Why Effective, Modern SEO Requires Technical, Creative, and Strategic Thinking – Whiteboard Friday

Posted by randfish

There’s no doubt that quite a bit has changed about SEO, and that the field is far more integrated with other aspects of online marketing than it once was. In today’s Whiteboard Friday, Rand pushes back against the idea that effective modern SEO doesn’t require any technical expertise, outlining a fantastic list of technical elements that today’s SEOs need to know about in order to be truly effective.

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 I’m going to do something unusual. I don’t usually point out these inconsistencies or sort of take issue with other folks’ content on the web, because I generally find that that’s not all that valuable and useful. But I’m going to make an exception here.

There is an article by Jayson DeMers, who I think might actually be here in Seattle — maybe he and I can hang out at some point — called “Why Modern SEO Requires Almost No Technical Expertise.” It was an article that got a shocking amount of traction and attention. On Facebook, it has thousands of shares. On LinkedIn, it did really well. On Twitter, it got a bunch of attention.

Some folks in the SEO world have already pointed out some issues around this. But because of the increasing popularity of this article, and because I think there’s, like, this hopefulness from worlds outside of kind of the hardcore SEO world that are looking to this piece and going, “Look, this is great. We don’t have to be technical. We don’t have to worry about technical things in order to do SEO.”

Look, I completely get the appeal of that. I did want to point out some of the reasons why this is not so accurate. At the same time, I don’t want to rain on Jayson, because I think that it’s very possible he’s writing an article for Entrepreneur, maybe he has sort of a commitment to them. Maybe he had no idea that this article was going to spark so much attention and investment. He does make some good points. I think it’s just really the title and then some of the messages inside there that I take strong issue with, and so I wanted to bring those up.

First off, some of the good points he did bring up.

One, he wisely says, “You don’t need to know how to code or to write and read algorithms in order to do SEO.” I totally agree with that. If today you’re looking at SEO and you’re thinking, “Well, am I going to get more into this subject? Am I going to try investing in SEO? But I don’t even know HTML and CSS yet.”

Those are good skills to have, and they will help you in SEO, but you don’t need them. Jayson’s totally right. You don’t have to have them, and you can learn and pick up some of these things, and do searches, watch some Whiteboard Fridays, check out some guides, and pick up a lot of that stuff later on as you need it in your career. SEO doesn’t have that hard requirement.

And secondly, he makes an intelligent point that we’ve made many times here at Moz, which is that, broadly speaking, a better user experience is well correlated with better rankings.

You make a great website that delivers great user experience, that provides the answers to searchers’ questions and gives them extraordinarily good content, way better than what’s out there already in the search results, generally speaking you’re going to see happy searchers, and that’s going to lead to higher rankings.

But not entirely. There are a lot of other elements that go in here. So I’ll bring up some frustrating points around the piece as well.

First off, there’s no acknowledgment — and I find this a little disturbing — that the ability to read and write code, or even HTML and CSS, which I think are the basic place to start, is helpful or can take your SEO efforts to the next level. I think both of those things are true.

So being able to look at a web page, view source on it, or pull up Firebug in Firefox or something and diagnose what’s going on and then go, “Oh, that’s why Google is not able to see this content. That’s why we’re not ranking for this keyword or term, or why even when I enter this exact sentence in quotes into Google, which is on our page, this is why it’s not bringing it up. It’s because it’s loading it after the page from a remote file that Google can’t access.” These are technical things, and being able to see how that code is built, how it’s structured, and what’s going on there, very, very helpful.

Some coding knowledge also can take your SEO efforts even further. I mean, so many times, SEOs are stymied by the conversations that we have with our programmers and our developers and the technical staff on our teams. When we can have those conversations intelligently, because at least we understand the principles of how an if-then statement works, or what software engineering best practices are being used, or they can upload something into a GitHub repository, and we can take a look at it there, that kind of stuff is really helpful.

Secondly, I don’t like that the article overly reduces all of this information that we have about what we’ve learned about Google. So he mentions two sources. One is things that Google tells us, and others are SEO experiments. I think both of those are true. Although I’d add that there’s sort of a sixth sense of knowledge that we gain over time from looking at many, many search results and kind of having this feel for why things rank, and what might be wrong with a site, and getting really good at that using tools and data as well. There are people who can look at Open Site Explorer and then go, “Aha, I bet this is going to happen.” They can look, and 90% of the time they’re right.

So he boils this down to, one, write quality content, and two, reduce your bounce rate. Neither of those things are wrong. You should write quality content, although I’d argue there are lots of other forms of quality content that aren’t necessarily written — video, images and graphics, podcasts, lots of other stuff.

And secondly, that just doing those two things is not always enough. So you can see, like many, many folks look and go, “I have quality content. It has a low bounce rate. How come I don’t rank better?” Well, your competitors, they’re also going to have quality content with a low bounce rate. That’s not a very high bar.

Also, frustratingly, this really gets in my craw. I don’t think “write quality content” means anything. You tell me. When you hear that, to me that is a totally non-actionable, non-useful phrase that’s a piece of advice that is so generic as to be discardable. So I really wish that there was more substance behind that.

The article also makes, in my opinion, the totally inaccurate claim that modern SEO really is reduced to “the happier your users are when they visit your site, the higher you’re going to rank.”

Wow. Okay. Again, I think broadly these things are correlated. User happiness and rank is broadly correlated, but it’s not a one to one. This is not like a, “Oh, well, that’s a 1.0 correlation.”

I would guess that the correlation is probably closer to like the page authority range. I bet it’s like 0.35 or something correlation. If you were to actually measure this broadly across the web and say like, “Hey, were you happier with result one, two, three, four, or five,” the ordering would not be perfect at all. It probably wouldn’t even be close.

There’s a ton of reasons why sometimes someone who ranks on Page 2 or Page 3 or doesn’t rank at all for a query is doing a better piece of content than the person who does rank well or ranks on Page 1, Position 1.

Then the article suggests five and sort of a half steps to successful modern SEO, which I think is a really incomplete list. So Jayson gives us;

  • Good on-site experience
  • Writing good content
  • Getting others to acknowledge you as an authority
  • Rising in social popularity
  • Earning local relevance
  • Dealing with modern CMS systems (which he notes most modern CMS systems are SEO-friendly)

The thing is there’s nothing actually wrong with any of these. They’re all, generally speaking, correct, either directly or indirectly related to SEO. The one about local relevance, I have some issue with, because he doesn’t note that there’s a separate algorithm for sort of how local SEO is done and how Google ranks local sites in maps and in their local search results. Also not noted is that rising in social popularity won’t necessarily directly help your SEO, although it can have indirect and positive benefits.

I feel like this list is super incomplete. Okay, I brainstormed just off the top of my head in the 10 minutes before we filmed this video a list. The list was so long that, as you can see, I filled up the whole whiteboard and then didn’t have any more room. I’m not going to bother to erase and go try and be absolutely complete.

But there’s a huge, huge number of things that are important, critically important for technical SEO. If you don’t know how to do these things, you are sunk in many cases. You can’t be an effective SEO analyst, or consultant, or in-house team member, because you simply can’t diagnose the potential problems, rectify those potential problems, identify strategies that your competitors are using, be able to diagnose a traffic gain or loss. You have to have these skills in order to do that.

I’ll run through these quickly, but really the idea is just that this list is so huge and so long that I think it’s very, very, very wrong to say technical SEO is behind us. I almost feel like the opposite is true.

We have to be able to understand things like;

  • Content rendering and indexability
  • Crawl structure, internal links, JavaScript, Ajax. If something’s post-loading after the page and Google’s not able to index it, or there are links that are accessible via JavaScript or Ajax, maybe Google can’t necessarily see those or isn’t crawling them as effectively, or is crawling them, but isn’t assigning them as much link weight as they might be assigning other stuff, and you’ve made it tough to link to them externally, and so they can’t crawl it.
  • Disabling crawling and/or indexing of thin or incomplete or non-search-targeted content. We have a bunch of search results pages. Should we use rel=prev/next? Should we robots.txt those out? Should we disallow from crawling with meta robots? Should we rel=canonical them to other pages? Should we exclude them via the protocols inside Google Webmaster Tools, which is now Google Search Console?
  • Managing redirects, domain migrations, content updates. A new piece of content comes out, replacing an old piece of content, what do we do with that old piece of content? What’s the best practice? It varies by different things. We have a whole Whiteboard Friday about the different things that you could do with that. What about a big redirect or a domain migration? You buy another company and you’re redirecting their site to your site. You have to understand things about subdomain structures versus subfolders, which, again, we’ve done another Whiteboard Friday about that.
  • Proper error codes, downtime procedures, and not found pages. If your 404 pages turn out to all be 200 pages, well, now you’ve made a big error there, and Google could be crawling tons of 404 pages that they think are real pages, because you’ve made it a status code 200, or you’ve used a 404 code when you should have used a 410, which is a permanently removed, to be able to get it completely out of the indexes, as opposed to having Google revisit it and keep it in the index.

Downtime procedures. So there’s specifically a… I can’t even remember. It’s a 5xx code that you can use. Maybe it was a 503 or something that you can use that’s like, “Revisit later. We’re having some downtime right now.” Google urges you to use that specific code rather than using a 404, which tells them, “This page is now an error.”

Disney had that problem a while ago, if you guys remember, where they 404ed all their pages during an hour of downtime, and then their homepage, when you searched for Disney World, was, like, “Not found.” Oh, jeez, Disney World, not so good.

  • International and multi-language targeting issues. I won’t go into that. But you have to know the protocols there. Duplicate content, syndication, scrapers. How do we handle all that? Somebody else wants to take our content, put it on their site, what should we do? Someone’s scraping our content. What can we do? We have duplicate content on our own site. What should we do?
  • Diagnosing traffic drops via analytics and metrics. Being able to look at a rankings report, being able to look at analytics connecting those up and trying to see: Why did we go up or down? Did we have less pages being indexed, more pages being indexed, more pages getting traffic less, more keywords less?
  • Understanding advanced search parameters. Today, just today, I was checking out the related parameter in Google, which is fascinating for most sites. Well, for Moz, weirdly, related:oursite.com shows nothing. But for virtually every other sit, well, most other sites on the web, it does show some really interesting data, and you can see how Google is connecting up, essentially, intentions and topics from different sites and pages, which can be fascinating, could expose opportunities for links, could expose understanding of how they view your site versus your competition or who they think your competition is.

Then there are tons of parameters, like in URL and in anchor, and da, da, da, da. In anchor doesn’t work anymore, never mind about that one.

I have to go faster, because we’re just going to run out of these. Like, come on. Interpreting and leveraging data in Google Search Console. If you don’t know how to use that, Google could be telling you, you have all sorts of errors, and you don’t know what they are.

  • Leveraging topic modeling and extraction. Using all these cool tools that are coming out for better keyword research and better on-page targeting. I talked about a couple of those at MozCon, like MonkeyLearn. There’s the new Moz Context API, which will be coming out soon, around that. There’s the Alchemy API, which a lot of folks really like and use.
  • Identifying and extracting opportunities based on site crawls. You run a Screaming Frog crawl on your site and you’re going, “Oh, here’s all these problems and issues.” If you don’t have these technical skills, you can’t diagnose that. You can’t figure out what’s wrong. You can’t figure out what needs fixing, what needs addressing.
  • Using rich snippet format to stand out in the SERPs. This is just getting a better click-through rate, which can seriously help your site and obviously your traffic.
  • Applying Google-supported protocols like rel=canonical, meta description, rel=prev/next, hreflang, robots.txt, meta robots, x robots, NOODP, XML sitemaps, rel=nofollow. The list goes on and on and on. If you’re not technical, you don’t know what those are, you think you just need to write good content and lower your bounce rate, it’s not going to work.
  • Using APIs from services like AdWords or MozScape, or hrefs from Majestic, or SEM refs from SearchScape or Alchemy API. Those APIs can have powerful things that they can do for your site. There are some powerful problems they could help you solve if you know how to use them. It’s actually not that hard to write something, even inside a Google Doc or Excel, to pull from an API and get some data in there. There’s a bunch of good tutorials out there. Richard Baxter has one, Annie Cushing has one, I think Distilled has some. So really cool stuff there.
  • Diagnosing page load speed issues, which goes right to what Jayson was talking about. You need that fast-loading page. Well, if you don’t have any technical skills, you can’t figure out why your page might not be loading quickly.
  • Diagnosing mobile friendliness issues
  • Advising app developers on the new protocols around App deep linking, so that you can get the content from your mobile apps into the web search results on mobile devices. Awesome. Super powerful. Potentially crazy powerful, as mobile search is becoming bigger than desktop.

Okay, I’m going to take a deep breath and relax. I don’t know Jayson’s intention, and in fact, if he were in this room, he’d be like, “No, I totally agree with all those things. I wrote the article in a rush. I had no idea it was going to be big. I was just trying to make the broader points around you don’t have to be a coder in order to do SEO.” That’s completely fine.

So I’m not going to try and rain criticism down on him. But I think if you’re reading that article, or you’re seeing it in your feed, or your clients are, or your boss is, or other folks are in your world, maybe you can point them to this Whiteboard Friday and let them know, no, that’s not quite right. There’s a ton of technical SEO that is required in 2015 and will be for years to come, I think, that SEOs have to have in order to be effective at their jobs.

All right, everyone. Look forward to some great comments, and we’ll see you again next time for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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Deconstructing the App Store Rankings Formula with a Little Mad Science

Posted by AlexApptentive

After seeing Rand’s “Mad Science Experiments in SEO” presented at last year’s MozCon, I was inspired to put on the lab coat and goggles and do a few experiments of my own—not in SEO, but in SEO’s up-and-coming younger sister, ASO (app store optimization).

Working with Apptentive to guide enterprise apps and small startup apps alike to increase their discoverability in the app stores, I’ve learned a thing or two about app store optimization and what goes into an app’s ranking. It’s been my personal goal for some time now to pull back the curtains on Google and Apple. Yet, the deeper into the rabbit hole I go, the more untested assumptions I leave in my way.

Hence, I thought it was due time to put some longstanding hypotheses through the gauntlet.

As SEOs, we know how much of an impact a single ranking can mean on a SERP. One tiny rank up or down can make all the difference when it comes to your website’s traffic—and revenue.

In the world of apps, ranking is just as important when it comes to standing out in a sea of more than 1.3 million apps. Apptentive’s recent mobile consumer survey shed a little more light this claim, revealing that nearly half of all mobile app users identified browsing the app store charts and search results (the placement on either of which depends on rankings) as a preferred method for finding new apps in the app stores. Simply put, better rankings mean more downloads and easier discovery.

Like Google and Bing, the two leading app stores (the Apple App Store and Google Play) have a complex and highly guarded algorithms for determining rankings for both keyword-based app store searches and composite top charts.

Unlike SEO, however, very little research and theory has been conducted around what goes into these rankings.

Until now, that is.

Over the course of five studies analyzing various publicly available data points for a cross-section of the top 500 iOS (U.S. Apple App Store) and the top 500 Android (U.S. Google Play) apps, I’ll attempt to set the record straight with a little myth-busting around ASO. In the process, I hope to assess and quantify any perceived correlations between app store ranks, ranking volatility, and a few of the factors commonly thought of as influential to an app’s ranking.

But first, a little context

Image credit: Josh Tuininga, Apptentive

Both the Apple App Store and Google Play have roughly 1.3 million apps each, and both stores feature a similar breakdown by app category. Apps ranking in the two stores should, theoretically, be on a fairly level playing field in terms of search volume and competition.

Of these apps, nearly two-thirds have not received a single rating and 99% are considered unprofitable. These studies, therefore, single out the rare exceptions to the rule—the top 500 ranked apps in each store.

While neither Apple nor Google have revealed specifics about how they calculate search rankings, it is generally accepted that both app store algorithms factor in:

  • Average app store rating
  • Rating/review volume
  • Download and install counts
  • Uninstalls (what retention and churn look like for the app)
  • App usage statistics (how engaged an app’s users are and how frequently they launch the app)
  • Growth trends weighted toward recency (how daily download counts changed over time and how today’s ratings compare to last week’s)
  • Keyword density of the app’s landing page (Ian did a great job covering this factor in a previous Moz post)

I’ve simplified this formula to a function highlighting the four elements with sufficient data (or at least proxy data) for our analysis:

Ranking = fn(Rating, Rating Count, Installs, Trends)

Of course, right now, this generalized function doesn’t say much. Over the next five studies, however, we’ll revisit this function before ultimately attempting to compare the weights of each of these four variables on app store rankings.

(For the purpose of brevity, I’ll stop here with the assumptions, but I’ve gone into far greater depth into how I’ve reached these conclusions in a 55-page report on app store rankings.)

Now, for the Mad Science.

Study #1: App-les to app-les app store ranking volatility

The first, and most straight forward of the five studies involves tracking daily movement in app store rankings across iOS and Android versions of the same apps to determine any trends of differences between ranking volatility in the two stores.

I went with a small sample of five apps for this study, the only criteria for which were that:

  • They were all apps I actively use (a criterion for coming up with the five apps but not one that influences rank in the U.S. app stores)
  • They were ranked in the top 500 (but not the top 25, as I assumed app store rankings would be stickier at the top—an assumption I’ll test in study #2)
  • They had an almost identical version of the app in both Google Play and the App Store, meaning they should (theoretically) rank similarly
  • They covered a spectrum of app categories

The apps I ultimately chose were Lyft, Venmo, Duolingo, Chase Mobile, and LinkedIn. These five apps represent the travel, finance, education banking, and social networking categories.

Hypothesis

Going into this analysis, I predicted slightly more volatility in Apple App Store rankings, based on two statistics:

Both of these assumptions will be tested in later analysis.

Results

7-Day App Store Ranking Volatility in the App Store and Google Play

Among these five apps, Google Play rankings were, indeed, significantly less volatile than App Store rankings. Among the 35 data points recorded, rankings within Google Play moved by as much as 23 positions/ranks per day while App Store rankings moved up to 89 positions/ranks. The standard deviation of ranking volatility in the App Store was, furthermore, 4.45 times greater than that of Google Play.

Of course, the same apps varied fairly dramatically in their rankings in the two app stores, so I then standardized the ranking volatility in terms of percent change to control for the effect of numeric rank on volatility. When cast in this light, App Store rankings changed by as much as 72% within a 24-hour period while Google Play rankings changed by no more than 9%.

Also of note, daily rankings tended to move in the same direction across the two app stores approximately two-thirds of the time, suggesting that the two stores, and their customers, may have more in common than we think.

Study #2: App store ranking volatility across the top charts

Testing the assumption implicit in standardizing the data in study No. 1, this one was designed to see if app store ranking volatility is correlated with an app’s current rank. The sample for this study consisted of the top 500 ranked apps in both Google Play and the App Store, with special attention given to those on both ends of the spectrum (ranks 1–100 and 401–500).

Hypothesis

I anticipated rankings to be more volatile the higher an app is ranked—meaning an app ranked No. 450 should be able to move more ranks in any given day than an app ranked No. 50. This hypothesis is based on the assumption that higher ranked apps have more installs, active users, and ratings, and that it would take a large margin to produce a noticeable shift in any of these factors.

Results

App Store Ranking Volatility of Top 500 Apps

One look at the chart above shows that apps in both stores have increasingly more volatile rankings (based on how many ranks they moved in the last 24 hours) the lower on the list they’re ranked.

This is particularly true when comparing either end of the spectrum—with a seemingly straight volatility line among Google Play’s Top 100 apps and very few blips within the App Store’s Top 100. Compare this section to the lower end, ranks 401–)500, where both stores experience much more turbulence in their rankings. Across the gamut, I found a 24% correlation between rank and ranking volatility in the Play Store and 28% correlation in the App Store.

To put this into perspective, the average app in Google Play’s 401–)500 ranks moved 12.1 ranks in the last 24 hours while the average app in the Top 100 moved a mere 1.4 ranks. For the App Store, these numbers were 64.28 and 11.26, making slightly lower-ranked apps more than five times as volatile as the highest ranked apps. (I say slightly as these “lower-ranked” apps are still ranked higher than 99.96% of all apps.)

The relationship between rank and volatility is pretty consistent across the App Store charts, while rank has a much greater impact on volatility at the lower end of Google Play charts (ranks 1-100 have a 35% correlation) than it does at the upper end (ranks 401-500 have a 1% correlation).

Study #3: App store rankings across the stars

The next study looks at the relationship between rank and star ratings to determine any trends that set the top chart apps apart from the rest and explore any ties to app store ranking volatility.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

As discussed in the introduction, this study relates directly to one of the factors commonly accepted as influential to app store rankings: average rating.

Getting started, I hypothesized that higher ranks generally correspond to higher ratings, cementing the role of star ratings in the ranking algorithm.

As far as volatility goes, I did not anticipate average rating to play a role in app store ranking volatility, as I saw no reason for higher rated apps to be less volatile than lower rated apps, or vice versa. Instead, I believed volatility to be tied to rating volume (as we’ll explore in our last study).

Results

Average App Store Ratings of Top Apps

The chart above plots the top 100 ranked apps in either store with their average rating (both historic and current, for App Store apps). If it looks a little chaotic, it’s just one indicator of the complexity of ranking algorithm in Google Play and the App Store.

If our hypothesis was correct, we’d see a downward trend in ratings. We’d expect to see the No. 1 ranked app with a significantly higher rating than the No. 100 ranked app. Yet, in neither store is this the case. Instead, we get a seemingly random plot with no obvious trends that jump off the chart.

A closer examination, in tandem with what we already know about the app stores, reveals two other interesting points:

  1. The average star rating of the top 100 apps is significantly higher than that of the average app. Across the top charts, the average rating of a top 100 Android app was 4.319 and the average top iOS app was 3.935. These ratings are 0.32 and 0.27 points, respectively, above the average rating of all rated apps in either store. The averages across apps in the 401–)500 ranks approximately split the difference between the ratings of the top ranked apps and the ratings of the average app.
  2. The rating distribution of top apps in Google Play was considerably more compact than the distribution of top iOS apps. The standard deviation of ratings in the Apple App Store top chart was over 2.5 times greater than that of the Google Play top chart, likely meaning that ratings are more heavily weighted in Google Play’s algorithm.

App Store Ranking Volatility and Average Rating

Looking next at the relationship between ratings and app store ranking volatility reveals a -15% correlation that is consistent across both app stores; meaning the higher an app is rated, the less its rank it likely to move in a 24-hour period. The exception to this rule is the Apple App Store’s calculation of an app’s current rating, for which I did not find a statistically significant correlation.

Study #4: App store rankings across versions

This next study looks at the relationship between the age of an app’s current version, its rank and its ranking volatility.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

In alteration of the above function, I’m using the age of a current app’s version as a proxy (albeit not a very good one) for trends in app store ratings and app quality over time.

Making the assumptions that (a) apps that are updated more frequently are of higher quality and (b) each new update inspires a new wave of installs and ratings, I’m hypothesizing that the older the age of an app’s current version, the lower it will be ranked and the less volatile its rank will be.

Results

How update frequency correlates with app store rank

The first and possibly most important finding is that apps across the top charts in both Google Play and the App Store are updated remarkably often as compared to the average app.

At the time of conducting the study, the current version of the average iOS app on the top chart was only 28 days old; the current version of the average Android app was 38 days old.

As hypothesized, the age of the current version is negatively correlated with the app’s rank, with a 13% correlation in Google Play and a 10% correlation in the App Store.

How update frequency correlates with app store ranking volatility

The next part of the study maps the age of the current app version to its app store ranking volatility, finding that recently updated Android apps have less volatile rankings (correlation: 8.7%) while recently updated iOS apps have more volatile rankings (correlation: -3%).

Study #5: App store rankings across monthly active users

In the final study, I wanted to examine the role of an app’s popularity on its ranking. In an ideal world, popularity would be measured by an app’s monthly active users (MAUs), but since few mobile app developers have released this information, I’ve settled for two publicly available proxies: Rating Count and Installs.

Hypothesis

Ranking = fn(Rating, Rating Count, Installs, Trends)

For the same reasons indicated in the second study, I anticipated that more popular apps (e.g., apps with more ratings and more installs) would be higher ranked and less volatile in rank. This, again, takes into consideration that it takes more of a shift to produce a noticeable impact in average rating or any of the other commonly accepted influencers of an app’s ranking.

Results

Apps with more ratings and reviews typically rank higher

The first finding leaps straight off of the chart above: Android apps have been rated more times than iOS apps, 15.8x more, in fact.

The average app in Google Play’s Top 100 had a whopping 3.1 million ratings while the average app in the Apple App Store’s Top 100 had 196,000 ratings. In contrast, apps in the 401–)500 ranks (still tremendously successful apps in the 99.96 percentile of all apps) tended to have between one-tenth (Android) and one-fifth (iOS) of the ratings count as that of those apps in the top 100 ranks.

Considering that almost two-thirds of apps don’t have a single rating, reaching rating counts this high is a huge feat, and a very strong indicator of the influence of rating count in the app store ranking algorithms.

To even out the playing field a bit and help us visualize any correlation between ratings and rankings (and to give more credit to the still-staggering 196k ratings for the average top ranked iOS app), I’ve applied a logarithmic scale to the chart above:

The relationship between app store ratings and rankings in the top 100 apps

From this chart, we can see a correlation between ratings and rankings, such that apps with more ratings tend to rank higher. This equates to a 29% correlation in the App Store and a 40% correlation in Google Play.

Apps with more ratings typically experience less app store ranking volatility

Next up, I looked at how ratings count influenced app store ranking volatility, finding that apps with more ratings had less volatile rankings in the Apple App Store (correlation: 17%). No conclusive evidence was found within the Top 100 Google Play apps.

Apps with more installs and active users tend to rank higher in the app stores

And last but not least, I looked at install counts as an additional proxy for MAUs. (Sadly, this is a statistic only listed in Google Play. so any resulting conclusions are applicable only to Android apps.)

Among the top 100 Android apps, this last study found that installs were heavily correlated with ranks (correlation: -35.5%), meaning that apps with more installs are likely to rank higher in Google Play. Android apps with more installs also tended to have less volatile app store rankings, with a correlation of -16.5%.

Unfortunately, these numbers are slightly skewed as Google Play only provides install counts in broad ranges (e.g., 500k–)1M). For each app, I took the low end of the range, meaning we can likely expect the correlation to be a little stronger since the low end was further away from the midpoint for apps with more installs.

Summary

To make a long post ever so slightly shorter, here are the nuts and bolts unearthed in these five mad science studies in app store optimization:

  1. Across the top charts, Apple App Store rankings are 4.45x more volatile than those of Google Play
  2. Rankings become increasingly volatile the lower an app is ranked. This is particularly true across the Apple App Store’s top charts.
  3. In both stores, higher ranked apps tend to have an app store ratings count that far exceeds that of the average app.
  4. Ratings appear to matter more to the Google Play algorithm, especially as the Apple App Store top charts experience a much wider ratings distribution than that of Google Play’s top charts.
  5. The higher an app is rated, the less volatile its rankings are.
  6. The 100 highest ranked apps in either store are updated much more frequently than the average app, and apps with older current versions are correlated with lower ratings.
  7. An app’s update frequency is negatively correlated with Google Play’s ranking volatility but positively correlated with ranking volatility in the App Store. This likely due to how Apple weighs an app’s most recent ratings and reviews.
  8. The highest ranked Google Play apps receive, on average, 15.8x more ratings than the highest ranked App Store apps.
  9. In both stores, apps that fall under the 401–500 ranks receive, on average, 10–20% of the rating volume seen by apps in the top 100.
  10. Rating volume and, by extension, installs or MAUs, is perhaps the best indicator of ranks, with a 29–40% correlation between the two.

Revisiting our first (albeit oversimplified) guess at the app stores’ ranking algorithm gives us this loosely defined function:

Ranking = fn(Rating, Rating Count, Installs, Trends)

I’d now re-write the function into a formula by weighing each of these four factors, where a, b, c, & d are unknown multipliers, or weights:

Ranking = (Rating * a) + (Rating Count * b) + (Installs * c) + (Trends * d)

These five studies on ASO shed a little more light on these multipliers, showing Rating Count to have the strongest correlation with rank, followed closely by Installs, in either app store.

It’s with the other two factors—rating and trends—that the two stores show the greatest discrepancy. I’d hazard a guess to say that the App Store prioritizes growth trends over ratings, given the importance it places on an app’s current version and the wide distribution of ratings across the top charts. Google Play, on the other hand, seems to favor ratings, with an unwritten rule that apps just about have to have at least four stars to make the top 100 ranks.

Thus, we conclude our mad science with this final glimpse into what it takes to make the top charts in either store:

Weight of factors in the Apple App Store ranking algorithm

Rating Count > Installs > Trends > Rating

Weight of factors in the Google Play ranking algorithm

Rating Count > Installs > Rating > Trends


Again, we’re oversimplifying for the sake of keeping this post to a mere 3,000 words, but additional factors including keyword density and in-app engagement statistics continue to be strong indicators of ranks. They simply lie outside the scope of these studies.

I hope you found this deep-dive both helpful and interesting. Moving forward, I also hope to see ASOs conducting the same experiments that have brought SEO to the center stage, and encourage you to enhance or refute these findings with your own ASO mad science experiments.

Please share your thoughts in the comments below, and let’s deconstruct the ranking formula together, one experiment at a time.

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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

I Can’t Drive 155: Meta Descriptions in 2015

Posted by Dr-Pete

For years now, we (and many others) have been recommending keeping your Meta Descriptions shorter than
about 155-160 characters. For months, people have been sending me examples of search snippets that clearly broke that rule, like this one (on a search for “hummingbird food”):

For the record, this one clocks in at 317 characters (counting spaces). So, I set out to discover if these long descriptions were exceptions to the rule, or if we need to change the rules. I collected the search snippets across the MozCast 10K, which resulted in 92,669 snippets. All of the data in this post was collected on April 13, 2015.

The Basic Data

The minimum snippet length was zero characters. There were 69 zero-length snippets, but most of these were the new generation of answer box, that appears organic but doesn’t have a snippet. To put it another way, these were misidentified as organic by my code. The other 0-length snippets were local one-boxes that appeared as organic but had no snippet, such as this one for “chichen itza”:

These zero-length snippets were removed from further analysis, but considering that they only accounted for 0.07% of the total data, they didn’t really impact the conclusions either way. The shortest legitimate, non-zero snippet was 7 characters long, on a search for “geek and sundry”, and appears to have come directly from the site’s meta description:

The maximum snippet length that day (this is a highly dynamic situation) was 372 characters. The winner appeared on a search for “benefits of apple cider vinegar”:

The average length of all of the snippets in our data set (not counting zero-length snippets) was 143.5 characters, and the median length was 152 characters. Of course, this can be misleading, since some snippets are shorter than the limit and others are being artificially truncated by Google. So, let’s dig a bit deeper.

The Bigger Picture

To get a better idea of the big picture, let’s take a look at the display length of all 92,600 snippets (with non-zero length), split into 20-character buckets (0-20, 21-40, etc.):

Most of the snippets (62.1%) cut off as expected, right in the 141-160 character bucket. Of course, some snippets were shorter than that, and didn’t need to be cut off, and some broke the rules. About 1% (1,010) of the snippets in our data set measured 200 or more characters. That’s not a huge number, but it’s enough to take seriously.

That 141-160 character bucket is dwarfing everything else, so let’s zoom in a bit on the cut-off range, and just look at snippets in the 120-200 character range (in this case, by 5-character bins):

Zooming in, the bulk of the snippets are displaying at lengths between about 146-165 characters. There are plenty of exceptions to the 155-160 character guideline, but for the most part, they do seem to be exceptions.

Finally, let’s zoom in on the rule-breakers. This is the distribution of snippets displaying 191+ characters, bucketed in 10-character bins (191-200, 201-210, etc.):

Please note that the Y-axis scale is much smaller than in the previous 2 graphs, but there is a pretty solid spread, with a decent chunk of snippets displaying more than 300 characters.

Without looking at every original meta description tag, it’s very difficult to tell exactly how many snippets have been truncated by Google, but we do have a proxy. Snippets that have been truncated end in an ellipsis (…), which rarely appears at the end of a natural description. In this data set, more than half of all snippets (52.8%) ended in an ellipsis, so we’re still seeing a lot of meta descriptions being cut off.

I should add that, unlike titles/headlines, it isn’t clear whether Google is cutting off snippets by pixel width or character count, since that cut-off is done on the server-side. In most cases, Google will cut before the end of the second line, but sometimes they cut well before this, which could suggest a character-based limit. They also cut off at whole words, which can make the numbers a bit tougher to interpret.

The Cutting Room Floor

There’s another difficulty with telling exactly how many meta descriptions Google has modified – some edits are minor, and some are major. One minor edit is when Google adds some additional information to a snippet, such as a date at the beginning. Here’s an example (from a search for “chicken pox”):

With the date (and minus the ellipsis), this snippet is 164 characters long, which suggests Google isn’t counting the added text against the length limit. What’s interesting is that the rest comes directly from the meta description on the site, except that the site’s description starts with “Chickenpox.” and Google has removed that keyword. As a human, I’d say this matches the meta description, but a bot has a very hard time telling a minor edit from a complete rewrite.

Another minor rewrite occurs in snippets that start with search result counts:

Here, we’re at 172 characters (with spaces and minus the ellipsis), and Google has even let this snippet roll over to a third line. So, again, it seems like the added information at the beginning isn’t counting against the length limit.

All told, 11.6% of the snippets in our data set had some kind of Google-generated data, so this type of minor rewrite is pretty common. Even if Google honors most of your meta description, you may see small edits.

Let’s look at our big winner, the 372-character description. Here’s what we saw in the snippet:

Jan 26, 2015 – Health• Diabetes Prevention: Multiple studies have shown a correlation between apple cider vinegar and lower blood sugar levels. … • Weight Loss: Consuming apple cider vinegar can help you feel more full, which can help you eat less. … • Lower Cholesterol: … • Detox: … • Digestive Aid: … • Itchy or Sunburned Skin: … • Energy Boost:1 more items

So, what about the meta description? Here’s what we actually see in the tag:

Were you aware of all the uses of apple cider vinegar? From cleansing to healing, to preventing diabetes, ACV is a pantry staple you need in your home.

That’s a bit more than just a couple of edits. So, what’s happening here? Well, there’s a clue on that same page, where we see yet another rule-breaking snippet:

You might be wondering why this snippet is any more interesting than the other one. If you could see the top of the SERP, you’d know why, because it looks something like this:

Google is automatically extracting list-style data from these pages to fuel the expansion of the Knowledge Graph. In one case, that data is replacing a snippet
and going directly into an answer box, but they’re performing the same translation even for some other snippets on the page.

So, does every 2nd-generation answer box yield long snippets? After 3 hours of inadvisable mySQL queries, I can tell you that the answer is a resounding “probably not”. You can have 2nd-gen answer boxes without long snippets and you can have long snippets without 2nd-gen answer boxes,
but there does appear to be a connection between long snippets and Knowledge Graph in some cases.

One interesting connection is that Google has begun bolding keywords that seem like answers to the query (and not just synonyms for the query). Below is an example from a search for “mono symptoms”. There’s an answer box for this query, but the snippet below is not from the site in the answer box:

Notice the bolded words – “fatigue”, “sore throat”, “fever”, “headache”, “rash”. These aren’t synonyms for the search phrase; these are actual symptoms of mono. This data isn’t coming from the meta description, but from a bulleted list on the target page. Again, it appears that Google is trying to use the snippet to answer a question, and has gone well beyond just matching keywords.

Just for fun, let’s look at one more, where there’s no clear connection to the Knowledge Graph. Here’s a snippet from a search for “sons of anarchy season 4”:

This page has no answer box, and the information extracted is odd at best. The snippet bears little or no resemblance to the site’s meta description. The number string at the beginning comes out of a rating widget, and some of the text isn’t even clearly available on the page. This seems to be an example of Google acknowledging IMDb as a high-authority site and desperately trying to match any text they can to the query, resulting in a Frankenstein’s snippet.

The Final Verdict

If all of this seems confusing, that’s probably because it is. Google is taking a lot more liberties with snippets these days, both to better match queries, to add details they feel are important, or to help build and support the Knowledge Graph.

So, let’s get back to the original question – is it time to revise the 155(ish) character guideline? My gut feeling is: not yet. To begin with, the vast majority of snippets are still falling in that 145-165 character range. In addition, the exceptions to the rule are not only atypical situations, but in most cases those long snippets don’t seem to represent the original meta description. In other words, even if Google does grant you extra characters, they probably won’t be the extra characters you asked for in the first place.

Many people have asked: “How do I make sure that Google shows my meta description as is?” I’m afraid the answer is: “You don’t.” If this is very important to you, I would recommend keeping your description below the 155-character limit, and making sure that it’s a good match to your target keyword concepts. I suspect Google is going to take more liberties with snippets over time, and we’re going to have to let go of our obsession with having total control over the SERPs.

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