​​Measure Your Mobile Rankings and Search Visibility in Moz Analytics

Posted by jon.white

We have launched a couple of new things in Moz Pro that we are excited to share with you all: Mobile Rankings and a Search Visibility score. If you want, you can jump right in by heading to a campaign and adding a mobile engine, or keep reading for more details!

Track your mobile vs. desktop rankings in Moz Analytics

Mobilegeddon came and went with slightly less fanfare than expected, somewhat due to the vast ‘Mobile Friendly’ updates we all did at super short notice (nice work everyone!). Nevertheless, mobile rankings visibility is now firmly on everyone’s radar, and will only become more important over time.

Now you can track your campaigns’ mobile rankings for all of the same keywords and locations you are tracking on desktop.

For this campaign my mobile visibility is almost 20% lower than my desktop visibility and falling;
I can drill down to find out why

Clicking on this will take you into a new Engines tab within your Keyword Rankings page where you can find a more detailed version of this chart as well as a tabular view by keyword for both desktop and mobile. Here you can also filter by label and location.

Here I can see Search Visibility across engines including mobile;
in this case, for my branded keywords.

We have given an extra engine to all campaigns

We’ve given customers an extra engine for each campaign, increasing the number from 3 to 4. Use the extra slot to add the mobile engine and unlock your mobile data!

We will begin to track mobile rankings within 24 hours of adding to a campaign. Once you are set up, you will notice a new chart on your dashboard showing visibility for Desktop vs. Mobile Search Visibility.

Measure your Search Visibility score vs. competitors

The overall Search Visibility for my campaign

Along with this change we have also added a Search Visibility score to your rankings data. Use your visibility score to track and report on your overall campaign ranking performance, compare to your competitors, and look for any large shifts that might indicate penalties or algorithm changes. For a deeper drill-down into your data you can also segment your visibility score by keyword labels or locations. Visit the rankings summary page on any campaign to get started.

How is Search Visibility calculated?

Good question!

The Search Visibility score is the percentage of clicks we estimate you receive based on your rankings positions, across all of your keywords.

We take each ranking position for each keyword, multiply by an estimated click-thru-rate, and then take the average of all of your keywords. You can think of it as the percentage of your SERPs that you own. The score is expressed as a percentage, though scores of 100% would be almost impossible unless you are tracking keywords using the “site:” modifier. It is probably more useful to measure yourself vs. your competitors rather than focus on the actual score, but, as a rule of thumb, mid-40s is probably the realistic maximum for non-branded keywords.

Jeremy, our Moz Analytics TPM, came up with this metaphor:

Think of the SERPs for your keywords as villages. Each position on the SERP is a plot of land in SERP-village. The Search Visibility score is the average amount of plots you own in each SERP-village. Prime real estate plots (i.e., better ranking positions, like #1) are worth more. A complete monopoly of real estate in SERP-village would equate to a score of 100%. The Search Visibility score equates to how much total land you own in all SERP-villages.

Some neat ways to use this feature

  • Label and group your keywords, particularly when you add them – As visibility score is an average of all of your keywords, when you add or remove keywords from your campaign you will likely see fluctuations in the score that are unrelated to performance. Solve this by getting in the habit of labeling keywords when you add them. Then segment your data by these labels to track performance of specific keyword groups over time.
  • See how location affects your mobile rankings – Using the Engines tab in Keyword Rankings, use the filters to select just local keywords. Look for big differences between Mobile and Desktop where Google might be assuming local intent for mobile searches but not for desktop. Check out how your competitors perform for these keywords. Can you use this data?

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!

Reblogged 2 years ago from tracking.feedpress.it

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.

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!

Reblogged 2 years ago from tracking.feedpress.it

Deconstructing the App Store Rankings Formula with a Little Mad Science

Posted by AlexApptentive

After seeing Rand’s “Mad Science Experiments in SEO” presented at last year’s MozCon, I was inspired to put on the lab coat and goggles and do a few experiments of my own—not in SEO, but in SEO’s up-and-coming younger sister, ASO (app store optimization).

Working with Apptentive to guide enterprise apps and small startup apps alike to increase their discoverability in the app stores, I’ve learned a thing or two about app store optimization and what goes into an app’s ranking. It’s been my personal goal for some time now to pull back the curtains on Google and Apple. Yet, the deeper into the rabbit hole I go, the more untested assumptions I leave in my way.

Hence, I thought it was due time to put some longstanding hypotheses through the gauntlet.

As SEOs, we know how much of an impact a single ranking can mean on a SERP. One tiny rank up or down can make all the difference when it comes to your website’s traffic—and revenue.

In the world of apps, ranking is just as important when it comes to standing out in a sea of more than 1.3 million apps. Apptentive’s recent mobile consumer survey shed a little more light this claim, revealing that nearly half of all mobile app users identified browsing the app store charts and search results (the placement on either of which depends on rankings) as a preferred method for finding new apps in the app stores. Simply put, better rankings mean more downloads and easier discovery.

Like Google and Bing, the two leading app stores (the Apple App Store and Google Play) have a complex and highly guarded algorithms for determining rankings for both keyword-based app store searches and composite top charts.

Unlike SEO, however, very little research and theory has been conducted around what goes into these rankings.

Until now, that is.

Over the course of five studies analyzing various publicly available data points for a cross-section of the top 500 iOS (U.S. Apple App Store) and the top 500 Android (U.S. Google Play) apps, I’ll attempt to set the record straight with a little myth-busting around ASO. In the process, I hope to assess and quantify any perceived correlations between app store ranks, ranking volatility, and a few of the factors commonly thought of as influential to an app’s ranking.

But first, a little context

Image credit: Josh Tuininga, Apptentive

Both the Apple App Store and Google Play have roughly 1.3 million apps each, and both stores feature a similar breakdown by app category. Apps ranking in the two stores should, theoretically, be on a fairly level playing field in terms of search volume and competition.

Of these apps, nearly two-thirds have not received a single rating and 99% are considered unprofitable. These studies, therefore, single out the rare exceptions to the rule—the top 500 ranked apps in each store.

While neither Apple nor Google have revealed specifics about how they calculate search rankings, it is generally accepted that both app store algorithms factor in:

  • Average app store rating
  • Rating/review volume
  • Download and install counts
  • Uninstalls (what retention and churn look like for the app)
  • App usage statistics (how engaged an app’s users are and how frequently they launch the app)
  • Growth trends weighted toward recency (how daily download counts changed over time and how today’s ratings compare to last week’s)
  • Keyword density of the app’s landing page (Ian did a great job covering this factor in a previous Moz post)

I’ve simplified this formula to a function highlighting the four elements with sufficient data (or at least proxy data) for our analysis:

Ranking = fn(Rating, Rating Count, Installs, Trends)

Of course, right now, this generalized function doesn’t say much. Over the next five studies, however, we’ll revisit this function before ultimately attempting to compare the weights of each of these four variables on app store rankings.

(For the purpose of brevity, I’ll stop here with the assumptions, but I’ve gone into far greater depth into how I’ve reached these conclusions in a 55-page report on app store rankings.)

Now, for the Mad Science.

Study #1: App-les to app-les app store ranking volatility

The first, and most straight forward of the five studies involves tracking daily movement in app store rankings across iOS and Android versions of the same apps to determine any trends of differences between ranking volatility in the two stores.

I went with a small sample of five apps for this study, the only criteria for which were that:

  • They were all apps I actively use (a criterion for coming up with the five apps but not one that influences rank in the U.S. app stores)
  • They were ranked in the top 500 (but not the top 25, as I assumed app store rankings would be stickier at the top—an assumption I’ll test in study #2)
  • They had an almost identical version of the app in both Google Play and the App Store, meaning they should (theoretically) rank similarly
  • They covered a spectrum of app categories

The apps I ultimately chose were Lyft, Venmo, Duolingo, Chase Mobile, and LinkedIn. These five apps represent the travel, finance, education banking, and social networking categories.

Hypothesis

Going into this analysis, I predicted slightly more volatility in Apple App Store rankings, based on two statistics:

Both of these assumptions will be tested in later analysis.

Results

7-Day App Store Ranking Volatility in the App Store and Google Play

Among these five apps, Google Play rankings were, indeed, significantly less volatile than App Store rankings. Among the 35 data points recorded, rankings within Google Play moved by as much as 23 positions/ranks per day while App Store rankings moved up to 89 positions/ranks. The standard deviation of ranking volatility in the App Store was, furthermore, 4.45 times greater than that of Google Play.

Of course, the same apps varied fairly dramatically in their rankings in the two app stores, so I then standardized the ranking volatility in terms of percent change to control for the effect of numeric rank on volatility. When cast in this light, App Store rankings changed by as much as 72% within a 24-hour period while Google Play rankings changed by no more than 9%.

Also of note, daily rankings tended to move in the same direction across the two app stores approximately two-thirds of the time, suggesting that the two stores, and their customers, may have more in common than we think.

Study #2: App store ranking volatility across the top charts

Testing the assumption implicit in standardizing the data in study No. 1, this one was designed to see if app store ranking volatility is correlated with an app’s current rank. The sample for this study consisted of the top 500 ranked apps in both Google Play and the App Store, with special attention given to those on both ends of the spectrum (ranks 1–100 and 401–500).

Hypothesis

I anticipated rankings to be more volatile the higher an app is ranked—meaning an app ranked No. 450 should be able to move more ranks in any given day than an app ranked No. 50. This hypothesis is based on the assumption that higher ranked apps have more installs, active users, and ratings, and that it would take a large margin to produce a noticeable shift in any of these factors.

Results

App Store Ranking Volatility of Top 500 Apps

One look at the chart above shows that apps in both stores have increasingly more volatile rankings (based on how many ranks they moved in the last 24 hours) the lower on the list they’re ranked.

This is particularly true when comparing either end of the spectrum—with a seemingly straight volatility line among Google Play’s Top 100 apps and very few blips within the App Store’s Top 100. Compare this section to the lower end, ranks 401–)500, where both stores experience much more turbulence in their rankings. Across the gamut, I found a 24% correlation between rank and ranking volatility in the Play Store and 28% correlation in the App Store.

To put this into perspective, the average app in Google Play’s 401–)500 ranks moved 12.1 ranks in the last 24 hours while the average app in the Top 100 moved a mere 1.4 ranks. For the App Store, these numbers were 64.28 and 11.26, making slightly lower-ranked apps more than five times as volatile as the highest ranked apps. (I say slightly as these “lower-ranked” apps are still ranked higher than 99.96% of all apps.)

The relationship between rank and volatility is pretty consistent across the App Store charts, while rank has a much greater impact on volatility at the lower end of Google Play charts (ranks 1-100 have a 35% correlation) than it does at the upper end (ranks 401-500 have a 1% correlation).

Study #3: App store rankings across the stars

The next study looks at the relationship between rank and star ratings to determine any trends that set the top chart apps apart from the rest and explore any ties to app store ranking volatility.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

As discussed in the introduction, this study relates directly to one of the factors commonly accepted as influential to app store rankings: average rating.

Getting started, I hypothesized that higher ranks generally correspond to higher ratings, cementing the role of star ratings in the ranking algorithm.

As far as volatility goes, I did not anticipate average rating to play a role in app store ranking volatility, as I saw no reason for higher rated apps to be less volatile than lower rated apps, or vice versa. Instead, I believed volatility to be tied to rating volume (as we’ll explore in our last study).

Results

Average App Store Ratings of Top Apps

The chart above plots the top 100 ranked apps in either store with their average rating (both historic and current, for App Store apps). If it looks a little chaotic, it’s just one indicator of the complexity of ranking algorithm in Google Play and the App Store.

If our hypothesis was correct, we’d see a downward trend in ratings. We’d expect to see the No. 1 ranked app with a significantly higher rating than the No. 100 ranked app. Yet, in neither store is this the case. Instead, we get a seemingly random plot with no obvious trends that jump off the chart.

A closer examination, in tandem with what we already know about the app stores, reveals two other interesting points:

  1. The average star rating of the top 100 apps is significantly higher than that of the average app. Across the top charts, the average rating of a top 100 Android app was 4.319 and the average top iOS app was 3.935. These ratings are 0.32 and 0.27 points, respectively, above the average rating of all rated apps in either store. The averages across apps in the 401–)500 ranks approximately split the difference between the ratings of the top ranked apps and the ratings of the average app.
  2. The rating distribution of top apps in Google Play was considerably more compact than the distribution of top iOS apps. The standard deviation of ratings in the Apple App Store top chart was over 2.5 times greater than that of the Google Play top chart, likely meaning that ratings are more heavily weighted in Google Play’s algorithm.

App Store Ranking Volatility and Average Rating

Looking next at the relationship between ratings and app store ranking volatility reveals a -15% correlation that is consistent across both app stores; meaning the higher an app is rated, the less its rank it likely to move in a 24-hour period. The exception to this rule is the Apple App Store’s calculation of an app’s current rating, for which I did not find a statistically significant correlation.

Study #4: App store rankings across versions

This next study looks at the relationship between the age of an app’s current version, its rank and its ranking volatility.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

In alteration of the above function, I’m using the age of a current app’s version as a proxy (albeit not a very good one) for trends in app store ratings and app quality over time.

Making the assumptions that (a) apps that are updated more frequently are of higher quality and (b) each new update inspires a new wave of installs and ratings, I’m hypothesizing that the older the age of an app’s current version, the lower it will be ranked and the less volatile its rank will be.

Results

How update frequency correlates with app store rank

The first and possibly most important finding is that apps across the top charts in both Google Play and the App Store are updated remarkably often as compared to the average app.

At the time of conducting the study, the current version of the average iOS app on the top chart was only 28 days old; the current version of the average Android app was 38 days old.

As hypothesized, the age of the current version is negatively correlated with the app’s rank, with a 13% correlation in Google Play and a 10% correlation in the App Store.

How update frequency correlates with app store ranking volatility

The next part of the study maps the age of the current app version to its app store ranking volatility, finding that recently updated Android apps have less volatile rankings (correlation: 8.7%) while recently updated iOS apps have more volatile rankings (correlation: -3%).

Study #5: App store rankings across monthly active users

In the final study, I wanted to examine the role of an app’s popularity on its ranking. In an ideal world, popularity would be measured by an app’s monthly active users (MAUs), but since few mobile app developers have released this information, I’ve settled for two publicly available proxies: Rating Count and Installs.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

For the same reasons indicated in the second study, I anticipated that more popular apps (e.g., apps with more ratings and more installs) would be higher ranked and less volatile in rank. This, again, takes into consideration that it takes more of a shift to produce a noticeable impact in average rating or any of the other commonly accepted influencers of an app’s ranking.

Results

Apps with more ratings and reviews typically rank higher

The first finding leaps straight off of the chart above: Android apps have been rated more times than iOS apps, 15.8x more, in fact.

The average app in Google Play’s Top 100 had a whopping 3.1 million ratings while the average app in the Apple App Store’s Top 100 had 196,000 ratings. In contrast, apps in the 401–)500 ranks (still tremendously successful apps in the 99.96 percentile of all apps) tended to have between one-tenth (Android) and one-fifth (iOS) of the ratings count as that of those apps in the top 100 ranks.

Considering that almost two-thirds of apps don’t have a single rating, reaching rating counts this high is a huge feat, and a very strong indicator of the influence of rating count in the app store ranking algorithms.

To even out the playing field a bit and help us visualize any correlation between ratings and rankings (and to give more credit to the still-staggering 196k ratings for the average top ranked iOS app), I’ve applied a logarithmic scale to the chart above:

The relationship between app store ratings and rankings in the top 100 apps

From this chart, we can see a correlation between ratings and rankings, such that apps with more ratings tend to rank higher. This equates to a 29% correlation in the App Store and a 40% correlation in Google Play.

Apps with more ratings typically experience less app store ranking volatility

Next up, I looked at how ratings count influenced app store ranking volatility, finding that apps with more ratings had less volatile rankings in the Apple App Store (correlation: 17%). No conclusive evidence was found within the Top 100 Google Play apps.

Apps with more installs and active users tend to rank higher in the app stores

And last but not least, I looked at install counts as an additional proxy for MAUs. (Sadly, this is a statistic only listed in Google Play. so any resulting conclusions are applicable only to Android apps.)

Among the top 100 Android apps, this last study found that installs were heavily correlated with ranks (correlation: -35.5%), meaning that apps with more installs are likely to rank higher in Google Play. Android apps with more installs also tended to have less volatile app store rankings, with a correlation of -16.5%.

Unfortunately, these numbers are slightly skewed as Google Play only provides install counts in broad ranges (e.g., 500k–)1M). For each app, I took the low end of the range, meaning we can likely expect the correlation to be a little stronger since the low end was further away from the midpoint for apps with more installs.

Summary

To make a long post ever so slightly shorter, here are the nuts and bolts unearthed in these five mad science studies in app store optimization:

  1. Across the top charts, Apple App Store rankings are 4.45x more volatile than those of Google Play
  2. Rankings become increasingly volatile the lower an app is ranked. This is particularly true across the Apple App Store’s top charts.
  3. In both stores, higher ranked apps tend to have an app store ratings count that far exceeds that of the average app.
  4. Ratings appear to matter more to the Google Play algorithm, especially as the Apple App Store top charts experience a much wider ratings distribution than that of Google Play’s top charts.
  5. The higher an app is rated, the less volatile its rankings are.
  6. The 100 highest ranked apps in either store are updated much more frequently than the average app, and apps with older current versions are correlated with lower ratings.
  7. An app’s update frequency is negatively correlated with Google Play’s ranking volatility but positively correlated with ranking volatility in the App Store. This likely due to how Apple weighs an app’s most recent ratings and reviews.
  8. The highest ranked Google Play apps receive, on average, 15.8x more ratings than the highest ranked App Store apps.
  9. In both stores, apps that fall under the 401–500 ranks receive, on average, 10–20% of the rating volume seen by apps in the top 100.
  10. Rating volume and, by extension, installs or MAUs, is perhaps the best indicator of ranks, with a 29–40% correlation between the two.

Revisiting our first (albeit oversimplified) guess at the app stores’ ranking algorithm gives us this loosely defined function:

Ranking = fn(Rating, Rating Count, Installs, Trends)

I’d now re-write the function into a formula by weighing each of these four factors, where a, b, c, & d are unknown multipliers, or weights:

Ranking = (Rating * a) + (Rating Count * b) + (Installs * c) + (Trends * d)

These five studies on ASO shed a little more light on these multipliers, showing Rating Count to have the strongest correlation with rank, followed closely by Installs, in either app store.

It’s with the other two factors—rating and trends—that the two stores show the greatest discrepancy. I’d hazard a guess to say that the App Store prioritizes growth trends over ratings, given the importance it places on an app’s current version and the wide distribution of ratings across the top charts. Google Play, on the other hand, seems to favor ratings, with an unwritten rule that apps just about have to have at least four stars to make the top 100 ranks.

Thus, we conclude our mad science with this final glimpse into what it takes to make the top charts in either store:

Weight of factors in the Apple App Store ranking algorithm

Rating Count > Installs > Trends > Rating

Weight of factors in the Google Play ranking algorithm

Rating Count > Installs > Rating > Trends


Again, we’re oversimplifying for the sake of keeping this post to a mere 3,000 words, but additional factors including keyword density and in-app engagement statistics continue to be strong indicators of ranks. They simply lie outside the scope of these studies.

I hope you found this deep-dive both helpful and interesting. Moving forward, I also hope to see ASOs conducting the same experiments that have brought SEO to the center stage, and encourage you to enhance or refute these findings with your own ASO mad science experiments.

Please share your thoughts in the comments below, and let’s deconstruct the ranking formula together, one experiment at a time.

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Reblogged 3 years ago from tracking.feedpress.it

Why Good Unique Content Needs to Die – Whiteboard Friday

Posted by randfish

We all know by now that not just any old content is going to help us rank in competitive SERPs. We often hear people talking about how it takes “good, unique content.” That’s the wrong bar. In today’s Whiteboard Friday, Rand talks about where we should be aiming, and how to get there.

For reference, here’s a still of this week’s whiteboard. Click on it to open a high resolution image in a new tab!

Video transcription

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we’re going to chat about something that I really have a problem with in the SEO world, and that is the phrase “good, unique content.” I’ll tell you why this troubles me so much. It’s because I get so many emails, I hear so many times at conferences and events with people I meet, with folks I talk to in the industry saying, “Hey, we created some good, unique content, but we don’t seem to be performing well in search.” My answer back to that is always that is not the bar for entry into SEO. That is not the bar for ranking.

The content quality scale

So I made this content quality scale to help illustrate what I’m talking about here. You can see that it starts all the way up at 10x, and down here I’ve got Panda Invasion. So quality, like Google Panda is coming for your site, it’s going to knock you out of the rankings. It’s going to penalize you, like your content is thin and largely useless.

Then you go up a little bit, and it’s like, well four out of five searchers find it pretty bad. They clicked the Back button. Maybe one out of five is thinking, “Well, this is all right. This solves my most basic problems.”

Then you get one level higher than that, and you have good, unique content, which I think many folks think of as where they need to get to. It’s essentially, hey, it’s useful enough. It answers the searcher’s query. It’s unique from any other content on the Web. If you read it, you wouldn’t vomit. It’s good enough, right? Good, unique content.

Problem is almost everyone can get here. They really can. It’s not a high bar, a high barrier to entry to say you need good, unique content. In fact, it can scale. So what I see lots of folks doing is they look at a search result or a set of search results in their industry. Say you’re in travel and vacations, and you look at these different countries and you’re going to look at the hotels or recommendations in those countries and then see all the articles there. You go, “Yeah, you know what, I think we could do something as good as what’s up there or almost.” Well, okay, that puts you in the range. That’s good, unique content.

But in my opinion, the minimum bar today for modern SEO is a step higher, and that is as good as the best in the search results on the search results page. If you can’t consistently say, “We’re the best result that a searcher could find in the search results,” well then, guess what? You’re not going to have an opportunity to rank. It’s much, much harder to get into those top 10 positions, page 1, page 2 positions than it was in the past because there are so many ranking signals that so many of these websites have already built up over the last 5, 10, 15 years that you need to go above and beyond.

Really, where I want folks to go and where I always expect content from Moz to go is here, and that is 10x, 10 times better than anything I can find in the search results today. If I don’t think I can do that, then I’m not going to try and rank for those keywords. I’m just not going to pursue it. I’m going to pursue content in areas where I believe I can create something 10 times better than the best result out there.

What changed?

Why is this? What changed? Well, a bunch of things actually.

  • User experience became a much bigger element in the ranking algorithms, and that’s direct influences, things that we’ve talked about here on Whiteboard Friday before like pogo-sticking, and lots of indirect ones like the links that you earn based on the user experience that you provide and Google rendering pages, Google caring about load speed and device rendering, mobile friendliness, all these kinds of things.
  • Earning links overtook link building. It used to be you put out a page and you built a bunch of links to it. Now that doesn’t so much work anymore because Google is very picky about the links that it’s going to consider. If you can’t earn links naturally, not only can you not get links fast enough and not get good ones, but you also are probably earning links that Google doesn’t even want to count or may even penalize you for. It’s nearly impossible to earn links with just good, unique content. If there’s something better out there on page one of the search results, why would they even bother to link to you? Someone’s going to do a search, and they’re going to find something else to link to, something better.
  • Third, the rise of content marketing over the last five, six years has meant that there’s just a lot more competition. This field is a lot more crowded than it used to be, with many people trying to get to a higher and higher quality bar.
  • Finally, as a result of many of these things, user expectations have gone crazy. Users expect pages to load insanely fast, even on mobile devices, even when their connection’s slow. They expect it to look great. They expect to be provided with an answer almost instantaneously. The quality of results that Google has delivered and the quality of experience that sites like Facebook, which everyone is familiar with, are delivering means that our brains have rewired themselves to expect very fast, very high quality results consistently.

How do we create “10x” content?

So, because of all these changes, we need a process. We need a process to choose, to figure out how we can get to 10x content, not good, unique content, 10x content. A process that I often like to use — this probably is not the only one, but you’re welcome to use it if you find it valuable — is to go, “All right, you know what? I’m going to perform some of these search queries.”

By the way, I would probably perform the search query in two places. One is in Google and their search results, and the other is actually in BuzzSumo, which I think is a great tool for this, where I can see the content that has been most shared. So if you haven’t already, check out BuzzSumo.com.

I might search for something like Costa Rica ecolodges, which I might be considering a Costa Rica vacation at some point in the future. I look at these top ranking results, probably the whole top 10 as well as the most shared content on social media.

Then I’m going to ask myself these questions;

  • What questions are being asked and answered by these search results?
  • What sort of user experience is provided? I look at this in terms of speed, in terms of mobile friendliness, in terms of rendering, in terms of layout and design quality, in terms of what’s required from the user to be able to get the information? Is it all right there, or do I need to click? Am I having trouble finding things?
  • What’s the detail and thoroughness of the information that’s actually provided? Is it lacking? Is it great?
  • What about use of visuals? Visual content can often take best in class all the way up to 10x if it’s done right. So I might check out the use of visuals.
  • The quality of the writing.
  • I’m going to look at information and data elements. Where are they pulling from? What are their sources? What’s the quality of that stuff? What types of information is there? What types of information is missing?

In fact, I like to ask, “What’s missing?” a lot.

From this, I can determine like, hey, here are the strengths and weaknesses of who’s getting all of the social shares and who’s ranking well, and here’s the delta between me and them today. This is the way that I can be 10 times better than the best results in there.

If you use this process or a process like this and you do this type of content auditing and you achieve this level of content quality, you have a real shot at rankings. One of the secret reasons for that is that the effort axis that I have here, like I go to Fiverr, I get Panda invasion. I make the intern write it. This is going to take a weekend to build versus there’s no way to scale this content.

This is a super power. When your competitors or other folks in the field look and say, “Hey, there’s no way that we can scale content quality like this. It’s just too much effort. We can’t keep producing it at this level,” well, now you have a competitive advantage. You have something that puts you in a category by yourself and that’s very hard for competitors to catch up to. It’s a huge advantage in search, in social, on the Web as a whole.

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

Video transcription by Speechpad.com

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Reblogged 3 years ago from tracking.feedpress.it

How to Combat 5 of the SEO World’s Most Infuriating Problems – Whiteboard Friday

Posted by randfish

These days, most of us have learned that spammy techniques aren’t the way to go, and we have a solid sense for the things we should be doing to rank higher, and ahead of our often spammier competitors. Sometimes, maddeningly, it just doesn’t work. In today’s Whiteboard Friday, Rand talks about five things that can infuriate SEOs with the best of intentions, why those problems exist, and what we can do about them.

For reference, here’s a still of this week’s whiteboard. Click on it to open a high resolution image in a new tab!

What SEO problems make you angry?

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we’re chatting about some of the most infuriating things in the SEO world, specifically five problems that I think plague a lot of folks and some of the ways that we can combat and address those.

I’m going to start with one of the things that really infuriates a lot of new folks to the field, especially folks who are building new and emerging sites and are doing SEO on them. You have all of these best practices list. You might look at a web developer’s cheat sheet or sort of a guide to on-page and on-site SEO. You go, “Hey, I’m doing it. I’ve got my clean URLs, my good, unique content, my solid keyword targeting, schema markup, useful internal links, my XML sitemap, and my fast load speed. I’m mobile friendly, and I don’t have manipulative links.”

Great. “Where are my results? What benefit am I getting from doing all these things, because I don’t see one?” I took a site that was not particularly SEO friendly, maybe it’s a new site, one I just launched or an emerging site, one that’s sort of slowly growing but not yet a power player. I do all this right stuff, and I don’t get SEO results.

This makes a lot of people stop investing in SEO, stop believing in SEO, and stop wanting to do it. I can understand where you’re coming from. The challenge is not one of you’ve done something wrong. It’s that this stuff, all of these things that you do right, especially things that you do right on your own site or from a best practices perspective, they don’t increase rankings. They don’t. That’s not what they’re designed to do.

1) Following best practices often does nothing for new and emerging sites

This stuff, all of these best practices are designed to protect you from potential problems. They’re designed to make sure that your site is properly optimized so that you can perform to the highest degree that you are able. But this is not actually rank boosting stuff unfortunately. That is very frustrating for many folks. So following a best practices list, the idea is not, “Hey, I’m going to grow my rankings by doing this.”

On the flip side, many folks do these things on larger, more well-established sites, sites that have a lot of ranking signals already in place. They’re bigger brands, they have lots of links to them, and they have lots of users and usage engagement signals. You fix this stuff. You fix stuff that’s already broken, and boom, rankings pop up. Things are going well, and more of your pages are indexed. You’re getting more search traffic, and it feels great. This is a challenge, on our part, of understanding what this stuff does, not a challenge on the search engine’s part of not ranking us properly for having done all of these right things.

2) My competition seems to be ranking on the back of spammy or manipulative links

What’s going on? I thought Google had introduced all these algorithms to kind of shut this stuff down. This seems very frustrating. How are they pulling this off? I look at their link profile, and I see a bunch of the directories, Web 2.0 sites — I love that the spam world decided that that’s Web 2.0 sites — article sites, private blog networks, and do follow blogs.

You look at this stuff and you go, “What is this junk? It’s terrible. Why isn’t Google penalizing them for this?” The answer, the right way to think about this and to come at this is: Are these really the reason that they rank? I think we need to ask ourselves that question.

One thing that we don’t know, that we can never know, is: Have these links been disavowed by our competitor here?

I’ve got my HulksIncredibleStore.com and their evil competitor Hulk-tastrophe.com. Hulk-tastrophe has got all of these terrible links, but maybe they disavowed those links and you would have no idea. Maybe they didn’t build those links. Perhaps those links came in from some other place. They are not responsible. Google is not treating them as responsible for it. They’re not actually what’s helping them.

If they are helping, and it’s possible they are, there are still instances where we’ve seen spam propping up sites. No doubt about it.

I think the next logical question is: Are you willing to loose your site or brand? What we don’t see anymore is we almost never see sites like this, who are ranking on the back of these things and have generally less legitimate and good links, ranking for two or three or four years. You can see it for a few months, maybe even a year, but this stuff is getting hit hard and getting hit frequently. So unless you’re willing to loose your site, pursuing their links is probably not a strategy.

Then what other signals, that you might not be considering potentially links, but also non-linking signals, could be helping them rank? I think a lot of us get blinded in the SEO world by link signals, and we forget to look at things like: Do they have a phenomenal user experience? Are they growing their brand? Are they doing offline kinds of things that are influencing online? Are they gaining engagement from other channels that’s then influencing their SEO? Do they have things coming in that I can’t see? If you don’t ask those questions, you can’t really learn from your competitors, and you just feel the frustration.

3) I have no visibility or understanding of why my rankings go up vs down

On my HulksIncredibleStore.com, I’ve got my infinite stretch shorts, which I don’t know why he never wears — he should really buy those — my soothing herbal tea, and my anger management books. I look at my rankings and they kind of jump up all the time, jump all over the place all the time. Actually, this is pretty normal. I think we’ve done some analyses here, and the average page one search results shift is 1.5 or 2 position changes daily. That’s sort of the MozCast dataset, if I’m recalling correctly. That means that, over the course of a week, it’s not uncommon or unnatural for you to be bouncing around four, five, or six positions up, down, and those kind of things.

I think we should understand what can be behind these things. That’s a very simple list. You made changes, Google made changes, your competitors made changes, or searcher behavior has changed in terms of volume, in terms of what they were engaging with, what they’re clicking on, what their intent behind searches are. Maybe there was just a new movie that came out and in one of the scenes Hulk talks about soothing herbal tea. So now people are searching for very different things than they were before. They want to see the scene. They’re looking for the YouTube video clip and those kind of things. Suddenly Hulk’s soothing herbal tea is no longer directing as well to your site.

So changes like these things can happen. We can’t understand all of them. I think what’s up to us to determine is the degree of analysis and action that’s actually going to provide a return on investment. Looking at these day over day or week over week and throwing up our hands and getting frustrated probably provides very little return on investment. Looking over the long term and saying, “Hey, over the last 6 months, we can observe 26 weeks of ranking change data, and we can see that in aggregate we are now ranking higher and for more keywords than we were previously, and so we’re going to continue pursuing this strategy. This is the set of keywords that we’ve fallen most on, and here are the factors that we’ve identified that are consistent across that group.” I think looking at rankings in aggregate can give us some real positive ROI. Looking at one or two, one week or the next week probably very little ROI.

4) I cannot influence or affect change in my organization because I cannot accurately quantify, predict, or control SEO

That’s true, especially with things like keyword not provided and certainly with the inaccuracy of data that’s provided to us through Google’s Keyword Planner inside of AdWords, for example, and the fact that no one can really control SEO, not fully anyway.

You get up in front of your team, your board, your manager, your client and you say, “Hey, if we don’t do these things, traffic will suffer,” and they go, “Well, you can’t be sure about that, and you can’t perfectly predict it. Last time you told us something, something else happened. So because the data is imperfect, we’d rather spend money on channels that we can perfectly predict, that we can very effectively quantify, and that we can very effectively control.” That is understandable. I think that businesses have a lot of risk aversion naturally, and so wanting to spend time and energy and effort in areas that you can control feels a lot safer.

Some ways to get around this are, first off, know your audience. If you know who you’re talking to in the room, you can often determine the things that will move the needle for them. For example, I find that many managers, many boards, many executives are much more influenced by competitive pressures than they are by, “We won’t do as well as we did before, or we’re loosing out on this potential opportunity.” Saying that is less powerful than saying, “This competitor, who I know we care about and we track ourselves against, is capturing this traffic and here’s how they’re doing it.”

Show multiple scenarios. Many of the SEO presentations that I see and have seen and still see from consultants and from in-house folks come with kind of a single, “Hey, here’s what we predict will happen if we do this or what we predict will happen if we don’t do this.” You’ve got to show multiple scenarios, especially when you know you have error bars because you can’t accurately quantify and predict. You need to show ranges.

So instead of this, I want to see: What happens if we do it a little bit? What happens if we really overinvest? What happens if Google makes a much bigger change on this particular factor than we expect or our competitors do a much bigger investment than we expect? How might those change the numbers?

Then I really do like bringing case studies, especially if you’re a consultant, but even in-house there are so many case studies in SEO on the Web today, you can almost always find someone who’s analogous or nearly analogous and show some of their data, some of the results that they’ve seen. Places like SEMrush, a tool that offers competitive intelligence around rankings, can be great for that. You can show, hey, this media site in our sector made these changes. Look at the delta of keywords they were ranking for versus R over the next six months. Correlation is not causation, but that can be a powerful influencer showing those kind of things.

Then last, but not least, any time you’re going to get up like this and present to a group around these topics, if you very possibly can, try to talk one-on-one with the participants before the meeting actually happens. I have found it almost universally the case that when you get into a group setting, if you haven’t had the discussions beforehand about like, “What are your concerns? What do you think is not valid about this data? Hey, I want to run this by you and get your thoughts before we go to the meeting.” If you don’t do that ahead of time, people can gang up and pile on. One person says, “Hey, I don’t think this is right,” and everybody in the room kind of looks around and goes, “Yeah, I also don’t think that’s right.” Then it just turns into warfare and conflict that you don’t want or need. If you address those things beforehand, then you can include the data, the presentations, and the “I don’t know the answer to this and I know this is important to so and so” in that presentation or in that discussion. It can be hugely helpful. Big difference between winning and losing with that.

5) Google is biasing to big brands. It feels hopeless to compete against them

A lot of people are feeling this hopelessness, hopelessness in SEO about competing against them. I get that pain. In fact, I’ve felt that very strongly for a long time in the SEO world, and I think the trend has only increased. This comes from all sorts of stuff. Brands now have the little dropdown next to their search result listing. There are these brand and entity connections. As Google is using answers and knowledge graph more and more, it’s feeling like those entities are having a bigger influence on where things rank and where they’re visible and where they’re pulling from.

User and usage behavior signals on the rise means that big brands, who have more of those signals, tend to perform better. Brands in the knowledge graph, brands growing links without any effort, they’re just growing links because they’re brands and people point to them naturally. Well, that is all really tough and can be very frustrating.

I think you have a few choices on the table. First off, you can choose to compete with brands where they can’t or won’t. So this is areas like we’re going after these keywords that we know these big brands are not chasing. We’re going after social channels or people on social media that we know big brands aren’t. We’re going after user generated content because they have all these corporate requirements and they won’t invest in that stuff. We’re going after content that they refuse to pursue for one reason or another. That can be very effective.

You better be building, growing, and leveraging your competitive advantage. Whenever you build an organization, you’ve got to say, “Hey, here’s who is out there. This is why we are uniquely better or a uniquely better choice for this set of customers than these other ones.” If you can leverage that, you can generally find opportunities to compete and even to win against big brands. But those things have to become obvious, they have to become well-known, and you need to essentially build some of your brand around those advantages, or they’re not going to give you help in search. That includes media, that includes content, that includes any sort of press and PR you’re doing. That includes how you do your own messaging, all of these things.

(C) You can choose to serve a market or a customer that they don’t or won’t. That can be a powerful way to go about search, because usually search is bifurcated by the customer type. There will be slightly different forms of search queries that are entered by different kinds of customers, and you can pursue one of those that isn’t pursued by the competition.

Last, but not least, I think for everyone in SEO we all realize we’re going to have to become brands ourselves. That means building the signals that are typically associated with brands — authority, recognition from an industry, recognition from a customer set, awareness of our brand even before a search has happened. I talked about this in a previous Whiteboard Friday, but I think because of these things, SEO is becoming a channel that you benefit from as you grow your brand rather than the channel you use to initially build your brand.

All right, everyone. Hope these have been helpful in combating some of these infuriating, frustrating problems and that we’ll see some great comments from you guys. I hope to participate in those as well, and we’ll catch you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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Reblogged 3 years ago from tracking.feedpress.it

​The 3 Most Common SEO Problems on Listings Sites

Posted by Dom-Woodman

Listings sites have a very specific set of search problems that you don’t run into everywhere else. In the day I’m one of Distilled’s analysts, but by night I run a job listings site, teflSearch. So, for my first Moz Blog post I thought I’d cover the three search problems with listings sites that I spent far too long agonising about.

Quick clarification time: What is a listings site (i.e. will this post be useful for you)?

The classic listings site is Craigslist, but plenty of other sites act like listing sites:

  • Job sites like Monster
  • E-commerce sites like Amazon
  • Matching sites like Spareroom

1. Generating quality landing pages

The landing pages on listings sites are incredibly important. These pages are usually the primary drivers of converting traffic, and they’re usually generated automatically (or are occasionally custom category pages) .

For example, if I search “Jobs in Manchester“, you can see nearly every result is an automatically generated landing page or category page.

There are three common ways to generate these pages (occasionally a combination of more than one is used):

  • Faceted pages: These are generated by facets—groups of preset filters that let you filter the current search results. They usually sit on the left-hand side of the page.
  • Category pages: These pages are listings which have already had a filter applied and can’t be changed. They’re usually custom pages.
  • Free-text search pages: These pages are generated by a free-text search box.

Those definitions are still bit general; let’s clear them up with some examples:

Amazon uses a combination of categories and facets. If you click on browse by department you can see all the category pages. Then on each category page you can see a faceted search. Amazon is so large that it needs both.

Indeed generates its landing pages through free text search, for example if we search for “IT jobs in manchester” it will generate: IT jobs in manchester.

teflSearch generates landing pages using just facets. The jobs in China landing page is simply a facet of the main search page.

Each method has its own search problems when used for generating landing pages, so lets tackle them one by one.


Aside

Facets and free text search will typically generate pages with parameters e.g. a search for “dogs” would produce:

www.mysite.com?search=dogs

But to make the URL user friendly sites will often alter the URLs to display them as folders

www.mysite.com/results/dogs/

These are still just ordinary free text search and facets, the URLs are just user friendly. (They’re a lot easier to work with in robots.txt too!)


Free search (& category) problems

If you’ve decided the base of your search will be a free text search, then we’ll have two major goals:

  • Goal 1: Helping search engines find your landing pages
  • Goal 2: Giving them link equity.

Solution

Search engines won’t use search boxes and so the solution to both problems is to provide links to the valuable landing pages so search engines can find them.

There are plenty of ways to do this, but two of the most common are:

  • Category links alongside a search

    Photobucket uses a free text search to generate pages, but if we look at example search for photos of dogs, we can see the categories which define the landing pages along the right-hand side. (This is also an example of URL friendly searches!)

  • Putting the main landing pages in a top-level menu

    Indeed also uses free text to generate landing pages, and they have a browse jobs section which contains the URL structure to allow search engines to find all the valuable landing pages.

Breadcrumbs are also often used in addition to the two above and in both the examples above, you’ll find breadcrumbs that reinforce that hierarchy.

Category (& facet) problems

Categories, because they tend to be custom pages, don’t actually have many search disadvantages. Instead it’s the other attributes that make them more or less desirable. You can create them for the purposes you want and so you typically won’t have too many problems.

However, if you also use a faceted search in each category (like Amazon) to generate additional landing pages, then you’ll run into all the problems described in the next section.

At first facets seem great, an easy way to generate multiple strong relevant landing pages without doing much at all. The problems appear because people don’t put limits on facets.

Lets take the job page on teflSearch. We can see it has 18 facets each with many options. Some of these options will generate useful landing pages:

The China facet in countries will generate “Jobs in China” that’s a useful landing page.

On the other hand, the “Conditional Bonus” facet will generate “Jobs with a conditional bonus,” and that’s not so great.

We can also see that the options within a single facet aren’t always useful. As of writing, I have a single job available in Serbia. That’s not a useful search result, and the poor user engagement combined with the tiny amount of content will be a strong signal to Google that it’s thin content. Depending on the scale of your site it’s very easy to generate a mass of poor-quality landing pages.

Facets generate other problems too. The primary one being they can create a huge amount of duplicate content and pages for search engines to get lost in. This is caused by two things: The first is the sheer number of possibilities they generate, and the second is because selecting facets in different orders creates identical pages with different URLs.

We end up with four goals for our facet-generated landing pages:

  • Goal 1: Make sure our searchable landing pages are actually worth landing on, and that we’re not handing a mass of low-value pages to the search engines.
  • Goal 2: Make sure we don’t generate multiple copies of our automatically generated landing pages.
  • Goal 3: Make sure search engines don’t get caught in the metaphorical plastic six-pack rings of our facets.
  • Goal 4: Make sure our landing pages have strong internal linking.

The first goal needs to be set internally; you’re always going to be the best judge of the number of results that need to present on a page in order for it to be useful to a user. I’d argue you can rarely ever go below three, but it depends both on your business and on how much content fluctuates on your site, as the useful landing pages might also change over time.

We can solve the next three problems as group. There are several possible solutions depending on what skills and resources you have access to; here are two possible solutions:

Category/facet solution 1: Blocking the majority of facets and providing external links
  • Easiest method
  • Good if your valuable category pages rarely change and you don’t have too many of them.
  • Can be problematic if your valuable facet pages change a lot

Nofollow all your facet links, and noindex and block category pages which aren’t valuable or are deeper than x facet/folder levels into your search using robots.txt.

You set x by looking at where your useful facet pages exist that have search volume. So, for example, if you have three facets for televisions: manufacturer, size, and resolution, and even combinations of all three have multiple results and search volume, then you could set you index everything up to three levels.

On the other hand, if people are searching for three levels (e.g. “Samsung 42″ Full HD TV”) but you only have one or two results for three-level facets, then you’d be better off indexing two levels and letting the product pages themselves pick up long-tail traffic for the third level.

If you have valuable facet pages that exist deeper than 1 facet or folder into your search, then this creates some duplicate content problems dealt with in the aside “Indexing more than 1 level of facets” below.)

The immediate problem with this set-up, however, is that in one stroke we’ve removed most of the internal links to our category pages, and by no-following all the facet links, search engines won’t be able to find your valuable category pages.

In order re-create the linking, you can add a top level drop down menu to your site containing the most valuable category pages, add category links elsewhere on the page, or create a separate part of the site with links to the valuable category pages.

The top level drop down menu you can see on teflSearch (it’s the search jobs menu), the other two examples are demonstrated in Photobucket and Indeed respectively in the previous section.

The big advantage for this method is how quick it is to implement, it doesn’t require any fiddly internal logic and adding an extra menu option is usually minimal effort.

Category/facet solution 2: Creating internal logic to work with the facets

  • Requires new internal logic
  • Works for large numbers of category pages with value that can change rapidly

There are four parts to the second solution:

  1. Select valuable facet categories and allow those links to be followed. No-follow the rest.
  2. No-index all pages that return a number of items below the threshold for a useful landing page
  3. No-follow all facets on pages with a search depth greater than x.
  4. Block all facet pages deeper than x level in robots.txt

As with the last solution, x is set by looking at where your useful facet pages exist that have search volume (full explanation in the first solution), and if you’re indexing more than one level you’ll need to check out the aside below to see how to deal with the duplicate content it generates.


Aside: Indexing more than one level of facets

If you want more than one level of facets to be indexable, then this will create certain problems.

Suppose you have a facet for size:

  • Televisions: Size: 46″, 44″, 42″

And want to add a brand facet:

  • Televisions: Brand: Samsung, Panasonic, Sony

This will create duplicate content because the search engines will be able to follow your facets in both orders, generating:

  • Television – 46″ – Samsung
  • Television – Samsung – 46″

You’ll have to either rel canonical your duplicate pages with another rule or set up your facets so they create a single unique URL.

You also need to be aware that each followable facet you add will multiply with each other followable facet and it’s very easy to generate a mass of pages for search engines to get stuck in. Depending on your setup you might need to block more paths in robots.txt or set-up more logic to prevent them being followed.

Letting search engines index more than one level of facets adds a lot of possible problems; make sure you’re keeping track of them.


2. User-generated content cannibalization

This is a common problem for listings sites (assuming they allow user generated content). If you’re reading this as an e-commerce site who only lists their own products, you can skip this one.

As we covered in the first area, category pages on listings sites are usually the landing pages aiming for the valuable search terms, but as your users start generating pages they can often create titles and content that cannibalise your landing pages.

Suppose you’re a job site with a category page for PHP Jobs in Greater Manchester. If a recruiter then creates a job advert for PHP Jobs in Greater Manchester for the 4 positions they currently have, you’ve got a duplicate content problem.

This is less of a problem when your site is large and your categories mature, it will be obvious to any search engine which are your high value category pages, but at the start where you’re lacking authority and individual listings might contain more relevant content than your own search pages this can be a problem.

Solution 1: Create structured titles

Set the <title> differently than the on-page title. Depending on variables you have available to you can set the title tag programmatically without changing the page title using other information given by the user.

For example, on our imaginary job site, suppose the recruiter also provided the following information in other fields:

  • The no. of positions: 4
  • The primary area: PHP Developer
  • The name of the recruiting company: ABC Recruitment
  • Location: Manchester

We could set the <title> pattern to be: *No of positions* *The primary area* with *recruiter name* in *Location* which would give us:

4 PHP Developers with ABC Recruitment in Manchester

Setting a <title> tag allows you to target long-tail traffic by constructing detailed descriptive titles. In our above example, imagine the recruiter had specified “Castlefield, Manchester” as the location.

All of a sudden, you’ve got a perfect opportunity to pick up long-tail traffic for people searching in Castlefield in Manchester.

On the downside, you lose the ability to pick up long-tail traffic where your users have chosen keywords you wouldn’t have used.

For example, suppose Manchester has a jobs program called “Green Highway.” A job advert title containing “Green Highway” might pick up valuable long-tail traffic. Being able to discover this, however, and find a way to fit it into a dynamic title is very hard.

Solution 2: Use regex to noindex the offending pages

Perform a regex (or string contains) search on your listings titles and no-index the ones which cannabalise your main category pages.

If it’s not possible to construct titles with variables or your users provide a lot of additional long-tail traffic with their own titles, then is a great option. On the downside, you miss out on possible structured long-tail traffic that you might’ve been able to aim for.

Solution 3: De-index all your listings

It may seem rash, but if you’re a large site with a huge number of very similar or low-content listings, you might want to consider this, but there is no common standard. Some sites like Indeed choose to no-index all their job adverts, whereas some other sites like Craigslist index all their individual listings because they’ll drive long tail traffic.

Don’t de-index them all lightly!

3. Constantly expiring content

Our third and final problem is that user-generated content doesn’t last forever. Particularly on listings sites, it’s constantly expiring and changing.

For most use cases I’d recommend 301’ing expired content to a relevant category page, with a message triggered by the redirect notifying the user of why they’ve been redirected. It typically comes out as the best combination of search and UX.

For more information or advice on how to deal with the edge cases, there’s a previous Moz blog post on how to deal with expired content which I think does an excellent job of covering this area.

Summary

In summary, if you’re working with listings sites, all three of the following need to be kept in mind:

  • How are the landing pages generated? If they’re generated using free text or facets have the potential problems been solved?
  • Is user generated content cannibalising the main landing pages?
  • How has constantly expiring content been dealt with?

Good luck listing, and if you’ve had any other tricky problems or solutions you’ve come across working on listings sites lets chat about them in the comments below!

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Reblogged 3 years ago from tracking.feedpress.it

The Incredible Shrinking SERP – 2015 Edition

Posted by Dr-Pete

In the beginning, there were 10 results, and it was good. Then, came expanded site-links and Google’s 
7-result SERP. Around the middle of 2014, we started to hear reports of SERPs with odd numbers of organic results – 9, 8, 6, 5, and even 4 page-1 results. At first, these were sporadic and hard to replicate, but they quietly expanded. This is a recent 4-result SERP for “autism speaks”:

By some counts, there are as many as 16 non-paid links on this page (not counting images), but by traditional SEO standards, there are only 4 true organic positions for which you can compete. So, what’s going on here? Is it just random, or is there a method to Google’s madness?

It’s all in the news

For a couple of months, I just assumed these strange result counts were some kind of glitch. Then I noticed an unusual pattern. Last October, Google rolled out the 
“In The News” Update. This update expanded news results to many new sources, but it also seemed to change the pattern of when news results appear. This is 28 days of data from MozCast’s Feature Graph (10K queries):

The presence of News results seemed to be cyclical, dipping early in the week and peaking later in the week. I don’t follow News results closely, so it was just a curiosity at first, until I saw another bit of data. This is the average page-1 result count for that same period:

While the scale of the change was much smaller (please note that both graphs have a restricted Y-axis to make the effect more visible), the opposing shapes of the curves seemed like more than a coincidence. As News results increased, the average page-1 organic result count decreased.

It’s a vertical, vertical world

Spot-checking various SERPs, I was able to confirm this effect. If page 1 had a News box, then the organic result count would be decreased by one (to either 9 results or 6, depending on the starting point). Here’s a sample SERP (I’ve removed snippets to simplify the image) for “samsung galaxy tab”:

This is a basic 10-result SERP, but when a News box comes into play, we’re only left with 9 organic results. This raised the question – were other verticals having a similar impact? Digging deeper, I found that, in addition to News results, Image results and In-depth Articles also occupied one organic position. Remember the example at the top of the post? It’s a brand query, resulting in a 7-result SERP, but it also has News results, Image results, and In-depth Articles. If we do the math: 7 – 1 – 1 – 1 = 4 results. It’s not random at all.

In the interest of being more methodical, what if we looked at the average page-1 organic result across every combination of verticals in our data set? We’ll stick with a starting point of 10 results, to keep the data clean. Here’s a table with the average counts by vertical combination:

I’ve taken the average out to two decimal places just to be more transparent, but what we’re seeing here is little more than a tiny bit of measurement error. Generally speaking, each instance of a vertical result type (as a whole, not individual links within these verticals) costs a 10-result SERP one organic ranking position. It’s worth nothing that SERPs with all 3 verticals are pretty rare, but when they occur, each of those 3 verticals costs one position and one opportunity for you to rank on page 1.

It’s always something

So, do the same rules apply to 7-result SERPs? Well, Google isn’t a big fan of making my life easy, so it turns out this gets a bit more complicated. When 7-result SERPs originally launched, our data showed that they almost always came with expanded sitelinks in the #1 organic position. By “expanded sitelinks”, I mean something like the following:

Sitelinks usually appear for queries that either have a strong brand connotation or at least a dominant interpretation. While we typically use 6-packs of expanded sitelinks as an example, actual counts can vary from 1 to 6. Originally, the presence of any sitelinks yielded a 7-result SERP. Now, it’s gotten a bit more complicated, as shown by the table below:

Since each row of sitelinks can contain up to 2 links, the general logic seems to be that 1 row of sitelinks equates to 1 additional organic result. If you have 3 rows of sitelinks, then Google will remove 3 organic results from page 1.

Google’s logic here seems to revolve around the actual display of information and length of the page. As they add some elements, they’re going to subtract others. Since the physical display length of of most elements can vary quite a bit, the rules right now are pretty simplistic, but the core logic seems to be based on constraining the total number of results displayed on page 1.

It’s time to rethink organic

All of this raises a difficult question – what is an organic result? As SEOs, we typically don’t think of vertical results as “organic” by our fairly narrow definition, but they’re much more organic than paid results or even Knowledge Graph. What’s more, Google is starting to blur the lines with verticals.

For example, in the past couple of weeks, Google has redesigned the look of In-depth Articles twice. You might think “So what? It’s just a design change,” but take a closer look. At the end of March, Googled removed the “In-depth articles” header. Here’s an example of the new design (for the query “jobs”):

While the thumbnail images and horizontal dividers still set these results apart somewhat, Google’s intent seems to be to make them appear more organic. Keep in mind, too, that other, organic results use thumbnails as well (including videos and recipes).

Then, just a couple of weeks later (our systems detected this on the morning of April 8th), Google went much farther, removing the thumbnails and even the byline. Here’s part of a screenshot for “Putin”:

Can you spot the true organic results here? They’re the first two – the rest of this screenshot is In-depth Articles. The only real clue, beside the count and source-code markers, is the horizontal divider on either end of the 3-pack. On mobile, even the dividers are gone, as every result is treated like a “card” (see below).

As an SEO, I’m still inclined to call these results “vertical” for two reasons: (1) historical precedent, and (2) these results play by different ranking rules. I think reason #2 is the more important one – In-depth Articles are currently dominated by a core set of big publishers, and the algorithm differs quite a bit from regular, organic results.

It’s only the beginning…

You wanna get really crazy? Let’s look at an entire SERP for “polar” on an Android device (Moto G). This result also includes In-depth Articles (warning: scrolling ahead):

Let’s do the math. For starters, it’s a branded result with expanded sitelinks, so we should have a 7-result page. Remember that those last 3 results are In-depth Articles, so we’ll subtract 1, leaving us with what should be 6 results. See the “app pack” in the middle? That’s an Android-specific vertical, and instead of counting the pack as just 1 result, Google is counting each link as a result. So, we’re only left with 3 traditional organic results on this SERP, despite it being packed with information.

I strongly suspect this trend will continue, and it will probably expand. The definition of “organic” is blurring, and I think that all of these vertical results represent SEO opportunities that can’t be ignored. If we’re stuck in the mindset of only one “true” organic, then our opportunities are going to keep shrinking every day.

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Reblogged 3 years ago from tracking.feedpress.it