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

Posted by jon.white

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

Track your mobile vs. desktop rankings in Moz Analytics

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

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

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

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

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

We have given an extra engine to all campaigns

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

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

Measure your Search Visibility score vs. competitors

The overall Search Visibility for my campaign

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

How is Search Visibility calculated?

Good question!

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

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

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

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

Some neat ways to use this feature

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

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Reblogged 4 years ago from tracking.feedpress.it

Controlling Search Engine Crawlers for Better Indexation and Rankings – Whiteboard Friday

Posted by randfish

When should you disallow search engines in your robots.txt file, and when should you use meta robots tags in a page header? What about nofollowing links? In today’s Whiteboard Friday, Rand covers these tools and their appropriate use in four situations that SEOs commonly find themselves facing.

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

Video transcription

Howdy Moz fans, and welcome to another edition of Whiteboard Friday. This week we’re going to talk about controlling search engine crawlers, blocking bots, sending bots where we want, restricting them from where we don’t want them to go. We’re going to talk a little bit about crawl budget and what you should and shouldn’t have indexed.

As a start, what I want to do is discuss the ways in which we can control robots. Those include the three primary ones: robots.txt, meta robots, and—well, the nofollow tag is a little bit less about controlling bots.

There are a few others that we’re going to discuss as well, including Webmaster Tools (Search Console) and URL status codes. But let’s dive into those first few first.

Robots.txt lives at yoursite.com/robots.txt, it tells crawlers what they should and shouldn’t access, it doesn’t always get respected by Google and Bing. So a lot of folks when you say, “hey, disallow this,” and then you suddenly see those URLs popping up and you’re wondering what’s going on, look—Google and Bing oftentimes think that they just know better. They think that maybe you’ve made a mistake, they think “hey, there’s a lot of links pointing to this content, there’s a lot of people who are visiting and caring about this content, maybe you didn’t intend for us to block it.” The more specific you get about an individual URL, the better they usually are about respecting it. The less specific, meaning the more you use wildcards or say “everything behind this entire big directory,” the worse they are about necessarily believing you.

Meta robots—a little different—that lives in the headers of individual pages, so you can only control a single page with a meta robots tag. That tells the engines whether or not they should keep a page in the index, and whether they should follow the links on that page, and it’s usually a lot more respected, because it’s at an individual-page level; Google and Bing tend to believe you about the meta robots tag.

And then the nofollow tag, that lives on an individual link on a page. It doesn’t tell engines where to crawl or not to crawl. All it’s saying is whether you editorially vouch for a page that is being linked to, and whether you want to pass the PageRank and link equity metrics to that page.

Interesting point about meta robots and robots.txt working together (or not working together so well)—many, many folks in the SEO world do this and then get frustrated.

What if, for example, we take a page like “blogtest.html” on our domain and we say “all user agents, you are not allowed to crawl blogtest.html. Okay—that’s a good way to keep that page away from being crawled, but just because something is not crawled doesn’t necessarily mean it won’t be in the search results.

So then we have our SEO folks go, “you know what, let’s make doubly sure that doesn’t show up in search results; we’ll put in the meta robots tag:”

<meta name="robots" content="noindex, follow">

So, “noindex, follow” tells the search engine crawler they can follow the links on the page, but they shouldn’t index this particular one.

Then, you go and run a search for “blog test” in this case, and everybody on the team’s like “What the heck!? WTF? Why am I seeing this page show up in search results?”

The answer is, you told the engines that they couldn’t crawl the page, so they didn’t. But they are still putting it in the results. They’re actually probably not going to include a meta description; they might have something like “we can’t include a meta description because of this site’s robots.txt file.” The reason it’s showing up is because they can’t see the noindex; all they see is the disallow.

So, if you want something truly removed, unable to be seen in search results, you can’t just disallow a crawler. You have to say meta “noindex” and you have to let them crawl it.

So this creates some complications. Robots.txt can be great if we’re trying to save crawl bandwidth, but it isn’t necessarily ideal for preventing a page from being shown in the search results. I would not recommend, by the way, that you do what we think Twitter recently tried to do, where they tried to canonicalize www and non-www by saying “Google, don’t crawl the www version of twitter.com.” What you should be doing is rel canonical-ing or using a 301.

Meta robots—that can allow crawling and link-following while disallowing indexation, which is great, but it requires crawl budget and you can still conserve indexing.

The nofollow tag, generally speaking, is not particularly useful for controlling bots or conserving indexation.

Webmaster Tools (now Google Search Console) has some special things that allow you to restrict access or remove a result from the search results. For example, if you have 404’d something or if you’ve told them not to crawl something but it’s still showing up in there, you can manually say “don’t do that.” There are a few other crawl protocol things that you can do.

And then URL status codes—these are a valid way to do things, but they’re going to obviously change what’s going on on your pages, too.

If you’re not having a lot of luck using a 404 to remove something, you can use a 410 to permanently remove something from the index. Just be aware that once you use a 410, it can take a long time if you want to get that page re-crawled or re-indexed, and you want to tell the search engines “it’s back!” 410 is permanent removal.

301—permanent redirect, we’ve talked about those here—and 302, temporary redirect.

Now let’s jump into a few specific use cases of “what kinds of content should and shouldn’t I allow engines to crawl and index” in this next version…

[Rand moves at superhuman speed to erase the board and draw part two of this Whiteboard Friday. Seriously, we showed Roger how fast it was, and even he was impressed.]

Four crawling/indexing problems to solve

So we’ve got these four big problems that I want to talk about as they relate to crawling and indexing.

1. Content that isn’t ready yet

The first one here is around, “If I have content of quality I’m still trying to improve—it’s not yet ready for primetime, it’s not ready for Google, maybe I have a bunch of products and I only have the descriptions from the manufacturer and I need people to be able to access them, so I’m rewriting the content and creating unique value on those pages… they’re just not ready yet—what should I do with those?”

My options around crawling and indexing? If I have a large quantity of those—maybe thousands, tens of thousands, hundreds of thousands—I would probably go the robots.txt route. I’d disallow those pages from being crawled, and then eventually as I get (folder by folder) those sets of URLs ready, I can then allow crawling and maybe even submit them to Google via an XML sitemap.

If I’m talking about a small quantity—a few dozen, a few hundred pages—well, I’d probably just use the meta robots noindex, and then I’d pull that noindex off of those pages as they are made ready for Google’s consumption. And then again, I would probably use the XML sitemap and start submitting those once they’re ready.

2. Dealing with duplicate or thin content

What about, “Should I noindex, nofollow, or potentially disallow crawling on largely duplicate URLs or thin content?” I’ve got an example. Let’s say I’m an ecommerce shop, I’m selling this nice Star Wars t-shirt which I think is kind of hilarious, so I’ve got starwarsshirt.html, and it links out to a larger version of an image, and that’s an individual HTML page. It links out to different colors, which change the URL of the page, so I have a gray, blue, and black version. Well, these four pages are really all part of this same one, so I wouldn’t recommend disallowing crawling on these, and I wouldn’t recommend noindexing them. What I would do there is a rel canonical.

Remember, rel canonical is one of those things that can be precluded by disallowing. So, if I were to disallow these from being crawled, Google couldn’t see the rel canonical back, so if someone linked to the blue version instead of the default version, now I potentially don’t get link credit for that. So what I really want to do is use the rel canonical, allow the indexing, and allow it to be crawled. If you really feel like it, you could also put a meta “noindex, follow” on these pages, but I don’t really think that’s necessary, and again that might interfere with the rel canonical.

3. Passing link equity without appearing in search results

Number three: “If I want to pass link equity (or at least crawling) through a set of pages without those pages actually appearing in search results—so maybe I have navigational stuff, ways that humans are going to navigate through my pages, but I don’t need those appearing in search results—what should I use then?”

What I would say here is, you can use the meta robots to say “don’t index the page, but do follow the links that are on that page.” That’s a pretty nice, handy use case for that.

Do NOT, however, disallow those in robots.txt—many, many folks make this mistake. What happens if you disallow crawling on those, Google can’t see the noindex. They don’t know that they can follow it. Granted, as we talked about before, sometimes Google doesn’t obey the robots.txt, but you can’t rely on that behavior. Trust that the disallow in robots.txt will prevent them from crawling. So I would say, the meta robots “noindex, follow” is the way to do this.

4. Search results-type pages

Finally, fourth, “What should I do with search results-type pages?” Google has said many times that they don’t like your search results from your own internal engine appearing in their search results, and so this can be a tricky use case.

Sometimes a search result page—a page that lists many types of results that might come from a database of types of content that you’ve got on your site—could actually be a very good result for a searcher who is looking for a wide variety of content, or who wants to see what you have on offer. Yelp does this: When you say, “I’m looking for restaurants in Seattle, WA,” they’ll give you what is essentially a list of search results, and Google does want those to appear because that page provides a great result. But you should be doing what Yelp does there, and make the most common or popular individual sets of those search results into category-style pages. A page that provides real, unique value, that’s not just a list of search results, that is more of a landing page than a search results page.

However, that being said, if you’ve got a long tail of these, or if you’d say “hey, our internal search engine, that’s really for internal visitors only—it’s not useful to have those pages show up in search results, and we don’t think we need to make the effort to make those into category landing pages.” Then you can use the disallow in robots.txt to prevent those.

Just be cautious here, because I have sometimes seen an over-swinging of the pendulum toward blocking all types of search results, and sometimes that can actually hurt your SEO and your traffic. Sometimes those pages can be really useful to people. So check your analytics, and make sure those aren’t valuable pages that should be served up and turned into landing pages. If you’re sure, then go ahead and disallow all your search results-style pages. You’ll see a lot of sites doing this in their robots.txt file.

That being said, I hope you have some great questions about crawling and indexing, controlling robots, blocking robots, allowing robots, and I’ll try and tackle those in the comments below.

We’ll look forward to seeing you again next week for another edition of Whiteboard Friday. Take care!

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Reblogged 4 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.

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

Reblogged 4 years ago from tracking.feedpress.it

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 4 years ago from tracking.feedpress.it

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

Posted by randfish

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

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

What SEO problems make you angry?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Video transcription by Speechpad.com

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Reblogged 4 years ago from tracking.feedpress.it

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

Posted by Dr-Pete

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

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

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

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

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

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

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

Tracking a long roll-out

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

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

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

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

Tracking potential losers

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

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

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

Changes beyond rankings

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

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

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

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

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

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

A bit of good news

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

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

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

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Reblogged 4 years ago from tracking.feedpress.it