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 3 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 4 years ago from tracking.feedpress.it

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

Posted by jon.white

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

Track your mobile vs. desktop rankings in Moz Analytics

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

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

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

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

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

We have given an extra engine to all campaigns

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

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

Measure your Search Visibility score vs. competitors

The overall Search Visibility for my campaign

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

How is Search Visibility calculated?

Good question!

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

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

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

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

Some neat ways to use this feature

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

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

Reblogged 4 years ago from tracking.feedpress.it

Deconstructing the App Store Rankings Formula with a Little Mad Science

Posted by AlexApptentive

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

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

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

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

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

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

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

Until now, that is.

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

But first, a little context

Image credit: Josh Tuininga, Apptentive

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

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

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

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

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

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

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

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

Now, for the Mad Science.

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

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

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

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

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

Hypothesis

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

Both of these assumptions will be tested in later analysis.

Results

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

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

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

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

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

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

Hypothesis

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

Results

App Store Ranking Volatility of Top 500 Apps

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

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

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

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

Study #3: App store rankings across the stars

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

Hypothesis

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

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

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

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

Results

Average App Store Ratings of Top Apps

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

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

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

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

App Store Ranking Volatility and Average Rating

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

Study #4: App store rankings across versions

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

Hypothesis

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

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

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

Results

How update frequency correlates with app store rank

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

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

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

How update frequency correlates with app store ranking volatility

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

Study #5: App store rankings across monthly active users

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

Hypothesis

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

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

Results

Apps with more ratings and reviews typically rank higher

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

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

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

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

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

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

Apps with more ratings typically experience less app store ranking volatility

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

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

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

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

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

Summary

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

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

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

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

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

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

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

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

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

Weight of factors in the Apple App Store ranking algorithm

Rating Count > Installs > Trends > Rating

Weight of factors in the Google Play ranking algorithm

Rating Count > Installs > Rating > Trends


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

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

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

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Reblogged 4 years ago from tracking.feedpress.it

I Can’t Drive 155: Meta Descriptions in 2015

Posted by Dr-Pete

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

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

The Basic Data

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

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

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

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

The Bigger Picture

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

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

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

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

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

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

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

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

The Cutting Room Floor

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Final Verdict

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

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

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

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​The 3 Most Common SEO Problems on Listings Sites

Posted by Dom-Woodman

Listings sites have a very specific set of search problems that you don’t run into everywhere else. In the day I’m one of Distilled’s analysts, but by night I run a job listings site, teflSearch. So, for my first Moz Blog post I thought I’d cover the three search problems with listings sites that I spent far too long agonising about.

Quick clarification time: What is a listings site (i.e. will this post be useful for you)?

The classic listings site is Craigslist, but plenty of other sites act like listing sites:

  • Job sites like Monster
  • E-commerce sites like Amazon
  • Matching sites like Spareroom

1. Generating quality landing pages

The landing pages on listings sites are incredibly important. These pages are usually the primary drivers of converting traffic, and they’re usually generated automatically (or are occasionally custom category pages) .

For example, if I search “Jobs in Manchester“, you can see nearly every result is an automatically generated landing page or category page.

There are three common ways to generate these pages (occasionally a combination of more than one is used):

  • Faceted pages: These are generated by facets—groups of preset filters that let you filter the current search results. They usually sit on the left-hand side of the page.
  • Category pages: These pages are listings which have already had a filter applied and can’t be changed. They’re usually custom pages.
  • Free-text search pages: These pages are generated by a free-text search box.

Those definitions are still bit general; let’s clear them up with some examples:

Amazon uses a combination of categories and facets. If you click on browse by department you can see all the category pages. Then on each category page you can see a faceted search. Amazon is so large that it needs both.

Indeed generates its landing pages through free text search, for example if we search for “IT jobs in manchester” it will generate: IT jobs in manchester.

teflSearch generates landing pages using just facets. The jobs in China landing page is simply a facet of the main search page.

Each method has its own search problems when used for generating landing pages, so lets tackle them one by one.


Aside

Facets and free text search will typically generate pages with parameters e.g. a search for “dogs” would produce:

www.mysite.com?search=dogs

But to make the URL user friendly sites will often alter the URLs to display them as folders

www.mysite.com/results/dogs/

These are still just ordinary free text search and facets, the URLs are just user friendly. (They’re a lot easier to work with in robots.txt too!)


Free search (& category) problems

If you’ve decided the base of your search will be a free text search, then we’ll have two major goals:

  • Goal 1: Helping search engines find your landing pages
  • Goal 2: Giving them link equity.

Solution

Search engines won’t use search boxes and so the solution to both problems is to provide links to the valuable landing pages so search engines can find them.

There are plenty of ways to do this, but two of the most common are:

  • Category links alongside a search

    Photobucket uses a free text search to generate pages, but if we look at example search for photos of dogs, we can see the categories which define the landing pages along the right-hand side. (This is also an example of URL friendly searches!)

  • Putting the main landing pages in a top-level menu

    Indeed also uses free text to generate landing pages, and they have a browse jobs section which contains the URL structure to allow search engines to find all the valuable landing pages.

Breadcrumbs are also often used in addition to the two above and in both the examples above, you’ll find breadcrumbs that reinforce that hierarchy.

Category (& facet) problems

Categories, because they tend to be custom pages, don’t actually have many search disadvantages. Instead it’s the other attributes that make them more or less desirable. You can create them for the purposes you want and so you typically won’t have too many problems.

However, if you also use a faceted search in each category (like Amazon) to generate additional landing pages, then you’ll run into all the problems described in the next section.

At first facets seem great, an easy way to generate multiple strong relevant landing pages without doing much at all. The problems appear because people don’t put limits on facets.

Lets take the job page on teflSearch. We can see it has 18 facets each with many options. Some of these options will generate useful landing pages:

The China facet in countries will generate “Jobs in China” that’s a useful landing page.

On the other hand, the “Conditional Bonus” facet will generate “Jobs with a conditional bonus,” and that’s not so great.

We can also see that the options within a single facet aren’t always useful. As of writing, I have a single job available in Serbia. That’s not a useful search result, and the poor user engagement combined with the tiny amount of content will be a strong signal to Google that it’s thin content. Depending on the scale of your site it’s very easy to generate a mass of poor-quality landing pages.

Facets generate other problems too. The primary one being they can create a huge amount of duplicate content and pages for search engines to get lost in. This is caused by two things: The first is the sheer number of possibilities they generate, and the second is because selecting facets in different orders creates identical pages with different URLs.

We end up with four goals for our facet-generated landing pages:

  • Goal 1: Make sure our searchable landing pages are actually worth landing on, and that we’re not handing a mass of low-value pages to the search engines.
  • Goal 2: Make sure we don’t generate multiple copies of our automatically generated landing pages.
  • Goal 3: Make sure search engines don’t get caught in the metaphorical plastic six-pack rings of our facets.
  • Goal 4: Make sure our landing pages have strong internal linking.

The first goal needs to be set internally; you’re always going to be the best judge of the number of results that need to present on a page in order for it to be useful to a user. I’d argue you can rarely ever go below three, but it depends both on your business and on how much content fluctuates on your site, as the useful landing pages might also change over time.

We can solve the next three problems as group. There are several possible solutions depending on what skills and resources you have access to; here are two possible solutions:

Category/facet solution 1: Blocking the majority of facets and providing external links
  • Easiest method
  • Good if your valuable category pages rarely change and you don’t have too many of them.
  • Can be problematic if your valuable facet pages change a lot

Nofollow all your facet links, and noindex and block category pages which aren’t valuable or are deeper than x facet/folder levels into your search using robots.txt.

You set x by looking at where your useful facet pages exist that have search volume. So, for example, if you have three facets for televisions: manufacturer, size, and resolution, and even combinations of all three have multiple results and search volume, then you could set you index everything up to three levels.

On the other hand, if people are searching for three levels (e.g. “Samsung 42″ Full HD TV”) but you only have one or two results for three-level facets, then you’d be better off indexing two levels and letting the product pages themselves pick up long-tail traffic for the third level.

If you have valuable facet pages that exist deeper than 1 facet or folder into your search, then this creates some duplicate content problems dealt with in the aside “Indexing more than 1 level of facets” below.)

The immediate problem with this set-up, however, is that in one stroke we’ve removed most of the internal links to our category pages, and by no-following all the facet links, search engines won’t be able to find your valuable category pages.

In order re-create the linking, you can add a top level drop down menu to your site containing the most valuable category pages, add category links elsewhere on the page, or create a separate part of the site with links to the valuable category pages.

The top level drop down menu you can see on teflSearch (it’s the search jobs menu), the other two examples are demonstrated in Photobucket and Indeed respectively in the previous section.

The big advantage for this method is how quick it is to implement, it doesn’t require any fiddly internal logic and adding an extra menu option is usually minimal effort.

Category/facet solution 2: Creating internal logic to work with the facets

  • Requires new internal logic
  • Works for large numbers of category pages with value that can change rapidly

There are four parts to the second solution:

  1. Select valuable facet categories and allow those links to be followed. No-follow the rest.
  2. No-index all pages that return a number of items below the threshold for a useful landing page
  3. No-follow all facets on pages with a search depth greater than x.
  4. Block all facet pages deeper than x level in robots.txt

As with the last solution, x is set by looking at where your useful facet pages exist that have search volume (full explanation in the first solution), and if you’re indexing more than one level you’ll need to check out the aside below to see how to deal with the duplicate content it generates.


Aside: Indexing more than one level of facets

If you want more than one level of facets to be indexable, then this will create certain problems.

Suppose you have a facet for size:

  • Televisions: Size: 46″, 44″, 42″

And want to add a brand facet:

  • Televisions: Brand: Samsung, Panasonic, Sony

This will create duplicate content because the search engines will be able to follow your facets in both orders, generating:

  • Television – 46″ – Samsung
  • Television – Samsung – 46″

You’ll have to either rel canonical your duplicate pages with another rule or set up your facets so they create a single unique URL.

You also need to be aware that each followable facet you add will multiply with each other followable facet and it’s very easy to generate a mass of pages for search engines to get stuck in. Depending on your setup you might need to block more paths in robots.txt or set-up more logic to prevent them being followed.

Letting search engines index more than one level of facets adds a lot of possible problems; make sure you’re keeping track of them.


2. User-generated content cannibalization

This is a common problem for listings sites (assuming they allow user generated content). If you’re reading this as an e-commerce site who only lists their own products, you can skip this one.

As we covered in the first area, category pages on listings sites are usually the landing pages aiming for the valuable search terms, but as your users start generating pages they can often create titles and content that cannibalise your landing pages.

Suppose you’re a job site with a category page for PHP Jobs in Greater Manchester. If a recruiter then creates a job advert for PHP Jobs in Greater Manchester for the 4 positions they currently have, you’ve got a duplicate content problem.

This is less of a problem when your site is large and your categories mature, it will be obvious to any search engine which are your high value category pages, but at the start where you’re lacking authority and individual listings might contain more relevant content than your own search pages this can be a problem.

Solution 1: Create structured titles

Set the <title> differently than the on-page title. Depending on variables you have available to you can set the title tag programmatically without changing the page title using other information given by the user.

For example, on our imaginary job site, suppose the recruiter also provided the following information in other fields:

  • The no. of positions: 4
  • The primary area: PHP Developer
  • The name of the recruiting company: ABC Recruitment
  • Location: Manchester

We could set the <title> pattern to be: *No of positions* *The primary area* with *recruiter name* in *Location* which would give us:

4 PHP Developers with ABC Recruitment in Manchester

Setting a <title> tag allows you to target long-tail traffic by constructing detailed descriptive titles. In our above example, imagine the recruiter had specified “Castlefield, Manchester” as the location.

All of a sudden, you’ve got a perfect opportunity to pick up long-tail traffic for people searching in Castlefield in Manchester.

On the downside, you lose the ability to pick up long-tail traffic where your users have chosen keywords you wouldn’t have used.

For example, suppose Manchester has a jobs program called “Green Highway.” A job advert title containing “Green Highway” might pick up valuable long-tail traffic. Being able to discover this, however, and find a way to fit it into a dynamic title is very hard.

Solution 2: Use regex to noindex the offending pages

Perform a regex (or string contains) search on your listings titles and no-index the ones which cannabalise your main category pages.

If it’s not possible to construct titles with variables or your users provide a lot of additional long-tail traffic with their own titles, then is a great option. On the downside, you miss out on possible structured long-tail traffic that you might’ve been able to aim for.

Solution 3: De-index all your listings

It may seem rash, but if you’re a large site with a huge number of very similar or low-content listings, you might want to consider this, but there is no common standard. Some sites like Indeed choose to no-index all their job adverts, whereas some other sites like Craigslist index all their individual listings because they’ll drive long tail traffic.

Don’t de-index them all lightly!

3. Constantly expiring content

Our third and final problem is that user-generated content doesn’t last forever. Particularly on listings sites, it’s constantly expiring and changing.

For most use cases I’d recommend 301’ing expired content to a relevant category page, with a message triggered by the redirect notifying the user of why they’ve been redirected. It typically comes out as the best combination of search and UX.

For more information or advice on how to deal with the edge cases, there’s a previous Moz blog post on how to deal with expired content which I think does an excellent job of covering this area.

Summary

In summary, if you’re working with listings sites, all three of the following need to be kept in mind:

  • How are the landing pages generated? If they’re generated using free text or facets have the potential problems been solved?
  • Is user generated content cannibalising the main landing pages?
  • How has constantly expiring content been dealt with?

Good luck listing, and if you’ve had any other tricky problems or solutions you’ve come across working on listings sites lets chat about them in the comments below!

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Reblogged 4 years ago from tracking.feedpress.it

The Most Important Link Penalty Removal Tool: Your Mindset

Posted by Eric Enge

Let’s face it. Getting slapped by a manual link penalty, or by the Penguin algorithm, really stinks. Once this has happened to you, your business is in a world of hurt. Worse still is the fact that you can’t get clear information from Google on which of your links are the bad ones. In today’s post, I am going to focus on the number one reason why people fail to get out from under these types of problems, and how to improve your chances of success.

The mindset

Success begins, continues, and ends with the right mindset. A large percentage of people I see who go through a link cleanup process are not aggressive enough about cleaning up their links. They worry about preserving some of that hard-won link juice they obtained over the years.

You have to start by understanding what a link cleanup process looks like, and just how long it can take. Some of the people I have spoken with have gone through a process like this one:

link removal timeline

In this fictitious timeline example, we see someone who spends four months working on trying to recover, and at the end of it all, they have not been successful.
A lot of time and money have been spent, and they have nothing to show for it. Then, the people at Google get frustrated and send them a message that basically tells them they are not getting it. At this point, they have no idea when they will be able to recover. The result is that the complete process might end up taking six months or more.

In contrast, imagine someone who is far more aggressive in removing and disavowing links. They are so aggressive that 20 percent of the links they cut out are actually ones that Google has not currently judged as being bad. They also start on March 9, and by April 30, the penalty has been lifted on their site.

Now they can begin rebuilding their business, five or months sooner than the person who does not take as aggressive an approach. Yes, they cut out some links that Google was not currently penalizing, but this is a small price to pay for getting your penalty cleared five months sooner. In addition, using our mindset-based approach, the 20 percent of links we cut out were probably not links that were helping much anyway, and that Google might also take action on them in the future.

Now that you understand the approach, it’s time to make the commitment. You have to make the decision that you are going to do whatever it takes to get this done, and that getting it done means cutting hard and deep, because that’s what will get you through it the fastest. Once you’ve got your head on straight about what it will take and have summoned the courage to go through with it, then and only then, you’re ready to do the work. Now let’s look at what that work entails.

Obtaining link data

We use four sources of data for links:

  1. Google Webmaster Tools
  2. Open Site Explorer
  3. Majestic SEO
  4. ahrefs

You will want to pull in data from all four of these sources, get them into one list, and then dedupe them to create a master list. Focus only on followed links as well, as nofollowed links are not an issue. The overall process is shown here:

pulling a link set

One other simplification is also possible at this stage. Once you have obtained a list of the followed links, there is another thing you can do to dramatically simplify your life.
You don’t need to look at every single link.

You do need to look at a small sampling of links from every domain that links to you. Chances are that this is a significantly smaller quantity of links to look at than all links. If a domain has 12 links to you, and you look at three of them, and any of those are bad, you will need to disavow the entire domain anyway.

I take the time to emphasize this because I’ve seen people with more than 1 million inbound links from 10,000 linking domains. Evaluating 1 million individual links could take a lifetime. Looking at 10,000 domains is not small, but it’s 100 times smaller than 1 million. But here is where the mindset comes in.
Do examine every domain.

This may be a grinding and brutal process, but there is no shortcut available here. What you don’t look at will hurt you. The sooner you start on the entire list, the sooner you will get the job done.

How to evaluate links

Now that you have a list, you can get to work. This is a key part where having the right mindset is critical. The first part of the process is really quite simple. You need to eliminate each and every one of these types of links:

  1. Article directory links
  2. Links in forum comments, or their related profiles
  3. Links in blog comments, or their related profiles
  4. Links from countries where you don’t operate/sell your products
  5. Links from link sharing schemes such as Link Wheels
  6. Any links you know were paid for

Here is an example of a foreign language link that looks somewhat out of place:

foreign language link

For the most part, you should also remove any links you have from web directories. Sure, if you have a link from DMOZ, Business.com, or BestofTheWeb.com, and the most important one or two directories dedicated to your market space, you can probably keep those.

For a decade I have offered people a rule for these types of directories, which is “no more than seven links from directories.” Even the good ones carry little to no value, and the bad ones can definitely hurt you. So there is absolutely no win to be had running around getting links from a bunch of directories, and there is no win in trying to keep them during a link cleanup process.

Note that I am NOT talking about local business directories such as Yelp, CityPages, YellowPages, SuperPages, etc. Those are a different class of directory that you don’t need to worry about. But general purpose web directories are, generally speaking, a poison.

Rich anchor text

Rich anchor text has been the downfall of many a publisher. Here is one of my favorite examples ever of rich anchor text:

The author wanted the link to say “buy cars,” but was too lazy to fit the two words into the same sentence! Of course, you may have many guest posts that you have written that are not nearly as obvious as this one. One great way to deal with that is to take your list of links that you built and sort them by URL and look at the overall mix of anchor text. You know it’s a problem if it looks anything like this:

overly optimized anchor text

The problem with the distribution in the above image is that the percentage of links that are non “rich” in nature is way too small. In the real world, most people don’t conveniently link to you using one of your key money phrases. Some do, but it’s normally a small percentage.

Other types of bad links

There is no way for me to cover every type of bad link in this post, but here are other types of links, or link scenarios, to be concerned about:

  1. If a large percentage of your links are coming from over on the right rail of sites, or in the footers of sites
  2. If there are sites that give you a site-wide link, or a very large number of links from one domain
  3. Links that come from sites whose IP address is identical in the A block, B block, and C block (read more about what these are here)
  4. Links from crappy sites

The definition of a crappy site may seem subjective, but if a site has not been updated in a while, or its information is of poor quality, or it just seems to have no one who cares about it, you can probably consider it a crappy site. Remember our discussion on mindset. Your objective is to be harsh in cleaning up your links.

In fact, the most important principle in evaluating links is this:
If you can argue that it’s a good link, it’s NOT. You don’t have to argue for good quality links. To put it another way, if they are not obviously good, then out they go!

Quick case study anecdote: I know of someone who really took a major knife to their backlinks. They removed and/or disavowed every link they had that was below a Moz Domain Authority of 70. They did not even try to justify or keep any links with lower DA than that. It worked like a champ. The penalty was lifted. If you are willing to try a hyper-aggressive approach like this one, you can avoid all the work evaluating links I just outlined above. Just get the Domain Authority data for all the links pointing to your site and bring out the hatchet.

No doubt that they ended up cutting out a large number of links that were perfectly fine, but their approach was way faster than doing the complete domain by domain analysis.

Requesting link removals

Why is it that we request link removals? Can’t we just build a
disavow file and submit that to Google? In my experience, for manual link penalties, the answer to this question is no, you can’t. (Note: if you have been hit by Penguin, and not a manual link penalty, you may not need to request link removals.)

Yes, disavowing a link is supposed to tell Google that you don’t want to receive any PageRank, or benefit, from it. However, there is a human element at play here.
Google likes to see that you put some effort into cleaning up the bad links that you have gotten that led to your penalty. The more bad links you have, the more important this becomes.

This does make the process a lot more expensive to get through, but if you approach this with the “whatever it takes” mindset, you dive into the requesting link removal process and go ahead and get it done.

I usually have people go through three rounds of requests asking people to remove links. This can be a very annoying process for those receiving your request, so you need to be aware of that. Don’t start your email with a line like “Your site is causing mine to be penalized …”, as that’s just plain offensive.

I’d be honest, and tell them “Hey, we’ve been hit by a penalty, and as part of our effort to recover we are trying to get many of the links we have gotten to our site removed. We don’t know which sites are causing the problem, but we’d appreciate your help …”

Note that some people will come back to you and ask for money to remove the link. Just ignore them, and put their domains in your disavow file.

Once you are done with the overall removal requests, and had whatever success you have had, take the rest of the domains and disavow them. There is a complete guide to
creating a disavow file here. The one incremental tip I would add is that you should nearly always disavow entire domains, not just the individual links you see.

This is important because even with the four tools we used to get information on as many links as we could, we still only have a subset of the total links. For example, the tools may have only seen one link from a domain, but in fact you have five. If you disavow only the one link, you still have four problem links, and that will torpedo your reconsideration request.

Disavowing the domain is a better-safe-than-sorry step you should take almost every time. As I illustrated at the beginning of this post, adding extra cleanup/reconsideration request loops is very expensive for your business.

The overall process

When all is said and done, the process looks something like this:

link removal process

If you run this process efficiently, and you don’t try to cut corners, you might be able to get out from your penalty in a single pass through the process. If so, congratulations!

What about tools?

There are some fairly well-known tools that are designed to help you with the link cleanup process. These include
Link Detox and Remove’em. In addition, at STC we have developed our own internal tool that we use with our clients.

These tools can be useful in flagging some of your links, but they are not comprehensive—they will help identify some really obvious offenders, but the great majority of links you need to deal with and remove/disavow are not identified. Plan on investing substantial manual time and effort to do the heavy lifting of a comprehensive review of all your links. Remember the “mindset.”

Summary

As I write this post, I have this sense of being heartless because I outline an approach that is often grueling to execute. But consider it tough love. Recovering from link penalties is indeed brutal.
In my experience, the winners are the ones who come with meat cleaver in hand, don’t try to cut corners, and take on the full task from the very start, no matter how extensive an effort it may be.

Does this type of process succeed? You bet. Here is an example of a traffic chart from a successful recovery:

manual penalty recovery graph

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Reblogged 4 years ago from tracking.feedpress.it