SearchCap: Google Updates Local Guides, Bing Rewards On MSN & New AdWords Tests

Below is what happened in search today, as reported on Search Engine Land and from other places across the web. From Search Engine Land: Google Using Points To Boost User Reviews, Beef Up Maps Content Nov 13, 2015 by Greg Sterling A couple of years ago, Google created a program in the mold of the…

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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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Reblogged 2 years ago from tracking.feedpress.it

From Editorial Calendars to SEO: Setting Yourself Up to Create Fabulous Content

Posted by Isla_McKetta

Quick note: This article is meant to apply to teams of all sizes, from the sole proprietor who spends all night writing their copy (because they’re doing business during the day) to the copy team who occupies an entire floor and produces thousands of pieces of content per week. So if you run into a section that you feel requires more resources than you can devote just now, that’s okay. Bookmark it and revisit when you can, or scale the step down to a more appropriate size for your team. We believe all the information here is important, but that does not mean you have to do everything right now.

If you thought ideation was fun, get ready for content creation. Sure, we’ve all written some things before, but the creation phase of content marketing is where you get to watch that beloved idea start to take shape.

Before you start creating, though, you want to get (at least a little) organized, and an editorial calendar is the perfect first step.

Editorial calendars

Creativity and organization are not mutually exclusive. In fact, they can feed each other. A solid schedule gives you and your writers the time and space to be wild and creative. If you’re just starting out, this document may be sparse, but it’s no less important. Starting early with your editorial calendar also saves you from creating content willy-nilly and then finding out months later that no one ever finished that pesky (but crucial) “About” page.

There’s no wrong way to set up your editorial calendar, as long as it’s meeting your needs. Remember that an editorial calendar is a living document, and it will need to change as a hot topic comes up or an author drops out.

There are a lot of different types of documents that pass for editorial calendars. You get to pick the one that’s right for your team. The simplest version is a straight-up calendar with post titles written out on each day. You could even use a wall calendar and a Sharpie.

Monday Tuesday Wednesday Thursday Friday
Title
The Five Colors of Oscar Fashion 12 Fabrics We’re Watching for Fall Is Charmeuse the New Corduroy? Hot Right Now: Matching Your Handbag to Your Hatpin Tea-length and Other Fab Vocab You Need to Know
Author Ellie James Marta Laila Alex

Teams who are balancing content for different brands at agencies or other more complex content environments will want to add categories, author information, content type, social promo, and more to their calendars.

Truly complex editorial calendars are more like hybrid content creation/editorial calendars, where each of the steps to create and publish the content are indicated and someone has planned for how long all of that takes. These can be very helpful if the content you’re responsible for crosses a lot of teams and can take a long time to complete. It doesn’t matter if you’re using Excel or a Google Doc, as long as the people who need the calendar can easily access it. Gantt charts can be excellent for this. Here’s a favorite template for creating a Gantt chart in Google Docs (and they only get more sophisticated).

Complex calendars can encompass everything from ideation through writing, legal review, and publishing. You might even add content localization if your empire spans more than one continent to make sure you have the currency, date formatting, and even slang right.

Content governance

Governance outlines who is taking responsibility for your content. Who evaluates your content performance? What about freshness? Who decides to update (or kill) an older post? Who designs and optimizes workflows for your team or chooses and manages your CMS?

All these individual concerns fall into two overarching components to governance: daily maintenance and overall strategy. In the long run it helps if one person has oversight of the whole process, but the smaller steps can easily be split among many team members. Read this to take your governance to the next level.

Finding authors

The scale of your writing enterprise doesn’t have to be limited to the number of authors you have on your team. It’s also important to consider the possibility of working with freelancers and guest authors. Here’s a look at the pros and cons of outsourced versus in-house talent.

In-house authors

Guest authors and freelancers

Responsible to

You

Themselves

Paid by

You (as part of their salary)

You (on a per-piece basis)

Subject matter expertise

Broad but shallow

Deep but narrow

Capacity for extra work

As you wish

Show me the Benjamins

Turnaround time

On a dime

Varies

Communication investment

Less

More

Devoted audience

Smaller

Potentially huge

From that table, it might look like in-house authors have a lot more advantages. That’s somewhat true, but do not underestimate the value of occasionally working with a true industry expert who has name recognition and a huge following. Whichever route you take (and there are plenty of hybrid options), it’s always okay to ask that the writers you are working with be professional about communication, payment, and deadlines. In some industries, guest writers will write for links. Consider yourself lucky if that’s true. Remember, though, that the final paycheck can be great leverage for getting a writer to do exactly what you need them to (such as making their deadlines).

Tools to help with content creation

So those are some things you need to have in place before you create content. Now’s the fun part: getting started. One of the beautiful things about the Internet is that new and exciting tools crop up every day to help make our jobs easier and more efficient. Here are a few of our favorites.

Calendars

You can always use Excel or a Google Doc to set up your editorial calendar, but we really like Trello for the ability to gather a lot of information in one card and then drag and drop it into place. Once there are actual dates attached to your content, you might be happier with something like a Google Calendar.

Ideation and research

If you need a quick fix for ideation, turn your keywords into wacky ideas with Portent’s Title Maker. You probably won’t want to write to the exact title you’re given (although “True Facts about Justin Bieber’s Love of Pickles” does sound pretty fascinating…), but it’s a good way to get loose and look at your topic from a new angle.

Once you’ve got that idea solidified, find out what your audience thinks about it by gathering information with Survey Monkey or your favorite survey tool. Or, use Storify to listen to what people are saying about your topic across a wide variety of platforms. You can also use Storify to save those references and turn them into a piece of content or an illustration for one. Don’t forget that a simple social ask can also do wonders.

Format

Content doesn’t have to be all about the words. Screencasts, Google+ Hangouts, and presentations are all interesting ways to approach content. Remember that not everyone’s a reader. Some of your audience will be more interested in visual or interactive content. Make something for everyone.

Illustration

Don’t forget to make your content pretty. It’s not that hard to find free stock images online (just make sure you aren’t violating someone’s copyright). We like Morgue File, Free Images, and Flickr’s Creative Commons. If you aren’t into stock images and don’t have access to in-house graphic design, it’s still relatively easy to add images to your content. Pull a screenshot with Skitch or dress up an existing image with Pixlr. You can also use something like Canva to create custom graphics.

Don’t stop with static graphics, though. There are so many tools out there to help you create gifs, quizzes and polls, maps, and even interactive timelines. Dream it, then search for it. Chances are whatever you’re thinking of is doable.

Quality, not quantity

Mediocre content will hurt your cause

Less is more. That’s not an excuse to pare your blog down to one post per month (check out our publishing cadence experiment), but it is an important reminder that if you’re writing “How to Properly Install a Toilet Seat” two days after publishing “Toilet Seat Installation for Dummies,” you might want to rethink your strategy.

The thing is, and I’m going to use another cliché here to drive home the point, you never get a second chance to make a first impression. Potential customers are roving the Internet right now looking for exactly what you’re selling. And if what they find is an only somewhat informative article stuffed with keywords and awful spelling and grammar mistakes… well, you don’t want that. Oh, and search engines think it’s spammy too…

A word about copyright

We’re not copyright lawyers, so we can’t give you the ins and outs on all the technicalities. What we can tell you (and you already know this) is that it’s not okay to steal someone else’s work. You wouldn’t want them to do it to you. This includes images. So whenever you can, make your own images or find images that you can either purchase the rights to (stock imagery) or license under Creative Commons.

It’s usually okay to quote short portions of text, as long as you attribute the original source (and a link is nice). In general, titles and ideas can’t be copyrighted (though they might be trademarked or patented). When in doubt, asking for permission is smart.

That said, part of the fun of the Internet is the remixing culture which includes using things like memes and gifs. Just know that if you go that route, there is a certain amount of risk involved.

Editing

Your content needs to go through at least one editing cycle by someone other than the original author. There are two types of editing, developmental (which looks at the underlying structure of a piece that happens earlier in the writing cycle) and copy editing (which makes sure all the words are there and spelled right in the final draft).

If you have a very small team or are in a rush (and are working with writers that have some skill), you can often skip the developmental editing phase. But know that an investment in that close read of an early draft is often beneficial to the piece and to the writer’s overall growth.

Many content teams peer-edit work, which can be great. Other organizations prefer to run their work by a dedicated editor. There’s no wrong answer, as long as the work gets edited.

Ensuring proper basic SEO

The good news is that search engines are doing their best to get closer and closer to understanding and processing natural language. So good writing (including the natural use of synonyms rather than repeating those keywords over and over and…) will take you a long way towards SEO mastery.

For that reason (and because it’s easy to get trapped in keyword thinking and veer into keyword stuffing), it’s often nice to think of your SEO check as a further edit of the post rather than something you should think about as you’re writing.

But there are still a few things you can do to help cover those SEO bets. Once you have that draft, do a pass for SEO to make sure you’ve covered the following:

  • Use your keyword in your title
  • Use your keyword (or long-tail keyword phrase) in an H2
  • Make sure the keyword appears at least once (though not more than four times, especially if it’s a phrase) in the body of the post
  • Use image alt text (including the keyword when appropriate)

Finding time to write when you don’t have any

Writing (assuming you’re the one doing the writing) can require a lot of energy—especially if you want to do it well. The best way to find time to write is to break each project down into little tasks. For example, writing a blog post actually breaks down into these steps (though not always in this order):

  • Research
  • Outline
  • Fill in outline
  • Rewrite and finish post
  • Write headline
  • SEO check
  • Final edit
  • Select hero image (optional)

So if you only have random chunks of time, set aside 15-30 minutes one day (when your research is complete) to write a really great outline. Then find an hour the next to fill that outline in. After an additional hour the following day, (unless you’re dealing with a research-heavy post) you should have a solid draft by the end of day three.

The magic of working this way is that you engage your brain and then give it time to work in the background while you accomplish other tasks. Hemingway used to stop mid-sentence at the end of his writing days for the same reason.

Once you have that draft nailed, the rest of the steps are relatively easy (even the headline, which often takes longer to write than any other sentence, is easier after you’ve immersed yourself in the post over a few days).

Working with design/development

Every designer and developer is a little different, so we can’t give you any blanket cure-alls for inter-departmental workarounds (aka “smashing silos”). But here are some suggestions to help you convey your vision while capitalizing on the expertise of your coworkers to make your content truly excellent.

Ask for feedback

From the initial brainstorm to general questions about how to work together, asking your team members what they think and prefer can go a long way. Communicate all the details you have (especially the unspoken expectations) and then listen.

If your designer tells you up front that your color scheme is years out of date, you’re saving time. And if your developer tells you that the interactive version of that timeline will require four times the resources, you have the info you need to fight for more budget (or reassess the project).

Check in

Things change in the design and development process. If you have interim check-ins already set up with everyone who’s working on the project, you’ll avoid the potential for nasty surprises at the end. Like finding out that no one has experience working with that hot new coding language you just read about and they’re trying to do a workaround that isn’t working.

Proofread

Your job isn’t done when you hand over the copy to your designer or developer. Not only might they need help rewriting some of your text so that it fits in certain areas, they will also need you to proofread the final version. Accidents happen in the copy-and-paste process and there’s nothing sadder than a really beautiful (and expensive) piece of content that wraps up with a typo:

Know when to fight for an idea

Conflict isn’t fun, but sometimes it’s necessary. The more people involved in your content, the more watered down the original idea can get and the more roadblocks and conflicting ideas you’ll run into. Some of that is very useful. But sometimes you’ll get pulled off track. Always remember who owns the final product (this may not be you) and be ready to stand up for the idea if it’s starting to get off track.

We’re confident this list will set you on the right path to creating some really awesome content, but is there more you’d like to know? Ask us your questions in the comments.

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Reblogged 2 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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