UX, Content Quality, and SEO – Whiteboard Friday

Posted by EricEnge

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

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

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

Video transcription

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

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

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

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

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

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

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

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

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

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

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

Thank you, Mozzers. Have a great day.

Video transcription by Speechpad.com

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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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Exposing The Generational Content Gap: Three Ways to Reach Multiple Generations

Posted by AndreaLehr

With more people of all ages online than ever before, marketers must create content that resonates with multiple generations. Successful marketers realize that each generation has unique expectations, values and experiences that influence consumer behaviors, and that offering your audience content that reflects their shared interests is a powerful way to connect with them and inspire them to take action.

We’re in the midst of a generational shift, with
Millennials expected to surpass Baby Boomers in 2015 as the largest living generation. In order to be competitive, marketers need to realize where key distinctions and similarities lie in terms of how these different generations consume content and share it with with others.

To better understand the habits of each generation,
BuzzStream and Fractl surveyed over 1,200 individuals and segmented their responses into three groups: Millennials (born between 1977–1995), Generation X (born between 1965–1976), and Baby Boomers (born between 1946–1964). [Eds note: The official breakdown for each group is as follows: Millennials (1981-1997), Generation X (1965-1980), and Boomers (1946-1964)]

Our survey asked them to identify their preferences for over 15 different content types while also noting their opinions on long-form versus short-form content and different genres (e.g., politics, technology, and entertainment).

We compared their responses and found similar habits and unique trends among all three generations.

Here’s our breakdown of the three key takeaways you can use to elevate your future campaigns:

1. Baby Boomers are consuming the most content

However, they have a tendency to enjoy it earlier in the day than Gen Xers and Millennials.

Although we found striking similarities between the younger generations, the oldest generation distinguished itself by consuming the most content. Over 25 percent of Baby Boomers consume 20 or more hours of content each week. Additional findings:

  • Baby Boomers also hold a strong lead in the 15–20 hours bracket at 17 percent, edging out Gen Xers and Millennials at 12 and 11 percent, respectively
  • A majority of Gen Xers and Millennials—just over 22 percent each—consume between 5 and 10 hours per week
  • Less than 10 percent of Gen Xers consume less than five hours of content a week—the lowest of all three groups

We also compared the times of day that each generation enjoys consuming content. The results show that most of our respondents—over 30 percent— consume content between 8 p.m. and midnight. However, there are similar trends that distinguish the oldest generation from the younger ones:

  • Baby Boomers consume a majority of their content in the morning. Nearly 40 percent of respondents are online between 5 a.m. and noon.
  • The least popular time for most respondents to engage with content online is late at night, between midnight and 5 a.m., earning less than 10 percent from each generation
  • Gen X is the only generation to dip below 10 percent in the three U.S. time zones: 5 a.m. to 9 a.m., 6 to 8 p.m., and midnight to 5 a.m.

When Do We Consume Content

When it comes to which device each generation uses to consume content, laptops are the most common, followed by desktops. The biggest distinction is in mobile usage: Over 50 percent of respondents who use their mobile as their primary device for content consumption are Millennials. Other results reveal:

  • Not only do Baby Boomers use laptops the most (43 percent), but they also use their tablets the most. (40 percent of all primary tablet users are Baby Boomers).
  • Over 25 percent of Millennials use a mobile device as their primary source for content
  • Gen Xers are the least active tablet users, with less than 8 percent of respondents using it as their primary device

Device To Consume Content2. Preferred content types and lengths span all three generations

One thing every generation agrees on is the type of content they enjoy seeing online. Our results reveal that the top four content types— blog articles, images, comments, and eBooks—are exactly the same for Baby Boomers, Gen Xers, and Millennials. Additional comparisons indicate:

  • The least preferred content types—flipbooks, SlideShares, webinars, and white papers—are the same across generations, too (although not in the exact same order)
  • Surprisingly, Gen Xers and Millennials list quizzes as one of their five least favorite content types

Most Consumed Content Type

All three generations also agree on ideal content length, around 300 words. Further analysis reveals:

  • Baby Boomers have the highest preference for articles under 200 words, at 18 percent
  • Gen Xers have a strong preference for articles over 500 words compared to other generations. Over 20 percent of respondents favor long-form articles, while only 15 percent of Baby Boomers and Millennials share the same sentiment.
  • Gen Xers also prefer short articles the least, with less than 10 percent preferring articles under 200 words

Content Length PreferencesHowever, in regards to verticals or genres, where they consume their content, each generation has their own unique preference:

  • Baby Boomers have a comfortable lead in world news and politics, at 18 percent and 12 percent, respectively
  • Millennials hold a strong lead in technology, at 18 percent, while Baby Boomers come in at 10 percent in the same category
  • Gen Xers fall between Millennials and Baby Boomers in most verticals, although they have slight leads in personal finance, parenting, and healthy living
  • Although entertainment is the top genre for each generation, Millennials and Baby Boomers prefer it slightly more than than Gen Xers do

Favorite Content Genres

3. Facebook is the preferred content sharing platform across all three generations

Facebook remains king in terms of content sharing, and is used by about 60 percent of respondents in each generation studied. Surprisingly, YouTube came in second, followed by Twitter, Google+, and LinkedIn, respectively. Additional findings:

  • Baby Boomers share on Facebook the most, edging out Millennials by only a fraction of a percent
  • Although Gen Xers use Facebook slightly less than other generations, they lead in both YouTube and Twitter, at 15 percent and 10 percent, respectively
  • Google+ is most popular with Baby Boomers, at 8 percent, nearly double that of both Gen Xers and Millennials

Preferred Social PlatformAlthough a majority of each generation is sharing content on Facebook, the type of content they are sharing, especially visuals, varies by each age group. The oldest generation prefers more traditional content, such as images and videos. Millennials prefer newer content types, such as memes and GIFs, while Gen X predictably falls in between the two generations in all categories except SlideShares. Other findings:

  • The most popular content type for Baby Boomers is video, at 27 percent
  • Parallax is the least popular type for every generation, earning 1 percent or less in each age group
  • Millennials share memes the most, while less than 10 percent of Baby Boomers share similar content

Most Shared Visual ContentMarketing to several generations can be challenging, given the different values and ideas that resonate with each group. With the number of online content consumers growing daily, it’s essential for marketers to understand the specific types of content that each of their audiences connect with, and align it with their content marketing strategy accordingly.

Although there is no one-size-fits-all campaign, successful marketers can create content that multiple generations will want to share. If you feel you need more information getting started, you can review this deck of additional insights, which includes the preferred video length and weekend consuming habits of each generation discussed in this post.

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Inverse Document Frequency and the Importance of Uniqueness

Posted by EricEnge

In my last column, I wrote about how to use term frequency analysis in evaluating your content vs. the competition’s. Term frequency (TF) is only one part of the TF-IDF approach to information retrieval. The other part is inverse document frequency (IDF), which is what I plan to discuss today.

Today’s post will use an explanation of how IDF works to show you the importance of creating content that has true uniqueness. There are reputation and visibility reasons for doing this, and it’s great for users, but there are also SEO benefits.

If you wonder why I am focusing on TF-IDF, consider these words from a Google article from August 2014: “This is the idea of the famous TF-IDF, long used to index web pages.” While the way that Google may apply these concepts is far more than the simple TF-IDF models I am discussing, we can still learn a lot from understanding the basics of how they work.

What is inverse document frequency?

In simple terms, it’s a measure of the rareness of a term. Conceptually, we start by measuring document frequency. It’s easiest to illustrate with an example, as follows:

IDF table

In this example, we see that the word “a” appears in every document in the document set. What this tells us is that it provides no value in telling the documents apart. It’s in everything.

Now look at the word “mobilegeddon.” It appears in 1,000 of the documents, or one thousandth of one percent of them. Clearly, this phrase provides a great deal more differentiation for the documents that contain them.

Document frequency measures commonness, and we prefer to measure rareness. The classic way that this is done is with a formula that looks like this:

idf equation

For each term we are looking at, we take the total number of documents in the document set and divide it by the number of documents containing our term. This gives us more of a measure of rareness. However, we don’t want the resulting calculation to say that the word “mobilegeddon” is 1,000 times more important in distinguishing a document than the word “boat,” as that is too big of a scaling factor.

This is the reason we take the Log Base 10 of the result, to dampen that calculation. For those of you who are not mathematicians, you can loosely think of the Log Base 10 of a number as being a count of the number of zeros – i.e., the Log Base 10 of 1,000,000 is 6, and the log base 10 of 1,000 is 3. So instead of saying that the word “mobilegeddon” is 1,000 times more important, this type of calculation suggests it’s three times more important, which is more in line with what makes sense from a search engine perspective.

With this in mind, here are the IDF values for the terms we looked at before:

idf table logarithm values

Now you can see that we are providing the highest score to the term that is the rarest.

What does the concept of IDF teach us?

Think about IDF as a measure of uniqueness. It helps search engines identify what it is that makes a given document special. This needs to be much more sophisticated than how often you use a given search term (e.g. keyword density).

Think of it this way: If you are one of 6.78 million web sites that comes up for the search query “super bowl 2015,” you are dealing with a crowded playing field. Your chances of ranking for this term based on the quality of your content are pretty much zero.

massive number of results for broad keyword

Overall link authority and other signals will be the only way you can rank for a term that competitive. If you are a new site on the landscape, well, perhaps you should chase something else.

That leaves us with the question of what you should target. How about something unique? Even the addition of a simple word like “predictions”—changing our phrase to “super bowl 2015 predictions”—reduces this playing field to 17,800 results.

Clearly, this is dramatically less competitive already. Slicing into this further, the phrase “super bowl 2015 predictions and odds” returns only 26 pages in Google. See where this is going?

What IDF teaches us is the importance of uniqueness in the content we create. Yes, it will not pay nearly as much money to you as it would if you rank for the big head term, but if your business is a new entrant into a very crowded space, you are not going to rank for the big head term anyway

If you can pick out a smaller number of terms with much less competition and create content around those needs, you can start to rank for these terms and get money flowing into your business. This is because you are making your content more unique by using rarer combinations of terms (leveraging what IDF teaches us).

Summary

People who do keyword analysis are often wired to pursue the major head terms directly, simply based on the available keyword search volume. The result from this approach can, in fact, be pretty dismal.

Understanding how inverse document frequency works helps us understand the importance of standing out. Creating content that brings unique angles to the table is often a very potent way to get your SEO strategy kick-started.

Of course, the reasons for creating content that is highly differentiated and unique go far beyond SEO. This is good for your users, and it’s good for your reputation, visibility, AND also your SEO.

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 4 years ago from tracking.feedpress.it

How to Create Boring-Industry Content that Gets Shared

Posted by ronell-smith

If you think creating content for boring industries is tough, try creating content for an expensive product that’ll be sold in a so-called boring industry. Such was the problem faced by Mike Jackson, head of sales for a large Denver-based company that was debuting a line of new high-end products for the fishing industry in 2009.

After years of pestering the executives of his traditional, non-flashy company to create a line of products that could be sold to anglers looking to buy premium items, he finally had his wish: a product so expensive only a small percentage of anglers could afford them.

(image source)

What looked like being boxed into a corner was actually part of the plan.

When asked how he could ever put his neck on the line for a product he’d find tough to sell and even tougher to market, he revealed his brilliant plan.

“I don’t need to sell one million of [these products] a year,” he said. “All I need to do is sell a few hundred thousand, which won’t be hard. And as far as marketing, that’s easy: I’m ignoring the folks who’ll buy the items. I’m targeting professional anglers, the folks the buyers are influenced by. If the pros, the influencers, talk about and use the products, people will buy them.”

Such was my first introduction to how it’s often wise to ignore who’ll buy the product in favor of marketing to those who’ll help you market and sell the product.

These influencers are a sweet spot in product marketing and they are largely ignored by many brands

Looking at content for boring industries all wrong

A few months back, I received a message in Google Plus that really piqued my interest: “What’s the best way to create content for my boring business? Just kidding. No one will read it, nor share information from a painter anyway.”

I went from being dismayed to disheartened. Dismayed because the business owner hadn’t yet found a way to connect with his prospects through meaningful content. Disheartened because he seemed to have given up trying.

You can successfully create content for boring industries. Doing so requires nothing out of the ordinary from what you’d normally do to create content for any industry. That’s the good news.

The bad news: Creating successful content for boring industries requires you think beyond content and SEO, focusing heavily on content strategy and outreach.

Successfully creating content for boring industries—or any industry, for that matter—comes down to who’ll share it and who’ll link to it, not who’ll read it, a point nicely summed up in this tweet:

So when businesses struggle with creating content for their respective industries, the culprits are typically easy to find:

  • They lack clarity on who they are creating content for (e.g., content strategy, personas)
  • There are no specific goals (e.g., traffic, links, conversions, etc.) assigned regarding the content, so measuring its effectiveness is impossible
  • They’re stuck in neutral thinking viral content is the only option, while ignoring the value of content amplification (e.g., PR/outreach)

Alone, these three elements are bad; taken together, though, they spell doom for your brand.

content does not equal amplification

If you lack clarity on who you’re creating content for, the best you can hope for is that sometimes you’ll create and share information members of your audience find useful, but you likely won’t be able to reach or engage them with the needed frequency to make content marketing successful.

Goals, or lack thereof, are the real bugaboo of content creation. The problem is even worse for boring industries, where the pressure is on to deliver a content vehicle that meets the threshold of interest to simply gain attention, much less, earn engagement.

For all the hype about viral content, it’s dismaying that so few marketers aren’t being honest on the topic: it’s typically hard to create, impossible to predict and typically has very, very little connection to conversions for most businesses.

What I’ve found is that businesses, regardless of category, struggle to create worthwhile content, leading me to believe there is no boring industry content, only content that’s boring.

“Whenever we label content as ‘boring,’ we’re really admitting we have no idea how to approach marketing something,” says Builtvisible’s Richard Baxter.

Now that we know what the impediments are to producing content for any industry, including boring industries, it’s time to tackle the solution.

Develop a link earning mindset

There are lots of article on the web regarding how to create content for boring industries, some of which have appeared on this very blog.

But, to my mind, the one issue they all suffer from is they all focus on what content should be created, not (a) what content is worthy of promotion, (b) how to identify those who could help with promotion, and (c) how to earn links from boring industry content. (Remember, much of the content that’s read is never shared; much of what’s shared is never read in its entirety; and some of the most linked-to content is neither heavily shared nor heavily read.)

This is why content creators in boring industries should scrap their notions of having the most-read and most-shared content, shifting their focus to creating content that can earn links in addition to generating traffic and social signals to the site.

After all, links and conversions are the main priorities for most businesses sharing content online, including so-called local businesses.

ranking factors survey results

(Image courtesy of the 2014 Moz Local Search Ranking Factors Survey)

If you’re ready to create link-earning, traffic-generating content for your boring-industry business follow the tips from the fictitious example of RZ’s Auto Repair, a Dallas, Texas, automobile shop.

With the Dallas-Forth Worth market being large and competitive, RZ’s has narrowed their speciality to storm repair, mainly hail damage, which is huge in the area. Even with the narrowed focus, however, they still have stiff competition from the major players in the vertical, including MAACO.

What the brand does have in its favor, however, is a solid website and a strong freelance copywriter to help produce content.

Remember, those three problems we mentioned above—lack of goals, lack of clarity and lack of focus on amplification—we’ll now put them to good use to drive our main objectives of traffic, links and conversions.

Setting the right goals

For RZ, this is easy: He needs sales, business (e.g., qualified leads and conversions), but he knows he must be patient since using paid media is not in the cards.

Therefore, he sits down with his partner, and they come up with what seems like the top five workable, important goals:

  1. Increased traffic on the website – He’s noticed that when traffic increases, so does his business.
  2. More phone calls – If they get a customer on the phone, the chances of closing the sale are around 75%.
  3. One blog per week on the site – The more often he blogs, the more web traffic, visits and phone calls increase.
  4. Links from some of the businesses in the area – He’s no dummy. He knows the importance of links, which are that much better when they come from a large company that could send him business.
  5. Develop relationships with small and midsize non-competing businesses in the area for cross promotions, events and the like.

Know the audience

marketing group discussing personas

(image source)

Too many businesses create cute blogs that might generate traffic but do nothing for sales. RZ isn’t falling for this trap. He’s all about identifying the audience who’s likely to do business with him.

Luckily, his secretary is a meticulous record keeper, allowing him to build a reasonable profile of his target persona based on past clients.

  • 21-35 years old
  • Drives a truck that’s less than fours years old
  • Has an income of $45,000-$59,000
  • Employed by a corporation with greater than 500 employees
  • Active on social media, especially Facebook and Twitter
  • Consumes most of their information online
  • Typically referred by a friend or a co-worker

This information will prove invaluable as he goes about creating content. Most important, these nuggets create a clearer picture of how he should go about looking for people and/or businesses to amplify his content.

PR and outreach: Your amplification engines

Armed with his goals and the knowledge of his audience, RZ can now focus on outreach for amplification, thinking along the lines of…

  • Who/what influences his core audience?
  • What could he offer them by way of content to earn their help?
  • What content would they find valuable enough to share and link to?
  • What challenges do they face that he could help them with?
  • How could his brand set itself apart from any other business looking for help from these potential outreach partners?

Putting it all together

Being the savvy businessperson he is, RZ pulls his small staff together and they put their thinking caps on.

Late spring through early fall is prime hail storm season in Dallas. The season accounts for 80 percent of his yearly business. (The other 20% is fender benders.) Also, they realize, many of the storms happen in the late afternoon/early evening, when people are on their way home from work and are stuck in traffic, or when they duck into the grocery store or hit the gym after work.

What’s more, says one of the staffers, often a huge group of clients will come at once, owing to having been parked in the same lot when a storm hits.

Eureka!

lightbulb

(image source)

That’s when RZ bolts out of his chair with the idea that could put his business on the map: Let’s create content for businesses getting a high volume of after-work traffic—sit-down restaurants, gyms, grocery stores, etc.

The businesses would be offering something of value to their customers, who’ll learn about precautions to take in the event of a hail storm, and RZ would have willing amplifiers for his content.

Content is only as boring as your outlook

First—and this is a fatal mistake too many content creators make—RZ visits the handful of local businesses he’d like to partner with. The key here, however, is he smartly makes them aware that he’s done his homework and is eager to help their patrons while making them aware of his service.

This is an integral part of outreach: there must be a clear benefit to the would-be benefactor.

After RZ learns that several of the businesses are amenable to sharing his business’s helpful information, he takes the next step and asks what form the content should take. For now, all he can get them to promote is a glossy one-sheeter, “How To Protect Your Vehicle Against Extensive Hail Damage,” that the biggest gym in the area will promote via a small display at the check-in in return for a 10% coupon for customers.

Three of the five others he talked to also agreed to promote the one-sheeter, though each said they’d be willing to promote other content investments provided they added value for their customers.

The untold truth about creating content for boring industries

When business owners reach out to me about putting together a content strategy for their boring brand, I make two things clear from the start:

  1. There are no boring brands. Those two words are a cop out. No matter what industry you serve, there are hoards of people who use the products or services who are quite smitten.
  2. What they see as boring, I see as an opportunity.

In almost every case, they want to discuss some of another big content piece that’s sure to draw eyes, engagement, and that maybe even leads to a few links. Sure, I say, if you have tons of money to spend.

big content example

(Amazing piece of interactive content created by BuiltVisible)

Assuming you don’t have money to burn, and you want a plan you can replicate easily over time, try what I call the 1-2-1 approach for monthly blog content:

1: A strong piece of local content (goal: organic reach, topical relevance, local SEO)

2: Two pieces of evergreen content (goal: traffic)

1: A link-worthy asset (goal: links)

This plan is not very hard at all to pull off, provided you have your ear to the street in the local market; have done your keyword research, identifying several long-tail keywords you have the ability to rank for; and you’re willing to continue with outreach.

What it does is allow the brand to create content with enough frequency to attain significance with the search engines, while also developing the habit of sharing, promoting and amplifying content as well. For example, all of the posts would be shared on Twitter, Google Plus, and Facebook. (Don’t sleep on paid promotion via Facebook.)

Also, for the link-worthy asset, there would be outreach in advance of its creation, then amplification, and continued promotion from the company and those who’ve agreed to support the content.

Create a winning trifecta: Outreach, promotion and amplification

To RZ’s credit, he didn’t dawdle, getting right to work creating worthwhile content via the 1-2-1 method:

1: “The Worst Places in Dallas to be When a Hail Storm Hits”
2: “Can Hail Damage Cause Structural Damage to Your Car?” and “Should You Buy a Car Damaged by Hail?”
1: “Big as Hail!” contest

This contest idea came from the owner of a large local gym. RZ’s will give $500 to the local homeowner who sends in the largest piece of hail, as judged by Facebook fans, during the season. In return, the gym will promote the contest at its multiple locations, link to the content promotion page on RZ’s website, and share images of its fans holding large pieces of hail via social media.

What does the gym get in return: A catchy slogan (e.g., it’s similar to “big as hell,” popular gym parlance) to market around during the hail season.

It’s a win-win for everyone involved, especially RZ.

He gets a link, but most important he realizes how to create content to nail each one of his goals. You can do the same. All it takes is a change in mindset. Away from content creation. Toward outreach, promote and amplify.

Summary

While the story of RZ’s entirely fictional, it is based on techniques I’ve used with other small and midsize businesses. The keys, I’ve found, are to get away from thinking about your industry/brand as being boring, even if it is, and marshal the resources to find the audience who’ll benefit from from your content and, most important, identify the influencers who’ll promote and amplify it.

What are your thoughts?

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Reblogged 4 years ago from tracking.feedpress.it

Using Term Frequency Analysis to Measure Your Content Quality

Posted by EricEnge

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

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

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

Conceptualizing page quality

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

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

Here are some things they might want:

  1. A way to buy golf clubs on your site (you would need to see a shopping cart of some sort).
  2. The ability to select specific brands, perhaps by links to other pages about those brands of golf clubs.
  3. Information on how to pick the club that is best for them.
  4. The ability to select specific types of clubs (drivers, putters, irons, etc.). Again, this may be via links to other pages.
  5. A site search box.
  6. Pricing info.
  7. Info on shipping costs.
  8. Expert analysis comparing different golf club brands.
  9. End user reviews of your company so they can determine if they want to do business with you.
  10. How your return policy works.
  11. How they can file a complaint.
  12. Information about your company. Perhaps an “about us” page.
  13. A link to a privacy policy page.
  14. Whether or not you have been “in the news” recently.
  15. Trust symbols that show that you are a reputable organization.
  16. A way to access pages to buy different products, such as golf balls or tees.
  17. Information about specific golf courses.
  18. Tips on how to improve their golf game.

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

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

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

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

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

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

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

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

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

Using TF analysis to evaluate your page

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

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

term frequency calculation

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

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

term frequency spreadsheet

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

  1. Are there any highly related words that all or most of your competitors are using that you don’t use at all?
  2. Are there any such words that you use significantly less, on average, than your competitors?
  3. Also look for words that you use significantly more than competitors.

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

second stage term frequency analysis spreadsheet

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

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

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

  1. It’s missing any mention of actor ryan reynolds
  2. It’s missing any mention of actor helen mirren
  3. The page has no reviews
  4. Words like “family” and “story” are not mentioned
  5. “Austrian” and “maria altmann” are not used at all
  6. The phrase “woman in gold” and words “billing” and “info” are used proportionally more than they are with the other pages

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

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

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

  1. A page that had no privacy policy, yet was taking personally identifiable info from users.
  2. A major lack of important synonyms that would indicate a real depth of available content.
  3. Comparatively low Domain Authority competitors ranking with in-depth content.

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

How do you execute this for yourself?

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

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

  1. Run all the URLs you are testing through the keyword density tool.
  2. Copy and paste all the one word, two word, and three word results into a tab on the spreadsheet.
  3. Sort them all so you get total word counts aligned by position as I have shown in the linked spreadsheet.
  4. Set up the formulas as I did in the demo spreadsheet (you can just use the demo spreadsheet).
  5. Then do your analysis!

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

Summary

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

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

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 4 years ago from tracking.feedpress.it

​1 Day After Mobilegeddon: How Far Did the Sky Fall?

Posted by Dr-Pete

Even clinging to the once towering bridge, the only thing Kayce could see was desert. Yesterday, San Francisco hummed with life, but now there was nothing but the hot hiss of the wind. Google’s Mobilegeddon blew out from Mountain View like Death’s last exhale, and for the first time since she regained consciousness, Kayce wondered if she was the last SEO left alive.

We have a penchant for melodrama, and the blogosphere loves a conspiracy, but after weeks of speculation bordering on hysteria, it’s time to see what the data has to say about Google’s Mobile Update. We’re going to do something a little different – this post will be updated periodically as new data comes in. Stay tuned to this post/URL.

If you watch MozCast, you may be unimpressed with this particular apocalypse:

Temperatures hit 66.1°F on the first official day of Google’s Mobile Update (the system is tuned to an average of 70°F). Of course, the problem is that this system only measures desktop temperatures, and as we know, Google’s Mobile Update should only impact mobile SERPs. So, we decided to build a MozCast Mobile, that would separately track mobile SERPs (Android, specifically) across the same 10K keyword set. Here’s what we saw for the past 7 days on MozCast Mobile:

While the temperature across mobile results on April 21st was slightly higher (73.7°F), you’ll also notice that most of the days are slightly higher and the pattern of change is roughly the same. It appears that the first day of the Mobile Update was a relatively quiet day.

There’s another metric we can look at, though. Since building MozCast Mobile, we’ve also been tracking how many page-1 URLs show the “Mobile-friendly” tag. Presumably, if mobile-friendly results are rewarded, we’ll expect that number to jump. Here’s the last 7 days of that stat:

As of the morning of April 22nd, 70.1% of the URLs we track carried the “Mobile-friendly” tag. That sounds like a lot, but that number hasn’t changed much the past few days. Interestingly, the number has creeped up over the past 2 weeks from a low of 66.3%. It’s unclear whether this is due to changes Google made or changes webmasters made, but I suspect this small uptick indicates sites making last minute changes to meet the mobile deadline. It appears Google is getting what they want from us, one way or another.

Tracking a long roll-out

Although Google has repeatedly cited April 21st, they’ve also said that this update could take days or weeks. If an update is spread out over weeks, can we accurately measure the flux? The short answer is: not very well. We can measure flux over any time-span, but search results naturally change over time – we have no real guidance to tell us what’s normal over longer periods.

The “Mobile-friendly” tag tracking is one solution – this should gradually increase – but there’s another metric we can look at. If mobile results continue to diverge from desktop results, than the same-day flux between the two sets of results should increase. In other words, mobile results should get increasingly different from desktop results with each day of the roll-out. Here’s what that cross-flux looks like:

I’m using raw flux data here, since the temperature conversion isn’t calibrated to this data. This comparison is tricky, because many sites use different URLs for mobile vs. desktop. I’ve stripped out the obvious cases (“m.” and “mobile.” sub-domains), but that still leaves a lot of variants.

Historically, we’re not seeing much movement on April 21st. The bump on April 15-16 is probably an error – Google made a change to In-depth Articles on mobile that created some bad data. So, again, not much going on here, but this should give us a view to see compounding changes over time.

Tracking potential losers

No sites are reporting major hits yet, but by looking at the “Mobile-friendly” tag for the top domains in MozCast Mobile, we can start to piece together who might get hit by the update. Here are the top 20 domains (in our 10K data set) as of April 21st, along with the percent of their ranking URLs that are tagged as mobile-friendly:

    1. en.m.wikipedia.org — 96.3%
    2. www.amazon.com — 62.3%
    3. m.facebook.com — 100.0%
    4. m.yelp.com — 99.9%
    5. m.youtube.com — 27.8%
    6. twitter.com — 99.8%
    7. www.tripadvisor.com — 92.5%
    8. www.m.webmd.com — 100.0%
    9. mobile.walmart.com — 99.5%
    10. www.pinterest.com — 97.5%
    11. www.foodnetwork.com — 69.9%
    12. www.ebay.com — 97.7%
    13. www.mayoclinic.org — 100.0%
    14. m.allrecipes.com — 97.1%
    15. m.medlineplus.gov — 100.0%
    16. www.bestbuy.com — 90.2%
    17. www.overstock.com — 98.6%
    18. m.target.com — 41.4%
    19. www.zillow.com — 99.6%
    20. www.irs.gov — 0.0%

I’ve bolded any site under 75% – the IRS is our big Top 20 trouble spot, although don’t expect IRS.gov to stop ranking at tax-time soon. Interestingly, YouTube’s mobile site only shows as mobile-friendly about a quarter of the time in our data set – this will be a key case to watch. Note that Google could consider a site mobile-friendly without showing the “Mobile-friendly” tag, but it’s the simplest/best proxy we have right now.

Changes beyond rankings

It’s important to note that, in many ways, mobile SERPs are already different from desktop SERPs. The most striking difference is design, but that’s not the only change. For examples, Google recently announced that they would be dropping domains in mobile display URLs. Here’s a sample mobile result from my recent post:

Notice the display URL, which starts with the brand name (“Moz”) instead of our domain name. That’s followed by a breadcrumb-style URL that uses part of the page name. Expect this to spread, and possibly even hit desktop results in the future.

While Google has said that vertical results wouldn’t change with the April 21st update, that statement is a bit misleading when it comes to local results. Google already uses different styles of local pack results for mobile, and those pack results appear in different proportions. For example, here’s a local “snack pack” on mobile (Android):

Snack packs appear in only 1.5% of the local rankings we track for MozCast Desktop, but they’re nearly 4X as prevalent (6.0%) on MozCast Mobile (for the same keywords and locations). As these new packs become more prevalent, they take away other styles of packs, and create new user behavior. So, to say local is the same just because the core algorithm may be the same is misleading at best.

Finally, mobile adds entirely new entities, like app packs on Android (from a search for “jobs”):

These app packs appear on a full 8.4% of the mobile SERPs we’re tracking, including many high-volume keywords. As I noted in my recent post, these app packs also consume page-1 organic slots.

A bit of good news

If you’re worried that you may be too late to the mobile game, it appears there is some good news. Google will most likely reprocess new mobile-friendly pages quickly. Just this past few days, Moz redesigned our blog to be mobile friendly. In less than 24 hours, some of our main blog pages were already showing the “Mobile-friendly” tag:

However big this update ultimately ends up being, Google’s push toward mobile-first design and their clear public stance on this issue strongly signal that mobile-friendly sites are going to have an advantage over time.

Stay tuned to this post (same URL) for the next week or two – I’ll be updating charts and data as the Mobile Update continues to roll out. If the update really does take days or weeks, we’ll do our best to measure the long-term impact and keep you informed.

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Reblogged 4 years ago from tracking.feedpress.it