Moz Local Officially Launches in the UK

Posted by David-Mihm

To all Moz Local fans in the UK, I’m excited to announce that your wait is over. As the sun rises “across the pond” this morning, Moz Local is officially live in the United Kingdom!

A bit of background

As many of you know, we released the US version of Moz Local in March 2014. After 12 months of terrific growth in the US, and a boatload of technical improvements and feature releases–especially for Enterprise customers–we released the Check Listing feature for a limited set of partner search engines and directories in the UK in April of this year.

Over 20,000 of you have checked your listings (or your clients’ listings) in the last 3-1/2 months. Those lookups have helped us refine and improve the background technology immensely (more on that below). We’ve been just as eager to release the fully-featured product as you’ve been to use it, and the technical pieces have finally fallen into place for us to do so.

How does it work?

The concept is the same as the US version of Moz Local: show you how accurately and completely your business is listed on the most important local search platforms and directories, and optimize and perfect as many of those business listings as we can on your behalf.

For customers specifically looking for you, accurate business listings are obviously important. For customers who might not know about you yet, they’re also among the most important factors for ranking in local searches on Google. Basically, the more times Google sees your name, address, phone, and website listed the same way on quality local websites, the more trust they have in your business, and the higher you’re likely to rank.

Moz Local is designed to help on both these fronts.

To use the product, you simply need to type a name and postcode at moz.com/local. We’ll then show you a list of the closest matching listings we found. We prioritize verified listing information that we find on Google or Facebook, and selecting one of those verified listings means we’ll be able to distribute it on your behalf.

Clicking on a result brings you to a full details report for that listing. We’ll show you how accurate and complete your listings are now, and where they could be after using our product.

Clicking the tabs beneath the Listing Score graphic will show you some of the incompletions and inconsistencies that publishing your listing with Moz Local will address.

For customers with hundreds or thousands of locations, bulk upload is also available using a modified version of your data from Google My Business–feel free to e-mail enterpriselocal@moz.com for more details.

Where do we distribute your data?

We’ve prioritized the most important commercial sites in the UK local search ecosystem, and made them the centerpieces of Moz Local. We’ll update your data directly on globally-important players Factual and Foursquare, and the UK-specific players CentralIndex, Thomson Local, and the Scoot network–which includes key directories like TouchLocal, The Independent, The Sun, The Mirror, The Daily Scotsman, and Wales Online.

We’ll be adding two more major destinations shortly, and for those of you who sign up before that time, your listings will be automatically distributed to the additional destinations when the integrations are complete.

How much does it cost?

The cost per listing is £84/year, which includes distribution to the sites mentioned above with unlimited updates throughout the year, monitoring of your progress over time, geographically- focused reporting, and the ability to find and close duplicate listings right from your Moz Local dashboard–all the great upgrades that my colleague Noam Chitayat blogged about here.

What’s next?

Well, as I mentioned just a couple paragraphs ago, we’ve got two additional destinations to which we’ll be sending your data in very short order. Once those integrations are complete, we’ll be just a few weeks away from releasing our biggest set of features since we launched. I look forward to sharing more about these features at BrightonSEO at the end of the summer!

For those of you around the world in Canada, Australia, and other countries, we know there’s plenty of demand for Moz Local overseas, and we’re working as quickly as we can to build additional relationships abroad. And to our friends in the UK, please let us know how we can continue to make the product even better!

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 3 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.

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 3 years ago from tracking.feedpress.it

Your Daily SEO Fix

Posted by Trevor-Klein

We at the Mozplex have noticed a recurring event. Somewhat regularly, one of our community members—sometimes even a Pro subscriber—will ask us if we know of any tools that’ll do a good job solving for a particular use case. They’ve got a need and are looking for a solution. That solution, it turns out, is available in our own tools—they just never made the connection.

This week, we began a series of video tutorials we’re calling the Moz Daily SEO Fix. The videos are shorter than two minutes each and are designed to offer you solutions to some of the most common problems faced by SEOs and online marketers of all stripes. A new video will be released every weekday for a month, and we’ll post a weekly roundup on Thursday afternoons.

Whether you’re a seasoned veteran of the old SEOmoz days or have never once used a Moz tool, we hope these videos will show you a way to make your marketing life a little easier. =)


Fix 1: How to reclaim links using Open Site Explorer

In today’s Daily SEO Fix, David explains how to use the Open Site Explorer’s top pages tab plus the filter for 4xx and 5xx errors to find the pages on your site with the most potential link equity that are broken and can be redirected. 301’ing these URLs to relevant pages on your site can give your rankings a serious boost.

.video-container {
position: relative;
padding-bottom: 56.25%;
padding-top: 30px; height: 0; overflow: hidden;
}
.video-container iframe,
.video-container object,
.video-container embed {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
}


Fix 2: How to build links using Fresh Web Explorer

In this Daily SEO Fix, Michael shows you how to set up an alert in Fresh Web Explorer for anyone who mentions (or links to) your two biggest competitors but not to you. Monitor your inbox for these alerts and you’ll find new link building opportunities, ripe for the picking.


Fix 3: How to find the best times to tweet using Followerwonk

Finding the best time to tweet is unique for everyone and figuring out what times work best for you is key to maximizing your presence on Twitter. In this Daily SEO Fix, Ellie shows you how to use Followerwonk to find the best times to tweet so your followers don’t miss out on your updates.


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

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 3 years ago from tracking.feedpress.it

How Much Has Link Building Changed in Recent Years?

Posted by Paddy_Moogan

I get asked this question a lot. It’s mainly asked by people who are considering buying my link building book and want to know whether it’s still up to date. This is understandable given that the first edition was published in February 2013 and our industry has a deserved reputation for always changing.

I find myself giving the same answer, even though I’ve been asked it probably dozens of times in the last two years—”not that much”. I don’t think this is solely due to the book itself standing the test of time, although I’ll happily take a bit of credit for that 🙂 I think it’s more a sign of our industry as a whole not changing as much as we’d like to think.

I started to question myself and if I was right and honestly, it’s one of the reasons it has taken me over two years to release the second edition of the book.

So I posed this question to a group of friends not so long ago, some via email and some via a Facebook group. I was expecting to be called out by many of them because my position was that in reality, it hasn’t actually changed that much. The thing is, many of them agreed and the conversations ended with a pretty long thread with lots of insights. In this post, I’d like to share some of them, share what my position is and talk about what actually has changed.

My personal view

Link building hasn’t changed as much we think it has.

The core principles of link building haven’t changed. The signals around link building have changed, but mainly around new machine learning developments that have indirectly affected what we do. One thing that has definitely changed is the mindset of SEOs (and now clients) towards link building.

I think the last big change to link building came in April 2012 when Penguin rolled out. This genuinely did change our industry and put to bed a few techniques that should never have worked so well in the first place.

Since then, we’ve seen some things change, but the core principles haven’t changed if you want to build a business that will be around for years to come and not run the risk of being hit by a link related Google update. For me, these principles are quite simple:

  • You need to deserve links – either an asset you create or your product
  • You need to put this asset in front of a relevant audience who have the ability to share it
  • You need consistency – one new asset every year is unlikely to cut it
  • Anything that scales is at risk

For me, the move towards user data driving search results + machine learning has been the biggest change we’ve seen in recent years and it’s still going.

Let’s dive a bit deeper into all of this and I’ll talk about how this relates to link building.

The typical mindset for building links has changed

I think that most SEOs are coming round to the idea that you can’t get away with building low quality links any more, not if you want to build a sustainable, long-term business. Spammy link building still works in the short-term and I think it always will, but it’s much harder than it used to be to sustain websites that are built on spam. The approach is more “churn and burn” and spammers are happy to churn through lots of domains and just make a small profit on each one before moving onto another.

For everyone else, it’s all about the long-term and not putting client websites at risk.

This has led to many SEOs embracing different forms of link building and generally starting to use content as an asset when it comes to attracting links. A big part of me feels that it was actually Penguin in 2012 that drove the rise of content marketing amongst SEOs, but that’s a post for another day…! For today though, this goes some way towards explain the trend we see below.

Slowly but surely, I’m seeing clients come to my company already knowing that low quality link building isn’t what they want. It’s taken a few years after Penguin for it to filter down to client / business owner level, but it’s definitely happening. This is a good thing but unfortunately, the main reason for this is that most of them have been burnt in the past by SEO companies who have built low quality links without giving thought to building good quality ones too.

I have no doubt that it’s this change in mindset which has led to trends like this:

The thing is, I don’t think this was by choice.

Let’s be honest. A lot of us used the kind of link building tactics that Google no longer like because they worked. I don’t think many SEOs were under the illusion that it was genuinely high quality stuff, but it worked and it was far less risky to do than it is today. Unless you were super-spammy, the low-quality links just worked.

Fast forward to a post-Penguin world, things are far more risky. For me, it’s because of this that we see the trends like the above. As an industry, we had the easiest link building methods taken away from us and we’re left with fewer options. One of the main options is content marketing which, if you do it right, can lead to good quality links and importantly, the types of links you won’t be removing in the future. Get it wrong and you’ll lose budget and lose the trust if your boss or client in the power of content when it comes to link building.

There are still plenty of other methods to build links and sometimes we can forget this. Just look at this epic list from Jon Cooper. Even with this many tactics still available to us, it’s hard work. Way harder than it used to be.

My summary here is that as an industry, our mindset has shifted but it certainly wasn’t a voluntary shift. If the tactics that Penguin targeted still worked today, we’d still be using them.

A few other opinions…

I definitely think too many people want the next easy win. As someone surfing the edge of what Google is bringing our way, here’s my general take—SEO, in broad strokes, is changing a lot, *but* any given change is more and more niche and impacts fewer people. What we’re seeing isn’t radical, sweeping changes that impact everyone, but a sort of modularization of SEO, where we each have to be aware of what impacts our given industries, verticals, etc.”

Dr. Pete

 

I don’t feel that techniques for acquiring links have changed that much. You can either earn them through content and outreach or you can just buy them. What has changed is the awareness of “link building” outside of the SEO community. This makes link building / content marketing much harder when pitching to journalists and even more difficult when pitching to bloggers.

“Link building has to be more integrated with other channels and struggles to work in its own environment unless supported by brand, PR and social. Having other channels supporting your link development efforts also creates greater search signals and more opportunity to reach a bigger audience which will drive a greater ROI.

Carl Hendy

 

SEO has grown up in terms of more mature staff and SEOs becoming more ingrained into businesses so there is a smarter (less pressure) approach. At the same time, SEO has become more integrated into marketing and has made marketing teams and decision makers more intelligent in strategies and not pushing for the quick win. I’m also seeing that companies who used to rely on SEO and building links have gone through IPOs and the need to build 1000s of links per quarter has rightly reduced.

Danny Denhard

Signals that surround link building have changed

There is no question about this one in my mind. I actually wrote about this last year in my previous blog post where I talked about signals such as anchor text and deep links changing over time.

Many of the people I asked felt the same, here are some quotes from them, split out by the types of signal.

Domain level link metrics

I think domain level links have become increasingly important compared with page level factors, i.e. you can get a whole site ranking well off the back of one insanely strong page, even with sub-optimal PageRank flow from that page to the rest of the site.

Phil Nottingham

I’d agree with Phil here and this is what I was getting at in my previous post on how I feel “deep links” will matter less over time. It’s not just about domain level links here, it’s just as much about the additional signals available for Google to use (more on that later).

Anchor text

I’ve never liked anchor text as a link signal. I mean, who actually uses exact match commercial keywords as anchor text on the web?

SEOs. 🙂

Sure there will be natural links like this, but honestly, I struggle with the idea that it took Google so long to start turning down the dial on commercial anchor text as a ranking signal. They are starting to turn it down though, slowly but surely. Don’t get me wrong, it still matters and it still works. But like pure link spam, the barrier is a lot more lower now in terms what of constitutes too much.

Rand feels that they matter more than we’d expect and I’d mostly agree with this statement:

Exact match anchor text links still have more power than you’d expect—I think Google still hasn’t perfectly sorted what is “brand” or “branded query” from generics (i.e. they want to start ranking a new startup like meldhome.com for “Meld” if the site/brand gets popular, but they can’t quite tell the difference between that and https://moz.com/learn/seo/redirection getting a few manipulative links that say “redirect”)

Rand Fishkin

What I do struggle with though, is that Google still haven’t figured this out and that short-term, commercial anchor text spam is still so effective. Even for a short burst of time.

I don’t think link building as a concept has changed loads—but I think links as a signal have, mainly because of filters and penalties but I don’t see anywhere near the same level of impact from coverage anymore, even against 18 months ago.

Paul Rogers

New signals have been introduced

It isn’t just about established signals changing though, there are new signals too and I personally feel that this is where we’ve seen the most change in Google algorithms in recent years—going all the way back to Panda in 2011.

With Panda, we saw a new level of machine learning where it almost felt like Google had found a way of incorporating human reaction / feelings into their algorithms. They could then run this against a website and answer questions like the ones included in this post. Things such as:

  • “Would you be comfortable giving your credit card information to this site?”
  • “Does this article contain insightful analysis or interesting information that is beyond obvious?”
  • “Are the pages produced with great care and attention to detail vs. less attention to detail?”

It is a touch scary that Google was able to run machine learning against answers to questions like this and write an algorithm to predict the answers for any given page on the web. They have though and this was four years ago now.

Since then, they’ve made various moves to utilize machine learning and AI to build out new products and improve their search results. For me, this was one of the biggest and went pretty unnoticed by our industry. Well, until Hummingbird came along I feel pretty sure that we have Ray Kurzweil to thank for at least some of that.

There seems to be more weight on theme/topic related to sites, though it’s hard to tell if this is mostly link based or more user/usage data based. Google is doing a good job of ranking sites and pages that don’t earn the most links but do provide the most relevant/best answer. I have a feeling they use some combination of signals to say “people who perform searches like this seem to eventually wind up on this website—let’s rank it.” One of my favorite examples is the Audubon Society ranking for all sorts of birding-related searches with very poor keyword targeting, not great links, etc. I think user behavior patterns are stronger in the algo than they’ve ever been.

– Rand Fishkin

Leading on from what Rand has said, it’s becoming more and more common to see search results that just don’t make sense if you look at the link metrics—but are a good result.

For me, the move towards user data driving search results + machine learning advanced has been the biggest change we’ve seen in recent years and it’s still going.

Edit: since drafting this post, Tom Anthony released this excellent blog post on his views on the future of search and the shift to data-driven results. I’d recommend reading that as it approaches this whole area from a different perspective and I feel that an off-shoot of what Tom is talking about is the impact on link building.

You may be asking at this point, what does machine learning have to do with link building?

Everything. Because as strong as links are as a ranking signal, Google want more signals and user signals are far, far harder to manipulate than established link signals. Yes it can be done—I’ve seen it happen. There have even been a few public tests done. But it’s very hard to scale and I’d venture a guess that only the top 1% of spammers are capable of doing it, let alone maintaining it for a long period of time. When I think about the process for manipulation here, I actually think we go a step beyond spammers towards hackers and more cut and dry illegal activity.

For link building, this means that traditional methods of manipulating signals are going to become less and less effective as these user signals become stronger. For us as link builders, it means we can’t keep searching for that silver bullet or the next method of scaling link building just for an easy win. The fact is that scalable link building is always going to be at risk from penalization from Google—I don’t really want to live a life where I’m always worried about my clients being hit by the next update. Even if Google doesn’t catch up with a certain method, machine learning and user data mean that these methods may naturally become less effective and cost efficient over time.

There are of course other things such as social signals that have come into play. I certainly don’t feel like these are a strong ranking factor yet, but with deals like this one between Google and Twitter being signed, I wouldn’t be surprised if that ever-growing dataset is used at some point in organic results. The one advantage that Twitter has over Google is it’s breaking news freshness. Twitter is still way quicker at breaking news than Google is—140 characters in a tweet is far quicker than Google News! Google know this which is why I feel they’ve pulled this partnership back into existence after a couple of years apart.

There is another important point to remember here and it’s nicely summarised by Dr. Pete:

At the same time, as new signals are introduced, these are layers not replacements. People hear social signals or user signals or authorship and want it to be the link-killer, because they already fucked up link-building, but these are just layers on top of on-page and links and all of the other layers. As each layer is added, it can verify the layers that came before it and what you need isn’t the magic signal but a combination of signals that generally matches what Google expects to see from real, strong entities. So, links still matter, but they matter in concert with other things, which basically means it’s getting more complicated and, frankly, a bit harder. Of course, on one wants to hear that.”

– Dr. Pete

The core principles have not changed

This is the crux of everything for me. With all the changes listed above, the key is that the core principles around link building haven’t changed. I could even argue that Penguin didn’t change the core principles because the techniques that Penguin targeted should never have worked in the first place. I won’t argue this too much though because even Google advised website owners to build directory links at one time.

You need an asset

You need to give someone a reason to link to you. Many won’t do it out of the goodness of their heart! One of the most effective ways to do this is to develop a content asset and use this as your reason to make people care. Once you’ve made someone care, they’re more likely to share the content or link to it from somewhere.

You need to promote that asset to the right audience

I really dislike the stance that some marketers take when it comes to content promotion—build great content and links will come.

No. Sorry but for the vast majority of us, that’s simply not true. The exceptions are people that sky dive from space or have huge existing audiences to leverage.

You simply have to spend time promoting your content or your asset for it to get shares and links. It is hard work and sometimes you can spend a long time on it and get little return, but it’s important to keep working at until you’re at a point where you have two things:

  • A big enough audience where you can almost guarantee at least some traffic to your new content along with some shares
  • Enough strong relationships with relevant websites who you can speak to when new content is published and stand a good chance of them linking to it

Getting to this point is hard—but that’s kind of the point. There are various hacks you can use along the way but it will take time to get right.

You need consistency

Leading on from the previous point. It takes time and hard work to get links to your content—the types of links that stand the test of time and you’re not going to be removing in 12 months time anyway! This means that you need to keep pushing content out and getting better each and every time. This isn’t to say you should just churn content out for the sake of it, far from it. I am saying that with each piece of content you create, you will learn to do at least one thing better the next time. Try to give yourself the leverage to do this.

Anything scalable is at risk

Scalable link building is exactly what Google has been trying to crack down on for the last few years. Penguin was the biggest move and hit some of the most scalable tactics we had at our disposal. When you scale something, you often lose some level of quality, which is exactly what Google doesn’t want when it comes to links. If you’re still relying on tactics that could fall into the scalable category, I think you need to be very careful and just look at the trend in the types of links Google has been penalizing to understand why.

The part Google plays in this

To finish up, I want to briefly talk about the part that Google plays in all of this and shaping the future they want for the web.

I’ve always tried to steer clear of arguments involving the idea that Google is actively pushing FUD into the community. I’ve preferred to concentrate more on things I can actually influence and change with my clients rather than what Google is telling us all to do.

However, for the purposes of this post, I want to talk about it.

General paranoia has increased. My bet is there are some companies out there carrying out zero specific linkbuilding activity through worry.

Dan Barker

Dan’s point is a very fair one and just a day or two after reading this in an email, I came across a page related to a client’s target audience that said:

“We are not publishing guest posts on SITE NAME any more. All previous guest posts are now deleted. For more information, see www.mattcutts.com/blog/guest-blogging/“.

I’ve reworded this as to not reveal the name of the site, but you get the point.

This is silly. Honestly, so silly. They are a good site, publish good content, and had good editorial standards. Yet they have ignored all of their own policies, hard work, and objectives to follow a blog post from Matt. I’m 100% confident that it wasn’t sites like this one that Matt was talking about in this blog post.

This is, of course, from the publishers’ angle rather than the link builders’ angle, but it does go to show the effect that statements from Google can have. Google know this so it does make sense for them to push out messages that make their jobs easier and suit their own objectives—why wouldn’t they? In a similar way, what did they do when they were struggling to classify at scale which links are bad vs. good and they didn’t have a big enough web spam team? They got us to do it for them 🙂

I’m mostly joking here, but you see the point.

The most recent infamous mobilegeddon update, discussed here by Dr. Pete is another example of Google pushing out messages that ultimately scared a lot of people into action. Although to be fair, I think that despite the apparent small impact so far, the broad message from Google is a very serious one.

Because of this, I think we need to remember that Google does have their own agenda and many shareholders to keep happy. I’m not in the camp of believing everything that Google puts out is FUD, but I’m much more sensitive and questioning of the messages now than I’ve ever been.

What do you think? I’d love to hear your feedback and thoughts in the comments.

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DWS 2.5 SEO Project Management Suite Released – Limited Spots Available

http://bit.ly/domainwebstudio Sign Up for the DWS 2.5 SEO Tool Suite before the doors close for the long awaited launch of DWS 3.0 – There are only a few spo…

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Check Your Local Business Listings in the UK

Posted by David-Mihm

One of the most consistent refrains from the Moz community as we’ve
released features over the last two years has been the desire to see Moz Local expand to countries outside the U.S. Today I’m pleased to announce that we’re embarking on our journey to global expansion with support for U.K. business listing searches in our Check Listing tool.

Some of you may remember limited U.K. functionality as part of GetListed.org, but as a very small company we couldn’t keep up with the maintenance required to present reliable results. It’s taken us longer than we would have liked to get here, but now with more resources, the Moz Local team has the bandwidth and important experience from the past year of Moz Local in the U.S. to fully support U.K. businesses.

How It Works

We’ve updated our search feature to accept both U.S. and U.K. postal codes, so just head on over to
moz.com/local/search to check it out!

After entering the name of your business and a U.K. postcode, we go out and ping Google and other important local search sites in the U.K., and return what we found. Simply select the closest-matching business and we’ll proceed to run a full audit of your listings across these sites.

You can click through and discover incomplete listings, inconsistent NAP information, duplicate listings, and more.

This check listing feature is free to all Moz community members.

You’ve no doubt noted in the screenshot above that we project a listing score improvement. We do plan to release a fully-featured U.K. version of Moz Local later this spring (with the same distribution, reporting, and duplicate-closure features that are available in the U.S.), and you can enter your email address—either on that page or right here—to be notified when we do!

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U.K.-Specific Partners

As I’ve mentioned in previous blog comments, there are a certain number of global data platforms (Google, Facebook, Yelp, Bing, Foursquare, and Factual, among others) where it’s valuable to be listed correctly and completely no matter which country you’re in.

But every country has its own unique set of domestically relevant players as well, and we’re pleased to have worked with two of them on this release: Central Index and Thomson Local. (Head on over to the
Moz Local Learning Center for more information about country-specific data providers.)

We’re continuing discussions with a handful of other prospective data partners in the U.K. If you’re interested in working with us, please
let us know!

What’s Next?

Requests for further expansion, especially to Canada and Australia, I’m sure will be loud and clear in the comments below! Further expansion is on our roadmap, but it’s balanced against a more complete feature set in the (more populous) U.S. and U.K. markets. We’ll continue to use our experience in those markets as we prioritize when and where to expand next.

A few lucky members of the Moz Local team are already on their way to
BrightonSEO. So if you’re attending that awesome event later this week, please stop by our booth and let us know what you’d like to see us work on next.

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

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Videos: Additional How-To videos to guide you through Majestic

Following our other updates this year, we have released three more videos in our “how-to” use Majestic online tutorial range. These are: How-to use the Backlink History Tool – which will show you how to use charts that illustrate your growth in backlinks and referring domains over a period of time; allowing you to analyse…

The post Videos: Additional How-To videos to guide you through Majestic appeared first on Majestic Blog.

Reblogged 3 years ago from blog.majestic.com