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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Reblogged 4 years ago from tracking.feedpress.it

UX, Content Quality, and SEO – Whiteboard Friday

Posted by EricEnge

Editor’s note: Today we’re featuring back-to-back episodes of Whiteboard Friday from our friends at Stone Temple Consulting. Make sure to also check out the first episode, “Becoming Better SEO Scientists” from Mark Traphagen.

User experience and the quality of your content have an incredibly broad impact on your SEO efforts. In this episode of Whiteboard Friday, Stone Temple’s Eric Enge shows you how paying attention to your users can benefit your position in the SERPs.

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

Hi, Mozzers. I’m Eric Enge, CEO of Stone Temple Consulting. Today I want to talk to you about one of the most underappreciated aspects of SEO, and that is the interaction between user experience, content quality, and your SEO rankings and traffic.

I’m going to take you through a little history first. You know, we all know about the Panda algorithm update that came out in February 23, 2011, and of course more recently we have the search quality update that came out in May 19, 2015. Our Panda friend had 27 different updates that we know of along the way. So a lot of stuff has gone on, but we need to realize that that is not where it all started.

The link algorithm from the very beginning was about search quality. Links allowed Google to have an algorithm that gave better results than the other search engines of their day, which were dependent on keywords. These things however, that I’ve just talked about, are still just the tip of the iceberg. Google goes a lot deeper than that, and I want to walk you through the different things that it does.

So consider for a moment, you have someone search on the phrase “men’s shoes” and they come to your website.

What is that they want when they come to your website? Do they want sneakers, sandals, dress shoes? Well, those are sort of the obvious things that they might want. But you need to think a little bit more about what the user really wants to be able to know before they buy from you.

First of all, there has to be a way to buy. By the way, affiliate sites don’t have ways to buy. So the line of thinking I’m talking about might not work out so well for affiliate sites and works better for people who can actually sell the product directly. But in addition to a way to buy, they might want a privacy policy. They might want to see an About Us page. They might want to be able to see your phone number. These are all different kinds of things that users look for when they arrive on the pages of your site.

So as we think about this, what is it that we can do to do a better job with our websites? Well, first of all, lose the focus on keywords. Don’t get me wrong, keywords haven’t gone entirely away. But the pages where we overemphasize one particular keyword over another or related phrases are long gone, and you need to have a broader focus on how you approach things.

User experience is now a big deal. You really need to think about how users are interacting with your page and how that shows your overall page quality. Think about the percent satisfaction. If I send a hundred users to your page from my search engine, how many of those users are going to be happy with the content or the products or everything that they see with your page? You need to think through the big picture. So at the end of the day, this impacts the content on your page to be sure, but a lot more than that it impacts the design, related items that you have on the page.

So let me just give you an example of that. I looked at one page recently that was for a flower site. It was a page about annuals on that site, and that page had no link to their perennials page. Well, okay, a fairly good percentage of people who arrive on a page about annuals are also going to want to have perennials as something they might consider buying. So that page was probably coming across as a poor user experience. So these related items concepts are incredibly important.

Then the links to your page is actually a way to get to some of those related items, and so those are really important as well. What are the related products that you link to?

Finally, really it impacts everything you do with your page design. You need to move past the old-fashioned way of thinking about SEO and into the era of: How am I doing with satisfying all the people who come to the pages of your site?

Thank you, Mozzers. Have a great day.

Video transcription by Speechpad.com

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Reblogged 4 years ago from tracking.feedpress.it

I Can’t Drive 155: Meta Descriptions in 2015

Posted by Dr-Pete

For years now, we (and many others) have been recommending keeping your Meta Descriptions shorter than
about 155-160 characters. For months, people have been sending me examples of search snippets that clearly broke that rule, like this one (on a search for “hummingbird food”):

For the record, this one clocks in at 317 characters (counting spaces). So, I set out to discover if these long descriptions were exceptions to the rule, or if we need to change the rules. I collected the search snippets across the MozCast 10K, which resulted in 92,669 snippets. All of the data in this post was collected on April 13, 2015.

The Basic Data

The minimum snippet length was zero characters. There were 69 zero-length snippets, but most of these were the new generation of answer box, that appears organic but doesn’t have a snippet. To put it another way, these were misidentified as organic by my code. The other 0-length snippets were local one-boxes that appeared as organic but had no snippet, such as this one for “chichen itza”:

These zero-length snippets were removed from further analysis, but considering that they only accounted for 0.07% of the total data, they didn’t really impact the conclusions either way. The shortest legitimate, non-zero snippet was 7 characters long, on a search for “geek and sundry”, and appears to have come directly from the site’s meta description:

The maximum snippet length that day (this is a highly dynamic situation) was 372 characters. The winner appeared on a search for “benefits of apple cider vinegar”:

The average length of all of the snippets in our data set (not counting zero-length snippets) was 143.5 characters, and the median length was 152 characters. Of course, this can be misleading, since some snippets are shorter than the limit and others are being artificially truncated by Google. So, let’s dig a bit deeper.

The Bigger Picture

To get a better idea of the big picture, let’s take a look at the display length of all 92,600 snippets (with non-zero length), split into 20-character buckets (0-20, 21-40, etc.):

Most of the snippets (62.1%) cut off as expected, right in the 141-160 character bucket. Of course, some snippets were shorter than that, and didn’t need to be cut off, and some broke the rules. About 1% (1,010) of the snippets in our data set measured 200 or more characters. That’s not a huge number, but it’s enough to take seriously.

That 141-160 character bucket is dwarfing everything else, so let’s zoom in a bit on the cut-off range, and just look at snippets in the 120-200 character range (in this case, by 5-character bins):

Zooming in, the bulk of the snippets are displaying at lengths between about 146-165 characters. There are plenty of exceptions to the 155-160 character guideline, but for the most part, they do seem to be exceptions.

Finally, let’s zoom in on the rule-breakers. This is the distribution of snippets displaying 191+ characters, bucketed in 10-character bins (191-200, 201-210, etc.):

Please note that the Y-axis scale is much smaller than in the previous 2 graphs, but there is a pretty solid spread, with a decent chunk of snippets displaying more than 300 characters.

Without looking at every original meta description tag, it’s very difficult to tell exactly how many snippets have been truncated by Google, but we do have a proxy. Snippets that have been truncated end in an ellipsis (…), which rarely appears at the end of a natural description. In this data set, more than half of all snippets (52.8%) ended in an ellipsis, so we’re still seeing a lot of meta descriptions being cut off.

I should add that, unlike titles/headlines, it isn’t clear whether Google is cutting off snippets by pixel width or character count, since that cut-off is done on the server-side. In most cases, Google will cut before the end of the second line, but sometimes they cut well before this, which could suggest a character-based limit. They also cut off at whole words, which can make the numbers a bit tougher to interpret.

The Cutting Room Floor

There’s another difficulty with telling exactly how many meta descriptions Google has modified – some edits are minor, and some are major. One minor edit is when Google adds some additional information to a snippet, such as a date at the beginning. Here’s an example (from a search for “chicken pox”):

With the date (and minus the ellipsis), this snippet is 164 characters long, which suggests Google isn’t counting the added text against the length limit. What’s interesting is that the rest comes directly from the meta description on the site, except that the site’s description starts with “Chickenpox.” and Google has removed that keyword. As a human, I’d say this matches the meta description, but a bot has a very hard time telling a minor edit from a complete rewrite.

Another minor rewrite occurs in snippets that start with search result counts:

Here, we’re at 172 characters (with spaces and minus the ellipsis), and Google has even let this snippet roll over to a third line. So, again, it seems like the added information at the beginning isn’t counting against the length limit.

All told, 11.6% of the snippets in our data set had some kind of Google-generated data, so this type of minor rewrite is pretty common. Even if Google honors most of your meta description, you may see small edits.

Let’s look at our big winner, the 372-character description. Here’s what we saw in the snippet:

Jan 26, 2015 – Health• Diabetes Prevention: Multiple studies have shown a correlation between apple cider vinegar and lower blood sugar levels. … • Weight Loss: Consuming apple cider vinegar can help you feel more full, which can help you eat less. … • Lower Cholesterol: … • Detox: … • Digestive Aid: … • Itchy or Sunburned Skin: … • Energy Boost:1 more items

So, what about the meta description? Here’s what we actually see in the tag:

Were you aware of all the uses of apple cider vinegar? From cleansing to healing, to preventing diabetes, ACV is a pantry staple you need in your home.

That’s a bit more than just a couple of edits. So, what’s happening here? Well, there’s a clue on that same page, where we see yet another rule-breaking snippet:

You might be wondering why this snippet is any more interesting than the other one. If you could see the top of the SERP, you’d know why, because it looks something like this:

Google is automatically extracting list-style data from these pages to fuel the expansion of the Knowledge Graph. In one case, that data is replacing a snippet
and going directly into an answer box, but they’re performing the same translation even for some other snippets on the page.

So, does every 2nd-generation answer box yield long snippets? After 3 hours of inadvisable mySQL queries, I can tell you that the answer is a resounding “probably not”. You can have 2nd-gen answer boxes without long snippets and you can have long snippets without 2nd-gen answer boxes,
but there does appear to be a connection between long snippets and Knowledge Graph in some cases.

One interesting connection is that Google has begun bolding keywords that seem like answers to the query (and not just synonyms for the query). Below is an example from a search for “mono symptoms”. There’s an answer box for this query, but the snippet below is not from the site in the answer box:

Notice the bolded words – “fatigue”, “sore throat”, “fever”, “headache”, “rash”. These aren’t synonyms for the search phrase; these are actual symptoms of mono. This data isn’t coming from the meta description, but from a bulleted list on the target page. Again, it appears that Google is trying to use the snippet to answer a question, and has gone well beyond just matching keywords.

Just for fun, let’s look at one more, where there’s no clear connection to the Knowledge Graph. Here’s a snippet from a search for “sons of anarchy season 4”:

This page has no answer box, and the information extracted is odd at best. The snippet bears little or no resemblance to the site’s meta description. The number string at the beginning comes out of a rating widget, and some of the text isn’t even clearly available on the page. This seems to be an example of Google acknowledging IMDb as a high-authority site and desperately trying to match any text they can to the query, resulting in a Frankenstein’s snippet.

The Final Verdict

If all of this seems confusing, that’s probably because it is. Google is taking a lot more liberties with snippets these days, both to better match queries, to add details they feel are important, or to help build and support the Knowledge Graph.

So, let’s get back to the original question – is it time to revise the 155(ish) character guideline? My gut feeling is: not yet. To begin with, the vast majority of snippets are still falling in that 145-165 character range. In addition, the exceptions to the rule are not only atypical situations, but in most cases those long snippets don’t seem to represent the original meta description. In other words, even if Google does grant you extra characters, they probably won’t be the extra characters you asked for in the first place.

Many people have asked: “How do I make sure that Google shows my meta description as is?” I’m afraid the answer is: “You don’t.” If this is very important to you, I would recommend keeping your description below the 155-character limit, and making sure that it’s a good match to your target keyword concepts. I suspect Google is going to take more liberties with snippets over time, and we’re going to have to let go of our obsession with having total control over the SERPs.

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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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