Is Australia the land of opportunity for your retail brand?

Australia has a resident population of more than 24 million and, according to eMarketer, the country’s ecommerce sales are predicted to reach A$32.56 billion by 2017. The country’s remote location in the APAC region means that unlike European countries or the USA, traditionally there have been a lack of global brands sold locally.

Of course, we also know that many expatriates, particularly from inside the Commonwealth, have made Australia their home and are keen to buy products they know and love from their country of origin.

All of these factors present a huge and potentially lucrative opportunity for non-Australian brands wanting to open up their new and innovative products to a fresh market, or compete for market share.

But it’s not just non-Australian retailers who are at an advantage here: Australia was late to the ecommerce party because native, established brands were trading well without it. Subsequently, Australian retailers’ ecommerce technology stacks are much more recent and not burdened by legacy systems. This makes it much easier to extend, or get started with, best-of-breed technologies and cash in on a market that’s booming. To put some of this into perspective, Magento’s innovative ecommerce platform currently takes 42% of Australia’s market share and the world’s first adopter of Magento 2.0 was an Australian brand.

The GST loophole

At the moment, local retailers are campaigning against a rule that exempts foreign websites from being charged a 10% general sales tax (GST) on purchases under A$1,000. And in 2013, Australian consumers made $3.11 billion worth of purchases under A$1,000.[1]

While the current GST break appears to put non-Australian retailers at an advantage, Australian-based brands such as Harvey Norman are using it to their advantage by setting up ecommerce operations in Asia to enjoy the GST benefit.

Australian consumers have also countered the argument by saying that price isn’t always the motivator when it comes to making purchasing decisions.

It’s not a place where no man has gone before

Often, concerns around meeting local compliance and lack of overseas business knowledge prevent outsiders from taking the leap into cross-border trade. However, this ecommerce passport, created by Ecommerce Worldwide and NORA, is designed to support those considering selling in Australia. The guide provides a comprehensive look into everything from the country’s economy and trade status, to logistics and dealing with international payments.

Global expansion success stories are also invaluable sources of information. For instance, it’s not just lower-end retailers that are fitting the bill, with brands like online luxury fashion retailer Net-a-Porter naming Australia as one of its biggest markets.

How tech-savvy are the Aussies?

One of the concerns you might have as a new entrant into the market is how you’ll reach and sell to your new audience, particularly without having a physical presence. The good news is that more than 80% of the country is digitally enabled and 60% of mobile phone users own a smartphone – so online is deeply rooted into the majority of Australians’ lives. [2]

Marketing your brand

Heard the saying “Fire bullets then fire cannonballs”? In any case, you’ll want to test the waters and gauge people’s reactions to your product or service.

It all starts with the website because, without it, you’re not discoverable or searchable, and you’ve nowhere to drive people to when running campaigns. SEO and SEM should definitely be a priority, and an online store that can handle multiple regions and storefronts, like Magento, will make your life easier. A mobile-first mentality and well thought-out UX will also place you in a good position.

Once your new web store is set up, you should be making every effort to collect visitors’ email addresses, perhaps via a popover. Why? Firstly, email is one of the top three priority areas for Australian retailers, because it’s a cost-effective, scalable marketing channel that enables true personalization.

Secondly, email marketing automation empowers you to deliver the customer experience today’s consumer expects, as well as enabling you to communicate with them throughout the lifecycle. Check out our ‘Do customer experience masters really exist?’ whitepaper for some real-life success stories.

Like the Magento platform, dotmailer is set up to handle multiple languages, regions and accounts, and is designed to grow with you.

In summary, there’s great scope for ecommerce success in Australia, whether you’re a native bricks-and-mortar retailer, a start-up or a non-Australian merchant. The barriers to cross-border trade are falling and Australia is one of APAC’s most developed regions in terms of purchasing power and tech savviness.

We recently worked with ecommerce expert Chloe Thomas to produce a whitepaper on cross-border trade, which goes into much more detail on how to market and sell successfully in new territories. You can download a free copy here.

[1] Australian Passport 2015: Cross-Border Trading Report

[2] Australian Passport 2015: Cross-Border Trading Report

Reblogged 2 years ago from blog.dotmailer.com

Distance from Perfect

Posted by wrttnwrd

In spite of all the advice, the strategic discussions and the conference talks, we Internet marketers are still algorithmic thinkers. That’s obvious when you think of SEO.

Even when we talk about content, we’re algorithmic thinkers. Ask yourself: How many times has a client asked you, “How much content do we need?” How often do you still hear “How unique does this page need to be?”

That’s 100% algorithmic thinking: Produce a certain amount of content, move up a certain number of spaces.

But you and I know it’s complete bullshit.

I’m not suggesting you ignore the algorithm. You should definitely chase it. Understanding a little bit about what goes on in Google’s pointy little head helps. But it’s not enough.

A tale of SEO woe that makes you go “whoa”

I have this friend.

He ranked #10 for “flibbergibbet.” He wanted to rank #1.

He compared his site to the #1 site and realized the #1 site had five hundred blog posts.

“That site has five hundred blog posts,” he said, “I must have more.”

So he hired a few writers and cranked out five thousand blogs posts that melted Microsoft Word’s grammar check. He didn’t move up in the rankings. I’m shocked.

“That guy’s spamming,” he decided, “I’ll just report him to Google and hope for the best.”

What happened? Why didn’t adding five thousand blog posts work?

It’s pretty obvious: My, uh, friend added nothing but crap content to a site that was already outranked. Bulk is no longer a ranking tactic. Google’s very aware of that tactic. Lots of smart engineers have put time into updates like Panda to compensate.

He started like this:

And ended up like this:
more posts, no rankings

Alright, yeah, I was Mr. Flood The Site With Content, way back in 2003. Don’t judge me, whippersnappers.

Reality’s never that obvious. You’re scratching and clawing to move up two spots, you’ve got an overtasked IT team pushing back on changes, and you’ve got a boss who needs to know the implications of every recommendation.

Why fix duplication if rel=canonical can address it? Fixing duplication will take more time and cost more money. It’s easier to paste in one line of code. You and I know it’s better to fix the duplication. But it’s a hard sell.

Why deal with 302 versus 404 response codes and home page redirection? The basic user experience remains the same. Again, we just know that a server should return one home page without any redirects and that it should send a ‘not found’ 404 response if a page is missing. If it’s going to take 3 developer hours to reconfigure the server, though, how do we justify it? There’s no flashing sign reading “Your site has a problem!”

Why change this thing and not that thing?

At the same time, our boss/client sees that the site above theirs has five hundred blog posts and thousands of links from sites selling correspondence MBAs. So they want five thousand blog posts and cheap links as quickly as possible.

Cue crazy music.

SEO lacks clarity

SEO is, in some ways, for the insane. It’s an absurd collection of technical tweaks, content thinking, link building and other little tactics that may or may not work. A novice gets exposed to one piece of crappy information after another, with an occasional bit of useful stuff mixed in. They create sites that repel search engines and piss off users. They get more awful advice. The cycle repeats. Every time it does, best practices get more muddled.

SEO lacks clarity. We can’t easily weigh the value of one change or tactic over another. But we can look at our changes and tactics in context. When we examine the potential of several changes or tactics before we flip the switch, we get a closer balance between algorithm-thinking and actual strategy.

Distance from perfect brings clarity to tactics and strategy

At some point you have to turn that knowledge into practice. You have to take action based on recommendations, your knowledge of SEO, and business considerations.

That’s hard when we can’t even agree on subdomains vs. subfolders.

I know subfolders work better. Sorry, couldn’t resist. Let the flaming comments commence.

To get clarity, take a deep breath and ask yourself:

“All other things being equal, will this change, tactic, or strategy move my site closer to perfect than my competitors?”

Breaking it down:

“Change, tactic, or strategy”

A change takes an existing component or policy and makes it something else. Replatforming is a massive change. Adding a new page is a smaller one. Adding ALT attributes to your images is another example. Changing the way your shopping cart works is yet another.

A tactic is a specific, executable practice. In SEO, that might be fixing broken links, optimizing ALT attributes, optimizing title tags or producing a specific piece of content.

A strategy is a broader decision that’ll cause change or drive tactics. A long-term content policy is the easiest example. Shifting away from asynchronous content and moving to server-generated content is another example.

“Perfect”

No one knows exactly what Google considers “perfect,” and “perfect” can’t really exist, but you can bet a perfect web page/site would have all of the following:

  1. Completely visible content that’s perfectly relevant to the audience and query
  2. A flawless user experience
  3. Instant load time
  4. Zero duplicate content
  5. Every page easily indexed and classified
  6. No mistakes, broken links, redirects or anything else generally yucky
  7. Zero reported problems or suggestions in each search engines’ webmaster tools, sorry, “Search Consoles”
  8. Complete authority through immaculate, organically-generated links

These 8 categories (and any of the other bazillion that probably exist) give you a way to break down “perfect” and help you focus on what’s really going to move you forward. These different areas may involve different facets of your organization.

Your IT team can work on load time and creating an error-free front- and back-end. Link building requires the time and effort of content and outreach teams.

Tactics for relevant, visible content and current best practices in UX are going to be more involved, requiring research and real study of your audience.

What you need and what resources you have are going to impact which tactics are most realistic for you.

But there’s a basic rule: If a website would make Googlebot swoon and present zero obstacles to users, it’s close to perfect.

“All other things being equal”

Assume every competing website is optimized exactly as well as yours.

Now ask: Will this [tactic, change or strategy] move you closer to perfect?

That’s the “all other things being equal” rule. And it’s an incredibly powerful rubric for evaluating potential changes before you act. Pretend you’re in a tie with your competitors. Will this one thing be the tiebreaker? Will it put you ahead? Or will it cause you to fall behind?

“Closer to perfect than my competitors”

Perfect is great, but unattainable. What you really need is to be just a little perfect-er.

Chasing perfect can be dangerous. Perfect is the enemy of the good (I love that quote. Hated Voltaire. But I love that quote). If you wait for the opportunity/resources to reach perfection, you’ll never do anything. And the only way to reduce distance from perfect is to execute.

Instead of aiming for pure perfection, aim for more perfect than your competitors. Beat them feature-by-feature, tactic-by-tactic. Implement strategy that supports long-term superiority.

Don’t slack off. But set priorities and measure your effort. If fixing server response codes will take one hour and fixing duplication will take ten, fix the response codes first. Both move you closer to perfect. Fixing response codes may not move the needle as much, but it’s a lot easier to do. Then move on to fixing duplicates.

Do the 60% that gets you a 90% improvement. Then move on to the next thing and do it again. When you’re done, get to work on that last 40%. Repeat as necessary.

Take advantage of quick wins. That gives you more time to focus on your bigger solutions.

Sites that are “fine” are pretty far from perfect

Google has lots of tweaks, tools and workarounds to help us mitigate sub-optimal sites:

  • Rel=canonical lets us guide Google past duplicate content rather than fix it
  • HTML snapshots let us reveal content that’s delivered using asynchronous content and JavaScript frameworks
  • We can use rel=next and prev to guide search bots through outrageously long pagination tunnels
  • And we can use rel=nofollow to hide spammy links and banners

Easy, right? All of these solutions may reduce distance from perfect (the search engines don’t guarantee it). But they don’t reduce it as much as fixing the problems.
Just fine does not equal fixed

The next time you set up rel=canonical, ask yourself:

“All other things being equal, will using rel=canonical to make up for duplication move my site closer to perfect than my competitors?”

Answer: Not if they’re using rel=canonical, too. You’re both using imperfect solutions that force search engines to crawl every page of your site, duplicates included. If you want to pass them on your way to perfect, you need to fix the duplicate content.

When you use Angular.js to deliver regular content pages, ask yourself:

“All other things being equal, will using HTML snapshots instead of actual, visible content move my site closer to perfect than my competitors?”

Answer: No. Just no. Not in your wildest, code-addled dreams. If I’m Google, which site will I prefer? The one that renders for me the same way it renders for users? Or the one that has to deliver two separate versions of every page?

When you spill banner ads all over your site, ask yourself…

You get the idea. Nofollow is better than follow, but banner pollution is still pretty dang far from perfect.

Mitigating SEO issues with search engine-specific tools is “fine.” But it’s far, far from perfect. If search engines are forced to choose, they’ll favor the site that just works.

Not just SEO

By the way, distance from perfect absolutely applies to other channels.

I’m focusing on SEO, but think of other Internet marketing disciplines. I hear stuff like “How fast should my site be?” (Faster than it is right now.) Or “I’ve heard you shouldn’t have any content below the fold.” (Maybe in 2001.) Or “I need background video on my home page!” (Why? Do you have a reason?) Or, my favorite: “What’s a good bounce rate?” (Zero is pretty awesome.)

And Internet marketing venues are working to measure distance from perfect. Pay-per-click marketing has the quality score: A codified financial reward applied for seeking distance from perfect in as many elements as possible of your advertising program.

Social media venues are aggressively building their own forms of graphing, scoring and ranking systems designed to separate the good from the bad.

Really, all marketing includes some measure of distance from perfect. But no channel is more influenced by it than SEO. Instead of arguing one rule at a time, ask yourself and your boss or client: Will this move us closer to perfect?

Hell, you might even please a customer or two.

One last note for all of the SEOs in the crowd. Before you start pointing out edge cases, consider this: We spend our days combing Google for embarrassing rankings issues. Every now and then, we find one, point, and start yelling “SEE! SEE!!!! THE GOOGLES MADE MISTAKES!!!!” Google’s got lots of issues. Screwing up the rankings isn’t one of them.

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

Reblogged 3 years ago from tracking.feedpress.it

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

Posted by randfish

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

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

Video Transcription

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

So let’s take a look.

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

Why does this happen?

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

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

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

How do you achieve this?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Video transcription by Speechpad.com

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Reblogged 3 years ago from tracking.feedpress.it

A Vision for Brand Engagement Online, or "The Goal"

Posted by EricEnge

Today’s post focuses on a vision for your online presence. This vision outlines what it takes to be the best, both from an overall reputation and visibility standpoint, as well as an SEO point of view. The reason these are tied together is simple: Your overall online reputation and visibility is a huge factor in your SEO. Period. Let’s start by talking about why.

Core ranking signals

For purposes of this post, let’s define three cornerstone ranking signals that most everyone agrees on:

Links

Links remain a huge factor in overall ranking. Both Cyrus Shepard and Marcus Tober re-confirmed this on the Periodic Table of SEO Ranking Factors session at the SMX Advanced conference in Seattle this past June.

On-page content

On-page content remains a huge factor too, but with some subtleties now thrown in. I wrote about some of this in earlier posts I did on Moz about Term Frequency and Inverse Document Frequency. Suffice it to say that on-page content is about a lot more than pure words on the page, but also includes the supporting pages that you link to.

User engagement with your site

This is not one of the traditional SEO signals from the early days of SEO, but most advanced SEO pros that I know consider it a real factor these days. One of the most popular concepts people talk about is called pogo-sticking, which is illustrated here:

You can learn more about the pogosticking concept by visiting this Whiteboard Friday video by a rookie SEO with a last name of Fishkin.

New, lesser-known signals

OK, so these are the more obvious signals, but now let’s look more broadly at the overall web ecosystem and talk about other types of ranking signals. Be warned that some of these signals may be indirect, but that just doesn’t matter. In fact, my first example below is an indirect factor which I will use to demonstrate why whether a signal is direct or indirect is not an issue at all.

Let me illustrate with an example. Say you spend $1 billion dollars building a huge brand around a product that is massively useful to people. Included in this is a sizable $100 million dollar campaign to support a highly popular charitable foundation, and your employees regularly donate time to help out in schools across your country. In short, the great majority of people love your brand.

Do you think this will impact the way people link to your site? Of course it does. Do you think it will impact how likely people are to be satisified with quality of the pages of your site? Consider this A/B test scenario of 2 pages from different “brands” (for the one on the left, imagine the image of Coca Cola or Pepsi Cola, whichever one you prefer):

Do you think that the huge brand will get a benefit of a doubt on their page that the no-name brand does not even though the pages are identical? Of course they will. Now let’s look at some simpler scenarios that don’t involve a $1 billion investment.

1. Cover major options related to a product or service on “money pages”

Imagine that a user arrives on your auto parts site after searching on the phrase “oil filter” at Google or Bing. Chances are pretty good that they want an oil filter, but here are some other items they may also want:

  • A guide to picking the right filter for their car
  • Oil
  • An oil filter wrench
  • A drainage pan to drain the old oil into

This is just the basics, right? But, you would be surprised with how many sites don’t include links or information on directly related products on their money pages. Providing this type of smart site and page design can have a major impact on user engagement with the money pages of your site.

2. Include other related links on money pages

In the prior item we covered the user’s most directly related needs, but they may have secondary needs as well. Someone who is changing a car’s oil is either a mechanic or a do-it-yourself-er. What else might they need? How about other parts, such as windshield wipers or air filters?

These are other fairly easy maintenance steps for someone who is working on their car to complete. Presence of these supporting products could be one way to improve user engagement with your pages.

3. Offer industry-leading non-commercial content on-site

Publishing world-class content on your site is a great way to produce links to your site. Of course, if you do this on a blog on your site, it may not provide links directly to your money pages, but it will nonetheless lift overall site authority.

In addition, if someone has consumed one or more pieces of great content on your site, the chance of their engaging in a more positive manner with your site overall go way up. Why? Because you’ve earned their trust and admiration.

4. Be everywhere your audiences are with more high-quality, relevant, non-commercial content

Are there major media sites that cover your market space? Do they consider you to be an expert? Will they quote you in articles they write? Can you provide them with guest posts or let you be a guest columnist? Will they collaborate on larger content projects with you?

All of these activities put you in front of their audiences, and if those audiences overlap with yours, this provides a great way to build your overall reputation and visibility. This content that you publish, or collaborate on, that shows up on 3rd-party sites will get you mentions and links. In addition, once again, it will provide you with a boost to your branding. People are now more likely to consume your other content more readily, including on your money pages.

5. Leverage social media

The concept here shares much in common with the prior point. Social media provides opportunities to get in front of relevant audiences. Every person that’s an avid follower of yours on a social media site is more likely to show very different behavior characteristics interacting with your site than someone that does not know you well at all.

Note that links from social media sites are nofollowed, but active social media behavior can lead to people implementing “real world” links to your site that are followed, from their blogs and media web sites.

6. Be active in the offline world as well

Think your offline activity doesn’t matter online? Think again. Relationships are still most easily built face-to-face. People you meet and spend time with can well become your most loyal fans online. This is particularly important when it comes to building relationships with influential people.

One great way to do that is to go to public events related to your industry, such as conferences. Better still, obtain speaking engagements at those conferences. This can even impact people who weren’t there to hear you speak, as they become aware that you have been asked to do that. This concept can also work for a small local business. Get out in your community and engage with people at local events.

The payoff here is similar to the payoff for other items: more engaged, highly loyal fans who engage with you across the web, sending more and more positive signals, both to other people and to search engines, that you are the real deal.

7. Provide great customer service/support

Whatever your business may be, you need to take care of your customers as best you can. No one can make everyone happy, that’s unrealistic, but striving for much better than average is a really sound idea. Having satisfied customers saying nice things about you online is a big impact item in the grand scheme of things.

8. Actively build relationships with influencers too

While this post is not about the value of influencer relationships, I include this in the list for illustration purposes, for two reasons:

  1. Some opportunities are worth extra effort. Know of someone who could have a major impact on your business? Know that they will be at a public event in the near future? Book your plane tickets and get your butt out there. No guarantee that you will get the result you are looking for, or that it will happen quickly, but your chances go WAY up if you get some face time with them.
  2. Influencers are worth special attention and focus, but your relationship-building approach to the web and SEO is not only about influencers. It’s about the entire ecosystem.

It’s an integrated ecosystem

The web provides a level of integrated, real-time connectivity of a kind that the world has never seen before. This is only going to increase. Do something bad to a customer in Hong Kong? Consumers in Boston will know within 5 minutes. That’s where it’s all headed.

Google and Bing (and any future search engine that may emerge) want to measure these types of signals because they tell them how to improve the quality of the experience on their platforms. There are may ways they can perform these measurements.

One simple concept is covered by Rand in this recent Whiteboard Friday video. The discussion is about a recent patent granted to Google that shows how the company can use search queries to detect who is an authority on a topic.

The example he provides is about people who search on “email finding tool”. If Google also finds that a number of people search on “voila norbert email tool”, Google may use that as an authority signal.

Think about that for a moment. How are you going to get people to search on your brand more while putting it together with a non-branded querly like that? (OK, please leave Mechanical Turk and other services like that out of the discussion).

Now you can start to see the bigger picture. Measurements like pogosticking and this recent search behavior related patent are just the tip of the iceberg. Undoubtedly, there are many other ways that search engines can measure what people like and engage with the most.

This is all part of SEO now. UX, product breadth, problem solving, UX, engaging in social media, getting face to face, creating great content that you publish in front of other people’s audiences, and more.

For the small local business, you can still win at this game, as your focus just needs to be on doing it better than your competitors. The big brands will never be hyper-local like you are, so don’t think you can’t play the game, because you can.

Whoever you are, get ready, because this new integrated ecosystem is already upon us, and you need to be a part of it.

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Reblogged 3 years ago from tracking.feedpress.it

Big Data, Big Problems: 4 Major Link Indexes Compared

Posted by russangular

Given this blog’s readership, chances are good you will spend some time this week looking at backlinks in one of the growing number of link data tools. We know backlinks continue to be one of, if not the most important
parts of Google’s ranking algorithm. We tend to take these link data sets at face value, though, in part because they are all we have. But when your rankings are on the line, is there a better way to get at which data set is the best? How should we go
about assessing these different link indexes like
Moz,
Majestic, Ahrefs and SEMrush for quality? Historically, there have been 4 common approaches to this question of index quality…

  • Breadth: We might choose to look at the number of linking root domains any given service reports. We know
    that referring domains correlates strongly with search rankings, so it makes sense to judge a link index by how many unique domains it has
    discovered and indexed.
  • Depth: We also might choose to look at how deep the web has been crawled, looking more at the total number of URLs
    in the index, rather than the diversity of referring domains.
  • Link Overlap: A more sophisticated approach might count the number of links an index has in common with Google Webmaster
    Tools.
  • Freshness: Finally, we might choose to look at the freshness of the index. What percentage of links in the index are
    still live?

There are a number of really good studies (some newer than others) using these techniques that are worth checking out when you get a chance:

  • BuiltVisible analysis of Moz, Majestic, GWT, Ahrefs and Search Metrics
  • SEOBook comparison of Moz, Majestic, Ahrefs, and Ayima
  • MatthewWoodward
    study of Ahrefs, Majestic, Moz, Raven and SEO Spyglass
  • Marketing Signals analysis of Moz, Majestic, Ahrefs, and GWT
  • RankAbove comparison of Moz, Majestic, Ahrefs and Link Research Tools
  • StoneTemple study of Moz and Majestic

While these are all excellent at addressing the methodologies above, there is a particular limitation with all of them. They miss one of the
most important metrics we need to determine the value of a link index: proportional representation to Google’s link graph
. So here at Angular Marketing, we decided to take a closer look.

Proportional representation to Google Search Console data

So, why is it important to determine proportional representation? Many of the most important and valued metrics we use are built on proportional
models. PageRank, MozRank, CitationFlow and Ahrefs Rank are proportional in nature. The score of any one URL in the data set is relative to the
other URLs in the data set. If the data set is biased, the results are biased.

A Visualization

Link graphs are biased by their crawl prioritization. Because there is no full representation of the Internet, every link graph, even Google’s,
is a biased sample of the web. Imagine for a second that the picture below is of the web. Each dot represents a page on the Internet,
and the dots surrounded by green represent a fictitious index by Google of certain sections of the web.

Of course, Google isn’t the only organization that crawls the web. Other organizations like Moz,
Majestic, Ahrefs, and SEMrush
have their own crawl prioritizations which result in different link indexes.

In the example above, you can see different link providers trying to index the web like Google. Link data provider 1 (purple) does a good job
of building a model that is similar to Google. It isn’t very big, but it is proportional. Link data provider 2 (blue) has a much larger index,
and likely has more links in common with Google that link data provider 1, but it is highly disproportional. So, how would we go about measuring
this proportionality? And which data set is the most proportional to Google?

Methodology

The first step is to determine a measurement of relativity for analysis. Google doesn’t give us very much information about their link graph.
All we have is what is in Google Search Console. The best source we can use is referring domain counts. In particular, we want to look at
what we call
referring domain link pairs. A referring domain link pair would be something like ask.com->mlb.com: 9,444 which means
that ask.com links to mlb.com 9,444 times.

Steps

  1. Determine the root linking domain pairs and values to 100+ sites in Google Search Console
  2. Determine the same for Ahrefs, Moz, Majestic Fresh, Majestic Historic, SEMrush
  3. Compare the referring domain link pairs of each data set to Google, assuming a
    Poisson Distribution
  4. Run simulations of each data set’s performance against each other (ie: Moz vs Maj, Ahrefs vs SEMrush, Moz vs SEMrush, et al.)
  5. Analyze the results

Results

When placed head-to-head, there seem to be some clear winners at first glance. In head-to-head, Moz edges out Ahrefs, but across the board, Moz and Ahrefs fare quite evenly. Moz, Ahrefs and SEMrush seem to be far better than Majestic Fresh and Majestic Historic. Is that really the case? And why?

It turns out there is an inversely proportional relationship between index size and proportional relevancy. This might seem counterintuitive,
shouldn’t the bigger indexes be closer to Google? Not Exactly.

What does this mean?

Each organization has to create a crawl prioritization strategy. When you discover millions of links, you have to prioritize which ones you
might crawl next. Google has a crawl prioritization, so does Moz, Majestic, Ahrefs and SEMrush. There are lots of different things you might
choose to prioritize…

  • You might prioritize link discovery. If you want to build a very large index, you could prioritize crawling pages on sites that
    have historically provided new links.
  • You might prioritize content uniqueness. If you want to build a search engine, you might prioritize finding pages that are unlike
    any you have seen before. You could choose to crawl domains that historically provide unique data and little duplicate content.
  • You might prioritize content freshness. If you want to keep your search engine recent, you might prioritize crawling pages that
    change frequently.
  • You might prioritize content value, crawling the most important URLs first based on the number of inbound links to that page.

Chances are, an organization’s crawl priority will blend some of these features, but it’s difficult to design one exactly like Google. Imagine
for a moment that instead of crawling the web, you want to climb a tree. You have to come up with a tree climbing strategy.

  • You decide to climb the longest branch you see at each intersection.
  • One friend of yours decides to climb the first new branch he reaches, regardless of how long it is.
  • Your other friend decides to climb the first new branch she reaches only if she sees another branch coming off of it.

Despite having different climb strategies, everyone chooses the same first branch, and everyone chooses the same second branch. There are only
so many different options early on.

But as the climbers go further and further along, their choices eventually produce differing results. This is exactly the same for web crawlers
like Google, Moz, Majestic, Ahrefs and SEMrush. The bigger the crawl, the more the crawl prioritization will cause disparities. This is not a
deficiency; this is just the nature of the beast. However, we aren’t completely lost. Once we know how index size is related to disparity, we
can make some inferences about how similar a crawl priority may be to Google.

Unfortunately, we have to be careful in our conclusions. We only have a few data points with which to work, so it is very difficult to be
certain regarding this part of the analysis. In particular, it seems strange that Majestic would get better relative to its index size as it grows,
unless Google holds on to old data (which might be an important discovery in and of itself). It is most likely that at this point we can’t make
this level of conclusion.

So what do we do?

Let’s say you have a list of domains or URLs for which you would like to know their relative values. Your process might look something like
this…

  • Check Open Site Explorer to see if all URLs are in their index. If so, you are looking metrics most likely to be proportional to Google’s link graph.
  • If any of the links do not occur in the index, move to Ahrefs and use their Ahrefs ranking if all you need is a single PageRank-like metric.
  • If any of the links are missing from Ahrefs’s index, or you need something related to trust, move on to Majestic Fresh.
  • Finally, use Majestic Historic for (by leaps and bounds) the largest coverage available.

It is important to point out that the likelihood that all the URLs you want to check are in a single index increases as the accuracy of the metric
decreases. Considering the size of Majestic’s data, you can’t ignore them because you are less likely to get null value answers from their data than
the others. If anything rings true, it is that once again it makes sense to get data
from as many sources as possible. You won’t
get the most proportional data without Moz, the broadest data without Majestic, or everything in-between without Ahrefs.

What about SEMrush? They are making progress, but they don’t publish any relative statistics that would be useful in this particular
case. Maybe we can hope to see more from them soon given their already promising index!

Recommendations for the link graphing industry

All we hear about these days is big data; we almost never hear about good data. I know that the teams at Moz,
Majestic, Ahrefs, SEMrush and others are interested in mimicking Google, but I would love to see some organization stand up against the
allure of
more data in favor of better data—data more like Google’s. It could begin with testing various crawl strategies to see if they produce
a result more similar to that of data shared in Google Search Console. Having the most Google-like data is certainly a crown worth winning.

Credits

Thanks to Diana Carter at Angular for assistance with data acquisition and Andrew Cron with statistical analysis. Thanks also to the representatives from Moz, Majestic, Ahrefs, and SEMrush for answering questions about their indices.

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Reblogged 3 years ago from tracking.feedpress.it

Creating Demand for Products, Services, and Ideas that Have Little to No Existing Search Volume – Whiteboard Friday

Posted by randfish

A lot of fantastic websites (and products, services, ideas, etc.) are in something of a pickle: The keywords they would normally think to target get next to no search volume. It can make SEO seem like a lost cause. In today’s Whiteboard Friday, Rand explains why that’s not the case, and talks about the one extra step that’ll help those organizations create the demand they want.

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

Video transcription

Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we’re going to chat about a particularly challenging problem in the world of SEO, and that is trying to do SEO or trying to do any type of web marketing when your product, service, or idea has no search volume around it. So nobody is already looking for what you offer. It’s a new thing, a new concept.

I’ll use the example here of a website that I’m very fond of, but which there’s virtually no search volume for, called Niice. It’s Niice.co.

It’s great. I searched for things in here. It brings me back all these wonderful visuals from places like Colossus and lots of design portals. I love this site. I use it all the time for inspiration, for visuals, for stuff that I might write about on blogs, for finding new artists. It’s just cool. I love it. I love the discovery aspect of it, and I think it can be really great for finding artists and designers and visuals.

But when I looked at the keyword research — and granted I didn’t go deep into the keyword research, but let’s imagine that I did — I looked for things like: “visual search engine” almost no volume; “search engine for designers” almost no volume; “graphical search engine” almost no volume; “find designer visuals” nada.

So when they look at their keyword research they go, “Man, we don’t even have keywords to target here really.” SEO almost feels like it’s not a channel of opportunity, and I think that’s where many, many companies and businesses make mistakes actually, because just because you don’t see keyword research around exactly around what you’re offering doesn’t mean that SEO can’t be a great channel. It just means we have to do an extra step of work, and that’s what I want to talk about today.

So I think when you encounter this type of challenge — and granted it might not be the challenge that there’s no keyword volume — it could be a challenge in your business, for your organization, for some ideas or products that you have or are launching that there’s just very little, and thus you’re struggling to come up with enough volume to create the quantity of leads, or free trials, or customers that you need. This process really can work.

Key questions to start.

1) Who’s the target audience?

In Niice’s case, that’s going to be a lot of designers. It might be people who are creating presentations. It might be those who are searching out designers or artists. It could be people seeking inspiration for all sorts of things. So they’re going to figure out who that is.

From there, they can look at the job title, interests, demographics of those people, and then you can do some cool stuff where you can figure out things like, “Oh, you know what? We could do some Facebook ad targeting to those right groups to help boost their interests in our product and potentially, well, create branded search volume down the road, attract direct visitors, build brand awareness for ourselves, and potentially get some traffic to the site directly as well. If we can convert some of that traffic, well, that’s fantastic.”

In their case, I think Niice is ad-supported right now, so all they really need is the traffic itself. But regardless, this is that same type of process you’d use.

2) What else do they search for?

What is that target audience searching for? Knowledge, products, tools, services, people, brands, whatever it is, if you know who the audience is, you can figure out what they’re searching for because they have needs. If they have a job title, if they have interests, if you have those profile features about the audience, you can figure out what else they’re going to be searching for, and in this case, knowing what designers are searching for, well, that’s probably relatively simplistic. The other parts of their audience might be more complex, but that one is pretty obvious.

From that, we can do content creation. We can do keyword targeting to be in front of those folks when they’re doing search by creating content that may not necessarily be exactly selling our tools, but that’s the idea of content marketing. We’re creating content to target people higher up in the funnel before they need our product.

We can use that, too, for product and feature inspiration in the product itself. So in this case, Niice might consider creating a design pattern library or several, pulling from different places, or hiring someone to come in and build one for them and then featuring that somewhere on the site if you haven’t done a search yet and then potentially trying to rank for that in the search engine, which then brings qualified visitors, the types of people who once they got exposed to Niice would be like, “Wow, this is great and it’s totally free. I love it.”

UX tool list, so list of tools for user experience, people on the design or UI side, maybe Photoshop tutorials, whatever it is that they feel like they’re competent and capable of creating and could potentially rank for, well, now you’re attracting the right audience to your site before they need your product.

3) Where do they go?

That audience, where are they going on the web? What do they do when they get there? To whom do they listen? Who are their influencers? How can we be visible in those locations? So from that I can get things like influencer targeting and outreach. I can get ad and sponsorship opportunities. I can figure out places to do partnership or guest content or business development.

In Niice’s case, that might be things like sponsor or speak at design events. Maybe they could create an awards project for Dribble. So they go to Dribble, they look at what’s been featured there, or they go to Colossus, or some of the other sites that they feature, and they find the best work of the week. At the end of the week, they feature the top 10 projects, and then they call out the designers who put them together.

Wow, that’s terrific. Now you’re getting in front of the audience whose work you’re featuring, which is going to, in turn, make them amplify Niice’s project and product to an audience who’s likely to be in their target audience. It’s sort of a win-win. That’s also going to help them build links, engagement, shares, and all sorts of signals that potentially will help them with their authority, both topically and domain-wide, which then means they can rank for all the content they create, building up this wonderful engine.

4) What types of content have achieved broad or viral distribution?

I think what we can glean from this is not just inspiration for content and keyword opportunities as we can from many other kinds of content, but also sites to target, in particular sites to target with advertising, sites to target for guest posting or sponsorship, or sites to target for business development or for partnerships, site to target in an ad network, sites to target psychographically or demographically for Facebook if we want to run ads like that, potentially bidding on ads in Google when people search for that website or for that brand name in paid search.

So if you’re Niice, you could think about contracting some featured artist to contribute visuals maybe for a topical news project. So something big is happening in the news or in the design community, you contract a few of the artists whose work you have featured or are featuring, or people from the communities whose work you’re featuring, and say, “Hey, we might not be able to pay you a lot, but we’re going to get in front of a ton of people. We’re going to build exposure for you, which is something we already do, FYI, and now you’ve got some wonderful content that has that potential to mimic that work.”

You could think about, and I love this just generally as a content marketing and SEO tactic, if you go find viral content, content that has had wide sharing success across the web from the past, say two, three, four, or five years ago, you have a great opportunity, especially if the initial creator of that content or project hasn’t continued on with it, to go say, “Hey, you know what? We can do a version of that. We’re going to modernize and update that for current audiences, current tastes, what’s currently going on in the market. We’re going to go build that, and we have a strong feeling that it’s going to be successful because it’s succeeded in the past.”

That, I think, is a great way to get content ideas from viral content and then to potentially overtake them in the search rankings too. If something from three or five years ago, that was particularly timely then still ranks today, if you produce it, you’re almost certainly going to come out on top due to Google’s bias for freshness, especially around things that have timely relevance.

5) Should brand advertisement be in our consideration set?

Then last one, I like to ask about brand advertising in these cases, because when there’s not search volume yet, a lot of times what you have to do is create awareness. I should change this from advertising to a brand awareness, because really there’s organic ways to do it and advertising ways to do it. You can think about, “Well, where are places that we can target where we could build that awareness? Should we invest in press and public relations?” Not press releases. “Then how do we own the market?” So I think one of the keys here is starting with that name or title or keyword phrase that encapsulates what the market will call your product, service or idea.

In the case of Niice, that could be, well, visual search engines. You can imagine the press saying, “Well, visual search engines like Niice have recently blah, blah, blah.” Or it could be designer search engines, or it could be graphical search engines, or it could be designer visual engines, whatever it is. You need to find what that thing is going to be and what’s going to resonate.

In the case of Nest, that was the smart home. In the case of Oculus, it was virtual reality and virtual reality gaming. In the case of Tesla, it was sort of already established. There’s electric cars, but they kind of own that market. If you know what those keywords are, you can own the market before it gets hot, and that’s really important because that means that all of the press and PR and awareness that happens around the organic rankings for that particular keyword phrase will all be owned and controlled by you.

When you search for “smart home,” Nest is going to dominate those top 10 results. When you search for “virtual reality gaming,” Oculus is going to dominate those top 10. It’s not necessarily dominate just on their own site, it’s dominate all the press and PR articles that are about that, all of the Wikipedia page about it, etc., etc. You become the brand that’s synonymous with the keyword or concept. From an SEO perspective, that’s a beautiful world to live in.

So, hopefully, for those of you who are struggling around demand for your keywords, for your volume, this process can be something that’s really helpful. I look forward to hearing from you in the comments. We’ll see you again next week for another edition of Whiteboard Friday. Take care.

Video transcription by Speechpad.com

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Reblogged 3 years ago from tracking.feedpress.it

Deconstructing the App Store Rankings Formula with a Little Mad Science

Posted by AlexApptentive

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

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

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

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

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

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

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

Until now, that is.

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

But first, a little context

Image credit: Josh Tuininga, Apptentive

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

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

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

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

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

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

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

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

Now, for the Mad Science.

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

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

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

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

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

Hypothesis

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

Both of these assumptions will be tested in later analysis.

Results

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

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

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

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

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

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

Hypothesis

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

Results

App Store Ranking Volatility of Top 500 Apps

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

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

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

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

Study #3: App store rankings across the stars

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

Hypothesis

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

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

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

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

Results

Average App Store Ratings of Top Apps

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

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

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

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

App Store Ranking Volatility and Average Rating

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

Study #4: App store rankings across versions

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

Hypothesis

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

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

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

Results

How update frequency correlates with app store rank

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

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

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

How update frequency correlates with app store ranking volatility

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

Study #5: App store rankings across monthly active users

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

Hypothesis

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

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

Results

Apps with more ratings and reviews typically rank higher

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

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

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

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

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

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

Apps with more ratings typically experience less app store ranking volatility

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

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

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

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

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

Summary

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

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

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

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

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

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

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

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

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

Weight of factors in the Apple App Store ranking algorithm

Rating Count > Installs > Trends > Rating

Weight of factors in the Google Play ranking algorithm

Rating Count > Installs > Rating > Trends


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

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

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

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Reblogged 3 years ago from tracking.feedpress.it