Uncategorized

Setting Up 4 Key Customer Loyalty Metrics in Google Analytics

Customer loyalty is one of the strongest assets a business can have, and one that any can aim to improve. However, improvement requires iteration and testing, and iteration and testing require measurement.

Traditionally, customer loyalty has been measured using customer surveys. The Net Promoter Score, for example, is based on the question (on a scale of one to ten) “How likely is it that you would recommend our company/product/service to a friend or colleague?”. Regularly monitoring metrics like this with any accuracy is going to get expensive (and/or annoying to customers), and is never going to be hugely meaningful, as advocacy is only one dimension of customer loyalty. Even with a wider range of questions, there’s also some risk that you end up tracking what your customers claim about their loyalty rather than their actual loyalty, although you might expect the two to be strongly correlated.

Common mistakes

Google Analytics and other similar platforms collect data that could give you more meaningful metrics for free. However, they don’t always make them completely obvious – before writing this post, I checked to be sure there weren’t any very similar ones already published, and I found some fairly dubious reoccurring recommendations. The most common of these was using % of return visitors as a sole or primary metric for customer loyalty. If the percentage of visitors to your site who are return visitors drops, there are plenty of reasons that could be behind that besides a drop in loyalty—a large number of new visitors from a successful marketing campaign, for example. Similarly, if the absolute number of return visitors rises, this could be as easily caused by an increase in general traffic levels as by an increase in the loyalty of existing customers.

Visitor frequency is another easily misinterpreted metric;  infrequent visits do not always indicate a lack of loyalty. If you were a loyal Mercedes customer, and never bought any car that wasn’t a new Mercedes, you wouldn’t necessarily visit their website on a weekly basis, and someone who did wouldn’t necessarily be a more loyal customer than you.

The metrics

Rather than starting with the metrics Google Analytics shows us and deciding what they mean about customer loyalty (or anything else), a better approach is to decide what metrics you want, then deciding how you can replicate them in Google Analytics.

To measure the various dimensions of (online) customer loyalty well, I felt the following metrics would make the most sense:

  • Proportion of visitors who want to hear more
  • Proportion of visitors who advocate
  • Proportion of visitors who return
  • Proportion of macro-converters who convert again

Note that a couple of these may not be what they initially seem. If your registration process contains an awkwardly worded checkbox for email signup, for example, it’s not a good measure of whether people want to hear more. Secondly, “proportion of visitors who return” is not the same as “proportion of visitors who are return visitors.”

1. Proportion of visitors who want to hear more

This is probably the simplest of the above metrics, especially if you’re already tracking newsletter signups as a micro-conversion. If you’re not, you probably should be, so see Google’s guidelines for event tracking using the analytics.js tracking snippet or Google Tag Manager, and set your new event as a goal in Google Analytics.

2. Proportion of visitors who advocate

It’s never possible to track every public or private recommendation, but there are two main ways that customer advocacy can be measured in Google Analytics: social referrals and social interactions. Social referrals may be polluted as a customer loyalty metric by existing campaigns, but these can be segmented out if properly tracked, leaving the social acquisition channel measuring only organic referrals.

Social interactions can also be tracked in Google Analytics, although surprisingly, with the exception of Google+, tracking them does require additional code on your site. Again, this is probably worth tracking anyway, so if you aren’t already doing so, see Google’s guidelines for analytics.js tracking snippets, or this excellent post for Google Tag Manager analytics implementations.

3. Proportion of visitors who return

As mentioned above, this isn’t the same as the proportion of visitors who are return visitors. Fortunately, Google Analytics does give us a feature to measure this.

Even though date of first session isn’t available as a dimension in reports, it can be used as a criteria for custom segments. This allows us to start building a data set for how many visitors who made their first visit in a given period have returned since.

There are a couple of caveats. First, we need to pick a sensible time period based on our frequency and recency data. Second, this data obviously takes a while to produce; I can’t tell how many of this month’s new visitors will make further visits at some point in the future.

In Distilled’s case, I chose 3 months as a sensible period within which I would expect the vast majority of loyal customers to visit the site at least once. Unfortunately, due to the 90-day limit on time periods for this segment, this required adding together the totals for two shorter periods. I was then able to compare the number of new visitors in each month with how many of those new visitors showed up again in the subsequent 3 months:

As ever with data analysis, the headline figure doesn’t tell the story. Instead, it’s something we should seek to explain. Looking at the above graph, it would be easy to conclude “Distilled’s customer loyalty has bombed recently; they suck.” However, the fluctuation in the above graph is mostly due to the enormous amount of organic traffic that’s been generated by Hannah‘s excellent blog post 4 Types of Content Every Site Needs.

Although many new visitors who discovered the Distilled site through this blog post have returned since, the return rate is unsurprisingly lower than some of the most business-orientated pages on the site. This isn’t a bad thing—it’s what you’d expect from top-of-funnel content like blog posts—but it’s a good example of why it’s worth keeping an eye out for this sort of thing if you want to analyse these metrics. If I wanted to dig a little deeper, I might start by segmenting this data to get a more controlled view of how new visitors are reacting to Distilled’s site over time.

4. Proportion of macro-converters who convert again

While a standard Google Analytics implementation does allow you to view how many users have made multiple purchases, it doesn’t allow you to see how these fell across their sessions. Similarly, if you can see how many users have had two sessions and two goal conversions, but you can’t see whether those conversions were in different visits, it’s entirely possible that some had one accidental visit that bounced, and one visit with two different conversions (note that you cannot perform the same conversion twice in one session).

It would be possible to create custom dimensions for first (and/or second, third, etc.) purchase dates using internal data, but this is a complex and site-specific implementation. Unfortunately, for the time being, I know of no good way of documenting user conversion patterns over multiple sessions using only Google Analytics, despite the fact that it collects all the data required to do this.

Contribute

These are only my favourite customer loyalty metrics. If you have any that you’re already tracking or are unsure how to track, please explain in the comments below.

About AuthorTom.Capper Analyst at Distilled London, specialising in web analytics.
Advertisements

One Content Metric to Rule Them All

Let’s face it: Measuring, analyzing, and reporting the success of content marketing is hard.

Not only that, but we’re all busy. In its latest report on B2B trends, the Content Marketing Institute quantified some of the greatest challenges faced by today’s content marketers, and a whopping 69% of companies cited a lack of time. We spend enough of our time sourcing, editing, and publishing the content, and anyone who has ever managed an editorial calendar knows that fires are constantly in need of dousing. With so little extra time on our hands, the last thing content marketers want to do is sift through a heaping pile of data that looks something like this:

Sometimes we want to dig into granular data. If a post does exceptionally well on Twitter, but just so-so everywhere else, that’s noteworthy. But when we look at individual metrics, it’s far too easy to read into them in all the wrong ways.

Here at Moz, it’s quite easy to think that a post isn’t doing well when it doesn’t have a bunch of thumbs up, or to think that we’ve made a horrible mistake when a post gets several thumbs down. The truth is, though, that we can’t simply equate metrics like thumbs to success. In fact, our most thumbed-down post in the last two years was one in which Carson Ward essentially predicted the recent demise of spammy guest blogging.

We need a solution. We need something that’s easy to track at a glance, but doesn’t lose the forest for the trees. We need a way to quickly sift through the noise and figure out which pieces of content were really successful, and which didn’t go over nearly as well. We need something that looks more like this:

This post walks through how we combined our content metrics for the Moz Blog into a single, easy-to-digest score, and better yet, almost completely automated it.

What it is not

It is not an absolute score. Creating an absolute score, while the math would be equally easy, simply wouldn’t be worthwhile. Companies that are just beginning their content marketing efforts would consistently score in the single digits, and it isn’t fair to compare a multi-million dollar push from a giant corporation to a best effort from a very small company. This metric isn’t meant to compare one organization’s efforts with any other; it’s meant to be used inside of a single organization.

What it is and what it measures

The One Metric is a single score that tells you how successful a piece of content was by comparing it to the average performance of the content that came before it. We made it by combining several other metrics, or “ingredients,” that fall into three equally weighted categories:

  1. Google Analytics
  2. On-page (in-house) metrics
  3. Social metrics

It would never do to simply smash all these metrics together, as the larger numbers would inherently carry more weight. In other words, we cannot simply take the average of 10,000 visits and 200 Facebook likes, as Facebook would be weighted far more heavily—moving from 200 to 201 likes would be an increase of 0.5%, and moving from 10,000 to 10,001 visits would be an increase of 0.01%. To ensure every one of the ingredients is weighted equally, we compare them to our expectations of them individually.

Let’s take a simple example using only one ingredient. If we wanted to get a sense for how well a particular post did on Twitter, we could obviously look at the number of tweets that link to it. But what does that number actually mean? How successful is a post that earns 100 tweets? 500? 2,000? In order to make sense of it, we use past performance. We take everything we’ve posted over the last two months, and find the average number of tweets each of those posts got. (We chose two months; you can use more or less if that works better for you.) That’s our benchmark—our expectation for how many tweets our future posts will get. Then, if our next post gets more than that expected number, we can safely say that it did well by our own standards. The actual number of tweets doesn’t really matter in this sense—it’s about moving up and to the right, striving to continually improve our work.

Here’s a more visual representation of how that looks:

Knowing a post did better or worse than expectations is quite valuable, but how much better or worse did it actually do? Did it barely miss the mark, or did it completely tank? It’s time to quantify.

It’s that percentage of the average (92% and 73% in the examples above) that we use to seed our One Metric. For any given ingredient, if we have 200% of the average, we have a post that did twice as well as normal. If we have 50%, we have a post that did half as well.

From there, we do the exact same thing for all the other ingredients we’d like to use, and then combine them:

This gives us a single metric that offers a quick overview of a post’s performance. In the above example, our overall performance came out to 113% of what we’d expect based on our average performance. We can say it outperformed expectations by 13%.

We don’t stop there, though. This percent of the average is quite useful… but we wanted this metric to be useful outside of our own minds. We wanted it to make sense to just about anyone who looked at it, so we needed a different scale. To that end, we took it one step farther and applied that percentage to a logarithmic scale, giving us a single two-digit score much like you see for Domain Authority and Page Authority.

If you’re curious, we used the following equation for our scale (though you should feel free to adjust that equation to create a scale more suitable for your needs):

Where y is the One Metric score, and x is the percent of a post’s expected performance it actually received. Essentially, a post that exactly meets expectations receives a score of 50.

For the above example, an overall percentage of expectations that comes out to 113% translates as follows:

Of course, you won’t need to calculate the value by hand; that’ll be done automatically in a spreadsheet. Which is actually a great segue…

The whole goal here is to make things easy, so what we’re going for is a spreadsheet where all you have to do is “fill down” for each new piece of content as it’s created. About 10-15 seconds of work for each piece. Unfortunately, I can’t simply give you a ready-to-go template, as I don’t have access to your Google Analytics, and have no clue how your on-page metrics might be set up. 

As a result, this might look a little daunting at first.

Once you get things working once, though, all it takes is copying the formulas into new rows for new pieces of content; the metrics will be filled automatically. It’s well worth the initial effort.

Ready? Start here:

Make a copy of that document so you can make edits (File > Make a Copy), then follow the steps below to adjust that spreadsheet based on your own preferences.

  1. You’ll want to add or remove columns from that sheet to match the ingredients you’ll be using. Do you not have any on-page metrics like thumbs or comments? No problem—just delete them. Do you want to add Pinterest repins as an ingredient? Toss it in there. It’s your metric, so make it a combination of the things that matter to you.
  2. Get some content in there. Since the performance of each new piece of content is based on the performance of what came before it, you need to add the “what came before it.” If you’ve got access to a database for your organization (or know someone who does), that might be easiest. You can also create a new tab in that spreadsheet, then use the =IMPORTFEED function to automatically pull a list of content from your RSS feed.
  3. Populate the first row. You’ll use a variety of functionality within Google Spreadsheets to pull the data you need in from various places on the web, and I go through many of them below. This is the most time-consuming part of setting this up; don’t give up!
  4. Got your data successfully imported for the first row? Fill down. Make sure it’s importing the right data for the rest of your initial content.
  5. Calculate the percentage of expectations. Depending on how many ingredients you’re using, this equation can look mighty intimidating, but that’s really just a product of the spreadsheet smooshing it all onto one line. Here’s a prettier version:
    All this is doing (remember Step 2 above, where we combined the ingredients) is comparing each individual metric to past performance, and then weighting them appropriately.

    And, here’s what that looks like in plain text for our metric (yours may vary):

    =((1/3)*(E48/(average(E2:E47))))+((1/3)*((F48/(average(F2:F47)))+(G48/(average(G2:G47))))/2)+((1/3)*((H48/(average(H2:H47)))+(I48/(average(I2:I47)))+(J48/(average(J2:J47)))/3))
    	

    Note that this equation goes from row 2 through row 47 because we had 46 pieces of content that served to create our “expectation.”

  6. Convert it to the One Metric score. This is a piece of cake. You can certainly use our logarithmic equation (referenced above): y = 27*ln(x) +50, where x is the percent of expectations you just finished calculating. Or, if you feel comfortable adjusting that to suit your own needs, feel free to do that as well.
  7. You’re all set! Add more content, fill down, and repeat!

Here are more detailed instructions for pulling various types of data into the spreadsheet:

Adding new rows with IFTTT

If This Then That (IFTTT) makes it brilliantly easy to have your new posts automatically added to the spreadsheet where you track your One Metric. The one catch is that your posts need to have an RSS feed set up (more on that from FeedBurner). Sign up for a free IFTTT account if you don’t already have one, and then set up a recipe that adds a row to a Google Spreadsheet for every new post in the RSS feed.

When creating that recipe, make sure you include “Entry URL” as one of the fields that’s recorded in the spreadsheet; that’ll be necessary for pulling in the rest of the metrics for each post.

Also, IFTTT shortens URLs by default, which you’ll want to turn off, since the shortened URLs won’t mean anything to the APIs we’re using later. You can find that setting in your account preferences.

Pulling Google Analytics

One of the beautiful things about using a Google Spreadsheet for tracking this metric is the easy integration with Google Analytics. There’s an add-on for Google Spreadsheets that makes pulling in just about any metric a simple process. The only downside is that even after setting things up correctly, you’ll still need to manually refresh the data.

To get started,  install the add-on. You’ll want to do so while using an account that has access to your Google Analytics.

Then, create a new report; you’ll find the option under “Add-ons > Google Analytics:”

Select the GA account info that contains the metrics you want to see, and choose the metrics you’d like to track. Put “Page” in the field for “Dimensions;” that’ll allow you to reference the resulting report by URL.

You can change the report’s configuration later on, and if you’d like extra help figuring out how to fiddle with it, check out Google’s documentation.

This will create (at least) two new tabs on your spreadsheet; one for Report Configuration, and one for each of the metrics you included when creating the report. On the Report Configuration tab, you’ll want to be sure you set the date range appropriately (I’d recommend setting the end date fairly far in the future, so you don’t have to go back and change it later). To make things run a bit quicker, I’d also recommend setting a filter for the section(s) of your site you’d like to evaluate. Last but not least, the default value for “Max Results” is 1,000, so if you have more pages than that, I’d change that, as well (the max value is 10,000).

Got it all set up? Run that puppy! Head to Add-ons > Google Analytics > Run Reports. Each time you return to this spreadsheet to update your info, you’ll want to click “Run Reports” again, to get the most up-to-date stats.

There’s one more step. Your data is now in a table on the wrong worksheet, so we need to pull it over using the VLOOKUP formula. Essentially, you’re telling Excel, “See that URL over there? Find it in the table on that report tab, and tell me what the number is next to it.” If you haven’t used VLOOKUP before, it’s well worth learning. There’s a fantastic  explanation over at Search Engine Watch if you could use a primer (or a refresher).

Pulling in social metrics with scripts

This is a little trickier, as Google Spreadsheets doesn’t include a way to pull in social metrics, and that info ins’t included in GA. The solution? We create our own functions for the spreadsheet to use.

Relax; it’s not as hard as you’d think. =)

I’ll go over Facebook, Twitter, and Google Plus here, though the process would undoubtedly be similar for any other social network you’d like to measure.

We start in the script editor, which you’ll find under the tools menu:

If you’ve been there before, you’ll see a list of scripts you’ve already made; just click “Create a New Project.” If you’re new to Google Scripts, it’ll plop you into a blank project—you can just dismiss the popup window that tries to get you started.

Google Scripts organizes what you create into “projects,” and each project can contain multiple scripts. You’ll only need one project here—just call it something like “Social Metrics Scripts”—and then create a new script within that project for each of the social networks you’d like to include as an ingredient in your One Metric.

Once you have a blank script ready for each network, go through one by one, and paste the respective code below into the large box in the script editor (make sure to replace the default “myFunction” code).

function fbshares(url) {
var jsondata = UrlFetchApp.fetch("http://api.facebook.com/restserver.php?method=links.getStats&format=json&urls="+url);
var object = Utilities.jsonParse(jsondata.getContentText());
return object[0].total_count;
Utilities.sleep(1000)
}
function tweets(url) {
var jsondata = UrlFetchApp.fetch("http://urls.api.twitter.com/1/urls/count.json?url="+url);
var object = Utilities.jsonParse(jsondata.getContentText());
Utilities.sleep(1000)
return object.count;
}
function plusones(url) {
var options =
{
"method" : "post",
"contentType" : "application/json",
"payload" :
'{"method":"pos.plusones.get","id":"p","params":{"nolog":true,"id":"'+url+'","source":"widget","userId":"@viewer","groupId":"@self"},"jsonrpc":"2.0","key":"p","apiVersion":"v1"}'
};
var response = UrlFetchApp.fetch("https://clients6.google.com/rpc?key=AIzaSyCKSbrvQasunBoV16zDH9R33D88CeLr9gQ", options);
var results = JSON.parse(response.getContentText());
if (results.result != undefined)
return results.result.metadata.globalCounts.count;
return "Error";
}

Make sure you save these scripts—that isn’t automatic like it is with most Google applications. Done? You’ve now got the following functions at your disposal in Google Spreadsheets:

  • =fbshares(url)
  • =tweets(url)
  • =plusones(url)

The (url) in each of those cases is where you’ll point to the URL of the post you’re trying to analyze, which should be pulled in automatically by IFTTT. Voila! Social metrics.

Pulling on-page metrics

You may also have metrics built into your site that you’d like to use. For example, Moz has thumbs up on each post, and we also frequently see great discussions in our comments section, so we use both of those as success metrics for our blog. Those can usually be pulled in through one of the following two methods.

But first, obligatory note: Both of these methods involve scraping a page for information, which is obviously fine if you’re scraping your own site, but it’s against the ToS for many services out there (such as Google’s properties and Twitter), so be careful with how you use these.

=IMPORTXML

While getting it set up correctly can be a little tricky, this is an incredibly handy function, as it allows you to scrape a piece of information from a page using an XPath. As long as your metric is displayed somewhere on the URL for your piece of content, you can use this function to pull it into your spreadsheet.

Here’s how you format the function:

If you’d like a full tutorial on XPaths (they’re quite useful), our friends at Distilled put together a really fantastic guide to using them for things just like this.  It’s well worth a look. You can skip that for now, if you’d rather, as you can find the XPath for any given element pretty quickly with a tool built into Chrome.

Right-click on the metric you’d like to pull, and click on “Inspect element.”

That’ll pull up the developer tools console at the bottom of the window, and will highlight the line of code that corresponds to what you clicked. Right-click on that line of code, and you’ll have the option to “Copy XPath.” Have at it.

That’ll copy the XPath to your clipboard, which you can then paste into the function in Google Spreadsheets.

Richard Baxter of BuiltVisible created a wonderful  guide to the IMPORTXML function a few years ago; it’s worth a look if you’d like more info.

Combining =INDEX with =IMPORTHTML

If your ingredient is housed in a <table> or a list (ordered or unordered) on your pages, this method might work just as well.

=IMPORTHTML simply plucks the information from a list or table on a given URL, and =INDEX pulls the value from a cell you specify within that table. Combining them creates a function something like this:

Note that without the INDEX function, the IMPORTHTML function will pull in the entire piece of content it’s given. So, if you have a 15-line table on your page and you import that using IMPORTHTML, you’ll get the entire table in 15 rows in your spreadsheet. INDEX is what restricts it to a single cell in that table. For more on this function, check out this quick tutorial.


Taking it to the next level

I’ve got a few ideas in the works for how to make this metric even better. 

Automatically check for outlier ingredients and flag them

One of the downsides of smooshing all of these ingredients together is missing out on the insights that individual metrics can offer. If one post did fantastically well on Facebook, for example, but ended up with a non-remarkable One Metric score, you might still want to know that it did really well on Facebook.

In the next iteration of the metric, my plan is to have the spreadsheet automatically calculate not only the average performance of past content, but also the standard deviation. Then, whenever a single piece differs by more than a couple of standard deviations (in either direction), that ingredient will get called out as an outlier for further review.

Break out the categories of ingredients

In the graphic above that combines the ingredients into categories in order to calculate an overall average, it might help to monitor those individual categories, too. You might, then, have a spreadsheet that looked something like this:

Make the weight of each category adjustable based on current goals

As it stands, each of those three categories is given equal weight in coming up with our One Metric scores. If we broke the categories out, though, they could be weighted differently to reflect our company’s changing goals. For example, if increased brand awareness was a goal, we could apply a heavier weight to social metrics. If retention became more important, on-page metrics from the existing community could be weighted more heavily. That weighting would adapt the metric to be a truer representation of the content’s performance against current company goals.



I hope this comes in as handy for everyone else’s analysis as it has for my own. If you have any questions and/or feedback, or any other interesting ways you think this metric could be used, I’d love to hear from you in the comments!

About Author — Trevor is the content strategist at Moz—a proud member of the content team. He manages the Moz Blog, helps craft and execute content strategy, and wrangles other projects in an effort to align Moz’s content with the company’s business objectives and to provide the most valuable experience possible for the Moz community.

Is Your Content Credible Enough to Share?

Insufficient credibility undermines digital marketing, particularly among SEOs who now produce or promote content as part of their job. People won’t share content that isn’t credible; they know the things they share reflect on them and impacts their own credibility. While the importance of credibility gets mentioned in passing, little has been said about how to actually build it, until now.

Your Guide to Establishing Credibility

You build credibility by signaling to the reader that you can be trusted. The signals of trust can come from the author, the site, and from within the content itself. Each signal will appeal to different types of readers in different contexts, but they come together to make content that is credible enough to share.

Rand mentioned credibility in his Content Marketing Manifesto as one of the things we need to build familiarity, linkability, and trust. Several studies have also shown credibility’s critical role in promoting and sharing. So, let’s build some credibility.

1. Establish expert credibility

Expert credibility comes from having knowledge others do not. People want experts they can understand and trust, especially when trying to understand complex or ambiguous topics like new technology, engineering, advanced science, or law.

Be an expert or hire an expert with insight

A Syracuse University study found “insightful” content was most correlated with users’ estimation of a blog’s credibility. You can’t offer interesting insight on a subject you know very little about, so obviously you need to be an expert or hire one.

Unless your expert has breaking news, he or she needs to provide quality analysis and opinion to add any value. Most successful non-news content is opinion and/or analysis, whether verbal, graphical, or textual.

If you’re creating video or text content for your site, the expert should also be able to clearly express complex subjects in a way readers can understand and follow. If he can’t then get a content writer to interview the expert and relay the information.

Source experts

Do not try to give your opinion as an expert in a field where you’re not one. It won’t work.

We’ve all read non-expert content on subjects where we’re knowledgeable. We know what expertly-written content looks like and can easy detect pretenders. If you pretend to be an expert and get one little detail wrong, you’ll blow all your credibility with the people who actually understand and influence the discussion. They won’t link to or share that piece of content and they may never share any of your content again. Don’t take that risk.

Rather than trying to fake expertise, try finding experts and incorporating their expertise into your post. Journalists have long understood this tactic. Even journalists who are experts use quotations from other experts in both news and analysis pieces. The front page of the Washington Post’s technology print section is usually littered with quotation marks and according-tos.

People running blogs can easily get a quote from someone knowledgeable enough to have an opinion that matters. Experts with strong opinions usually want to share them.

Be passionate to build trust

The Syracuse University study and this University of Pennsylvania study show that passion is key to judgments on credibility and sharing. Readers don’t just want an expert who can explain things; they want an expert who cares.

Experts who know what they’re talking about tend to have nuanced and sophisticated opinions about subjects they understand. Don’t undercut that understanding with a shallow piece of content. Expert pieces should be deep and thoughtful.

Legal experts who really care about Citizens United vs. Federal Election Commission simply wouldn’t take the time to write a bland essay on what the ruling said and how it might impact the future of politics. SEO experts don’t want to report on the fact that Google penalized guest post networks. They care, and want to explain why it’s good or bad.

Expert opinion shouldn’t be confused with argument, and it doesn’t require you to start a firefight with anyone who’s taken the other stance.

Cite sources

Cite the sources for all your expert insight. Citing expert sources is the most obvious way to back up your claims and gain trust. Often citing a source is as simple as linking to the webpage from which you got your information.

Don’t use weasel words like, “it has been said,” or, “many people believe,” to skirt the citation responsibility. Experienced writers and editors instinctively close the tab on any content attempting to unnecessarily blur their sources.

Show data

Sometimes, instead of breaking news, you can add to it with data. Data lends credibility to your post in a unique way because with numbers, your sources and methodology are more important than the author’s history and popularity. The data, if it’s compiled and analyzed correctly, speaks for itself.

For example, when the CableTV team heard about the potential Comcast/Time Warner merger, we knew simply sharing the news would be a waste of time. Every major news outlet would easily drown out our site, and opinion pieces where popping up everywhere. Instead, we crunched some numbers, comparing U.S. Census data with coverage data, producing a coverage and population analysis people could see and understand. A few large news organizations used the data in ongoing analysis, Reddit’s founder (Alexis Ohanian) shared the post, and roughly 60,000 people ended up seeing it.

JavaScript libraries and HTML 5 tools are springing up everywhere to help non-technical users visualize data in interesting ways. Mapping examples include SimpleMaps (used in our post), MapBox, Google Fusion Tables, etc. Graphing and other data options are all over, but this site is a good place to start. Compile data in-between writing stories related to your niche with Census data or any of these data sources so you’re ready to go when news hits. For more tips, Kane Jamison always has tips on data-driven content marketing, including the presentation below:

 

2. Harness hierarchical credibility

Hierarchical or rank-based credibility comes from a person’s position or title. High-ranking members of an organization have a better chance of being taken seriously simply by nature of their perceived authority, especially when the organization is well-known.

Have important people write important things

People lend more credibility to an article written by an unknown CEO than a writer they don’t know—even if the writer knows more about the topic than the CEO. For better or worse, people are simply influenced by official job titles and standing within hierarchy.

Your definition of what’s important may vary. Almost everything on the popular 42floors blog is written by a founder, while CEOs of larger companies will probably have less time and less interest in regular blogging.

Use executives for guest posts

I know – I’m the guy who wrote guest posting had gone too far. Google thought so too based on its latest round of penalties. I believe, however, the lack of credibility and expertise in many guest articles was a major cause for Google’s (perhaps disproportionate) response to guest blogging networks.

Don’t waste an executive’s time on small unknown sites no one would ever read. Instead, consider pitching an article written by an executive or other well-known figure to well-known sites. Trulia is a good example with high-ranking members adding guest posts for Google, The Wall Street Journal, and interviewing with sites like Business Insider. Moz, of course, is another place to see founders adding posts and video frequently.

Better job titles

If you want your content to be shared, make your authors experts in both title and in truth. Changing titles for title’s sake may sound stupid, but titles like managing editor, [subject] correspondent, [subject expert], or even [subject] writer have more gravitas than a plain “author” or “contributor.” Think about what the title says to a person reading your content (or email). The flip side: writers should actually be subject-matter experts.

You should also re-think giving quirky titles to everyone, as they can hurt credibility. I can’t imagine the Wall Street Journal quoting a “digital ninja” or “marketing cowboy” in their story – unless that story is about job titles.

Leadership quotes

You can also make use of another person’s position to lend credibility to your content. This works especially well if you’re looking for insight into a recent news event. Quotes from company executives, government officials, and other high-title positions give you something unique and show you’re not just another blogger summarizing the news built on someone else’s journalism.

3. Seek referent credibility

When someone trustworthy shares something with positive sentiment, we immediately trust the shared item. The referrer lends his or her credibility to the referee. The Moz audience will have no problem understanding referent credibility, as it’s the primary method Google uses to prioritize content that seems equally relevant to a user query. People also rely on referent credibility to decide whether a post is worth sharing. Those referrals build more credibility, and viral content is born. How do you get some referent credibility to radiate onto your content?

Publish on credible sites

This post will receive some measure of credibility simply by being published on the main Moz blog. Anything on or linked to from well-known sites and authors receives referent credibility.

Share referrals and testimonials

You’ll commonly see “as featured on” lists or testimonials from recognizable personalities. Testimonials from anyone at Google or Microsoft with an impressive-sounding position could go a long way for a B2B product. Referent credibility is the reason celebrity endorsements work.

Leveraging referent credibility in a press push generally works well if your company is involved in something newsworthy. Consider requesting and using quotes from relevant and well-known people in press releases or even outreach emails if you’ve done something worth announcing.

Analysis pieces are a little trickier: pointing out past coverage can lend some credibility to a blog post or press release, but it can also look a little desperate if done incorrectly. High relevance and low frequency are key. A good offline analogy is that person who mentions that time they met a celebrity every chance they get, whether it’s relevant or not. Name-droppers are trying (too hard) to build credibility, but it’s actually just sad and annoying. The same celebrity encounter might actually generate interest and credibility if it’s relevant to the conversation and you haven’t told the story to death. Feel free to talk about times well-known people shared or endorsed you, but make sure it’s relevant and don’t overdo it.

Appeal to credible people

When a well-known person shares your content, more links and shares often follow. Find credible people, see what they talk about and share, and then try make something great that appeals to them. This idea has already been covered extensively here on Moz.

4. Take advantage of associative credibility

People make associations between one trait and another, creating a Halo effect. For example, several studies (1, 2, 3) have found that attractive people often receive higher pay and are seen as more intelligent, when in reality there is no correlation. Users do the same thing with websites, so making your website look and feel like other credible sites is important.

Use trusted design as a guide

Don’t run in and steal the Times’ CSS file. I’m pretty sure that’s illegal. It’s also probably not going to work unless you’re running a national multi-channel newspaper. But you should be aware that people associate design elements on a site with the credibility of the site. You can help or hinder your credibility through web design in hundreds of ways. Start by looking at legitimate sites and incorporating some of their design elements into your own. Then check out some untrustworthy and unknown sites to see the difference and determine what to avoid.

Obviously you want your site to be unique, but be carefully unique. If you stray from trusted convention, know why you’re doing it. Maybe you want to kill hamburger icons on mobile – just make sure you have a well-considered alternative.

When in doubt, test

Split tests tend to focus on conversion and sales, and too often the blog/news design gets dragged along for the ride. Given the importance of content and sharing on visibility, testing the impact of site elements on sharing should be as important as the tests we do on sales funnels.

You can test different design elements as they relate to sharing by creating posts and pages with a page-level variable and a canonical tag back to the original post. Always test URLs with variables against other URLs with variables to account for site owners manually removing them. This setup may also be useful for testing different content promotion channels and methods.

Tracking results externally requires a different URL. You may use a modified URL rather than a variable, but only for single-page tests. Note that results will be a little erratic with variables people might remove, but trends will still emerge.

Consider your domain name

You have probably read a news article and wanted to share it, but then looked for a more reputable source to post to Reddit or Twitter.

Sometimes I’ll share content from a site I’ve never heard of, but usually I want the content I’m sharing to come from a site with a name that evokes trust. Everything in this article goes into a decision on whether to share, but domain name is a surprisingly large factor. When I post an article, I don’t want the first response or comment to be something snarky like, “Oh, according to goodbusinessnews4u.com – sounds legit.”

Domain will also impact click-through on social networks and social sharing sites. A couple years ago I wrote about choosing the right domain for your branding strategy, and I think its message still holds true.

Domain name will also influence what content seems appropriate. You don’t want people asking, “Why is highspeedinternet.com writing about cooking recipes?” Make sure content strategy aligns with your domain and branding strategy.

Write like a writer; build profiles

You must have credibility in your writing if you want your content to be shared. Follow these simple tips:

  • Write clearly, hire writers, or don’t waste your time on text content. Even a credible expert will have a hard time being trusted enough to share unless they write clearly with native-level grammar.
  • Build author profiles, use full names, and use author images. Nothing says, “I’m not proud of this” like a partial name without an image.
  • Build a full section about your company. Be as specific as possible, and avoid vague statements on the value your site adds.
  • Craft headlines that are easy to follow, avoid trick/cute headlines unless you have a great reason for tricking or confusing users about what the content will deliver.
  • Be consistent with surrounding articles. Jumbled topics and unrelated surrounding articles make sites look inconsistent.

Avoid clip art and stock images

Just ask Ian Lurie what he thinks about stock images. When I wrote “How Google’s Algorithm Silences Minority Opinions” I had the image in my head of Googlebot placing a gag on a user. Thankfully one of CLEARLINK‘s talented designers had a better (and less aggressive) idea:

A Google logo would have been easy, but boring. The custom image added a strong visual to the argument, emphasizing key points: a computer algorithm silencing a person, the person not caring too much. It also sent the associative message to users that the site is legitimate enough to use unique images.

Most of us can’t get custom illustrations or photographs for each post, but you should consider it for high-investment pieces or pieces you think have a good shot at success.

Final thoughts

Unless you have inside information on a rumor or are willing to burn your credibility going forward, your content must project credibility. Smaller sites and lesser-known brands have the most to gain by understanding how users and journalists make judgments on credibility and working to improve those factors. You don’t necessarily need to employ every strategy and tactic, but the best coverage and links will always require a high level of credibility. 

About AuthorCarson Ward is an online marketing manager at Clearlink, an enterprise digital marketing company.

Google Announces the End of Author Photos in Search: What You Should Know

Even so, it came as a surprise when John Mueller announced Google will soon drop authorship photos from most search results.

This one hits particularly hard, as I’m known as the guy who optimized his Google author photo. Along with many other SEOs, I constantly advise webmasters to connect their content writers with Google authorship. Up until now, would-be authors clamored to verify authorship, both for the potential of increased click-through rates, and also for greater brand visibility by introducing real people into search results.

As of today, the MozCast feature graph shows an immediate 10% decline in traditional authorship snippets, almost overnight. We expect to see this roll out further over the next several days.

How are author photos changing?

The announcement means author photos in most Google search results are going away. John Mueller indicated the change will roll out globally over the next few days.

Up until now, if you verified your authorship through Google+, and Google choose to display it, you might have seen your author photo displayed in Google search results. This included both your author photo and your Google circle count.

Going forward, Google plans to only display the author’s name in the search snippet, dropping the photo and the circle count.

Google News adds a different twist. 

In this case, Google’s plans show them adding a small author photo next to Google News snippets, in addition to a larger news photo snippet. 

At this time, we’re not sure how authorship in Google News will display in mobile results.

Why did Google drop author photos?

In his announcement, John Mueller said they were working to clean up the visual design of search results, and also to create a “better mobile experience and a more consistent design across devices.”

This makes sense in the way Google has embraced mobile-first design. Those photos take up a lot of real estate on small screens. 

On the other hand, it also leaves many webmasters scratching their heads as most seemed to enjoy the author photos and most of the web is moving towards a more visual experience.

John Mueller indicated that testing shows that “click-through behavior” with the new results is about the same, but we don’t know exactly what that means. One of the reasons authors like the photos in search results was the belief that a good photo could result in more clicks (although this was never a certainty). 

Will the new SERPs result in the same amount of clicks for authorship results? For now, it’s hard to say.

Critics argue that the one thing that will actually become more visible as a result of this change will be Google’s ads at the top and sides of the page.

What isn’t changing?

Despite this very drastic visual change in Google search results, several things are not changing:

1. Authorship is still here

As Mark Traphagen eloquently pointed out on Google+, the loss of photos does not mean Google authorship itself is going anywhere. 

“Google Authorship continues. Qualifying authors will still get a byline on search results, so Google hasn’t abandoned it.”

2. Authors’ names still appear in search results

In the new system, authors still get their name displayed in search results, which presumably clicks through to their Google+ profile. Will this be enough to sway searchers into clicking a link? Time will tell.

3. Your rankings don’t change

Authorship does not influence rankings for most search results. (exceptions for certain results like In-depth articles) Sometimes the photo led to more clicks for some people, but the new change should not alter the order of results.

4. You must still verify authorship for enhanced snippets

Google isn’t changing the guidelines for establishing authorship. This can be accomplished either through email verification or linking your content to your Google+ profile, and adding a link back to your website from your Google+ contributor section.

Tracking your authorship CTR

If you have authorship set up, you can easily track changes to your click-through rate using Google Webmaster Tools. Navigate to Labs > Author Stats to see how many time your author information has appeared in search results, along with total number of clicks and average position.

In the example above, search results associated with my authorship receive around 50,000 impressions a day, with an average of 1831 clicks, for an overall CTR of 3.6%

If you track your CTR immediately before and after the Google authorship change (by adjusting the dates in Webmaster Tools) you might notice any changes caused by the shakeup.

Keep in mind that CTR is highly determined by rank, or average position. Small fluctuations in rank can mean a large difference in the number of clicks each URL receives.

Is Google Authorship still worth it?

For many, scoring photos in search results was the only incentive people had to verify authorship. Whether or not it increased click-through rates, it was an ego boost, and it was great to show clients. With the photos gone, it’s likely fewer people will work to get verified.

Even with the photos gone, there is still ample reason to verify authorship, and I highly recommend you continue to do so. 

  • Even though a byline is much less visible than a photo, across the hundreds or thousands of search impressions you receive each day, those bylines can make a measurable difference in your traffic, and may improve your online visibility.
  • Google continues to work on promoting authoritative authors in search results, and authorship is one of the better ways for Google to establish “identity” on the web. Google continues to make statements explaining how important identity in content is, as explained by Matt Cutts both publicly and in this rarely seen interview.

Facing the future

If Google begins to incorporate more “Author Rank” signals into its search algorithm, establishing yourself as a trusted authority now could pay off big down the road. Disappearing author photos today may someday be replaced by actual higher rankings for credible authors, but there are no guarantees. 

At this point, it’s hard to say exactly where the future of authorship lies, especially given the unknown future of Google+ itself.

Personally, I will be sad to see author photos disappear. Let’s hope for something better down the road.

More from across the web:
Google Removes Author Photos From Search: Why And What Does It Mean?

About  Author — Cyrus-Shepard leads the Content and in-house SEO team at Moz. Follow him on Twitter and Google+. Read more posts by Cyrus.

Is Google Analytics Hurting your Business?

Given that everybody in the conference hall was involved in digital marketing in some way, and how much of website visitor tracking is done through Google Analytics, you might even speculate that was a foolhardy statement and that the only thing that saved the speaker was the cordon of riot police brought in specially for this talk. But then the man on the platform was Ammon Johns – a man with almost 20 years of SEO experience who is recognised by the industry as someone with a huge amount of SEO knowledge and who speaks at some of the largest digital marketing conferences around – so the riot police were little troubled, although many eyebrows were raised.

It turns out that the main aim of the talk wasn’t actually to get everybody in the room to boycott Google, but to make us think. And that’s what I’d like you to do throughout this post – question the common wisdom that Google Analytics is the best thing since hypertext protocols and ask yourself whether it might actually be harming your business.

Why is Google Analytics so great?

It is a truth universally acknowledged that Google Analytics is brilliant for four reasons:

  1. It’s very easy to use
  2. Everyone else uses it, so it must be the best
  3. It integrates brilliantly with AdWords
  4. It’s free. Who can argue with free?

The big question is, are these really the right reasons for choosing an analytics tool? Does “easy to use” mean “easy to get actionable insights from” or something else? With Google being a hugely successful corporation, are they really giving me a huge chunk of data for free or am I paying in some other way?

Is Google Analytics actually easy to use?

Google Analytics is definitely easy to set up. It’s also easy to get data out of and it’s easy to get rid of data you don’t want. But spitting out data isn’t the point of a web analytics. The point is to provide insights that let you build testable hypothesis and so improve the performance of your platform.

We’ve all seen the Google Analytics home screen – now the Audience Overview screen – with its visitor graphs and its language breakdowns. But have you really studied it? Head over to Analytics, take a look at that Audience Overview screen and ask yourself “how can I improve my business with these data and these data alone?” I’ll give you a few minutes of thinking time.

Did you manage to find anything? I would be very surprised if you did. Now that’s quite a shocking statement: you went to the first – and so by definition most important – screen of a tool that millions of people use every day and I don’t expect you to have found anything useful. Ouch.

That’s because while Google Analytics is very easy to set up and it’s very easy to see the data it spits out, it’s actually very difficult to get real insight. Almost every valuable analysis requires creating a custom report. You want to use cohort analysis to determine whether you have taken the right approach on a channel? Custom report. You want to see which blog posts drive the most and best engagement? Set up JavaScript events then build a custom report. You want to integrate offline sales data from your CRM? No can do; you will be able to when you get Universal Analytics, but only using (all together now) a custom report.

So there are plenty of things in Analytics that could be easier. But how can we make them easier? The problem here comes not from the data being collected but from the way it’s displayed. One option is to suck the data straight in from the API to your own set of reports that can not only be branded nicely but will only show the graphs you want to see, set up in the way you want. It’s not actually all that difficult for a good developer to do, and if it saves you time each week or month then you can make a good business case for investing in such a solution.

If you can make the business case for building a custom interface for Google Analytics, though, it might be worth asking yourself the question posed at the start of this post: “is Google Analytics really the best solution out there for me or can I justify investing in something else?” Take a couple of hours to explore the web analytics ecosystem and see if you can find a solution that would make it easier to deliver real, actionable insight.

Just because everyone else uses it, is Google Analytics really the best?

I started the last section off with a challenge, so I’ll do the same here. Don’t worry, this will be a simple one with no trips off to Analytics. Ready? Define “the best”. Go!

OK, so that’s actually what a mathematician would define as “complex”: a question that’s easy to ask but difficult to answer. The reason it’s difficult to answer is twofold:

  1. This is probably the first time we’ve ever asked ourselves this question
  2. The answer depends hugely on context: who is asking questions of our data, why they want answers, who is going to do the analysis, and a whole range of other factors

The reason I asked the question is that if we can’t define what “the best” means, how can we say Google Analytics is the best solution?

There are some things it does brilliantly. Tracking visitor flow, aggregating data over multiple pages and channels, letting us look at engagement. But there are some questions it simply cannot answer. For example, what would your reply be if your boss asked:

  • “The average time spent on this landing page is two minutes. Is that because they were reading the copy or because they were comparing our product to our competitors?”
  • “How well are the videos on our site engaging visitors?
  • “People jump from their mobile, to their work PC, back to their mobile on the train home, then onto their home computer. How can we track this happening to get a real picture of cross-device behaviour?”
  • “What happens if people have cookies turned off?”

Hands up all those who said “ermmm”.

There are tools out there that can do these things:

  • Crazy Egg gives you heatmaps showing what proportion of people have scrolled down a page and how many have clicked links on a given page (I personally love Crazy Egg. No affiliation, they just make a great product).
  • Digital Analytix from comScore lets you track individuals across devices. Universal Analytics will bring in this behaviour to some extent, but only for people who sign in to their Google accounts while browsing
  • While you could cobble together a video analysis using time on page, JavaScript events, and a pinch of salt, Digital Analytix gives you data on browser behaviour during video streaming
  • Piwik is an open source (read “free and fully customisable”) analytics tool that doesn’t use cookies, so doesn’t give you the problem of not being able to track people who have turned off cookies

A screenshot from Crazy Egg used on the Optimizely blog. When a CRO tools company starts using a web analytics tool it could be interesting to take a look (Image credit: Crazy Egg)

For a lot of people those are some pretty fundamental questions that can’t be answered. But some people know enough about JavaScript – or employ people who do – that they can set up event listeners to get a portion of this data. And some people are not asking these questions. But think about whether Google Analytics has ever not given you the answer to a question, or even if you haven’t asked a question because you know it can’t be answered; if this has happened a few times then it might be a good time to head off and do that research into other providers.

Anything free is amazing. But is Analytics really free?

Now I imagine that a lot of people reading that heading have straight away thought “of course it’s really free, we don’t give them a penny”. But think about this: in using Analytics you give Google all of the data. That gives them knowledge about you and your customers, and knowledge, as we all know, is power. So you might not be paying Google cash, but you are definitely helping them keep their position as one of the most powerful companies on the planet.

But more than that, if knowledge is power and power is money then surely gaining knowledge about data and its manipulation is a great learning opportunity and one that will make you a fair return one day. As Ammon said in his talk, “Using Google Analytics doesn’t make you good with data, just with Google Analytics”. Because if you just accept what Analytics pukes out at you, are you really asking the difficult questions that will help your business to improve?

One last thought: the data that Google Analytics gets is yours for free anyway. It’s your information about people coming to your website and interacting with your services, not Google’s. Lots of companies are moving towards data warehouses now, keeping all of their information within their own domain instead of giving it to third parties. And if you have any concerns about privacy following the recent revelations about the NSA and GCHQ then you might consider them pretty sensible people.

When is “Good Enough” good enough?

This was actually going to be the title of this post, but I don’t quite have Ammon’s nerve (and it’s a great topic for a project management post so has been filed away for later use).

As we’ve seen, Google Analytics is not the best solution out there. It’s not even the best free solution out there for some people. But what it is is “good enough”. It’s good enough to get some profound insights out of if you work with it, and like Excel, even better if you can build a custom dashboard. It’s good enough if you value those insights over privacy. It’s good enough if you can’t invest the time to learn a new tool that will give you similar insights. It’s good enough if you ask it the right sort of questions.

It might be for him, but is it for you? (Image credit The Meme Wiki)

But – and it’s a big but – for you that might not be enough for you and your company. Do you work for a “data-driven organisation”? Do you want to ask hard questions, make big changes, and get big improvements as a result of the data in your hands? Do you want to stand out from all of the other companies and agencies out there who do analytics in the same way?

If “good enough” suits your needs, dismiss this post with a wave of the hand. But if you think that you might need more than “good enough” in the future, or if you really want to be a properly data-driven decision maker, or if you think that big changes will give you big results I urge you to think about your choices. Explore the options out there; even if you go back to Google Analytics, you’ll come back with more knowledge than you had before. But if you don’t go back, you can look forward to a long, exciting, and rewarding journey.

About Author – Benjamin Morel is an agency-based digital marketeer and project manager working for Obergine in Oxford, UK. He is passionate about inbound marketing, especially building strategies centered around data and communication. Follow him on Twitter @BenjaminMorel or on Google +.

What Can Mid-Century Design Teach You About User Experience?

Verner Panton, a design revolutionary, once said, “You sit more comfortably on colours you like.” A statement that seems to disregard logic, and focus strictly on the intangible relationships which dictate preferences.

So what does this statement say about design, and more importantly, how can YOU apply this to your online marketing strategies?

The answer is an investment in user experience: understanding how design can impact cognitive science and drive decisions. Here are a few stories around Panton’s designs and the insights they lend in creating successful online user experiences today. 

Why invest in UX?

Panton Neon Swimming Pool

Panton worked during a wonderfully whimsy time for furniture design, helping to shaping the late 1950’s Pop movement by making waves with his neon swimming pool design. Panton’s focus on design that provides function and evokes emotion can be seen across his eccentric pieces and even in current day design practices.

User experience is largely a subjective field, making it difficult to directly correlate qualitative metrics to various UX efforts and initiatives. For online efforts, attribution may prove difficult as it deals with users’ emotions, an increase in conversions and drop in bounce rates are signs in line with the intentions of enhanced user experience.

Analysis by the Design Management Institute shows how design-driven companies outperformed others by 228% through efforts like creating streamlined user experiences. Design-driven companies have effectively sold more product and made more profit, by providing unique experiences, at each touch point of their relationship with customers. Facilitating a stakeholder workshop can effectively gather requirements while increasing alignment among stakeholders. 

How do you validate UX design?

Panton Cone Chair

Panton’s Cone chair was a piece he created for his parents’ restaurant. The Cone chair was so admired by a restaurant customer they offered to put it into production. Post-production the Cone chair was briefly on display in a Fifth Avenue shop in New York, where it was removed due to the large crowds it attracted.

User experience is centered on perception. With the proliferation of user interfaces it is of the utmost importance to focus on the individual user’s experience, while considering the collective experiences of the target audience. 

In order to validate your user’s/users’ experience concepts, it is important to take a systematic approach. The following Validation stack, by Cennyd Bowles, shows the close relationship between design theory, user research, and evidence; together, these effectively validate UX concepts.

UX Validation Stack

The validation stack requires you to provide recommendations that build off of one another and are driven by data. Backing up your argument with early buy-in from stakeholders and iterative user testing can both improve your argument for UX and strengthen your concepts.

 

Ways to improve UX

Panton S Chair

Panton’s S chair, a single legless piece of cantilevered plastic, graced VOGUE in 1995 with Kate Moss sitting naked atop it. The chair remains an icon of pop movement design, and is rumored to have been inspired by a pile of plastic buckets.

The design was made to maintain consistency, with the choice of one seamless material, and functionality, with its smooth stacking ability.

UX design calls for both consistency and functionality in order to limit distractions and guide users’ decisions.

  • Usability: Increase ease of use 
    Examine the full user path by watching them go through the site and conversion funnel. Asking the user how they think about or through the site and its use to them.
    (Tool to use: UserTesting.com
  • Informational design: Create visual hierarchy 
    Use data to drive design decisions. Track common on-site behaviors to adjust site layout or page layout.
    (Tool to use: Simple Mouse tracking)
  •  Content strategy: Incorporate personality
    Track your brand’s tone of voice across all platforms.
    (Tools to use are discussed in Distilled’s Content Guide)

An array of potential users should be observed over time, as users’ experiences and influences continually affect their decision-making process.

How does brand communication improve UX?

Panton Living Tower

Panton’s Living Tower is an impressive 2-meter high structure with unique cut-outs, designed to encourage communication. The oddly amoeba-esque cut outs in the furniture encouraged people to sit in seemingly un-conventional positions, while prompting conversation.

User experience efforts can be amplified by creating a space and prompt for conversation. Brands engaging with users on social and feedback channels should have the goal to meet their target market where they are or host a conversation their user/audience would like to have. Before building or creating a social strategy for a brand it is important to ask the following questions…

  • How is the social platform aligned to the brand? 
  • Why would users choose to engage in a dialogue with a brand on this platform? 
  • What value-add could the social platform provide for users?
  • When would it be most helpful for a user to communicate with the brand? 

Researching the types of discussions users are already prompting, about your competitors or industry, can help to uncover potential opportunities for social media strategy and content creation. Then measure social channels’ impact through network referrals, conversions, and landing page visit analytics. 

Why user-centered design for user experience? 

Arne Jacobsen Ant Chair

Panton studied under Arne Jacobsen, who worked with him to create the Ant chair. The chair was commissioned specifically for a large Danish pharmaceutical company‘s cafeteria. The chair base was designed to be comfortable, lightweight and stackable. The choice to use only three legs was in an attempt to minimize hitting furniture against people’s legs or other furniture, during their lunch hour.

User experience efforts should be grounded in similar methodologies, giving users additional functionality without compromising on a seamless experience. Striking a balance of trust, motivation and functionality can ultimately drive a greater user experience. Working with and learning from users’ patterns, through both qualitative and quantitative testing and tracking.

Using Kimono Labs to Scrape the Web for Free

Historically, I have written and presented about big data—using data to create insights, and how to automate your data ingestion process by connecting to APIs and leveraging advanced database technologies.

Recently I spoke at SMX West about leveraging the rich data in webmaster tools. After the panel, I was approached by the in-house SEO of a small company, who asked me how he could extract and leverage all the rich data out there without having a development team or large budget. I pointed him to the CSV exports and some of the more hidden tools to extract Google data, such as the GA Query Builder and the YouTube Analytics Query Builder

However, what do you do if there is no API? What do you do if you want to look at unstructured data, or use a data source that does not provide an export?

For today’s analytics pros, the world of scraping—or content extraction (sounds less black hat)—has evolved a lot, and there are lots of great technologies and tools out there to help solve those problems. To do so, many companies have emerged that specialize in programmatic content extraction such as MozendaScraperWikiImprtIO, and Outwit, but for today’s example I will use Kimono Labs. Kimono is simple and easy to use and offers very competitive pricing (including a very functional free version). I should also note that I have no connection to Kimono; it’s simply the tool I used for this example.

Before we get into the actual “scraping” I want to briefly discuss how these tools work.

The purpose of a tool like Kimono is to take unstructured data (not organized or exportable) and convert it into a structured format. The prime example of this is any ranking tool. A ranking tool reads Google’s results page, extracts the information and, based on certain rules, it creates a visual view of the data which is your ranking report.

Kimono Labs allows you to extract this data either on demand or as a scheduled job. Once you’ve extracted the data, it then allows you to either download it via a file or extract it via their own API. This is where Kimono really shines—it basically allows you to take any website or data source and turn it into an API or automated export.

For today’s exercise I would like to create two scrapers.

A. A ranking tool that will take Google’s results and store them in a data set, just like any other ranking tool. (Disclaimer: this is meant only as an example, as scraping Google’s results is against Google’s Terms of Service).

B. A ranking tool for Slideshare. We will simulate a Slideshare search and then extract all the results including some additional metrics. Once we have collected this data, we will look at the types of insights you are able to generate.

1. Sign up

Signup is simple; just go to http://www.kimonolabs.com/signup and complete the form. You will then be brought to a welcome page where you will be asked to drag their bookmarklet into your bookmarks bar.

The Kimonify Bookmarklet is the trigger that will start the application.

2. Building a ranking tool

Simply navigate your browser to Google and perform a search; in this example I am going to use the term “scraping.” Once the results pages are displayed, press the kimonify button (in some cases you might need to search again). Once you complete your search you should see a screen like the one below:

It is basically the default results page, but on the top you should see the Kimono Tool Bar. Let’s have a close look at that:

The bar is broken down into a few actions:

  • URL – Is the current URL you are analyzing.
  • ITEM NAME – Once you define an item to collect, you should name it.
  • ITEM COUNT – This will show you the number of results in your current collection.
  • NEW ITEM – Once you have completed the first item, you can click this to start to collect the next set.
  • PAGINATION – You use this mode to define the pagination link.
  • UNDO – I hope I don’t have to explain this 😉
  • EXTRACTOR VIEW – The mode you see in the screenshot above.
  • MODEL VIEW – Shows you the data model (the items and the type).
  • DATA VIEW – Shows you the actual data the current page would collect.
  • DONE – Saves your newly created API.

After you press the bookmarklet you need to start tagging the individual elements you want to extract. You can do this simply by clicking on the desired elements on the page (if you hover over it, it changes color for collectable elements).

Kimono will then try to identify similar elements on the page; it will highlight some suggested ones and you can confirm a suggestion via the little checkmark:

A great way to make sure you have the correct elements is by looking at the count. For example, we know that Google shows 10 results per page, therefore we want to see “10” in the item count box, which indicates that we have 10 similar items marked. Now go ahead and name your new item group. Each collection of elements should have a unique name. In this page, it would be “Title”.

Now it’s time to confirm the data; just click on the little Data icon to see a preview of the actual data this page would collect. In the data view you can switch between different formats (JSON, CSV and RSS). If everything went well, it should look like this:

As you can see, it not only extracted the visual title but also the underlying link. Good job!

To collect some more info, click on the Extractor icon again and pick out the next element.

Now click on the Plus icon and then on the description of the first listing. Since the first listing contains site links, it is not clear to Kimono what the structure is, so we need to help it along and click on the next description as well.

As soon as you do this, Kimono will identify some other descriptions; however, our count only shows 8 instead of the 10 items that are actually on that page. As we scroll down, we see some entries with author markup; Kimono is not sure if they are part of the set, so click the little checkbox to confirm. Your count should jump to 10.

Now that you identified all 10 objects, go ahead and name that group; the process is the same as in the Title example. In order to make our Tool better than others, I would like to add one more set— the author info.

Once again, click the Plus icon to start a new collection and scroll down to click on the author name. Because this is totally unstructured, Google will make a few recommendations; in this case, we are working on the exclusion process, so press the X for everything that’s not an author name. Since the word “by” is included, highlight only the name and not “by” to exclude that (keep in mind you can always undo if things get odd).

Once you’ve highlighted both names, results should look like the one below, with the count in the circle being 2 representing the two authors listed on this page.

Out of interest I did the same for the number of people in their Google+ circles. Once you have done that, click on the Model View button, and you should see all the fields. If you click on the Data View you should see the data set with the authors and circles.

As a final step, let’s go back to the Extractor view and define the pagination; just click the Pagination button (it looks like a book) and select the next link. Once you have done that, click Done.

You will be presented with a screen similar to this one:

Here you simply name your API, define how often you want this data to be extracted and how many pages you want to crawl. All of these settings can be changed manually; I would leave it with On demand and 10 pages max to not overuse your credits.

Once you’ve saved your API, there are a ton of options (too many to review here). Kimono has a great learning section you can check out any time.

To collect the listings requires a quick setup. Click on the pagination tab, turn it on and set your schedule to On demand to pull data when you ask it to. Your screen should look like this:

Now press Crawl and Kimono will start collecting your data. If you see any issues, you can always click on Edit API and go back to the extraction screen.

Once the crawl is completed, go to the Test Endpoint tab to view or download your data (I prefer CSV because you can easily open it in Excel, CSV, Spotfire, etc.) A possible next step here would be doing this for multiple keywords and then analyzing the impact of, say, G+ Authority on rankings. Again, many of you might say that a ranking tool can already do this, and that’s true, but I wanted to cover the basics before we dive into the next one.

3. Extracting SlideShare data

With Slideshare’s recent growth in popularity it has become a document sharing tool of choice for many marketers. But what’s really on Slideshare, who are the influencers, what makes it tick? We can utilize a custom scraper to extract that kind data from Slideshare.

To get started, point your browser to Slideshare and pick a keyword to search for.

For our example I want to look at presentations that talk about PPC in English, sorted by popularity, so the URL would be:
http://www.slideshare.net/search/slideshow?ft=presentations&lang=en&page=1&q=ppc&qf=qf1&sort=views&ud=any

Once you are on that page, pick the Kimonify button as you did earlier and tag the elements. In this case I will tag:

  • Title
  • Description
  • Category
  • Author
  • Likes
  • Slides

Once you have tagged those, go ahead and add the pagination as described above.

That will make a nice rich dataset which should look like this:

Hit Done and you’re finished. In order to quickly highlight the benefits of this rich data, I am going to load the data into Spotfire to get some interesting statics (I hope).

4. Insights

Rather than do a step-by-step walktrough of how to build dashboards, which you can find here, I just want to show you some insights you can glean from this data:

  • Most Popular Authors by Category. This shows you the top contributors and the categories they are in for PPC (squares sized by Likes)

  • Correlations. Is there a correlation between the numbers of slides vs. the number of likes? Why not find out?

  • Category with the most PPC content. Discover where your content works best (most likes).

5. Output

One of the great things about Kimono we have not really covered is that it actually converts websites into APIs. That means you build them once, and each time you need the data you can call it up. As an example, if I call up the Slideshare API again tomorrow, the data will be different. So you basically appified Slisdeshare. The interesting part here is the flexibility that Kimono offers. If you go to the How to Use slide, you will see the way Kimono treats the Source URL In this case it looks like this:

The way you can pull data from Kimono aside from the export is their own API; in this case you call the default URL,
http://www.kimonolabs.com/api/YOURPAIID?apikey=YO…

You would get the default data from the original URL; however, as illustrated in the table above, you can dynamically adjust elements of the source URL.

For example, if you append “&q=SEO”
(http://www.kimonolabs.com/api/YOURPAIID?apikey=YOURAPIKEY&q=SEO)
you would get the top slides for SEO instead of PPC. You can change any of the URL options easily.

I know this was a lot of information, but believe me when I tell you, we just scratched the surface. Tools like Kimono offer a variety of advanced functions that really open up the possibilities. Once you start to realize the potential, you will come up with some amazing, innovative ideas. I would love to see some of them here shared in the comments. So get out there and start scraping … and please feel free to tweet at me or reply below with any questions or comments!

About AuthorCatalystSEM, Attributed articles are authored by a variety of Catalyst employees. Catalyst, a pioneer in search engine marketing since 1998, is renowned for developing strategic digital marketing solutions for Fortune 500 brands. The firm is the search agency of record for many of the world’s top companies. Headquartered in Boston with offices in New York, Chicago, Seattle, Toronto, and Montreal, Catalyst serves numerous B2B and B2C clients in a variety of industries. The search engine marketing firm prides itself on being THE Search Innovator of the industry. Learn more about Catalyst at www.catalystsearchmarketing.com

How to Win Big in International SEO

First Steps on International SEO

Up until a few weeks ago, my first piece of advice to anyone looking to expand their site to an international audience would have been to just get your keywords right. In English, this is a no-brainer, and not that difficult. In other languages, keyword research and targeting can be quite difficult. Choosing correct keywords is even more important in non-English search simply because Google’s algorithm is not as robust, and you won’t benefit from synonym-matching and spelling correction.

It doesn’t do you any good just to grab a primary keyword from Google Translate or the Google Keyword Planner. If the suggested word isn’t one that a native speaker would naturally use, you will be unlikely to see the international search traffic you are expecting, and the users that would find the site wouldn’t think your content is very high quality.

Don’t Assume

My tune has changed. Now I would suggest that the most important thing for anyone looking to do international SEO to know is: Don’t make assumptions. There are multiple examples of companies that have gotten themselves into some pretty big PR disasters by using faulty assumptions in their global expansion. For example, KFC famously launched in China with a marketing slogan that meant, “Eat your fingers” in Chinese. In hindsight, it seems that some very basic research would have informed KFC that they were making a big mistake with their messaging.

It’s not that I didn’t think avoiding assumptions wasn’t important; it’s only that I thought faulty assumptions would be invalidated by keyword research. What changed for me were the surprising results from a survey I ran during the Mozinar I conducted on international keyword research.

I really enjoyed preparing for the Mozinar as it gave me a chance to step back from my daily international SEO practices and chronicle them for outsiders. Using my typical keyword research processes, I found what I thought to be interesting examples for the makeup of the audience I thought would be in attendance. However, in my preparation, I made a huge blunder that I realized would never have been corrected by just keyword research. Had this been an actual marketing campaign, my entire message would have fallen flat even if my keywords were solid.

I assumed that I would be speaking to primarily a US-based audience who was interested in learning more about marketing to non-English and non-US users.

In fact, as the results from the survey I ran during the Mozinar showed, this was probably not the case. More than half (55%) of the respondents were not based in the US. They weren’t just close-by in Canada and Mexico, but they were from as far away as Spain, India, Bulgaria, and Kosovo. In a follow-up question that asked respondents which countries they targeted with international SEO, some even answered the US alongside the other popular countries of Canada, France, and Italy.

Survey results

My reasoning for assuming that the attendees would primarily be in the US was actually quite logical. Mozinars are held at a time that are quite convenient for anyone in the Alaskan through Eastern time zones but fall outside of working hours for almost everyone else. Additionally, Moz content is only available in English, which I thought would be fairly limiting to online marketers who live in non-English speaking countries.

Broad Assumptions Kill Campaigns

Yet, even with my solid logic, it seems that my assumption was dead wrong. Only 20% of the Mozinar attendees filled out my survey, and while this was not a large enough of a sample size to consider the results to be truly representative, the responses were convincing enough for me to toss my original assumptions and alter my advice for anyone looking to expand to a global audience since a faulty assumption can have far reaching impacts on any marketing effort. 

A classic assumption you would make in targeting a global audience is to believe that because something works domestically it will also work globally. This could not be further from the truth. A stock image that you use on your US targeted webpage could be considered vastly over (or under) dressed in other cultures. People not familiar with US politics will likely misunderstand a reference to Red or Blue states.

Conservative Assumptions Make Marketing Boring

On the flipside, marketers might be overly cautious about what other cultures might understand and avoid using references that they could have used.

I am sure any marketer is well aware that a US holiday such as Thanksgiving isn’t going to make a lot of sense to a non-US audience and would avoid using it in marketing copy. As a result, you might also assume that the reference to the day after Thanksgiving of Black Friday wouldn’t make sense to someone outside the US. This would be an incorrect assumption. Black Friday has been successfully exported around the globe and last year there were even stores in the UK that had Black Friday riots.

Don’t Stereotype

Along the same lines, you also want to avoid stereotyping cultures and languages. There are no countries called LatAm, Europe, and APAC. These names might be convenient buckets for allocating marketing dollars, but by no means will the same marketing message work across an entire region. Aside from the differing languages, a user in the UK has very different characteristics from someone in Germany. There are even significant differences between a user in Mexico and a user in Colombia both in the kinds of keywords they use, and in the types of messaging that they will respond to.Use

Data When It’s Available

As anyone who has been working in online marketing for a while knows, there isn’t always data to prove or disprove every decision that has to be made, and many times it will make sense to implement and then only analyze after the fact. In this reality, assumptions certainly have their place, but you should certainly try to validate global assumptions first.

Had I had a way to survey potential Mozinar attendees before I began gathering my material, I would have had a better idea of who exactly the target audience would be and tailored my content appropriately.

The other responses to my Mozinar survey were also interesting but not nearly as shocking as the discovery that potentially half of the attendees were outside the US.

  • There was an even split between in-house and outside marketers. A majority of marketers in the US worked in-house while it was the opposite for those outside the US.
  • Company sizes ranged from one employee all the way up to 900 employees. The majority of respondents were at companies with more than 10 employees.
  • Respondents were asked to rank marketing activities in the order that they prioritize the time, and this is the list in weighted order:
  1. Content creation
  2. Analytics
  3. Keyword research
  4. Brand strategy
  5. Link building
  6. Content curation
  7. Public relations
  8. Reputation management
  • The majority of attendees had been in the field of online marketing for 3 years or less, and of those who answered that they currently focus on international SEO, 45% had been doing it less than 1 year.

The most common reason shared in the survey for not focusing on international SEO is employee resources. This is quite understandable, as it is more than a full-time job to focus on SEO just for a domestic market; adding additional markets and languages can make the SEO job infinitely harder.

International SEO is a MUST

If you didn’t already know it, the potential customers for your online business or website includes every person in the entire world. While you might think of your competitors as the handful of companies in your local market that offer similar products and services to you, your most formidable competitor might actually be thousands of miles–or even a continent–away. In today’s globalized state of search, a US-based web company is just as likely to lose market share to a startup in Sao Paolo or Moscow, as it is to lose to one in New York or Chicago.

Competing on a global scale means that it becomes a race for who can internationalize first and start grabbing market share. Even companies like Amazon that have a dominant share of the ecommerce market are in a race to open up their site to new countries and languages before they find themselves too distracted by foreign competitors on their home turf.

For example, Japan’s largest ecommerce company Rakuten has already gained a foothold in the US ecommerce market with their acquisition of Buy.com, and China’s Alibaba, arguably the world largest ecommerce company, is about to be flush with cash from a huge IPO that could be used for marketing campaigns.

So, jump into your analytics package and see how many visitors you have coming from countries and languages you have not targeted. What you will find is that you are most likely receiving visitors from the entire world, and these visitors are probably a lot less engaged than visitors from your focus markets. Since you’re receiving this traffic already, why not try a little bit harder to target international customers with just a little bit of SEO effort?

”              

Google Analytics

Basic International SEO is Not that Hard

Expanding your SEO efforts globally does not have to be prohibitively expensive or technically difficult. For example, you can make small changes as simple as explaining your primary product offering in another language can help. Say your site sells books written in English about Blue Widgets. If your entire site content is in English, your only non-English search traffic will be from users who conduct a search in English. However, if you translated your marketing content into Spanish, you can now draw in users who conduct their searches in Spanish. These users will still have to buy a book in English, but they will at least know that the book exists if they want it.

With just a few pages written in another language, your site can take a significant step towards acquiring a global audience. If you really want to take your global SEO efforts further, there is a lot more you are going to need to do, but just having new content is a great start.

There really are baby steps that can be taken towards international SEO without getting in over your head. You can have just a handful of your marketing pages translated into languages where you are already seeing visitors. There will be a bit of work that has to go into translating and optimizing for a new language, but you should see a significant return on your investment. Just remember: If you are going to try to internationalize you site and product, do your research and don’t make assumptions. 

Help with (Not Provided): New Landing Pages Report in Moz Analytics

There’s been a lot of talk about what (not provided) means for SEO practitioners, with Mozzers weighing in on broader implications and alternative strategies.

When it comes down to it, we’re stuck with a frustrating predicament that’s forcing us to change our process. Activities like keyword gap analysis, editorial brainstorming, basic rank tracking, and  even SEM planning have become harder to execute.

More fundamentally, we’re having a tough time answering simple questions like:

“Which keywords are sending traffic to my site(s)?”

“Which keyword opportunities am I not leveraging? Where are the gaps?”

So where do we go from here? Many of you have already changed your reporting strategies to reflect the new SEO reality. Focusing on topics, landing pages, and broader content strategies is a solid pivot, and we’ll be working hard at Moz to support those efforts.

But we also recognize that keyword-level data remains an important signal for many SEOs.

With that in mind, we’re happy to announce a new report in Moz Analytics that will help answer some of those (not provided) questions.

Quick Note: The new feature is only available to Moz Analytics campaigns connected to Google Analytics. If you’re using the old version of our software (the SEOmoz PRO app) and want to take advantage, be sure to switch your campaigns over to Moz Analytics and connect GA.

The Landing Pages report

TL;DR We’ve grouped your tracked keywords by landing page and correlated them with a new metric, Estimated Traffic Share. Use the report to determine which keywords are your strongest traffic drivers.

In the new data view, your tracked keywords are grouped by landing page and correlated with both ranking position and visits.

The Estimated Traffic Share metric is our best guess at the percentage of visits each keyword contributes. The value is based on a combination of landing page traffic, keyword ranking position, estimated search volume, and SERP click-through-rates.

Let’s look at a quick example from Rand’s blog:

We know that Rand’s evergreen post about stock options at startups received 170 organic visits. We also know it ranks decently for a couple of relevant phrases including “startup stock options.”

Based on ranking position and search volume, however, it’s a safe bet we’re missing at least a few of the most important keyword targets. A peek at the Opportunities tab confirms our assumption:

Tracking phrases like “understanding stock starting a company” provides additional insight into the post’s organic footprint and gives back some of the basic data (not provided) took away.

Sometimes you’ll see the opposite: Estimated Traffic Shares that sum to a big chunk of the landing page total.

In those situations you can make an educated guess that the primary traffic drivers are being tracked.

Quick Note: This is obvious but worth stating: in order to get the most out of the new report you need to add tracked keywords to your Moz Analytics campaigns. Not only that, you’ll probably want to add a healthy selection of terms to gain the most insight. For inspiration, take a peek at the Opportunities tab.

Back to the big picture

SEOs know how to adapt. The increase in (not provided) isn’t the first time we’ve lost a valuable data source, and it’s probably not the last. With the Landing Pages report and other Moz Analytics updates, we’ll do our best to address the changing search landscape. 

Have a look at the latest release and share your thoughts. As always, if you have any insights or feedback feel free to shoot a message to our extra-helpful Help Team (help (at) moz.com) or sound off in the comments.

Your Google Algorithm Cheat Sheet: Panda, Penguin, and Hummingbird

If you’re reading the Moz blog, then you probably have a decent understanding of Google and its algorithm changes. However, there is probably a good percentage of the Moz audience that is still confused about the effects that Panda, Penguin, and Hummingbird can have on your site. I did write a post last year about the main  differences between Penguin and a Manual Unnautral Links Penalty, and if you haven’t read that, it’ll give you a good primer.

The point of this article is to explain very simply what each of these algorithms are meant to do. It is hopefully a good reference that you can point your clients to if you want to explain an algorithm change and not overwhelm them with technical details about 301s, canonicals, crawl errors, and other confusing SEO terminologies.

What is an algorithm change?

First of all, let’s start by discussing the Google algorithm. It’s immensely complicated and continues to get more complicated as Google tries its best to provide searchers with the information that they need. When search engines were first created, early search marketers were able to easily find ways to make the search engine think that their client’s site was the one that should rank well. In some cases it was as simple as putting in some code on the website called a meta keywords tag. The meta keywords tag would tell search engines what the page was about.

As Google evolved, its engineers, who were primarily focused on making the search engine results as relevant to users as possible, continued to work on ways to stop people from cheating, and looked at other ways to show the most relevant pages at the top of their searches. The algorithm now looks at hundreds of different factors. There are some that we know are significant such as having a good descriptive title (between the <title></title> tags in the code.) And there are many that are the subject of speculation such as  whether or not Google +1’s contribute to a site’s rankings.

In the past, the Google algorithm would change very infrequently. If your site was sitting at #1 for a certain keyword, it was guaranteed to stay there until the next update which might not happen for weeks or months. Then, they would push out another update and things would change. They would stay that way until the next update happened. If you’re interested in reading about how Google used to push updates out of its index, you may find this  Webmaster World forum thread from 2002 interesting. (Many thanks to Paul Macnamara  for explaining to me how algo changes used to work on Google in the past and pointing me to the Webmaster World thread.)

This all changed with launch of “Caffeine” in 2010. Since Caffeine launched, the search engine results have been changing several times a day rather than every few weeks. Google makes over 600 changes to its algorithm in a year, and the vast majority of these are not announced. But, when Google makes a really big change, they give it a name, usually make an announcement, and everyone in the SEO world goes crazy trying to figure out how to understand the changes and use them to their advantage.

Three of the biggest changes that have happened in the last few years are the Panda algorithm, the Penguin algorithm and Hummingbird.

What is the Panda algorithm?

Panda first launched on February 23, 2011. It was a big deal. The purpose of Panda was to try to show high-quality sites higher in search results and demote sites that may be of lower quality. This algorithm change was unnamed when it first came out, and many of us called it the “Farmer” update as it seemed to affect content farms. (Content farms are sites that aggregate information from many sources, often stealing that information from other sites, in order to create large numbers of pages with the sole purpose of ranking well in Google for many different keywords.) However, it affected a very large number of sites. The algorithm change was eventually officially named after one of its creators, Navneet Panda.

When Panda first happened, a lot of SEOs in forums thought that this algorithm was targeting sites with unnatural backlink patterns. However, it turns out that links are most likely not a part of the Panda algorithm. It is all about on-site quality.

In most cases, sites that were affected by Panda were hit quite hard. But, I have also seen sites that have taken a slight loss on the date of a Panda update. Panda tends to be a site-wide issue which means that it doesn’t just demote certain pages of your site in the search engine results, but instead, Google considers the entire site to be of lower quality. In some cases though Panda can affect just a section of a site such as a news blog or one particular subdomain.

Whenever a Google employee is asked about what needs to be done to recover from Panda, they refer to a  blog post by Google Employee Amit Singhal that gives a checklist that you can use on your site to determine if your site really is high quality or not. Here is the list:

  • Would you trust the information presented in this article?
  • Is this article written by an expert or enthusiast who knows the topic well, or is it more shallow in nature?
  • Does the site have duplicate, overlapping, or redundant articles on the same or similar topics with slightly different keyword variations?
  • Would you be comfortable giving your credit card information to this site?
  • Does this article have spelling, stylistic, or factual errors?
  • Are the topics driven by genuine interests of readers of the site, or does the site generate content by attempting to guess what might rank well in search engines?
  • Does the article provide original content or information, original reporting, original research, or original analysis?
  • Does the page provide substantial value when compared to other pages in search results?
  • How much quality control is done on content?
  • Does the article describe both sides of a story?
  • Is the site a recognized authority on its topic?
  • Is the content mass-produced by or outsourced to a large number of creators, or spread across a large network of sites, so that individual pages or sites don’t get as much attention or care?
  • Was the article edited well, or does it appear sloppy or hastily produced?
  • For a health related query, would you trust information from this site?
  • Would you recognize this site as an authoritative source when mentioned by name?
  • Does this article provide a complete or comprehensive description of the topic?
  • Does this article contain insightful analysis or interesting information that is beyond obvious?
  • Is this the sort of page you’d want to bookmark, share with a friend, or recommend?
  • Does this article have an excessive amount of ads that distract from or interfere with the main content?
  • Would you expect to see this article in a printed magazine, encyclopedia or book?
  • Are the articles short, unsubstantial, or otherwise lacking in helpful specifics?
  • Are the pages produced with great care and attention to detail vs. less attention to detail?
  • Would users complain when they see pages from this site?

Phew! That list is pretty overwhelming! These questions do not necessarily mean that Google tries to algorithmically figure out whether your articles are interesting or whether you have told both sides of a story. Rather, the questions are there because all of these factors can contribute to how real-life users would rate the quality of your site. No one really knows all of the factors that Google uses in determining the quality of your site through the eyes of Panda. Ultimately though, the focus is on creating the best site possible for your users.  It is also important that only your best stuff is given to Google to have in its index. There are a few factors that are widely accepted as important things to look at in regards to Panda:

Thin content

A “thin” page is a page that adds little or no value to someone who is reading it. It doesn’t necessarily mean that a page has to be a certain number of words, but quite often, pages with very few words are not super-helpful. If you have a large number of pages on your site that contain just one or two sentences and those pages are all included in the Google index, then the Panda algorithm may determine that the majority of your indexed pages are of low quality.

Having the odd thin page is not going to cause you to run in to Panda problems. But, if a big enough portion of your site contains pages that are not helpful to users, then that is not good.

Duplicate content

There are several ways that duplicate content can cause your site to be viewed as a low-quality site by the Panda algorithm. The first is when a site has a large amount of content that is copied from other sources on the web. Let’s say that you have a blog on your site and you populate that blog with articles that are taken from other sources. Google is pretty good at figuring out that you are not the creator of this content. If the algorithm can see that a large portion of your site is made up of content that exists on other sites then this can cause Panda to look at you unfavorably.

You can also run into problems with duplicated content on your own site. One example would be for a site that has a large number of products for sale. Perhaps each product has a separate page for each color variation and size. But, all of these pages are essentially the same. If one product comes in 20 different colors and each of those come in 6 different sizes, then that means that you have 120 pages for the same product, all of which are almost identical. Now, imagine that you sell 4,000 products. This means that you’ve got almost half a million pages in the Google index when really 4,000 pages would suffice. In this type of situation, the fix for this problem is to use something called a canonical tag. Moz has got a really good guide on using canonical tags  here, and Dr. Pete has also written this great article on canonical tag use

Low-quality content

When I write an article and publish it on one of my websites, the only type of information that I want to present to Google is information that is the absolute best of its kind. In the past, many SEOs have given advice to site owners saying that it was important to blog every day and make sure that you are always adding content for Google to index. But, if what you are producing is not high quality content, then you could be doing more harm than good. A lot of Amit Singhal’s questions listed above are asking whether the content on your site is valuable to readers. Let’s say that I have an SEO blog and every day I take a short blurb from each of the interesting SEO articles that I have read online and publish it as a blog post on my site. Is Google going to want to show searchers my summary of these articles, or would they rather show them the actual articles? Of course my summary is not going to be as valuable as the real thing! Now, let’s say that I have done this every day for 4 years. Now my site has over 4,000 pages that contain information that is not unique and not as valuable as other sites on the same topics.

Here is another example. Let’s say that I am a plumber. I’ve been told that I should blog regularly, so several times a week I write a 2-3 paragraph article on things like, “How to fix a leaky faucet” or “How to unclog a toilet.” But, I’m busy and don’t have much time to put into my website so each article I’ve written contains keywords in the title and a few times in the content, but the content is not in depth and is not that helpful to readers. If the majority of the pages on my site contain information that no one is engaging with, then this can be a sign of low quality in the eyes of the Panda algorithm.

There are other factors that probably play a roll in the Panda algorithm.  Glenn Gabe recently wrote an  excellent article on his evaluation of sites affected by the most recent Panda update.  His bullet point list of things to improve upon when affected by Panda is extremely thorough.

How to recover from a Panda hit

Google refreshes the Panda algorithm approximately monthly. They used to announce whenever they were refreshing the algorithm, but now they only do this if there is a really big change to the Panda algorithm. What happens when the Panda algorithm refreshes is that Google takes a new look at each site on the web and determines whether or not it looks like a quality site in regards to the criteria that the Panda algorithm looks at. If your site was adversely affected by Panda and you have made changes such as removing thin and duplicate content then, when Panda refreshes, you should see that things improve. However, for some sites it can take a couple of Panda refreshes to see the full extent of the improvements. This is because it can sometimes take several months for Google to revisit all of your pages and recognize the changes that you have made.

Every now and then, instead of just refreshing the algorithm, Google does what they call an update. When an update happens, this means that Google has changed the criteria that they use to determine what is and isn’t considered high quality. On May 20, 2014, Google did a major update which they called Panda 4.0. This caused a lot of sites to see significant changes in regards to Panda:

Not all Panda recoveries are as dramatic as this one. But, if you have been affected by Panda and you work hard to make changes to your site, you really should see some improvement.

What is the Penguin algorithm?

Penguin

The Penguin algorithm initially rolled out on April 24, 2012. The goal of Penguin is to reduce the trust that Google has in sites that have cheated by creating unnatural backlinks in order to gain an advantage in the Google results. While the primary focus of Penguin is on unnatural links, there can be other  factors that can affect a site in the eyes of Penguin as well. Links, though, are known to be by far the most important thing to look at.

Why are links important?

A link is like a vote for your site. If a well respected site links to your site, then this is a recommendation for your site. If a small, unknown site links to you then this vote is not going to count for as much as a vote from an authoritative site. Still, if you can get a large number of these small votes, they really can make a difference. This is why, in the past, SEOs would try to get as many links as they could from any possible source.

Another thing that is important in the Google algorithms is anchor text. Anchor text is the text that is underlined in a link. So, in this link to a great  SEO blog, the anchor text would be “SEO blog.” If Moz.com gets a number of sites linking to them using the anchor text “SEO blog,” that is a hint to Google that people searching for “SEO blog” probably want to see sites like Moz in their search results.

It’s not hard to see how people could manipulate this part of the algorithm. Let’s say that I am doing SEO for a landscaping company in Orlando. In the past, one of the ways that I could cheat the algorithm into thinking that my company should be ranked highly would be to create a bunch of self made links and use anchor text in these links that contain phrases like Orlando Landscaping Company, Landscapers in Orlando and Orlando Landscaping. While an authoritative link from a well respected site is good, what people discovered is that creating a large number of links from low quality sites was quite effective. As such, what SEOs would do is create links from easy to get places like directory listings, self made articles, and links in comments and forum posts.

While we don’t know exactly what factors the Penguin algorithm looks at, what we do know is that this type of low quality, self made link is what the algorithm is trying to detect. In my mind, the Penguin algorithm is sort of like Google putting a “trust factor” on your links. I used to tell people that Penguin could affect a site on a page or even a keyword level, but Google employee John Mueller has said several times now that Penguin is a sitewide algorithm. This means that if the Penguin algorithm determines that a large number of the links to your site are untrustworthy, then this reduces Google’s trust in your entire site. As such, the whole site will see a reduction in rankings.  

While Penguin affected a lot of sites drastically, I have seen many sites that saw a small reduction in rankings.  The difference, of course, depends on the amount of link manipulation that has been done.

How to recover from a Penguin hit?

Penguin is a filter just like Panda. What that means, is that the algorithm is re-run periodically and sites are re-evaluated with each re-run. At this point it is not run very often at all. The last update was October 4, 2013 which means that we have currently been waiting eight months for a new Penguin update. In order to recover from Penguin, you need to identify the unnatural links pointing to your site and either remove them, or if you can’t remove them you can ask Google to no longer count them by using the  disavow tool. Then, the next time that Penguin refreshes or updates, if you have done a good enough job at cleaning up your unnatural links, you will once again regain trust in Google’s eyes.  In some cases, it can take a couple of refreshes in order for a site to completely escape Penguin because it can take up to 6 months for all of a site’s disavow file to be completely processed.

If you are not certain how to identify which links to your site are unnatural, here are some good resources for you:

The disavow tool is something that you probably should only be using if you really understand how it works. It is potentially possible for you to do more harm than good to your site if you disavow the wrong links. Here is some information on using the disavow tool:

It’s important to note that when sites “recover” from Penguin, they often don’t skyrocket up to top rankings once again as those previously high rankings were probably based on the power of links that are now considered unnatural. Here is some information on  what to expect when you have recovered from a link based penalty or algorithmic issue.

Also, the Penguin algorithm is not the same thing as a manual unnatural links penalty. You do not need to file a reconsideration request to recover from Penguin. You also do not need to document the work that you have done in order to get links removed as no Google employee will be manually reviewing your work. As mentioned previously, here is more information on the  difference between the Penguin algorithm and a manual unnatural links penalty.

What is Hummingbird?

Hummingbird is a completely different animal than Penguin or Panda. (Yeah, I know…that was a bad pun.) I will commonly get people emailing me telling me that Hummingbird destroyed their rankings. I would say that in almost every case that I have evalutated, this was not true. Google made their announcement about Hummingbird on September 26, 2013. However, at that time, they announced that Hummingbird had already been live for about a month. If the Hummingbird algorithm was truly responsible for catastrophic ranking fluctuations then we really should have seen an outcry from the SEO world of something drastic happening in August of 2013, and this did not happen. There did seem to be some type of fluctuation that happened around August 21 as reported here on Search Engine Round Table, but there were not many sites that reported huge ranking changes on that day.

If you think that Hummingbird affected you, it’s not a bad idea to look at your traffic to see if you noticed a drop on October 4, 2013 which was actually a refresh of the Penguin algorithm. I believe that a lot of people who thought that they were affected by Hummingbird were actually affected by Penguin which happened just a week after Google made their announcement about Hummingbird.

There are some excellent articles on Hummingbird here and here. Hummingbird was a complete overhaul of the entire Google algorithm. As Danny Sullivan put it, if you consider the Google algorithm as an engine, Panda and Penguin are algorithm changes that were like putting a new part in the engine such as a filter or a fuel pump. But, Hummingbird wasn’t just a new part; it was a completely new engine. That new engine still makes use of many of the old parts (such as Panda and Penguin) but a good amount of the engine is completely original.

The goal of the Hummingbird algorithm is for Google to better understand a user’s query. Bill Slawski who writes about Google patents has a great example of this in his post here. He explains that when someone searches for “What is the best place to find and eat Chicago deep dish style pizza?”, Hummingbird is able to discern that by “place” the user likely would be interested in results that show “restaurants”. There is speculation that these changes were necessary in order for Google’s voice search to be more effective. When we’re typing a search query, we might type, “best Seattle SEO company” but when we’re speaking a query (i.e. via Google Glass or via Google Now) we’re more likely to say something like, “Which firm in Seattle offers the best SEO services?” The point of Hummingbird is to better understand what users mean when they have queries like this.

So how do I recover or improve in the eyes of Hummingbird?

If you read the posts referenced above, the answer to this question is essentially to create content that answers users queries rather than just trying to rank for a particular keyword. But really, this is what you should already be doing!

It appears that Google’s goal with all of these algorithm changes (Panda, Penguin and Hummingbird) is to encourage webmasters to publish content that is the best of its kind. Google’s goal is to deliver answers to people who are searching. If you can produce content that answers people’s questions, then you’re on the right track.

I know that that is a really vague answer when it comes to “recovering” from Hummingbird. Hummingbird really is different than Panda and Penguin. When a site has been demoted by the Panda or Penguin algorithm, it’s because Google has lost some trust in the site’s quality, whether it is on-site quality or the legitimacy of its backlinks. If you fix those quality issues you can regain the algorithm’s trust and subsequently see improvements. But, if your site seems to be doing poorly since the launch of Hummingbird, then there really isn’t a way to recover those keyword rankings that you once held. You can, however, get new traffic by finding ways to be more thorough and complete in what your website offers.

Do you have more questions?

My goal in writing this article was to have a resource to point people to when they had basic questions about Panda, Penguin and Hummingbird. Recently, when I published my penalty newsletter, I had a small business owner comment that it was very interesting but that most of it went over their head. I realized that many people outside of the SEO world are greatly affected by these algorithm changes, but don’t have much information on why they have affected their website.

Do you have more questions about Panda, Penguin or Hummingbird? If so, I’d be happy to address them in the comments. I also would love for those of you who are experienced with dealing with websites affected by these issues to comment as well.

About Author — Marie Haynes is the author of Unnatural Links – The Complete Guide to Recovery. She is the founder of HIS Web Marketing, a company that primarily focuses on helping businesses with Google penalties. You can find Marie on Twitter where she tweets tips about unnatural links problems at @Marie_Haynes and you can sign up for her newsletter on Google penalties and algorithm changes here.