The Magento Xcelerate program: A positive sum game

As an open source ecommerce platform, Magento is flexible and accessible for developers to work with and as a result, an active community of developers emerged on online forums and at offline meetups all over the world. Many of these were happily plugging away independently of Magento until the split from eBay in early 2015.

Free from the reins of eBay, Magento has decisively been reaching out to, promoting and rewarding the individuals, agencies and technology providers that make up its ecosystem. Last February they announced the Magento Masters Program, empowering the top platform advocates, frequent forum contributors and the innovative solution implementers. Then at April‘s Magento Imagine conference (the largest yet) the theme emerged as ‘We are Magento”, in celebration of the community.

The new Xcelerate Technology Partner Program focuses not on individuals but on business partnerships formed with the technology companies that offer tools for Magento merchants to implement.

 Sharing ideas, opportunities and successes:

This is the Xcelerate Program tagline, which acts as a sort of mission statement to get the technology partners involved moving with regards to continuously considering Magento in their own technology roadmap and jointly communicating successes and learnings from working on implementations with merchants.

“In turn, the program offers members the tools to get moving, through events, resources and contacts. Our goal is to enable you to be an integral part of the Magento ecosystem” Jon Carmody, Head of Technology Partners

The program in practice:

The new program is accompanied by the new Marketplace from which the extensions can be purchased and downloaded. The program splits the extensions into 3 partnership levels:

Registered Partners – these are technology extensions that the new Magento Marketplace team test for code quality. Extensions must now pass this initial level to be eligible for the Marketplace. With each merchant having on average 15 extensions for their site, this is a win for merchants when it comes to extension trustworthiness.

Select Partners – extensions can enter this second tier if the technology falls into one of the strategic categories identified by Magento and if they pass an in-depth technical review. These will be marked as being ‘Select’ in the Marketplace.

Premier Partners – this level is by invitation only, chosen as providing crucial technology to Magento merchants (such as payments, marketing, tax software). The Magento team’s Extension Quality Program looks at coding structure, performance, scalability, security and compatibility but influence in the Community is also a consideration. dotmailer is proud to be the first Premier Technology Partner in the marketing space for Magento.

All in all, the latest move from Magento in illuminating its ecosystem should be positive for all; the merchants who can now choose from a vetted list of extensions and know when to expect tight integration, the technology partners building extensions now with clearer merchant needs/extension gaps in mind and guidance from Magento, and of course the solution implementers recommending the best extension for the merchant now knowing it will be maintained.

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Link Miner: On Page Link Analyzer

Jon Cooper from Point Blank SEO has just integrated Majestic into his on page Link Analyzer extension, called “Link Miner” for Chrome. When it is configured properly, it will show you backlink counts and referring domain counts for any page you are viewing in Chrome. Take the Link Miner page itself: The elements in Green…

The post Link Miner: On Page Link Analyzer appeared first on Majestic Blog.

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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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Understanding and Applying Moz’s Spam Score Metric – Whiteboard Friday

Posted by randfish

This week, Moz released a new feature that we call Spam Score, which helps you analyze your link profile and weed out the spam (check out the blog post for more info). There have been some fantastic conversations about how it works and how it should (and shouldn’t) be used, and we wanted to clarify a few things to help you all make the best use of the tool.

In today’s Whiteboard Friday, Rand offers more detail on how the score is calculated, just what those spam flags are, and how we hope you’ll benefit from using it.

For reference, here’s a still of this week’s whiteboard. 

Click on the image above to open a high resolution version in a new tab!

Video transcription

Howdy Moz fans, and welcome to another edition of Whiteboard Friday. This week, we’re going to chat a little bit about Moz’s Spam Score. Now I don’t typically like to do Whiteboard Fridays specifically about a Moz project, especially when it’s something that’s in our toolset. But I’m making an exception because there have been so many questions and so much discussion around Spam Score and because I hope the methodology, the way we calculate things, the look at correlation and causation, when it comes to web spam, can be useful for everyone in the Moz community and everyone in the SEO community in addition to being helpful for understanding this specific tool and metric.

The 17-flag scoring system

I want to start by describing the 17 flag system. As you might know, Spam Score is shown as a score from 0 to 17. You either fire a flag or you don’t. Those 17 flags you can see a list of them on the blog post, and we’ll show that in there. Essentially, those flags correlate to the percentage of sites that we found with that count of flags, not those specific flags, just any count of those flags that were penalized or banned by Google. I’ll show you a little bit more in the methodology.

Basically, what this means is for sites that had 0 spam flags, none of the 17 flags that we had fired, that actually meant that 99.5% of those sites were not penalized or banned, on average, in our analysis and 0.5% were. At 3 flags, 4.2% of those sites, that’s actually still a huge number. That’s probably in the millions of domains or subdomains that Google has potentially still banned. All the way down here with 11 flags, it’s 87.3% that we did find banned. That seems pretty risky or penalized. It seems pretty risky. But 12.7% of those is still a very big number, again probably in the hundreds of thousands of unique websites that are not banned but still have these flags.

If you’re looking at a specific subdomain and you’re saying, “Hey, gosh, this only has 3 flags or 4 flags on it, but it’s clearly been penalized by Google, Moz’s score must be wrong,” no, that’s pretty comfortable. That should fit right into those kinds of numbers. Same thing down here. If you see a site that is not penalized but has a number of flags, that’s potentially an indication that you’re in that percentage of sites that we found not to be penalized.

So this is an indication of percentile risk, not a “this is absolutely spam” or “this is absolutely not spam.” The only caveat is anything with, I think, more than 13 flags, we found 100% of those to have been penalized or banned. Maybe you’ll find an odd outlier or two. Probably you won’t.

Correlation ≠ causation

Correlation is not causation. This is something we repeat all the time here at Moz and in the SEO community. We do a lot of correlation studies around these things. I think people understand those very well in the fields of social media and in marketing in general. Certainly in psychology and electoral voting and election polling results, people understand those correlations. But for some reason in SEO we sometimes get hung up on this.

I want to be clear. Spam flags and the count of spam flags correlates with sites we saw Google penalize. That doesn’t mean that any of the flags or combinations of flags actually cause the penalty. It could be that the things that are flags are not actually connected to the reasons Google might penalize something at all. Those could be totally disconnected.

We are not trying to say with the 17 flags these are causes for concern or you need to fix these. We are merely saying this feature existed on this website when we crawled it, or it had this feature, maybe it still has this feature. Therefore, we saw this count of these features that correlates to this percentile number, so we’re giving you that number. That’s all that the score intends to say. That’s all it’s trying to show. It’s trying to be very transparent about that. It’s not trying to say you need to fix these.

A lot of flags and features that are measured are perfectly fine things to have on a website, like no social accounts or email links. That’s a totally reasonable thing to have, but it is a flag because we saw it correlate. A number in your domain name, I think it’s fine if you want to have a number in your domain name. There’s plenty of good domains that have a numerical character in them. That’s cool.

TLD extension that happens to be used by lots of spammers, like a .info or a .cc or a number of other ones, that’s also totally reasonable. Just because lots of spammers happen to use those TLD extensions doesn’t mean you are necessarily spam because you use one.

Or low link diversity. Maybe you’re a relatively new site. Maybe your niche is very small, so the number of folks who point to your site tends to be small, and lots of the sites that organically naturally link to you editorially happen to link to you from many of their pages, and there’s not a ton of them. That will lead to low link diversity, which is a flag, but it isn’t always necessarily a bad thing. It might still nudge you to try and get some more links because that will probably help you, but that doesn’t mean you are spammy. It just means you fired a flag that correlated with a spam percentile.

The methodology we use

The methodology that we use, for those who are curious — and I do think this is a methodology that might be interesting to potentially apply in other places — is we brainstormed a large list of potential flags, a huge number. We cut that down to the ones we could actually do, because there were some that were just unfeasible for our technology team, our engineering team to do.

Then, we got a huge list, many hundreds of thousands of sites that were penalized or banned. When we say banned or penalized, what we mean is they didn’t rank on page one for either their own domain name or their own brand name, the thing between the
www and the .com or .net or .info or whatever it was. If you didn’t rank for either your full domain name, www and the .com or Moz, that would mean we said, “Hey, you’re penalized or banned.”

Now you might say, “Hey, Rand, there are probably some sites that don’t rank on page one for their own brand name or their own domain name, but aren’t actually penalized or banned.” I agree. That’s a very small number. Statistically speaking, it probably is not going to be impactful on this data set. Therefore, we didn’t have to control for that. We ended up not controlling for that.

Then we found which of the features that we ideated, brainstormed, actually correlated with the penalties and bans, and we created the 17 flags that you see in the product today. There are lots things that I thought were going to correlate, for example spammy-looking anchor text or poison keywords on the page, like Viagra, Cialis, Texas Hold’em online, pornography. Those things, not all of them anyway turned out to correlate well, and so they didn’t make it into the 17 flags list. I hope over time we’ll add more flags. That’s how things worked out.

How to apply the Spam Score metric

When you’re applying Spam Score, I think there are a few important things to think about. Just like domain authority, or page authority, or a metric from Majestic, or a metric from Google, or any other kind of metric that you might come up with, you should add it to your toolbox and to your metrics where you find it useful. I think playing around with spam, experimenting with it is a great thing. If you don’t find it useful, just ignore it. It doesn’t actually hurt your website. It’s not like this information goes to Google or anything like that. They have way more sophisticated stuff to figure out things on their end.

Do not just disavow everything with seven or more flags, or eight or more flags, or nine or more flags. I think that we use the color coding to indicate 0% to 10% of these flag counts were penalized or banned, 10% to 50% were penalized or banned, or 50% or above were penalized or banned. That’s why you see the green, orange, red. But you should use the count and line that up with the percentile. We do show that inside the tool as well.

Don’t just take everything and disavow it all. That can get you into serious trouble. Remember what happened with Cyrus. Cyrus Shepard, Moz’s head of content and SEO, he disavowed all the backlinks to its site. It took more than a year for him to rank for anything again. Google almost treated it like he was banned, not completely, but they seriously took away all of his link power and didn’t let him back in, even though he changed the disavow file and all that.

Be very careful submitting disavow files. You can hurt yourself tremendously. The reason we offer it in disavow format is because many of the folks in our customer testing said that’s how they wanted it so they could copy and paste, so they could easily review, so they could get it in that format and put it into their already existing disavow file. But you should not do that. You’ll see a bunch of warnings if you try and generate a disavow file. You even have to edit your disavow file before you can submit it to Google, because we want to be that careful that you don’t go and submit.

You should expect the Spam Score accuracy. If you’re doing spam investigation, you’re probably looking at spammier sites. If you’re looking at a random hundred sites, you should expect that the flags would correlate with the percentages. If I look at a random hundred 4 flag Spam Score sites, 7.5% of those I would expect on average to be penalized or banned. If you are therefore seeing sites that don’t fit those, they probably fit into the percentiles that were not penalized, or up here were penalized, down here weren’t penalized, that kind of thing.

Hopefully, you find Spam Score useful and interesting and you add it to your toolbox. We would love to hear from you on iterations and ideas that you’ve got for what we can do in the future, where else you’d like to see it, and where you’re finding it useful/not useful. That would be great.

Hopefully, you’ve enjoyed this edition of Whiteboard Friday and will join us again next week. Thanks so much. Take care.

Video transcription by Speechpad.com

ADDITION FROM RAND: I also urge folks to check out Marie Haynes’ excellent Start-to-Finish Guide to Using Google’s Disavow Tool. We’re going to update the feature to link to that as well.

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Firefox Extension Upgraded to include Topical Trust Flow

If you are (like me) a Firefox fan, then you’ll be delighted to know that our Firefox extension has now got an upgrade to show Topical Trust Flow® for both inbound links and Anchor Text tabs. This follows our Chrome upgrade announced on the 2nd of March. How to get the Upgrade If you already…

The post Firefox Extension Upgraded to include Topical Trust Flow appeared first on Majestic Blog.

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Majestic Chrome Extension Upgraded to include Topical Trust Flow

Majestic seems to be on a bit of a roll with new functionality so far in 2015. Today I am pleased to announce that we have upgraded our Google Chrome link analyzer extension to embed Topical Trust Flow data in both the back links tab AND in the Anchor Text tab. The screenshot at the…

The post Majestic Chrome Extension Upgraded to include Topical Trust Flow appeared first on Majestic Blog.

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International SEO Study: How Searchers Perceive Country Code Top-Level Domains

Posted by 5le

The decision to focus your site on an international audience is a big step and one fraught with complexities. There are, of course, issues to deal with around language and user experience, but in addition there are some big technical choices to make including what domains to use.

Any authoritative
international SEO guide will elaborate on the differences between the options of subdirectory, subdomain, and country-code top level domain (CCTLD). One of the most common suggestions is for a site to opt to use a ccTLD (e.g. domain.co.uk) as the domain extension. The reasoning behind this is the theory that the ccTLD extension will “hint” to search engines and users exactly who your target audience should be versus the other, less explicit options. For example, a search engine and human user would know, even without clicking into a site, that a site that ends with .co.uk is targeting a user looking for UK content. 

We have solid data from
Google that a ccTLD does indicate country targeting; however, when it comes to users there is only an assumption that users even notice and make choices based on the ccTLD. However, this is a fairly broad assumption that doesn’t address whether a ccTLD is more important than a brand name in the domain or the quality of a website’s content. To test this theory, we ran a survey to discover what users really thought.

User knowledge of TLDs

Even before trying to understand how users related to ccTLDs it is essential to validate the assumption that users even know that general TLDs exist. To establish this fact, we asked respondents to pick which TLD might be the one in use by a non-profit. Close to
100% of respondents correctly identified a TLD ending with .org as the one most likely to be used by a non-profit. Interestingly, only 4% of people in the US stated that they were unsure of the correct TLD compared to 13% of Australians. Predictably, nearly all marketers (98%) chose the .org answer.

Another popular TLD is the .edu in use by educational assumptions, and we wanted to understand if users thought that content coming from a .edu domain might be more trustworthy. We asked users if they received an unsolicited email about water quality in their town whether they would place more trust in a sender’s email address that ended with .edu or .com.
89% of respondents in the US chose the .edu as more trustworthy, while only 79% said the same in Australia. Quite interestingly, the marketer responses (from the survey posted on Inbound.org were exactly the same as the Australians with 79% declaring the .edu to be more trustworthy.

.org cctld survey australia

If users can identify a .org as the correct TLD for a non-profit, and a .edu as a TLD that might be more trustworthy, it is likely that users are familiar with the existence of TLDs and how they might be used. The next question to answer is if users are aware of the connection between TLDs and locations.

Country relationship awareness

Next, we asked respondents to identify the location of a local business using a .ca TLD extension. The majority of respondents across all three surveys correctly chose Canada; and nearly all marketers (92%) got this correct. Oddly, more Australians (67%) correctly identified Canada than Americans (62%). We would have thought Americans should have been more familiar with the TLD of a neighboring country. Additionally, more Americans (23%) fell for the trick answer of California than Australians (15%). Regardless, we were able to conclude that most Internet users are aware of TLDs and that they are tied to a specific country.

canada cctld survey

To really gauge how much users know about TLDs and countries, we asked users to pick the right domain extension for a website in another country. In the US survey, we asked users to pick the correct TLD for an Australian company, and in the Australian survey we used a British company. In each of the questions we gave one correct answer possibility, one almost correct, and two entire wrong choices.For example, we gave .co.uk and .uk as answer choices to Australians.

In both the US and Australia, the majority of respondents chose the correct TLD, although Americans seem to have been confused by whether Australia’s TLD was .AU (35%) or .com.AU (24%).

There is a common practice of using country-code domain extensions as a vanity URL for content that is not geotargeted. For example, .ly is the domain extension for Libya, but it is frequently used on domains that have a word that ends with “ly.” Additionally, .me is the domain extension for Montenegro; however, the TLD is used for many purposes other than Montenegro content.

We wanted to understand if users noticed this type of TLD usage or if they thought the content might still be related to another country. We asked respondents what might be on a website that ended with .TV which is the TLD for the island nation of Tuvalu and is also a popular TLD for TV show websites. 51% of US respondents thought it might be a TV show and 42% chose the “it could be anything” answer. In Australia, 43% thought the site would be a TV show, and 44% said “it could be anything”.

tuvalu cctld survey

One of the answer options was that it could be a website in Tuvalu and interestingly twice as many Australian (9%) chose this option vs US respondents (4.5%). This question was one of the areas where marketers’ answers were very different from those in the US and Australia. 77% of marketers chose the TV show option and only 19% said it could be anything.

Based on the these three results, it is apparent that
users recognize TLDs, know that they are from other countries, and appear to make some judgments around the content based on the TLD.

Decision making using TLDs

Since users know that TLDs are an important part of a URL that is tied to a country of origin, it is important to understand how the TLD factors into their decision-making processes about whether or not they visit certain websites.

We asked users whether they thought medical content on a foreign TLD would be as reliable as similar content found on their local TLD. In the US, only 24% thought the content on the non-local TLD (.co.uk) was less reliable than content on a .com. In Australia, the results were nearly identical to what we saw in the US with only 28% answering that the non-local TLD (.co.uk) was less reliable than the content on a .com.au. Even 24% of marketers answered that the content was less reliable. The remaining respondents chose either that the content equally reliable or they just didn’t know. Based on these results, the TLD (at least as long as it was a reputable one)
does not seem to impact user trust.

UK cctld survey

Digging into the idea of trust and TLD a bit further, we asked the same reliability question about results on Google.com vs Google.de. In the US, 56% of respondents said that the results on Google.de are equally reliable to those on Google.com, and in Australia, 51% said the same thing when compared to Google.com.au. In the marketer survey, 66% of respondents said the results were equally reliable. The fact that the majority of respondents stated that results are equally reliable should mean that users are more focused on the brand portion of a domain rather than its country extension.

CcTLD’s impact on ecommerce

Making the decision to use a ccTLD on a website can be costly, so it is important to justify this cost with an actual revenue benefit. Therefore the real test of TLD choice is how it impacts revenue. This type of answer is of course hard to gauge in a survey where customers are not actually buying products, but we did want to try to see if there might be a way to measure purchasing decisions.

To achieve this result, we compared two different online retailers and asked respondents to choose the establishment that they thought would have the most reliable express shipping. In the US survey, we compared Amazon.co.jp to BestBuy.com. In the Australian survey, we compared Bigw.com.au (a well known online retailer) to Target.com. (Interesting fact: there is a Target in Australia that is not affiliated with Target in the US and their website is target.com.au) The intent of the question was to see if users zeroed in on the recognizable brand name or the domain extension.

cctld trust survey

In the US, while 39% said that both websites would offer reliable shipping, 42% still said that Best Buy would be the better option. Australians may have been confused by the incorrect Target website, since 61% said both websites would have reliable shipping, but 34% chose Big W. Even marketers didn’t seem oblivious to domain names with only 34% choosing the equally reliable option, and 49% choosing Best Buy. The data in this question is a bit inconclusive, but we can definitively say that while a large portion of users are blind to domain names, however, when selling online it would be best to use a familiar domain extension.

cctld trust survey australia

New TLDs

Late last year, ICANN (the Internet governing body) announced that they would be releasing dozens of new
GTLDs, which opened up a new domain name land grab harkening back to the early days of the Internet. Many of these domain names can be quite expensive, and we wanted to discover whether they even mattered to users.

gtld survey

We asked users if, based solely on the domain name, they were more likely to trust an insurance quote from a website ending in .insurance.
62% of Americans, 53% of Australians, and 67% of marketers said they were unlikely to trust the quote based on the domain alone. Based on this result, if you’re looking to invest in a new TLD simply to drive more conversions, you should probably do more research first. 

A new gTLD is probably not a silver bullet.

Methodology

For this survey, I collaborated with
Sam Mallikarjunan at HubSpot and we decided that the two assumptions we absolutely needed to validate where 1) whether users even notice ccTLDs and 2) if so do they really prefer the TLD of their country. While we received 101 responses from a version of the survey targeted at marketers on an Inbound.org discussion, we primarily used SurveyMonkey Audience, which allowed us to get answers from a statistically significant random selection of people in both the United States and Australia.

We created two nearly identical surveys with one targeted to a US-only audience and the other targeted to an Australian-only audience. A proper sample set is essential when conducting any survey that attempts to draw conclusions about people’s general behavior and preferences. And in this case, the minimum number of respondents we needed in order to capture a representative example was 350 for the U.S. and 300 for Australia.

Additionally, in order for a sample to be valid, the respondents have to be chosen completely at random. SurveyMonkey Audience recruits its 4-million+ members from SurveyMonkey’s 40 million annual unique visitors, and members are not paid for their participation. Instead, they are rewarded for taking surveys with charitable donations, made on their behalf by SurveyMonkey.

When tested against much larger research projects, Audience data has been exactly in line with larger sample sizes. For example, an Audience survey with just 400 respondents about a new Lay’s potato chip flavor had the same results as a wider contest that had 3 million participants.

SurveyMonkey’s survey research team was also able to use SurveyMonkey Audience to accurately predict election results in both 2012 and 2013. With a US sample size of 458 respondents and an Australian one of 312 all drawn at random, our ccTLD user preferences should reliably mirror the actual reality.

Summary

There will be many reasons that you may or may not want to use ccTLDs for your website, and a survey alone can never answer whether a ccTLD is the right strategy for any particular site. If you are thinking about making any big decisions about TLDs on your site, you should absolutely conduct some testing or surveying of your own before relying on just the recommendations of those who advise a TLD as the best strategy or the others that tell you it doesn’t matter at all.

Launching a PPC campaign with a landing page on a ccTLD and measuring CTRs against a control is far cheaper than replicating your entire site on a new TLD.

Based on our survey results, here’s what you should keep in mind when it comes to whether or not investing your time and money in a ccTLD is worth it:

  1. Users are absolutely aware of the TLDs and how they might relate to the contents of a website
  2. Users are aware of the connection between TLDs and countries
  3. Users do make decisions about websites based on the TLD; however there are no absolutes. Brand and content absolutely matter.

As to whether a ccTLD will work for you on your own site, give it a try and report back!

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MaximumSEO – Best Seo Extension For Magento

Best Magento Seo Extension for increasing your rank and search visibility on google and other search Engines. http://sidewebs.com/magento/Maximum-Seo-Extension/ you don’t need a magento seo…

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