Stop Ghost Spam in Google Analytics with One Filter

Posted by CarloSeo

The spam in Google Analytics (GA) is becoming a serious issue. Due to a deluge of referral spam from social buttons, adult sites, and many, many other sources, people are starting to become overwhelmed by all the filters they are setting up to manage the useless data they are receiving.

The good news is, there is no need to panic. In this post, I’m going to focus on the most common mistakes people make when fighting spam in GA, and explain an efficient way to prevent it.

But first, let’s make sure we understand how spam works. A couple of months ago, Jared Gardner wrote an excellent article explaining what referral spam is, including its intended purpose. He also pointed out some great examples of referral spam.

Types of spam

The spam in Google Analytics can be categorized by two types: ghosts and crawlers.

Ghosts

The vast majority of spam is this type. They are called ghosts because they never access your site. It is important to keep this in mind, as it’s key to creating a more efficient solution for managing spam.

As unusual as it sounds, this type of spam doesn’t have any interaction with your site at all. You may wonder how that is possible since one of the main purposes of GA is to track visits to our sites.

They do it by using the Measurement Protocol, which allows people to send data directly to Google Analytics’ servers. Using this method, and probably randomly generated tracking codes (UA-XXXXX-1) as well, the spammers leave a “visit” with fake data, without even knowing who they are hitting.

Crawlers

This type of spam, the opposite to ghost spam, does access your site. As the name implies, these spam bots crawl your pages, ignoring rules like those found in robots.txt that are supposed to stop them from reading your site. When they exit your site, they leave a record on your reports that appears similar to a legitimate visit.

Crawlers are harder to identify because they know their targets and use real data. But it is also true that new ones seldom appear. So if you detect a referral in your analytics that looks suspicious, researching it on Google or checking it against this list might help you answer the question of whether or not it is spammy.

Most common mistakes made when dealing with spam in GA

I’ve been following this issue closely for the last few months. According to the comments people have made on my articles and conversations I’ve found in discussion forums, there are primarily three mistakes people make when dealing with spam in Google Analytics.

Mistake #1. Blocking ghost spam from the .htaccess file

One of the biggest mistakes people make is trying to block Ghost Spam from the .htaccess file.

For those who are not familiar with this file, one of its main functions is to allow/block access to your site. Now we know that ghosts never reach your site, so adding them here won’t have any effect and will only add useless lines to your .htaccess file.

Ghost spam usually shows up for a few days and then disappears. As a result, sometimes people think that they successfully blocked it from here when really it’s just a coincidence of timing.

Then when the spammers later return, they get worried because the solution is not working anymore, and they think the spammer somehow bypassed the barriers they set up.

The truth is, the .htaccess file can only effectively block crawlers such as buttons-for-website.com and a few others since these access your site. Most of the spam can’t be blocked using this method, so there is no other option than using filters to exclude them.

Mistake #2. Using the referral exclusion list to stop spam

Another error is trying to use the referral exclusion list to stop the spam. The name may confuse you, but this list is not intended to exclude referrals in the way we want to for the spam. It has other purposes.

For example, when a customer buys something, sometimes they get redirected to a third-party page for payment. After making a payment, they’re redirected back to you website, and GA records that as a new referral. It is appropriate to use referral exclusion list to prevent this from happening.

If you try to use the referral exclusion list to manage spam, however, the referral part will be stripped since there is no preexisting record. As a result, a direct visit will be recorded, and you will have a bigger problem than the one you started with since. You will still have spam, and direct visits are harder to track.

Mistake #3. Worrying that bounce rate changes will affect rankings

When people see that the bounce rate changes drastically because of the spam, they start worrying about the impact that it will have on their rankings in the SERPs.

bounce.png

This is another mistake commonly made. With or without spam, Google doesn’t take into consideration Google Analytics metrics as a ranking factor. Here is an explanation about this from Matt Cutts, the former head of Google’s web spam team.

And if you think about it, Cutts’ explanation makes sense; because although many people have GA, not everyone uses it.

Assuming your site has been hacked

Another common concern when people see strange landing pages coming from spam on their reports is that they have been hacked.

landing page

The page that the spam shows on the reports doesn’t exist, and if you try to open it, you will get a 404 page. Your site hasn’t been compromised.

But you have to make sure the page doesn’t exist. Because there are cases (not spam) where some sites have a security breach and get injected with pages full of bad keywords to defame the website.

What should you worry about?

Now that we’ve discarded security issues and their effects on rankings, the only thing left to worry about is your data. The fake trail that the spam leaves behind pollutes your reports.

It might have greater or lesser impact depending on your site traffic, but everyone is susceptible to the spam.

Small and midsize sites are the most easily impacted – not only because a big part of their traffic can be spam, but also because usually these sites are self-managed and sometimes don’t have the support of an analyst or a webmaster.

Big sites with a lot of traffic can also be impacted by spam, and although the impact can be insignificant, invalid traffic means inaccurate reports no matter the size of the website. As an analyst, you should be able to explain what’s going on in even in the most granular reports.

You only need one filter to deal with ghost spam

Usually it is recommended to add the referral to an exclusion filter after it is spotted. Although this is useful for a quick action against the spam, it has three big disadvantages.

  • Making filters every week for every new spam detected is tedious and time-consuming, especially if you manage many sites. Plus, by the time you apply the filter, and it starts working, you already have some affected data.
  • Some of the spammers use direct visits along with the referrals.
  • These direct hits won’t be stopped by the filter so even if you are excluding the referral you will sill be receiving invalid traffic, which explains why some people have seen an unusual spike in direct traffic.

Luckily, there is a good way to prevent all these problems. Most of the spam (ghost) works by hitting GA’s random tracking-IDs, meaning the offender doesn’t really know who is the target, and for that reason either the hostname is not set or it uses a fake one. (See report below)

Ghost-Spam.png

You can see that they use some weird names or don’t even bother to set one. Although there are some known names in the list, these can be easily added by the spammer.

On the other hand, valid traffic will always use a real hostname. In most of the cases, this will be the domain. But it also can also result from paid services, translation services, or any other place where you’ve inserted GA tracking code.

Valid-Referral.png

Based on this, we can make a filter that will include only hits that use real hostnames. This will automatically exclude all hits from ghost spam, whether it shows up as a referral, keyword, or pageview; or even as a direct visit.

To create this filter, you will need to find the report of hostnames. Here’s how:

  1. Go to the Reporting tab in GA
  2. Click on Audience in the lefthand panel
  3. Expand Technology and select Network
  4. At the top of the report, click on Hostname

Valid-list

You will see a list of all hostnames, including the ones that the spam uses. Make a list of all the valid hostnames you find, as follows:

  • yourmaindomain.com
  • blog.yourmaindomain.com
  • es.yourmaindomain.com
  • payingservice.com
  • translatetool.com
  • anotheruseddomain.com

For small to medium sites, this list of hostnames will likely consist of the main domain and a couple of subdomains. After you are sure you got all of them, create a regular expression similar to this one:

yourmaindomain\.com|anotheruseddomain\.com|payingservice\.com|translatetool\.com

You don’t need to put all of your subdomains in the regular expression. The main domain will match all of them. If you don’t have a view set up without filters, create one now.

Then create a Custom Filter.

Make sure you select INCLUDE, then select “Hostname” on the filter field, and copy your expression into the Filter Pattern box.

filter

You might want to verify the filter before saving to check that everything is okay. Once you’re ready, set it to save, and apply the filter to all the views you want (except the view without filters).

This single filter will get rid of future occurrences of ghost spam that use invalid hostnames, and it doesn’t require much maintenance. But it’s important that every time you add your tracking code to any service, you add it to the end of the filter.

Now you should only need to take care of the crawler spam. Since crawlers access your site, you can block them by adding these lines to the .htaccess file:

## STOP REFERRER SPAM 
RewriteCond %{HTTP_REFERER} semalt\.com [NC,OR] 
RewriteCond %{HTTP_REFERER} buttons-for-website\.com [NC] 
RewriteRule .* - [F]

It is important to note that this file is very sensitive, and misplacing a single character it it can bring down your entire site. Therefore, make sure you create a backup copy of your .htaccess file prior to editing it.

If you don’t feel comfortable messing around with your .htaccess file, you can alternatively make an expression with all the crawlers, then and add it to an exclude filter by Campaign Source.

Implement these combined solutions, and you will worry much less about spam contaminating your analytics data. This will have the added benefit of freeing up more time for you to spend actually analyze your valid data.

After stopping spam, you can also get clean reports from the historical data by using the same expressions in an Advance Segment to exclude all the spam.

Bonus resources to help you manage spam

If you still need more information to help you understand and deal with the spam on your GA reports, you can read my main article on the subject here: http://www.ohow.co/what-is-referrer-spam-how-stop-it-guide/.

Additional information on how to stop spam can be found at these URLs:

In closing, I am eager to hear your ideas on this serious issue. Please share them in the comments below.

(Editor’s Note: All images featured in this post were created by the author.)

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Reblogged 3 years ago from tracking.feedpress.it

The Linkbait Bump: How Viral Content Creates Long-Term Lift in Organic Traffic – Whiteboard Friday

Posted by randfish

A single fantastic (or “10x”) piece of content can lift a site’s traffic curves long beyond the popularity of that one piece. In today’s Whiteboard Friday, Rand talks about why those curves settle into a “new normal,” and how you can go about creating the content that drives that change.

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

Video Transcription

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we’re chatting about the linkbait bump, classic phrase in the SEO world and almost a little dated. I think today we’re talking a little bit more about viral content and how high-quality content, content that really is the cornerstone of a brand or a website’s content can be an incredible and powerful driver of traffic, not just when it initially launches but over time.

So let’s take a look.

This is a classic linkbait bump, viral content bump analytics chart. I’m seeing over here my traffic and over here the different months of the year. You know, January, February, March, like I’m under a thousand. Maybe I’m at 500 visits or something, and then I have this big piece of viral content. It performs outstandingly well from a relative standpoint for my site. It gets 10,000 or more visits, drives a ton more people to my site, and then what happens is that that traffic falls back down. But the new normal down here, new normal is higher than the old normal was. So the new normal might be at 1,000, 1,500 or 2,000 visits whereas before I was at 500.

Why does this happen?

A lot of folks see an analytics chart like this, see examples of content that’s done this for websites, and they want to know: Why does this happen and how can I replicate that effect? The reasons why are it sort of feeds back into that viral loop or the flywheel, which we’ve talked about in previous Whiteboard Fridays, where essentially you start with a piece of content. That content does well, and then you have things like more social followers on your brand’s accounts. So now next time you go to amplify content or share content socially, you’re reaching more potential people. You have a bigger audience. You have more people who share your content because they’ve seen that that content performs well for them in social. So they want to find other content from you that might help their social accounts perform well.

You see more RSS and email subscribers because people see your interesting content and go, “Hey, I want to see when these guys produce something else.” You see more branded search traffic because people are looking specifically for content from you, not necessarily just around this viral piece, although that’s often a big part of it, but around other pieces as well, especially if you do a good job of exposing them to that additional content. You get more bookmark and type in traffic, more searchers biased by personalization because they’ve already visited your site. So now when they search and they’re logged into their accounts, they’re going to see your site ranking higher than they normally would otherwise, and you get an organic SEO lift from all the links and shares and engagement.

So there’s a ton of different factors that feed into this, and you kind of want to hit all of these things. If you have a piece of content that gets a lot of shares, a lot of links, but then doesn’t promote engagement, doesn’t get more people signing up, doesn’t get more people searching for your brand or searching for that content specifically, then it’s not going to have the same impact. Your traffic might fall further and more quickly.

How do you achieve this?

How do we get content that’s going to do this? Well, we’re going to talk through a number of things that we’ve talked about previously on Whiteboard Friday. But there are some additional ones as well. This isn’t just creating good content or creating high quality content, it’s creating a particular kind of content. So for this what you want is a deep understanding, not necessarily of what your standard users or standard customers are interested in, but a deep understanding of what influencers in your niche will share and promote and why they do that.

This often means that you follow a lot of sharers and influencers in your field, and you understand, hey, they’re all sharing X piece of content. Why? Oh, because it does this, because it makes them look good, because it helps their authority in the field, because it provides a lot of value to their followers, because they know it’s going to get a lot of retweets and shares and traffic. Whatever that because is, you have to have a deep understanding of it in order to have success with viral kinds of content.

Next, you want to have empathy for users and what will give them the best possible experience. So if you know, for example, that a lot of people are coming on mobile and are going to be sharing on mobile, which is true of almost all viral content today, FYI, you need to be providing a great mobile and desktop experience. Oftentimes that mobile experience has to be different, not just responsive design, but actually a different format, a different way of being able to scroll through or watch or see or experience that content.

There are some good examples out there of content that does that. It makes a very different user experience based on the browser or the device you’re using.

You also need to be aware of what will turn them off. So promotional messages, pop-ups, trying to sell to them, oftentimes that diminishes user experience. It means that content that could have been more viral, that could have gotten more shares won’t.

Unique value and attributes that separate your content from everything else in the field. So if there’s like ABCD and whoa, what’s that? That’s very unique. That stands out from the crowd. That provides a different form of value in a different way than what everyone else is doing. That uniqueness is often a big reason why content spreads virally, why it gets more shared than just the normal stuff.

I’ve talk about this a number of times, but content that’s 10X better than what the competition provides. So unique value from the competition, but also quality that is not just a step up, but 10X better, massively, massively better than what else you can get out there. That makes it unique enough. That makes it stand out from the crowd, and that’s a very hard thing to do, but that’s why this is so rare and so valuable.

This is a critical one, and I think one that, I’ll just say, many organizations fail at. That is the freedom and support to fail many times, to try to create these types of effects, to have this impact many times before you hit on a success. A lot of managers and clients and teams and execs just don’t give marketing teams and content teams the freedom to say, “Yeah, you know what? You spent a month and developer resources and designer resources and spent some money to go do some research and contracted with this third party, and it wasn’t a hit. It didn’t work. We didn’t get the viral content bump. It just kind of did okay. You know what? We believe in you. You’ve got a lot of chances. You should try this another 9 or 10 times before we throw it out. We really want to have a success here.”

That is something that very few teams invest in. The powerful thing is because so few people are willing to invest that way, the ones that do, the ones that believe in this, the ones that invest long term, the ones that are willing to take those failures are going to have a much better shot at success, and they can stand out from the crowd. They can get these bumps. It’s powerful.

Not a requirement, but it really, really helps to have a strong engaged community, either on your site and around your brand, or at least in your niche and your topic area that will help, that wants to see you, your brand, your content succeed. If you’re in a space that has no community, I would work on building one, even if it’s very small. We’re not talking about building a community of thousands or tens of thousands. A community of 100 people, a community of 50 people even can be powerful enough to help content get that catalyst, that first bump that’ll boost it into viral potential.

Then finally, for this type of content, you need to have a logical and not overly promotional match between your brand and the content itself. You can see many sites in what I call sketchy niches. So like a criminal law site or a casino site or a pharmaceutical site that’s offering like an interactive musical experience widget, and you’re like, “Why in the world is this brand promoting this content? Why did they even make it? How does that match up with what they do? Oh, it’s clearly just intentionally promotional.”

Look, many of these brands go out there and they say, “Hey, the average web user doesn’t know and doesn’t care.” I agree. But the average web user is not an influencer. Influencers know. Well, they’re very, very suspicious of why content is being produced and promoted, and they’re very skeptical of promoting content that they don’t think is altruistic. So this kills a lot of content for brands that try and invest in it when there’s no match. So I think you really need that.

Now, when you do these linkbait bump kinds of things, I would strongly recommend that you follow up, that you consider the quality of the content that you’re producing. Thereafter, that you invest in reproducing these resources, keeping those resources updated, and that you don’t simply give up on content production after this. However, if you’re a small business site, a small or medium business, you might think about only doing one or two of these a year. If you are a heavy content player, you’re doing a lot of content marketing, content marketing is how you’re investing in web traffic, I’d probably be considering these weekly or monthly at the least.

All right, everyone. Look forward to your experiences with the linkbait bump, and I will see you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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Reblogged 3 years ago from tracking.feedpress.it

The Meta Referrer Tag: An Advancement for SEO and the Internet

Posted by Cyrus-Shepard

The movement to make the Internet more secure through HTTPS brings several useful advancements for webmasters. In addition to security improvements, HTTPS promises future technological advances and potential SEO benefits for marketers.

HTTPS in search results is rising. Recent MozCast data from Dr. Pete shows nearly 20% of first page Google results are now HTTPS.

Sadly, HTTPS also has its downsides.

Marketers run into their first challenge when they switch regular HTTP sites over to HTTPS. Technically challenging, the switch typically involves routing your site through a series of 301 redirects. Historically, these types of redirects are associated with a loss of link equity (thought to be around 15%) which can lead to a loss in rankings. This can offset any SEO advantage that Google claims switching.

Ross Hudgens perfectly summed it up in this tweet:

Many SEOs have anecdotally shared stories of HTTPS sites performing well in Google search results (and our soon-to-be-published Ranking Factors data seems to support this.) However, the short term effect of a large migration can be hard to take. When Moz recently switched to HTTPS to provide better security to our logged-in users, we saw an 8-9% dip in our organic search traffic.

Problem number two is the subject of this post. It involves the loss of referral data. Typically, when one site sends traffic to another, information is sent that identifies the originating site as the source of traffic. This invaluable data allows people to see where their traffic is coming from, and helps spread the flow of information across the web.

SEOs have long used referrer data for a number of beneficial purposes. Oftentimes, people will link back or check out the site sending traffic when they see the referrer in their analytics data. Spammers know this works, as evidenced by the recent increase in referrer spam:

This process stops when traffic flows from an HTTPS site to a non-secure HTTP site. In this case, no referrer data is sent. Webmasters can’t know where their traffic is coming from.

Here’s how referral data to my personal site looked when Moz switched to HTTPS. I lost all visibility into where my traffic came from.

Its (not provided) all over again!

Enter the meta referrer tag

While we can’t solve the ranking challenges imposed by switching a site to HTTPS, we can solve the loss of referral data, and it’s actually super-simple.

Almost completely unknown to most marketers, the relatively new meta referrer tag (it’s actually been around for a few years) was designed to help out in these situations.

Better yet, the tag allows you to control how your referrer information is passed.

The meta referrer tag works with most browsers to pass referrer information in a manner defined by the user. Traffic remains encrypted and all the benefits of using HTTPS remain in place, but now you can pass referrer data to all websites, even those that use HTTP.

How to use the meta referrer tag

What follows are extremely simplified instructions for using the meta referrer tag. For more in-depth understanding, we highly recommend referring to the W3C working draft of the spec.

The meta referrer tag is placed in the <head> section of your HTML, and references one of five states, which control how browsers send referrer information from your site. The five states are:

  1. None: Never pass referral data
    <meta name="referrer" content="none">
    
  2. None When Downgrade: Sends referrer information to secure HTTPS sites, but not insecure HTTP sites
    <meta name="referrer" content="none-when-downgrade">
    
  3. Origin Only: Sends the scheme, host, and port (basically, the subdomain) stripped of the full URL as a referrer, i.e. https://moz.com/example.html would simply send https://moz.com
    <meta name="referrer" content="origin">
    

  4. Origin When Cross-Origin: Sends the full URL as the referrer when the target has the same scheme, host, and port (i.e. subdomain) regardless if it’s HTTP or HTTPS, while sending origin-only referral information to external sites. (note: There is a typo in the official spec. Future versions should be “origin-when-cross-origin”)
    <meta name="referrer" content="origin-when-crossorigin">
    
  5. Unsafe URL: Always passes the URL string as a referrer. Note if you have any sensitive information contained in your URL, this isn’t the safest option. By default, URL fragments, username, and password are automatically stripped out.
    <meta name="referrer" content="unsafe-url">
    

The meta referrer tag in action

By clicking the link below, you can get a sense of how the meta referrer tag works.

Check Referrer

Boom!

We’ve set the meta referrer tag for Moz to “origin”, which means when we link out to another site, we pass our scheme, host, and port. The end result is you see http://moz.com as the referrer, stripped of the full URL path (/meta-referrer-tag).

My personal site typically receives several visits per day from Moz. Here’s what my analytics data looked like before and after we implemented the meta referrer tag.

For simplicity and security, most sites may want to implement the “origin” state, but there are drawbacks.

One negative side effect was that as soon as we implemented the meta referrer tag, our AdRoll analytics, which we use for retargeting, stopped working. It turns out that AdRoll uses our referrer information for analytics, but the meta referrer tag “origin” state meant that the only URL they ever saw reported was https://moz.com.

Conclusion

We love the meta referrer tag because it keeps information flowing on the Internet. It’s the way the web is supposed to work!

It helps marketers and webmasters see exactly where their traffic is coming from. It encourages engagement, communication, and even linking, which can lead to improvements in SEO.

Useful links:

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Reblogged 3 years ago from tracking.feedpress.it

Becoming Better SEO Scientists – Whiteboard Friday

Posted by MarkTraphagen

Editor’s note: Today we’re featuring back-to-back episodes of Whiteboard Friday from our friends at Stone Temple Consulting. Make sure to also check out the second episode, “UX, Content Quality, and SEO” from Eric Enge.

Like many other areas of marketing, SEO incorporates elements of science. It becomes problematic for everyone, though, when theories that haven’t been the subject of real scientific rigor are passed off as proven facts. In today’s Whiteboard Friday, Stone Temple Consulting’s Mark Traphagen is here to teach us a thing or two about the scientific method and how it can be applied to our day-to-day work.

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

Video transcription

Howdy, Mozzers. Mark Traphagen from Stone Temple Consulting here today to share with you how to become a better SEO scientist. We know that SEO is a science in a lot of ways, and everything I’m going to say today applies not only to SEO, but testing things like your AdWords, how does that work, quality scores. There’s a lot of different applications you can make in marketing, but we’ll focus on the SEO world because that’s where we do a lot of testing. What I want to talk to you about today is how that really is a science and how we need to bring better science in it to get better results.

The reason is in astrophysics, things like that we know there’s something that they’re talking about these days called dark matter, and dark matter is something that we know it’s there. It’s pretty much accepted that it’s there. We can’t see it. We can’t measure it directly. We don’t even know what it is. We can’t even imagine what it is yet, and yet we know it’s there because we see its effect on things like gravity and mass. Its effects are everywhere. And that’s a lot like search engines, isn’t it? It’s like Google or Bing. We see the effects, but we don’t see inside the machine. We don’t know exactly what’s happening in there.

An artist’s depiction of how search engines work.

So what do we do? We do experiments. We do tests to try to figure that out, to see the effects, and from the effects outside we can make better guesses about what’s going on inside and do a better job of giving those search engines what they need to connect us with our customers and prospects. That’s the goal in the end.

Now, the problem is there’s a lot of testing going on out there, a lot of experiments that maybe aren’t being run very well. They’re not being run according to scientific principles that have been proven over centuries to get the best possible results.

Basic data science in 10 steps

So today I want to give you just very quickly 10 basic things that a real scientist goes through on their way to trying to give you better data. Let’s see what we can do with those in our SEO testing in the future.

So let’s start with number one. You’ve got to start with a hypothesis. Your hypothesis is the question that you want to solve. You always start with that, a good question in mind, and it’s got to be relatively narrow. You’ve got to narrow it down to something very specific. Something like how does time on page effect rankings, that’s pretty narrow. That’s very specific. That’s a good question. Might be able to test that. But something like how do social signals effect rankings, that’s too broad. You’ve got to narrow it down. Get it down to one simple question.

Then you choose a variable that you’re going to test. Out of all the things that you could do, that you could play with or you could tweak, you should choose one thing or at least a very few things that you’re going to tweak and say, “When we tweak this, when we change this, when we do this one thing, what happens? Does it change anything out there in the world that we are looking at?” That’s the variable.

The next step is to set a sample group. Where are you going to gather the data from? Where is it going to come from? That’s the world that you’re working in here. Out of all the possible data that’s out there, where are you going to gather your data and how much? That’s the small circle within the big circle. Now even though it’s smaller, you’re probably not going to get all the data in the world. You’re not going to scrape every search ranking that’s possible or visit every URL.

You’ve got to ask yourself, “Is it large enough that we’re at least going to get some validity?” If I wanted to find out what is the typical person in Seattle and I might walk through just one part of the Moz offices here, I’d get some kind of view. But is that a typical, average person from Seattle? I’ve been around here at Moz. Probably not. But this was large enough.

Also, it should be randomized as much as possible. Again, going back to that example, if I just stayed here within the walls of Moz and do research about Mozzers, I’d learn a lot about what Mozzers do, what Mozzers think, how they behave. But that may or may not be applicable to the larger world outside, so you randomized.

We want to control. So we’ve got our sample group. If possible, it’s always good to have another sample group that you don’t do anything to. You do not manipulate the variable in that group. Now, why do you have that? You have that so that you can say, to some extent, if we saw a change when we manipulated our variable and we did not see it in the control group, the same thing didn’t happen, more likely it’s not just part of the natural things that happen in the world or in the search engine.

If possible, even better you want to make that what scientists call double blind, which means that even you the experimenter don’t know who that control group is out of all the SERPs that you’re looking at or whatever it is. As careful as you might be and honest as you might be, you can end up manipulating the results if you know who is who within the test group? It’s not going to apply to every test that we do in SEO, but a good thing to have in mind as you work on that.

Next, very quickly, duration. How long does it have to be? Is there sufficient time? If you’re just testing like if I share a URL to Google +, how quickly does it get indexed in the SERPs, you might only need a day on that because typically it takes less than a day in that case. But if you’re looking at seasonality effects, you might need to go over several years to get a good test on that.

Let’s move to the second group here. The sixth thing keep a clean lab. Now what that means is try as much as possible to keep anything that might be dirtying your results, any kind of variables creeping in that you didn’t want to have in the test. Hard to do, especially in what we’re testing, but do the best you can to keep out the dirt.

Manipulate only one variable. Out of all the things that you could tweak or change choose one thing or a very small set of things. That will give more accuracy to your test. The more variables that you change, the more other effects and inner effects that are going to happen that you may not be accounting for and are going to muddy your results.

Make sure you have statistical validity when you go to analyze those results. Now that’s beyond the scope of this little talk, but you can read up on that. Or even better, if you are able to, hire somebody or work with somebody who is a trained data scientist or has training in statistics so they can look at your evaluation and say the correlations or whatever you’re seeing, “Does it have a statistical significance?” Very important.

Transparency. As much as possible, share with the world your data set, your full results, your methodology. What did you do? How did you set up the study? That’s going to be important to our last step here, which is replication and falsification, one of the most important parts of any scientific process.

So what you want to invite is, hey we did this study. We did this test. Here’s what we found. Here’s how we did it. Here’s the data. If other people ask the same question again and run the same kind of test, do they get the same results? Somebody runs it again, do they get the same results? Even better, if you have some people out there who say, “I don’t think you’re right about that because I think you missed this, and I’m going to throw this in and see what happens,” aha they falsify. That might make you feel like you failed, but it’s success because in the end what are we after? We’re after the truth about what really works.

Think about your next test, your next experiment that you do. How can you apply these 10 principles to do better testing, get better results, and have better marketing? Thanks.

Video transcription by Speechpad.com

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Reblogged 3 years ago from tracking.feedpress.it

The Importance of Being Different: Creating a Competitive Advantage With Your USP

Posted by TrentonGreener

“The one who follows the crowd will usually go no further than the crowd. Those who walk alone are likely to find themselves in places no one has ever been before.”

While this quote has been credited to everyone from Francis Phillip Wernig, under the pseudonym Alan Ashley-Pitt, to Einstein himself, the powerful message does not lose its substance no matter whom you choose to credit. There is a very important yet often overlooked effect of not heeding this warning. One which can be applied to all aspects of life. From love and happiness, to business and marketing, copying what your competitors are doing and failing to forge your own path can be a detrimental mistake.

While as marketers we are all acutely aware of the importance of differentiation, we’ve been trained for the majority of our lives to seek out the norm.

We spend the majority of our adolescent lives trying desperately not to be different. No one has ever been picked on for being too normal or not being different enough. We would beg our parents to buy us the same clothes little Jimmy or little Jamie wore. We’d want the same backpack and the same bike everyone else had. With the rise of the cell phone and later the smartphone, on hands and knees, we begged and pleaded for our parents to buy us the Razr, the StarTAC (bonus points if you didn’t have to Google that one), and later the iPhone. Did we truly want these things? Yes, but not just because they were cutting edge and nifty. We desired them because the people around us had them. We didn’t want to be the last to get these devices. We didn’t want to be different.

Thankfully, as we mature we begin to realize the fallacy that is trying to be normal. We start to become individuals and learn to appreciate that being different is often seen as beautiful. However, while we begin to celebrate being different on a personal level, it does not always translate into our business or professional lives.

We unconsciously and naturally seek out the normal, and if we want to be different—truly different in a way that creates an advantage—we have to work for it.

The truth of the matter is, anyone can be different. In fact, we all are very different. Even identical twins with the same DNA will often have starkly different personalities. As a business, the real challenge lies in being different in a way that is relevant, valuable to your audience, and creates an advantage.

“Strong products and services are highly differentiated from all other products and services. It’s that simple. It’s that difficult.” – Austin McGhie, Brand Is a Four Letter Word

Let’s explore the example of Revel Hotel & Casino. Revel is a 70-story luxury casino in Atlantic City that was built in 2012. There is simply not another casino of the same class in Atlantic City, but there might be a reason for this. Even if you’re not familiar with the city, a quick jump onto Atlantic City’s tourism website reveals that of the five hero banners that rotate, not one specifically mentions gambling, but three reference the boardwalk. This is further illustrated when exploring their internal linking structure. The beaches, boardwalk, and shopping all appear before a single mention of casinos. There simply isn’t as much of a market for high-end gamblers in the Atlantic City area; in the states Las Vegas serves that role. So while Revel has a unique advantage, their ability to attract customers to their resort has not resulted in profitable earnings reports. In Q2 2012, Revel had a gross operating loss of $35.177M, and in Q3 2012 that increased to $36.838M.

So you need to create a unique selling proposition (also known as unique selling point and commonly referred to as a USP), and your USP needs to be valuable to your audience and create a competitive advantage. Sounds easy enough, right? Now for the kicker. That advantage needs to be as sustainable as physically possible over the long term.

“How long will it take our competitors to duplicate our advantage?”

You really need to explore this question and the possible solutions your competitors could utilize to play catch-up or duplicate what you’ve done. Look no further than Google vs Bing to see this in action. No company out there is going to just give up because your USP is so much better; most will pivot or adapt in some way.

Let’s look at a Seattle-area coffee company of which you may or may not be familiar. Starbucks has tried quite a few times over the years to level-up their tea game with limited success, but the markets that Starbucks has really struggled to break into are the pastry, breads, dessert, and food markets.

Other stores had more success in these markets, and they thought that high-quality teas and bakery items were the USPs that differentiated them from the Big Bad Wolf that is Starbucks. And while they were right to think that their brick house would save them from the Big Bad Wolf for some time, this fable doesn’t end with the Big Bad Wolf in a boiling pot.

Never underestimate your competitor’s ability to be agile, specifically when overcoming a competitive disadvantage.

If your competitor can’t beat you by making a better product or service internally, they can always choose to buy someone who can.

After months of courting, on June 4th, 2012 Starbucks announced that they had come to an agreement to purchase La Boulange in order to “elevate core food offerings and build a premium, artisanal bakery brand.” If you’re a small-to-medium sized coffee shop and/or bakery that even indirectly competed with Starbucks, a new challenger approaches. And while those tea shops momentarily felt safe within the brick walls that guarded their USP, on the final day of that same year, the Big Bad Wolf huffed and puffed and blew a stack of cash all over Teavana. Making Teavana a wholly-owned subsidiary of Starbucks for the low, low price of $620M.

Sarcasm aside, this does a great job of illustrating the ability of companies—especially those with deep pockets—to be agile, and demonstrates that they often have an uncanny ability to overcome your company’s competitive advantage. In seven months, Starbucks went from a minor player in these markets to having all the tools they need to dominate tea and pastries. Have you tried their raspberry pound cake? It’s phenomenal.

Why does this matter to me?

Ok, we get it. We need to be different, and in a way that is relevant, valuable, defensible, and sustainable. But I’m not the CEO, or even the CMO. I cannot effect change on a company level; why does this matter to me?

I’m a firm believer that you effect change no matter what the name plate on your desk may say. Sure, you may not be able to call an all-staff meeting today and completely change the direction of your company tomorrow, but you can effect change on the parts of the business you do touch. No matter your title or area of responsibility, you need to know your company’s, client’s, or even a specific piece of content’s USP, and you need to ensure it is applied liberally to all areas of your work.

Look at this example SERP for “Mechanics”:

While yes, this search is very likely to be local-sensitive, that doesn’t mean you can’t stand out. Every single AdWords result, save one, has only the word “Mechanics” in the headline. (While the top of page ad is pulling description line 1 into the heading, the actual headline is still only “Mechanic.”) But even the one headline that is different doesn’t do a great job of illustrating the company’s USP. Mechanics at home? Whose home? Mine or theirs? I’m a huge fan of Steve Krug’s “Don’t Make Me Think,” and in this scenario there are too many questions I need answered before I’m willing to click through. “Mechanics; We Come To You” or even “Traveling Mechanics” illustrates this point much more clearly, and still fits within the 25-character limit for the headline.

If you’re an AdWords user, no matter how big or small your monthly spend may be, take a look at your top 10-15 keywords by volume and evaluate how well you’re differentiating yourself from the other brands in your industry. Test ad copy that draws attention to your USP and reap the rewards.

Now while this is simply an AdWords text ad example, the same concept can be applied universally across all of marketing.

Title tags & meta descriptions

As we alluded to above, not only do companies have USPs, but individual pieces of content can, and should, have their own USP. Use your title tag and meta description to illustrate what differentiates your piece of content from the competition and do so in a way that attracts the searcher’s click. Use your USP to your advantage. If you have already established a strong brand within a specific niche, great! Now use it to your advantage. Though it’s much more likely that you are competing against a strong brand, and in these scenarios ask yourself, “What makes our content different from theirs?” The answer you come up with is your content’s USP. Call attention to that in your title tag and meta description, and watch the CTR climb.

I encourage you to hop into your own site’s analytics and look at your top 10-15 organic landing pages and see how well you differentiate yourself. Even if you’re hesitant to negatively affect your inbound gold mines by changing the title tags, run a test and change up your meta description to draw attention to your USP. In an hour’s work, you just may make the change that pushes you a little further up those SERPs.

Branding

Let’s break outside the world of digital marketing and look at the world of branding. Tom’s Shoes competes against some heavy hitters in Nike, Adidas, Reebok, and Puma just to name a few. While Tom’s can’t hope to compete against the marketing budgets of these companies in a fair fight, they instead chose to take what makes them different, their USP, and disseminate it every chance they get. They have labeled themselves “The One for One” company. It’s in their homepage’s title tag, in every piece of marketing they put out, and it smacks you in the face when you land on their site. They even use the call-to-action “Get Good Karma” throughout their site.

Now as many of us may know, partially because of the scandal it created in late 2013, Tom’s is not actually a non-profit organization. No matter how you feel about the matter, this marketing strategy has created a positive effect on their bottom line. Fast Company conservatively estimated their revenues in 2013 at $250M, with many estimates being closer to the $300M mark. Not too bad of a slice of the pie when competing against the powerhouses Tom’s does.

Wherever you stand on this issue, Tom’s Shoes has done a phenomenal job of differentiating their brand from the big hitters in their industry.

Know your USP and disseminate it every chance you get.

This is worth repeating. Know your USP and disseminate it every chance you get, whether that be in title tags, ad copy, on-page copy, branding, or any other segment of your marketing campaigns. Online or offline, be different. And remember the quote that we started with, “The one who follows the crowd will usually go no further than the crowd. Those who walk alone are likely to find themselves in places no one has ever been before.”

The amount of marketing knowledge that can be taken from this one simple statement is astounding. Heed the words, stand out from the crowd, and you will have success.

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Reblogged 3 years ago from tracking.feedpress.it

Misuses of 4 Google Analytics Metrics Debunked

Posted by Tom.Capper

In this post I’ll pull apart four of the most commonly used metrics in Google Analytics, how they are collected, and why they are so easily misinterpreted.

Average Time on Page

Average time on page should be a really useful metric, particularly if you’re interested in engagement with content that’s all on a single page. Unfortunately, this is actually its worst use case. To understand why, you need to understand how time on page is calculated in Google Analytics:

Time on Page: Total across all pageviews of time from pageview to last engagement hit on that page (where an engagement hit is any of: next pageview, interactive event, e-commerce transaction, e-commerce item hit, or social plugin). (Source)

If there is no subsequent engagement hit, or if there is a gap between the last engagement hit on a site and leaving the site, the assumption is that no further time was spent on the site. Below are some scenarios with an intuitive time on page of 20 seconds, and their Google Analytics time on page:

Scenario

Intuitive time on page

GA time on page

0s: Pageview
10s: Social plugin
20s: Click through to next page

20s

20s

0s: Pageview
10s: Social plugin
20s: Leave site

20s

10s

0s: Pageview
20s: Leave site

20s

0s

Google doesn’t want exits to influence the average time on page, because of scenarios like the third example above, where they have a time on page of 0 seconds (source). To avoid this, they use the following formula (remember that Time on Page is a total):

Average Time on Page: (Time on Page) / (Pageviews – Exits)

However, as the second example above shows, this assumption doesn’t always hold. The second example feeds into the top half of the average time on page faction, but not the bottom half:

Example 2 Average Time on Page: (20s+10s+0s) / (3-2) = 30s

There are two issues here:

  1. Overestimation
    Excluding exits from the second half of the average time on page equation doesn’t have the desired effect when their time on page wasn’t 0 seconds—note that 30s is longer than any of the individual visits. This is why average time on page can often be longer than average visit duration. Nonetheless, 30 seconds doesn’t seem too far out in the above scenario (the intuitive average is 20s), but in the real world many pages have much higher exit rates than the 67% in this example, and/or much less engagement with events on page.
  2. Ignored visits
    Considering only visitors who exit without an engagement hit, whether these visitors stayed for 2 seconds, 10 minutes or anything inbetween, it doesn’t influence average time on page in the slightest. On many sites, a 10 minute view of a single page without interaction (e.g. a blog post) would be considered a success, but it wouldn’t influence this metric.

Solution: Unfortunately, there isn’t an easy solution to this issue. If you want to use average time on page, you just need to keep in mind how it’s calculated. You could also consider setting up more engagement events on page (like a scroll event without the “nonInteraction” parameter)—this solves issue #2 above, but potentially worsens issue #1.

Site Speed

If you’ve used the Site Speed reports in Google Analytics in the past, you’ve probably noticed that the numbers can sometimes be pretty difficult to believe. This is because the way that Site Speed is tracked is extremely vulnerable to outliers—it starts with a 1% sample of your users and then takes a simple average for each metric. This means that a few extreme values (for example, the occasional user with a malware-infested computer or a questionable wifi connection) can create a very large swing in your data.

The use of an average as a metric is not in itself bad, but in an area so prone to outliers and working with such a small sample, it can lead to questionable results.

Fortunately, you can increase the sampling rate right up to 100% (or the cap of 10,000 hits per day). Depending on the size of your site, this may still only be useful for top-level data. For example, if your site gets 1,000,000 hits per day and you’re interested in the performance of a new page that’s receiving 100 hits per day, Google Analytics will throttle your sampling back to the 10,000 hits per day cap—1%. As such, you’ll only be looking at a sample of 1 hit per day for that page.

Solution: Turn up the sampling rate. If you receive more than 10,000 hits per day, keep the sampling rate in mind when digging into less visited pages. You could also consider external tools and testing, such as Pingdom or WebPagetest.

Conversion Rate (by channel)

Obviously, conversion rate is not in itself a bad metric, but it can be rather misleading in certain reports if you don’t realise that, by default, conversions are attributed using a last non-direct click attribution model.

From Google Analytics Help:

“…if a person clicks over your site from google.com, then returns as “direct” traffic to convert, Google Analytics will report 1 conversion for “google.com / organic” in All Traffic.”

This means that when you’re looking at conversion numbers in your acquisition reports, it’s quite possible that every single number is different to what you’d expect under last click—every channel other than direct has a total that includes some conversions that occurred during direct sessions, and direct itself has conversion numbers that don’t include some conversions that occurred during direct sessions.

Solution: This is just something to be aware of. If you do want to know your last-click numbers, there’s always the Multi-Channel Funnels and Attribution reports to help you out.

Exit Rate

Unlike some of the other metrics I’ve discussed here, the calculation behind exit rate is very intuitive—”for all pageviews to the page, Exit Rate is the percentage that were the last in the session.” The problem with exit rate is that it’s so often used as a negative metric: “Which pages had the highest exit rate? They’re the problem with our site!” Sometimes this might be true: Perhaps, for example, if those pages are in the middle of a checkout funnel.

Often, however, a user will exit a site when they’ve found what they want. This doesn’t just mean that a high exit rate is ok on informational pages like blog posts or about pages—it could also be true of product pages and other pages with a highly conversion-focused intent. Even on ecommerce sites, not every visitor has the intention of converting. They might be researching towards a later online purchase, or even planning to visit your physical store. This is particularly true if your site ranks well for long tail queries or is referenced elsewhere. In this case, an exit could be a sign that they found the information they wanted and are ready to purchase once they have the money, the need, the right device at hand or next time they’re passing by your shop.

Solution: When judging a page by its exit rate, think about the various possible user intents. It could be useful to take a segment of visitors who exited on a certain page (in the Advanced tab of the new segment menu), and investigate their journey in User Flow reports, or their landing page and acquisition data.

Discussion

If you know of any other similarly misunderstood metrics, you have any questions or you have something to add to my analysis, tweet me at @THCapper or leave a comment below.

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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.

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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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Reblogged 3 years ago from tracking.feedpress.it