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

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UX, Content Quality, and SEO – Whiteboard Friday

Posted by EricEnge

Editor’s note: Today we’re featuring back-to-back episodes of Whiteboard Friday from our friends at Stone Temple Consulting. Make sure to also check out the first episode, “Becoming Better SEO Scientists” from Mark Traphagen.

User experience and the quality of your content have an incredibly broad impact on your SEO efforts. In this episode of Whiteboard Friday, Stone Temple’s Eric Enge shows you how paying attention to your users can benefit your position in the SERPs.

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

Hi, Mozzers. I’m Eric Enge, CEO of Stone Temple Consulting. Today I want to talk to you about one of the most underappreciated aspects of SEO, and that is the interaction between user experience, content quality, and your SEO rankings and traffic.

I’m going to take you through a little history first. You know, we all know about the Panda algorithm update that came out in February 23, 2011, and of course more recently we have the search quality update that came out in May 19, 2015. Our Panda friend had 27 different updates that we know of along the way. So a lot of stuff has gone on, but we need to realize that that is not where it all started.

The link algorithm from the very beginning was about search quality. Links allowed Google to have an algorithm that gave better results than the other search engines of their day, which were dependent on keywords. These things however, that I’ve just talked about, are still just the tip of the iceberg. Google goes a lot deeper than that, and I want to walk you through the different things that it does.

So consider for a moment, you have someone search on the phrase “men’s shoes” and they come to your website.

What is that they want when they come to your website? Do they want sneakers, sandals, dress shoes? Well, those are sort of the obvious things that they might want. But you need to think a little bit more about what the user really wants to be able to know before they buy from you.

First of all, there has to be a way to buy. By the way, affiliate sites don’t have ways to buy. So the line of thinking I’m talking about might not work out so well for affiliate sites and works better for people who can actually sell the product directly. But in addition to a way to buy, they might want a privacy policy. They might want to see an About Us page. They might want to be able to see your phone number. These are all different kinds of things that users look for when they arrive on the pages of your site.

So as we think about this, what is it that we can do to do a better job with our websites? Well, first of all, lose the focus on keywords. Don’t get me wrong, keywords haven’t gone entirely away. But the pages where we overemphasize one particular keyword over another or related phrases are long gone, and you need to have a broader focus on how you approach things.

User experience is now a big deal. You really need to think about how users are interacting with your page and how that shows your overall page quality. Think about the percent satisfaction. If I send a hundred users to your page from my search engine, how many of those users are going to be happy with the content or the products or everything that they see with your page? You need to think through the big picture. So at the end of the day, this impacts the content on your page to be sure, but a lot more than that it impacts the design, related items that you have on the page.

So let me just give you an example of that. I looked at one page recently that was for a flower site. It was a page about annuals on that site, and that page had no link to their perennials page. Well, okay, a fairly good percentage of people who arrive on a page about annuals are also going to want to have perennials as something they might consider buying. So that page was probably coming across as a poor user experience. So these related items concepts are incredibly important.

Then the links to your page is actually a way to get to some of those related items, and so those are really important as well. What are the related products that you link to?

Finally, really it impacts everything you do with your page design. You need to move past the old-fashioned way of thinking about SEO and into the era of: How am I doing with satisfying all the people who come to the pages of your site?

Thank you, Mozzers. Have a great day.

Video transcription by Speechpad.com

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8 Ways Content Marketers Can Hack Facebook Multi-Product Ads

Posted by Alan_Coleman

The trick most content marketers are missing

Creating great content is the first half of success in content marketing. Getting quality content read by, and amplified to, a relevant audience is the oft overlooked second half of success. Facebook can be a content marketer’s best friend for this challenge. For reach, relevance and amplification potential, Facebook is unrivaled.

  1. Reach: 1 in 6 mobile minutes on planet earth is somebody reading something on Facebook.
  2. Relevance: Facebook is a lean mean interest and demo targeting machine. There is no online or offline media that owns as much juicy interest and demographic information on its audience and certainly no media has allowed advertisers to utilise this information as effectively as Facebook has.
  3. Amplification: Facebook is literally built to encourage sharing. Here’s the first 10 words from their mission statement: “Facebook’s mission is to give people the power to share…”, Enough said!

Because of these three digital marketing truths, if a content marketer gets their paid promotion* right on Facebook, the battle for eyeballs and amplification is already won.

For this reason it’s crucial that content marketers keep a close eye on Facebook advertising innovations and seek out ways to use them in new and creative ways.

In this post I will share with you eight ways we’ve hacked a new Facebook ad format to deliver content marketing success.

Multi-Product Ads (MPAs)

In 2014, Facebook unveiled multi-product ads (MPAs) for US advertisers, we got them in Europe earlier this year. They allow retailers to show multiple products in a carousel-type ad unit.

They look like this:

If the user clicks on the featured product, they are guided directly to the landing page for that specific product, from where they can make a purchase.

You could say MPAs are Facebook’s answer to Google Shopping.

Facebook’s mistake is a content marketer’s gain

I believe Facebook has misunderstood how people want to use their social network and the transaction-focused format is OK at best for selling products. People aren’t really on Facebook to hit the “buy now” button. I’m a daily Facebook user and I can’t recall a time this year where I have gone directly from Facebook to an e-commerce website and transacted. Can you remember a recent time when you did?

So, this isn’t an innovation that removes a layer of friction from something that we are all doing online already (as the most effective innovations do). Instead, it’s a bit of a “hit and hope” that, by providing this functionality, Facebook would encourage people to try to buy online in a way they never have before.

The Wolfgang crew felt the MPA format would be much more useful to marketers and users if they were leveraging Facebook for the behaviour we all demonstrate on the platform every day, guiding users to relevant content. We attempted to see if Facebook Ads Manager would accept MPAs promoting content rather than products. We plugged in the images, copy and landing pages, hit “place order”, and lo and behold the ads became active. We’re happy to say that the engagement rates, and more importantly the amplification rates, are fantastic!

Multi-Content Ads

We’ve re-invented the MPA format for multi-advertisers in multi-ways, eight ways to be exact! Here’s eight MPA Hacks that have worked well for us. All eight hacks use the MPA format to promote content rather than promote products.

Hack #1: Multi-Package Ads

Our first variation wasn’t a million miles away from multi-product ads; we were promoting the various packages offered by a travel operator.

By looking at the number of likes, comments, and shares (in blue below the ads) you can see the ads were a hit with Facebook users and they earned lots of free engagement and amplification.

NB: If you have selected “clicks to website” as your advertising objective, all those likes, comments and shares are free!

Independent Travel Multi Product Ad

The ad sparked plenty of conversation amongst Facebook friends in the comments section.

Comments on a Facebook MPA

Hack #2: Multi-Offer Ads

Everybody knows the Internet loves a bargain. So we decided to try another variation moving away from specific packages, focusing instead on deals for a different travel operator.

Here’s how the ads looked:

These ads got valuable amplification beyond the share. In the comments section, you can see people tagging specific friends. This led to the MPAs receiving further amplification, and a very targeted and personalised form of amplification to boot.

Abbey Travel Facebook Ad Comments

Word of mouth referrals have been a trader’s best friend since the stone age. These “personalised” word of mouth referrals en masse are a powerful marketing proposition. It’s worth mentioning again that those engagements are free!

Hack #3: Multi-Locations Ads

Putting the Lo in SOLOMO.

This multi-product feed ad was hacked to promote numerous locations of a waterpark. “Where to go?” is among the first questions somebody asks when researching a holiday. In creating this top of funnel content, we can communicate with our target audience at the very beginning of their research process. A simple truth of digital marketing is: the more interactions you have with your target market on their journey to purchase, the more likely they are to seal the deal with you when it comes time to hit the “buy now” button. Starting your relationship early gives you an advantage over those competitors who are hanging around the bottom of the purchase funnel hoping to make a quick and easy conversion.

Abbey Travel SplashWorld Facebook MPA

What was surprising here, was that because we expected to reach people at the very beginning of their research journey, we expected the booking enquiries to be some time away. What actually happened was these ads sparked an enquiry frenzy as Facebook users could see other people enquiring and the holidays selling out in real time.

Abbey Travel comments and replies

In fact nearly all of the 35 comments on this ad were booking enquiries. This means what we were measuring as an “engagement” was actually a cold hard “conversion”! You don’t need me to tell you a booking enquiry is far closer to the money than a Facebook like.

The three examples outlined so far are for travel companies. Travel is a great fit for Facebook as it sits naturally in the Facebook feed, my Facebook feed is full of envy-inducing friends’ holiday pictures right now. Another interesting reason why travel is a great fit for Facebook ads is because typically there are multiple parties to a travel purchase. What happened here is the comments section actually became a very visible and measurable forum for discussion between friends and family before becoming a stampede inducing medium of enquiry.

So, stepping outside of the travel industry, how do other industries fare with hacked MPAs?

Hack #3a: Multi-Location Ads (combined with location targeting)

Location, location, location. For a property listings website, we applied location targeting and repeated our Multi-Location Ad format to advertise properties for sale to people in and around that location.

Hack #4: Multi-Big Content Ad

“The future of big content is multi platform”

– Cyrus Shepard

The same property website had produced a report and an accompanying infographic to provide their audience with unique and up-to-the-minute market information via their blog. We used the MPA format to promote the report, the infographic and the search rentals page of the website. This brought their big content piece to a larger audience via a new platform.

Rental Report Multi Product Ad

Hack #5: Multi-Episode Ad

This MPA hack was for an online TV player. As you can see we advertised the most recent episodes of a TV show set in a fictional Dublin police station, Red Rock.

Engagement was high, opinion was divided.

TV3s Red Rock viewer feedback

LOL.

Hack #6: Multi-People Ads

In the cosmetic surgery world, past patients’ stories are valuable marketing material. Particularly when the past patients are celebrities. We recycled some previously published stories from celebrity patients using multi-people ads and targeted them to a very specific audience.

Avoca Clinic Multi People Ads

Hack #7: Multi-UGC Ads

Have you witnessed the power of user generated content (UGC) in your marketing yet? We’ve found interaction rates with authentic UGC images can be up to 10 fold of those of the usual stylised images. In order to encourage further UGC, we posted a number of customer’s images in our Multi-UGC Ads.

The CTR on the above ads was 6% (2% is the average CTR for Facebook News feed ads according to our study). Strong CTRs earn you more traffic for your budget. Facebook’s relevancy score lowers your CPC as your CTR increases.

When it comes to the conversion, UGC is a power player, we’ve learned that “customers attracting new customers” is a powerful acquisition tool.

Hack #8: Target past customers for amplification

“Who will support and amplify this content and why?”

– Rand Fishkin

Your happy customers Rand, that’s the who and the why! Check out these Multi-Package Ads targeted to past customers via custom audiences. The Camino walkers have already told all their friends about their great trip, now allow them to share their great experiences on Facebook and connect the tour operator with their Facebook friends via a valuable word of mouth referral. Just look at the ratio of share:likes and shares:comments. Astonishingly sharable ads!

Camino Ways Mulit Product Ads

Targeting past converters in an intelligent manner is a super smart way to find an audience ready to share your content.

How will hacking Multi-Product Ads work for you?

People don’t share ads, but they do share great content. So why not hack MPAs to promote your content and reap the rewards of the world’s greatest content sharing machine: Facebook.

MPAs allow you to tell a richer story by allowing you to promote multiple pieces of content simultaneously. So consider which pieces of content you have that will work well as “content bundles” and who the relevant audience for each “content bundle” is.

As Hack #8 above illustrates, the big wins come when you match a smart use of the format with the clever and relevant targeting Facebook allows. We’re massive fans of custom audiences so if you aren’t sure where to start, I’d suggest starting there.

So ponder your upcoming content pieces, consider your older content you’d like to breathe some new life into and perhaps you could become a Facebook Ads Hacker.

I’d love to hear about your ideas for turning Multi-Product Ads into Multi-Content Ads in the comments section below.

We could even take the conversation offline at Mozcon!

Happy hacking.


*Yes I did say paid promotion, it’s no secret that Facebook’s organic reach continues to dwindle. The cold commercial reality is you need to pay to play on FB. The good news is that if you select ‘website clicks’ as your objective you only pay for website traffic and engagement while amplification by likes, comments, and shares are free! Those website clicks you pay for are typically substantially cheaper than Adwords, Taboola, Outbrain, Twitter or LinkedIn. How does it compare to display? It doesn’t. Paying for clicks is always preferable to paying for impressions. If you are spending money on display advertising I’d urge you to fling a few spondoolas towards Facebook ads and compare results. You will be pleasantly surprised.

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Deconstructing the App Store Rankings Formula with a Little Mad Science

Posted by AlexApptentive

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

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

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

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

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

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

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

Until now, that is.

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

But first, a little context

Image credit: Josh Tuininga, Apptentive

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

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

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

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

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

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

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

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

Now, for the Mad Science.

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

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

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

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

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

Hypothesis

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

Both of these assumptions will be tested in later analysis.

Results

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

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

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

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

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

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

Hypothesis

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

Results

App Store Ranking Volatility of Top 500 Apps

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

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

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

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

Study #3: App store rankings across the stars

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

Hypothesis

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

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

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

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

Results

Average App Store Ratings of Top Apps

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

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

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

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

App Store Ranking Volatility and Average Rating

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

Study #4: App store rankings across versions

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

Hypothesis

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

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

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

Results

How update frequency correlates with app store rank

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

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

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

How update frequency correlates with app store ranking volatility

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

Study #5: App store rankings across monthly active users

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

Hypothesis

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

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

Results

Apps with more ratings and reviews typically rank higher

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

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

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

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

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

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

Apps with more ratings typically experience less app store ranking volatility

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

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

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

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

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

Summary

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

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

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

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

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

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

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

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

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

Weight of factors in the Apple App Store ranking algorithm

Rating Count > Installs > Trends > Rating

Weight of factors in the Google Play ranking algorithm

Rating Count > Installs > Rating > Trends


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

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

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

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Has Google Gone Too Far with the Bias Toward Its Own Content?

Posted by ajfried

Since the beginning of SEO time, practitioners have been trying to crack the Google algorithm. Every once in a while, the industry gets a glimpse into how the search giant works and we have opportunity to deconstruct it. We don’t get many of these opportunities, but when we do—assuming we spot them in time—we try to take advantage of them so we can “fix the Internet.”

On Feb. 16, 2015, news started to circulate that NBC would start removing images and references of Brian Williams from its website.

This was it!

A golden opportunity.

This was our chance to learn more about the Knowledge Graph.

Expectation vs. reality

Often it’s difficult to predict what Google is truly going to do. We expect something to happen, but in reality it’s nothing like we imagined.

Expectation

What we expected to see was that Google would change the source of the image. Typically, if you hover over the image in the Knowledge Graph, it reveals the location of the image.

Keanu-Reeves-Image-Location.gif

This would mean that if the image disappeared from its original source, then the image displayed in the Knowledge Graph would likely change or even disappear entirely.

Reality (February 2015)

The only problem was, there was no official source (this changed, as you will soon see) and identifying where the image was coming from proved extremely challenging. In fact, when you clicked on the image, it took you to an image search result that didn’t even include the image.

Could it be? Had Google started its own database of owned or licensed images and was giving it priority over any other sources?

In order to find the source, we tried taking the image from the Knowledge Graph and “search by image” in images.google.com to find others like it. For the NBC Nightly News image, Google failed to even locate a match to the image it was actually using anywhere on the Internet. For other television programs, it was successful. Here is an example of what happened for Morning Joe:

Morning_Joe_image_search.png

So we found the potential source. In fact, we found three potential sources. Seemed kind of strange, but this seemed to be the discovery we were looking for.

This looks like Google is using someone else’s content and not referencing it. These images have a source, but Google is choosing not to show it.

Then Google pulled the ol’ switcheroo.

New reality (March 2015)

Now things changed and Google decided to put a source to their images. Unfortunately, I mistakenly assumed that hovering over an image showed the same thing as the file path at the bottom, but I was wrong. The URL you see when you hover over an image in the Knowledge Graph is actually nothing more than the title. The source is different.

Morning_Joe_Source.png

Luckily, I still had two screenshots I took when I first saw this saved on my desktop. Success. One screen capture was from NBC Nightly News, and the other from the news show Morning Joe (see above) showing that the source was changed.

NBC-nightly-news-crop.png

(NBC Nightly News screenshot.)

The source is a Google-owned property: gstatic.com. You can clearly see the difference in the source change. What started as a hypothesis in now a fact. Google is certainly creating a database of images.

If this is the direction Google is moving, then it is creating all kinds of potential risks for brands and individuals. The implications are a loss of control for any brand that is looking to optimize its Knowledge Graph results. As well, it seems this poses a conflict of interest to Google, whose mission is to organize the world’s information, not license and prioritize it.

How do we think Google is supposed to work?

Google is an information-retrieval system tasked with sourcing information from across the web and supplying the most relevant results to users’ searches. In recent months, the search giant has taken a more direct approach by answering questions and assumed questions in the Answer Box, some of which come from un-credited sources. Google has clearly demonstrated that it is building a knowledge base of facts that it uses as the basis for its Answer Boxes. When it sources information from that knowledge base, it doesn’t necessarily reference or credit any source.

However, I would argue there is a difference between an un-credited Answer Box and an un-credited image. An un-credited Answer Box provides a fact that is indisputable, part of the public domain, unlikely to change (e.g., what year was Abraham Lincoln shot? How long is the George Washington Bridge?) Answer Boxes that offer more than just a basic fact (or an opinion, instructions, etc.) always credit their sources.

There are four possibilities when it comes to Google referencing content:

  • Option 1: It credits the content because someone else owns the rights to it
  • Option 2: It doesn’t credit the content because it’s part of the public domain, as seen in some Answer Box results
  • Option 3: It doesn’t reference it because it owns or has licensed the content. If you search for “Chicken Pox” or other diseases, Google appears to be using images from licensed medical illustrators. The same goes for song lyrics, which Eric Enge discusses here: Google providing credit for content. This adds to the speculation that Google is giving preference to its own content by displaying it over everything else.
  • Option 4: It doesn’t credit the content, but neither does it necessarily own the rights to the content. This is a very gray area, and is where Google seemed to be back in February. If this were the case, it would imply that Google is “stealing” content—which I find hard to believe, but felt was necessary to include in this post for the sake of completeness.

Is this an isolated incident?

At Five Blocks, whenever we see these anomalies in search results, we try to compare the term in question against others like it. This is a categorization concept we use to bucket individuals or companies into similar groups. When we do this, we uncover some incredible trends that help us determine what a search result “should” look like for a given group. For example, when looking at searches for a group of people or companies in an industry, this grouping gives us a sense of how much social media presence the group has on average or how much media coverage it typically gets.

Upon further investigation of terms similar to NBC Nightly News (other news shows), we noticed the un-credited image scenario appeared to be a trend in February, but now all of the images are being hosted on gstatic.com. When we broadened the categories further to TV shows and movies, the trend persisted. Rather than show an image in the Knowledge Graph and from the actual source, Google tends to show an image and reference the source from Google’s own database of stored images.

And just to ensure this wasn’t a case of tunnel vision, we researched other categories, including sports teams, actors and video games, in addition to spot-checking other genres.

Unlike terms for specific TV shows and movies, terms in each of these other groups all link to the actual source in the Knowledge Graph.

Immediate implications

It’s easy to ignore this and say “Well, it’s Google. They are always doing something.” However, there are some serious implications to these actions:

  1. The TV shows/movies aren’t receiving their due credit because, from within the Knowledge Graph, there is no actual reference to the show’s official site
  2. The more Google moves toward licensing and then retrieving their own information, the more biased they become, preferring their own content over the equivalent—or possibly even superior—content from another source
  3. If feels wrong and misleading to get a Google Image Search result rather than an actual site because:
    • The search doesn’t include the original image
    • Considering how poor Image Search results are normally, it feels like a poor experience
  4. If Google is moving toward licensing as much content as possible, then it could make the Knowledge Graph infinitely more complicated when there is a “mistake” or something unflattering. How could one go about changing what Google shows about them?

Google is objectively becoming subjective

It is clear that Google is attempting to create databases of information, including lyrics stored in Google Play, photos, and, previously, facts in Freebase (which is now Wikidata and not owned by Google).

I am not normally one to point my finger and accuse Google of wrongdoing. But this really strikes me as an odd move, one bordering on a clear bias to direct users to stay within the search engine. The fact is, we trust Google with a heck of a lot of information with our searches. In return, I believe we should expect Google to return an array of relevant information for searchers to decide what they like best. The example cited above seems harmless, but what about determining which is the right religion? Or even who the prettiest girl in the world is?

Religion-and-beauty-queries.png

Questions such as these, which Google is returning credited answers for, could return results that are perceived as facts.

Should we next expect Google to decide who is objectively the best service provider (e.g., pizza chain, painter, or accountant), then feature them in an un-credited answer box? The direction Google is moving right now, it feels like we should be calling into question their objectivity.

But that’s only my (subjective) opinion.

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Why Good Unique Content Needs to Die – Whiteboard Friday

Posted by randfish

We all know by now that not just any old content is going to help us rank in competitive SERPs. We often hear people talking about how it takes “good, unique content.” That’s the wrong bar. In today’s Whiteboard Friday, Rand talks about where we should be aiming, and how to get there.

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 something that I really have a problem with in the SEO world, and that is the phrase “good, unique content.” I’ll tell you why this troubles me so much. It’s because I get so many emails, I hear so many times at conferences and events with people I meet, with folks I talk to in the industry saying, “Hey, we created some good, unique content, but we don’t seem to be performing well in search.” My answer back to that is always that is not the bar for entry into SEO. That is not the bar for ranking.

The content quality scale

So I made this content quality scale to help illustrate what I’m talking about here. You can see that it starts all the way up at 10x, and down here I’ve got Panda Invasion. So quality, like Google Panda is coming for your site, it’s going to knock you out of the rankings. It’s going to penalize you, like your content is thin and largely useless.

Then you go up a little bit, and it’s like, well four out of five searchers find it pretty bad. They clicked the Back button. Maybe one out of five is thinking, “Well, this is all right. This solves my most basic problems.”

Then you get one level higher than that, and you have good, unique content, which I think many folks think of as where they need to get to. It’s essentially, hey, it’s useful enough. It answers the searcher’s query. It’s unique from any other content on the Web. If you read it, you wouldn’t vomit. It’s good enough, right? Good, unique content.

Problem is almost everyone can get here. They really can. It’s not a high bar, a high barrier to entry to say you need good, unique content. In fact, it can scale. So what I see lots of folks doing is they look at a search result or a set of search results in their industry. Say you’re in travel and vacations, and you look at these different countries and you’re going to look at the hotels or recommendations in those countries and then see all the articles there. You go, “Yeah, you know what, I think we could do something as good as what’s up there or almost.” Well, okay, that puts you in the range. That’s good, unique content.

But in my opinion, the minimum bar today for modern SEO is a step higher, and that is as good as the best in the search results on the search results page. If you can’t consistently say, “We’re the best result that a searcher could find in the search results,” well then, guess what? You’re not going to have an opportunity to rank. It’s much, much harder to get into those top 10 positions, page 1, page 2 positions than it was in the past because there are so many ranking signals that so many of these websites have already built up over the last 5, 10, 15 years that you need to go above and beyond.

Really, where I want folks to go and where I always expect content from Moz to go is here, and that is 10x, 10 times better than anything I can find in the search results today. If I don’t think I can do that, then I’m not going to try and rank for those keywords. I’m just not going to pursue it. I’m going to pursue content in areas where I believe I can create something 10 times better than the best result out there.

What changed?

Why is this? What changed? Well, a bunch of things actually.

  • User experience became a much bigger element in the ranking algorithms, and that’s direct influences, things that we’ve talked about here on Whiteboard Friday before like pogo-sticking, and lots of indirect ones like the links that you earn based on the user experience that you provide and Google rendering pages, Google caring about load speed and device rendering, mobile friendliness, all these kinds of things.
  • Earning links overtook link building. It used to be you put out a page and you built a bunch of links to it. Now that doesn’t so much work anymore because Google is very picky about the links that it’s going to consider. If you can’t earn links naturally, not only can you not get links fast enough and not get good ones, but you also are probably earning links that Google doesn’t even want to count or may even penalize you for. It’s nearly impossible to earn links with just good, unique content. If there’s something better out there on page one of the search results, why would they even bother to link to you? Someone’s going to do a search, and they’re going to find something else to link to, something better.
  • Third, the rise of content marketing over the last five, six years has meant that there’s just a lot more competition. This field is a lot more crowded than it used to be, with many people trying to get to a higher and higher quality bar.
  • Finally, as a result of many of these things, user expectations have gone crazy. Users expect pages to load insanely fast, even on mobile devices, even when their connection’s slow. They expect it to look great. They expect to be provided with an answer almost instantaneously. The quality of results that Google has delivered and the quality of experience that sites like Facebook, which everyone is familiar with, are delivering means that our brains have rewired themselves to expect very fast, very high quality results consistently.

How do we create “10x” content?

So, because of all these changes, we need a process. We need a process to choose, to figure out how we can get to 10x content, not good, unique content, 10x content. A process that I often like to use — this probably is not the only one, but you’re welcome to use it if you find it valuable — is to go, “All right, you know what? I’m going to perform some of these search queries.”

By the way, I would probably perform the search query in two places. One is in Google and their search results, and the other is actually in BuzzSumo, which I think is a great tool for this, where I can see the content that has been most shared. So if you haven’t already, check out BuzzSumo.com.

I might search for something like Costa Rica ecolodges, which I might be considering a Costa Rica vacation at some point in the future. I look at these top ranking results, probably the whole top 10 as well as the most shared content on social media.

Then I’m going to ask myself these questions;

  • What questions are being asked and answered by these search results?
  • What sort of user experience is provided? I look at this in terms of speed, in terms of mobile friendliness, in terms of rendering, in terms of layout and design quality, in terms of what’s required from the user to be able to get the information? Is it all right there, or do I need to click? Am I having trouble finding things?
  • What’s the detail and thoroughness of the information that’s actually provided? Is it lacking? Is it great?
  • What about use of visuals? Visual content can often take best in class all the way up to 10x if it’s done right. So I might check out the use of visuals.
  • The quality of the writing.
  • I’m going to look at information and data elements. Where are they pulling from? What are their sources? What’s the quality of that stuff? What types of information is there? What types of information is missing?

In fact, I like to ask, “What’s missing?” a lot.

From this, I can determine like, hey, here are the strengths and weaknesses of who’s getting all of the social shares and who’s ranking well, and here’s the delta between me and them today. This is the way that I can be 10 times better than the best results in there.

If you use this process or a process like this and you do this type of content auditing and you achieve this level of content quality, you have a real shot at rankings. One of the secret reasons for that is that the effort axis that I have here, like I go to Fiverr, I get Panda invasion. I make the intern write it. This is going to take a weekend to build versus there’s no way to scale this content.

This is a super power. When your competitors or other folks in the field look and say, “Hey, there’s no way that we can scale content quality like this. It’s just too much effort. We can’t keep producing it at this level,” well, now you have a competitive advantage. You have something that puts you in a category by yourself and that’s very hard for competitors to catch up to. It’s a huge advantage in search, in social, on the Web as a whole.

All right everyone, hope you’ve enjoyed this edition of Whiteboard Friday, and we’ll see you again next week. Take care.

Video transcription by Speechpad.com

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