent
one from SearchMetrics although any list would do as long as it is accurate. Second, I proceeded to run a Panda questionnaire with 10 questions on random pages from each of the sites (both the winners and losers). You can run your own Panda survey by following Distilled and Moz’s instructions here or just use PandaRisk like I did. After completing these analyses, we simply compare the scores across the board to determine whether they continue to reflect what we would expect given the original goals of the Panda algorithm.

The aggregate results

I actually want to do this a little bit backwards to drive home a point. Normally we would build to the aggregate results, starting with the details and leaving you with the big picture. But Panda
is a big-picture kind of algorithmic update. It is specially focused on the intersection of myriad features, the sum is greater than the parts. While breaking down these features can give us some insight, at the end of the day we need to stay acutely aware that unless we do well across the board, we are at risk.

Below is a graph of the average cumulative scores across the winners and losers. The top row are winners, the bottom row are losers. The left and right red circles indicate the lowest and highest scores within those categories, and the blue circle represents the average. There is something very important that I want to point out on this graph.
The highest individual average score of all the losers is less than the lowest average score of the winners. This means that in our randomly selected data set, not a single loser averaged as high a score as the worst winner. When we aggregate the data together, even with a crude system of averages rather than the far more sophisticated machine learning techniques employed by Google, there is a clear disparity between the sites that survive Panda and those that do not.

It is also worth pointing out here that there is no
positive Panda algorithm to our knowledge. Sites that perform well on Panda do not see boosts because they are being given ranking preference by Google, rather their competitors have seen rankings loss or their own previous Panda penalties have been lifted. In either scenario, we should remember that performing well on Panda assessments isn’t going to necessarily increase your rankings, but it should help you sustain them.

Now, let’s move on to some of the individual questions. We are going to start with the least correlated questions and move to those which most strongly correlate with performance in Panda 4.1. While all of the questions had positive correlations, a few lacked statistical significance.


Insignificant correlation

The first question which was not statistically significant in its correlation with Panda performance was “This page has visible errors on it”. The scores have been inverted here so that the higher the score, the fewer the number of people who reported that the page has errors. You can see that while more respondents did say that the winners had no visible errors, the difference was very slight. In fact, there was only a 5.35% difference between the two. I will save comment on this until after we discuss the next question.

The second question which was not statistically significant in its correlation with Panda performance was “This page has too many ads”. The scores have once again been inverted here so that the higher the score, the fewer the number of people who reported that the page has too many ads. This was even closer. The winners performed only 2.3% better than the losers in Panda 4.1.

I think there is a clear takeaway from these two questions. Nearly everyone gets the easy stuff right, but that isn’t enough. First, a lot of pages just have no ads whatsoever because that isn’t their business model. Even those that do have ads have caught on for the most part and optimized their pages accordingly, especially given that Google has other layout algorithms in place aside from Panda. Moreover, content inaccuracy is more likely to impact scrapers and content spinners than most sites, so it is unsurprising that few if any reported that the pages were filled with errors. If you score poorly on either of these, you have only begun to scratch the surface, because most websites get these right enough.


Moderate correlation

A number of Panda questions drew statistically significant difference in means but there was still substantial crossover between the winners and losers.
Whenever the average of the losers was greater than the lowest of the winners, I considered it only a moderate correlation. While the difference between means remained strong, there was still a good deal of variance in the scores. 

The first of these to consider was the question as to whether the content was “trustworthy”. You will notice a trend in a lot of these questions that there is a great deal of subjective human opinion. This subjectivity plays itself out quite a bit when the topics of the site might deal with very different categories of knowledge. For example, a celebrity fact site might be very trustworthy (although the site might be ad-laden) and an opinion piece in the New Yorker on the same celebrity might not be seen as trustworthy – even though it is plainly labeled as opinion. The trustworthy question ties back to the “does this page have errors” question quite nicely, drawing attention to the difference between a subjective and objective question and the way it can spread the means out nicely when you ask a respondent to give more of a personal opinion. This might seem unfair, but in the real world your site and Google itself is being judged by that subjective opinion, so it is understandable why Google wants to get at it algorithmically. Nevertheless, there was a strong difference in means between winners and losers of 12.57%, more than double the difference we saw between winners and losers on the question of Errors.

Original content has long been a known requirement of organic search success, so no one was surprised when it made its way into the Panda questionnaire. It still remains an influential piece of the puzzle with a difference in mean of nearly 20%. It was barely ruled out from being a heavily correlated feature due to one loser edging out a loss against the losers’ average mean. Notice though that one of the winners scored a perfect 100% on the survey. This perfect score was received despite hundreds of respondents.
It can be done.

As you can imagine, perception on what is and is not an authority is very subjective. This question is powerful because it pulls in all kinds of assumptions and presuppositions about brand, subject matter, content quality, design, justification, citations, etc. This likely explains why this question is beleaguered by one of the highest variances on the survey. Nevertheless, there was a 13.42% difference in means. And, on the other side of the scale, we did see what it is like to have a site that is clearly not an authority, scoring the worst possible 0% on this question. This is what happens when you include highly irrelevant content on your site just for the purpose of picking up either links or traffic. Be wary.

Everyone hates the credit card question, and luckily there is huge variance in answers. At least one site survived Panda despite scoring 5% on this question. Notice that there is a huge overlap between the lowest winner and the average of the losing sites. Also, if you notice by the placement of the mean (blue circle) in the winners category, the average wasn’t skewed to the right indicating just one outlier. There was strong variance in the responses across the board. The same was true of the losers. However, with a +15% difference in means, there was a clear average differentiation between the performance of winners and losers. Once again, though, we are drawn back to that aggregate score at the top, where we see how Google can use all these questions together to build a much clearer picture of site and content quality. For example, it is possible that Google pays more attention to this question when it is analyzing a site that has other features like the words “shopping cart” or “check out” on the homepage. 

I must admit that the bookmarking question surprised me. I always considered it to be the most subjective of the bunch. It seemed unfair that a site might be judged because it has material that simply doesn’t appeal to the masses. The survey just didn’t bear this out though. There was a clear difference in means, but after comparing the sites that were from similar content categories, there just wasn’t any reason to believe that a bias was created by subject matter. The 14.64% difference seemed to be, editorially speaking, related more to the construction of the page and the quality of the content, not the topic being discussed. Perhaps a better way to think about this question is:
would you be embarrassed if your friends knew THIS was the site you were getting your information from rather than another.

This wraps up the 5 questions that had good correlations but substantial enough variance that it was possible for the highest loser to beat out the average winner. I think one clear takeaway from this section is that these questions, while harder to improve upon than the Low Ads and No Errors questions before, are completely within the webmaster’s grasp. Making your content and site appear original, trustworthy, authoritative, and worthy of bookmarking aren’t terribly difficult. Sure, it takes some time and effort, but these goals, unlike the next, don’t appear that far out of reach.


Heavy correlation

The final three questions that seemed to distinguish the most between the winners and losers of Panda 4.1 all had high difference-in-means and, more importantly, had little to no crossover between the highest loser and lowest winner. In my opinion, these questions are also the hardest for the webmaster to address. They require thoughtful design, high quality content, and real, expert human authors.

The first question that met this classification was “could this content could appear in print”. With a difference in mean of 22.62%, the winners thoroughly trounced the losers in this category. Their sites and content were just better designed and better written. They showed the kind of editorial oversight you would expect in a print publication. The content wasn’t trite and unimportant, it was thorough and timely. 

The next heavily correlated question was whether the page was written by experts. With over a 34% difference in means between the winners and losers, and
literally no overlap at all between the winners’ and losers’ individual averages, it was clearly the strongest question. You can see why Google would want to look into things like authorship when they knew that expertise was such a powerful distinguisher between Panda winners and losers. This really begs the question – who is writing your content and do your readers know it?

Finally, insightful analysis had a huge difference in means of +32% between winners and losers. It is worth noting that the highest loser is an outlier, which is typified by the skewed mean (blue circle) being closer to the bottom that the top. Most of the answers were closer to the lower score than the top. Thus, the overlap is exaggerated a bit. But once again, this just draws us back to the original conclusion – that the devil is not in the details, the devil is in the aggregate. You might be able to score highly on one or two of the questions, but it won’t be enough to carry you through.


The takeaways

OK, so hopefully it is clear that Panda really hasn’t changed all that much. The same questions we looked at for Panda 1.0 still matter. In fact, I would argue that Google is just getting better at algorithmically answering those same questions, not changing them. They are still the right way to judge a site in Google’s eyes. So how should you respond?

The first and most obvious thing is you should run a Panda survey on your (or your clients’) sites. Select a random sample of pages from the site. The easiest way to do this is get an export of all of the pages of your site, perhaps from Open Site Explorer, put them in Excel and shuffle them. Then choose the top 10 that come up.  You can follow the Moz instructions I linked to above, do it at PandaRisk, or just survey your employees, friends, colleagues, etc. While the latter probably will be positively biased, it is still better than nothing. Go ahead and get yourself a benchmark.

The next step is to start pushing those scores up one at a time. I
give some solid examples on the Panda 4.0 release article about improving press release sites, but there is another better resource that just came out as well. Josh Bachynski released an amazing set of known Panda factors over at his website The Moral Concept. It is well worth a thorough read. There is a lot to take in, but there are tons of easy-to-implement improvements that could help you out quite a bit. Once you have knocked out a few for each of your low-scoring questions, run the exact same survey again and see how you improve. Keep iterating this process until you beat out each of the question averages for winners. At that point, you can rest assured that your site is safe from the Panda by beating the devil in the aggregate. 

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