Simple Steps for Conducting Creative Content Research

Posted by Hannah_Smith

Most frequently, the content we create at Distilled is designed to attract press coverage, social shares, and exposure (and links) on sites our clients’ target audience reads. That’s a tall order.

Over the years we’ve had our hits and misses, and through this we’ve recognised the value of learning about what makes a piece of content successful. Coming up with a great idea is difficult, and it can be tough to figure out where to begin. Today, rather than leaping headlong into brainstorming sessions, we start with creative content research.

What is creative content research?

Creative content research enables you to answer the questions:

“What are websites publishing, and what are people sharing?”

From this, you’ll then have a clearer view on what might be successful for your client.

A few years ago this required quite an amount of work to figure out. Today, happily, it’s much quicker and easier. In this post I’ll share the process and tools we use.

Whoa there… Why do I need to do this?

I think that the value in this sort of activity lies in a couple of directions:

a) You can learn a lot by deconstructing the success of others…

I’ve been taking stuff apart to try to figure out how it works for about as long as I can remember, so applying this process to content research felt pretty natural to me. Perhaps more importantly though, I think that deconstructing content is actually easier when it isn’t your own. You’re not involved, invested, or in love with the piece so viewing it objectively and learning from it is much easier.

b) Your research will give you a clear overview of the competitive landscape…

As soon as a company elects to start creating content, they gain a whole raft of new competitors. In addition to their commercial competitors (i.e. those who offer similar products or services), the company also gains content competitors. For example, if you’re a sports betting company and plan to create content related to the sports events that you’re offering betting markets on; then you’re competing not just with other betting companies, but every other publisher who creates content about these events. That means major news outlets, sports news site, fan sites, etc. To make matters even more complicated, it’s likely that you’ll actually be seeking coverage from those same content competitors. As such, you need to understand what’s already being created in the space before creating content of your own.

c) You’re giving yourself the data to create a more compelling pitch…

At some point you’re going to need to pitch your ideas to your client (or your boss if you’re working in-house). At Distilled, we’ve found that getting ideas signed off can be really tough. Ultimately, a great idea is worthless if we can’t persuade our client to give us the green light. This research can be used to make a more compelling case to your client and get those ideas signed off. (Incidentally, if getting ideas signed off is proving to be an issue you might find this framework for pitching creative ideas useful).

Where to start

Good ideas start with a good brief, however it can be tough to pin clients down to get answers to a long list of questions.

As a minimum you’ll need to know the following:

  • Who are they looking to target?
    • Age, sex, demographic
    • What’s their core focus? What do they care about? What problems are they looking to solve?
    • Who influences them?
    • What else are they interested in?
    • Where do they shop and which brands do they buy?
    • What do they read?
    • What do they watch on TV?
    • Where do they spend their time online?
  • Where do they want to get coverage?
    • We typically ask our clients to give us a wishlist of 10 or so sites they’d love to get coverage on
  • Which topics are they comfortable covering?
    • This question is often the toughest, particularly if a client hasn’t created content specifically for links and shares before. Often clients are uncomfortable about drifting too far away from their core business—for example, if they sell insurance, they’ll typically say that they really want to create a piece of content about insurance. Whilst this is understandable from the clients’ perspective it can severely limit their chances of success. It’s definitely worth offering up a gentle challenge at this stage—I’ll often cite Red Bull, who are a great example of a company who create content based on what their consumers love, not what they sell (i.e. Red Bull sell soft drinks, but create content about extreme sports because that’s the sort of content their audience love to consume). It’s worth planting this idea early, but don’t get dragged into a fierce debate at this stage—you’ll be able to make a far more compelling argument once you’ve done your research and are pitching concrete ideas.

Processes, useful tools and sites

Now you have your brief, it’s time to begin your research.

Given that we’re looking to uncover “what websites are publishing and what’s being shared,” It won’t surprise you to learn that I pay particular attention to pieces of content and the coverage they receive. For each piece that I think is interesting I’ll note down the following:

  • The title/headline
  • A link to the coverage (and to the original piece if applicable)
  • How many social shares the coverage earned (and the original piece earned)
  • The number of linking root domains the original piece earned
  • Some notes about the piece itself: why it’s interesting, why I think it got shares/coverage
  • Any gaps in the content, whether or not it’s been executed well
  • How we might do something similar (if applicable)

Whilst I’m doing this I’ll also make a note of specific sites I see being frequently shared (I tend to check these out separately later on), any interesting bits of research (particularly if I think there might be an opportunity to do something different with the data), interesting threads on forums etc.

When it comes to kicking off your research, you can start wherever you like, but I’d recommend that you cover off each of the areas below:

What does your target audience share?

Whilst this activity might not uncover specific pieces of successful content, it’s a great way of getting a clearer understanding of your target audience, and getting a handle on the sites they read and the topics which interest them.

  • Review social profiles / feeds
    • If the company you’re working for has a Facebook page, it shouldn’t be too difficult to find some people who’ve liked the company page and have a public profile. It’s even easier on Twitter where most profiles are public. Whilst this won’t give you quantitative data, it does put a human face to your audience data and gives you a feel for what these people care about and share. In addition to uncovering specific pieces of content, this can also provide inspiration in terms of other sites you might want to investigate further and ideas for topics you might want to explore.
  • Demographics Pro
    • This service infers demographic data from your clients’ Twitter followers. I find it particularly useful if the client doesn’t know too much about their audience. In addition to demographic data, you get a breakdown of professions, interests, brand affiliations, and the other Twitter accounts they follow and who they’re most influenced by. This is a paid-for service, but there are pay-as-you-go options in addition to pay monthly plans.

Finding successful pieces of content on specific sites

If you’ve a list of sites you know your target audience read, and/or you know your client wants to get coverage on, there are a bunch of ways you can uncover interesting content:

  • Using your link research tool of choice (e.g. Open Site Explorer, Majestic, ahrefs) you can run a domain level report to see which pages have attracted the most links. This can also be useful if you want to check out commercial competitors to see which pieces of content they’ve created have attracted the most links.
  • There are also tools which enable you to uncover the most shared content on individual sites. You can use Buzzsumo to run content analysis reports on individual domains which provide data on average social shares per post, social shares by network, and social shares by content type.
  • If you just want to see the most shared content for a given domain you can run a simple search on Buzzsumo using the domain; and there’s also the option to refine by topic. For example a search like [guardian.com big data] will return the most shared content on the Guardian related to big data. You can also run similar reports using ahrefs’ Content Explorer tool.

Both Buzzsumo and ahrefs are paid tools, but both offer free trials. If you need to explore the most shared content without using a paid tool, there are other alternatives. Check out Social Crawlytics which will crawl domains and return social share data, or alternatively, you can crawl a site (or section of a site) and then run the URLs through SharedCount‘s bulk upload feature.

Finding successful pieces of content by topic

When searching by topic, I find it best to begin with a broad search and then drill down into more specific areas. For example, if I had a client in the financial services space, I’d start out looking at a broad topic like “money” rather than shooting straight to topics like loans or credit cards.

As mentioned above, both Buzzsumo and ahrefs allow you to search for the most shared content by topic and both offer advanced search options.

Further inspiration

There are also several sites I like to look at for inspiration. Whilst these sites don’t give you a great steer on whether or not a particular piece of content was actually successful, with a little digging you can quickly find the original source and pull link and social share data:

  • Visually has a community area where users can upload creative content. You can search by topic to uncover examples.
  • TrendHunter have a searchable archive of creative ideas, they feature products, creative campaigns, marketing campaigns, advertising and more. It’s best to keep your searches broad if you’re looking at this site.
  • Check out Niice (a moodboard app) which also has a searchable archive of handpicked design inspiration.
  • Searching Pinterest can allow you to unearth some interesting bits and pieces as can Google image searches and regular Google searches around particular topics.
  • Reviewing relevant sections of discussion sites like Quora can provide insight into what people are asking about particular topics which may spark a creative idea.

Moving from data to insight

By this point you’ve (hopefully) got a long list of content examples. Whilst this is a great start, effectively what you’ve got here is just data, now you need to convert this to insight.

Remember, we’re trying to answer the questions: “What are websites publishing, and what are people sharing?”

Ordinarily as I go through the creative content research process, I start to see patterns or themes emerge. For example, across a variety of topics areas you’ll see that the most shared content tends to be news. Whilst this is good to know, it’s not necessarily something that’s going to be particularly actionable. You’ll need to dig a little deeper—what else (aside from news) is given coverage? Can you split those things into categories or themes?

This is tough to explain in the abstract, so let me give you an example. We’d identified a set of music sites (e.g. Rolling Stone, NME, CoS, Stereogum, Pitchfork) as target publishers for a client.

Here’s a summary of what I concluded following my research:

The most-shared content on these music publications is news: album launches, new singles, videos of performances etc. As such, if we can work a news hook into whatever we create, this could positively influence our chances of gaining coverage.

Aside from news, the content which gains traction tends to fall into one of the following categories:

Earlier in this post I mentioned that it can be particularly tough to create content which attracts coverage and shares if clients feel strongly that they want to do something directly related to their product or service. The example I gave at the outset was a client who sold insurance and was really keen to create something about insurance. You’re now in a great position to win an argument with data, as thanks to your research you’ll be able to cite several pieces of insurance-related content which have struggled to gain traction. But it’s not all bad news as you’ll also be able to cite other topics which are relevant to the client’s target audience and stand a better chance of gaining coverage and shares.

Avoiding the pitfalls

There are potential pitfalls when it comes to creative content research in that it’s easy to leap to erroneous conclusions. Here’s some things to watch out for:

Make sure you’re identifying outliers…

When seeking out successful pieces of content you need to be certain that what you’re looking at is actually an outlier. For example, the average post on BuzzFeed gets over 30k social shares. As such, that post you found with just 10k shares is not an outlier. It’s done significantly worse than average. It’s therefore not the best post to be holding up as a fabulous example of what to create to get shares.

Don’t get distracted by formats…

Pay more attention to the idea than the format. For example, the folks at Mashable, kindly covered an infographic about Instagram which we created for a client. However, the takeaway here is not that Instagram infographics get coverage on Mashable. Mashable didn’t cover this because we created an infographic. They covered the piece because it told a story in a compelling and unusual way.

You probably shouldn’t create a listicle…

This point is related to the point above. In my experience, unless you’re a publisher with a huge, engaged social following, that listicle of yours is unlikely to gain traction. Listicles on huge publisher sites get shares, listicles on client sites typically don’t. This is doubly important if you’re also seeking coverage, as listicles on clients sites don’t typically get links or coverage on other sites.

How we use the research to inform our ideation process

At Distilled, we typically take a creative brief and complete creative content research and then move into the ideation process. A summary of the research is included within the creative brief, and this, along with a copy of the full creative content research is shared with the team.

The research acts as inspiration and direction and is particularly useful in terms of identifying potential topics to explore but doesn’t mean team members don’t still do further research of their own.

This process by no means acts as a silver bullet, but it definitely helps us come up with ideas.


Thanks for sticking with me to the end!

I’d love to hear more about your creative content research processes and any tips you have for finding inspirational content. Do let me know via the comments.

Image credits: Research, typing, audience, inspiration, kitteh.

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How You Can Build a Meaningful Brand

Posted by Hannah_Smith

Earlier this year I wrote a post about
the future of marketing. In it, I made a handful of predictions; arguably the most ‘out there’ of which was this: in the future, only brands which ‘mean something’ to consumers will survive. 

In today’s post I’ll be exploring what it takes to become a meaningful brand, and how you might go about building one.

SEO is not dead

Just so we’re clear, I’m not saying SEO is dead, or that organic search is not an important channel 🙂
These stats speak for themselves:

However, what we’re being asked to do as SEOs is constantly evolving. 

It used to be that you could build a very successful business online just by being great at SEO. But today, the SERPs are changing, and ranking first doesn’t mean what it used to:

The BBC still rank first organically for ‘weather’—but their listing is pushed beneath the fold. Plus, given the that the information the searcher is seeking is displayed right there in the SERP, I’m guessing they’re not receiving as much traffic from this term as they once were.

But it’s not just informational queries:

Skyscanner still rank first for the term ‘flights to paris’, but again here their organic listing is pushed beneath the fold thanks to paid search listings and the proprietary Google flight product.

Google is even going so far as to show its proprietary products against branded searches (hat-tip to 
Barry Adams for pointing this out):

MoneySuperMarket’s organic listing is above the fold, but Google is nonetheless being very aggressive.

As a consequence of these changes, as SEOs, we’re being asked to do different things. Clients of yesteryear used to say things like:

Get us links!

But today they’re saying things like:

Get us press coverage, social shares and exposure [links] on sites our target audience reads.

Whilst they may not explicitly be asking us to build a brand, nonetheless much of what we do today looks a lot like brand building. But where do we start?

What does ‘brand’ mean?

Before we kick off I think it’s worth exploring what brand really means. We have a tendency to use ‘brand’ and ‘company’ or ‘organisation’ interchangeably, but in reality they are two distinctly different things.

Here’s a definition:

brand – to impress firmly; fix ineradicably; place indelibly

Therefore a brand is not a brand unless it leaves a lasting impression, and of course, it needs to be a favourable impression. Essentially companies or organisations need to build brands that mean something to people.

However, right now companies and organisations are struggling to do this effectively:

“In Europe and the US, consumers would not care if 92% of brands ceased to exist” 


source

That means that consumers would only miss 8% of brands. 

Clearly we have a mountain to climb. How do we go about building meaningful brands? Particularly on SEO retainer budgets?

You can learn a lot by deconstructing the success of others

Like many in the search industry, I’m a fan of taking stuff apart to figure out how it works. So, when trying to figure out how to go about building a meaningful brand, I started by looking at what meaningful brands are doing right now.

I uncovered three core principles—some meaningful brands do all three; some just do one or two—I’ll deal with them each in turn.

1) Meaningful brands find opportunities to delight customers

Most people’s interactions with brands suck. But great interactions stand out and are shared. Let’s take a look at some examples:

@smartcarusa

Here’s how @smartcarusa responded when someone suggested that a single bird dropping would total one of their cars:

Now the takeaway here is not to rush out and make a bunch of infographics on disparate topics.
Out of context, the infographic is neither remarkable, nor particularly interesting, and I don’t think it would have garnered coverage had it not been created in response to this tweet.
But I think a lesson we can take from this is that going the extra mile to respond in a novel way can yield out-sized returns.

@ArgosHelpers

This is how @ArgosHelpers responded to a customer asking when PS4s would be back in stock:

The takeaway here is not people love brands who use slang—I think this is actually a very artfully worded response. See how the brand has taken care to use the same language as their customer without being in any way condescending? That’s what you need to shoot for.

@TescoMobile

This is how @TescoMobile responded when someone described their network as a ‘turn off’:

Whoa! 

The lesson here is definitely not ‘be a dick to people who are dicks to you’; I think the lesson here is that a well-judged, cheeky response can travel.

Ultimately you need to tread carefully if you want to use this type of tactic. I think @TescoMobile got away with this one—but it is really close to the line. To do this sort of thing you need to have a deep understanding of your audience—what’s considered funny and what’s just plain rude? This can vary hugely depending on the niche you’re working in and the public perception of your brand.

Moreover, if you’re a brand engaging in this sort of activity, you need to consider not only your own response, but the potential response from your audience, too. Some brands have an army of loyal advocates. But if brands aren’t careful, they may unwittingly encourage said army to attack an individual with a response like this.

Of course it’s not just interactions that have the capacity to delight—sometimes being nimble is enough:

@Arbys

When Pharrell turned up to the GRAMMYs wearing *that hat* here’s how @Arbys responded:

The takeaway here is not that you need a bit of luck, instead it’s that you need to be ready, willing and able to take advantage of opportunities as and when they arise. I think that if @Arbys hadn’t tweeted that, then someone else would have done and their brand wouldn’t have benefited.

Hopefully you can see where I’m going with this; let’s move on to principle two:

2) Meaningful brands give people the ability to define themselves to others

Have you ever thought about why you share what you share on social media? Most of us don’t think about it too much, but
The New York Times did a
study on the psychology of sharing in which 68% of respondents said they share things via social media to give others a better sense of who they are and what they care about.

For example, I might share an article from
hbr.org because I want you to think I’m the sort of person who reads Harvard Business Review. Or I might share an Oatmeal comic because I want you to think I have an excellent sense of humour. I might share something about the Lean In movement because I want to let you know where I stand on important issues.

If you’re seeking to create a meaningful brand, this can be an excellent space to play in because brands can give people the ability to define themselves to others. Now I don’t necessarily mean by creating content like
this which literally allows people to define themselves:

Brands can also help people define themselves by creating things people ‘look good’ sharing—let’s take a look at some examples:

GE’s #6SecondScience

The takeaway here is to create things which are tangentially related to your brand, that people ‘look good’ sharing. When people shared this content they were sharing stuff that was more than just ‘cool’—by sharing this content they were also able to express their enthusiasm for science.

In a similar vein meaningful brands create commercials that don’t feel like commercials—again, these are things that people ‘look good’ sharing:

Wren’s First Kiss

This film definitely got people talking. To date it’s received over 94 million YouTube views and coverage on over 1300 sites. But this isn’t just a video content play…

Oreo

When Oreo turned 100, they created 100 pieces of content over 100 days:

This campaign got over 1m Facebook ‘likes’ and thousands of pieces of press coverage. 

But actually, I think the smartest thing about this campaign was that it was highly topical content which put the cookie right in the centre of people’s conversations without being self-serving.

Still with me? Let’s move on to principle three:

3) Meaningful brands stand for something above and beyond their products or services

This is difficult to explain in the abstract, so I’m going to shoot straight to some examples.

BrewDog

BrewDog is a craft beer company. Their brand values are drawn from punk subculture—they’re anti-establishment and believe in individual freedom.
So when Dead Pony Club ale was ‘banned’ because the phrase “rip it up down empty streets” was printed on the label, their response was to issue a
press release apologising for ‘not giving a shit’ over the marketing rules breach.
Their fans loved their response:

The takeaway here isn’t that sweary press releases get attention (although they undoubtedly do)—by refusing to take the ruling lying down BrewDog showed people they were a brand which stood for something beyond great beer.

Nike

A core value for Nike is “if you have a body, you are an athlete”, and these values have inspired incredible creative like this:

I think that this advert is powerful because Nike isn’t talking about how their trainers enhance your performance, they’re talking about celebrating everyone’s athletic endeavours. It’s about more than just their products. 

OKCupid

I think that taking the decision to stand for something is perhaps most potent when it could actually cost a brand customers. When Mozilla appointed a new CEO, OKCupid showed this message to Firefox users:

They went on to say:

The takeaway here is not ‘align your brand with a cause and win the the Internet’, but rather, taking a bold stance on a relevant issue, even if it could actually hurt your business, can create a lasting impression.

What do I mean by ‘actually hurt your business’? Sadly, not everyone believes in equal rights for gay couples, as such, taking this stance could cost OK Cupid.

Using these principles day-to-day

The reality for me is that right now, much of this I can’t affect—sadly no clients have dropped several million into my lap and asked me to create them an ad like Nike’s 🙂

That said, I do think that it’s helped me to clarify my thinking on what it means to be a meaningful brand and how to figure out how to get there. At Distilled (the company who is good enough to employ me), the place we play most frequently is principle two—we create content which allows people to define themselves to others; things that people ‘look good sharing’. 

Perhaps more importantly, we’re taking the time to understand the companies we’re working with better so that our creative work is better aligned with their brand values. 

And so, dear reader, over to you—I’d love to hear what you think it takes to build a meaningful brand, and what’s working (and not working) for you, do let me know via the comments.

This post is based on a session I presented at SearchLove; those who are interested can view the full deck below:

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Panda 4.1: The Devil Is in the Aggregate

Posted by russvirante

I wish I didn’t have to say this. I wish I could look in the eyes of every victim of the last Panda 4.1 update and tell them it was something new, something unforeseeable, something out of their control. I wish I could tell them that Google pulled a fast one that no one saw coming. But I can’t.

Like many in the industry, I have been studying Panda closely since its inception. Google gave us a rare glimpse behind the curtain by providing us with the very guidelines they set in place to build their massive machine-learned algorithm which came to be known as Panda. Three and a half years later, Panda is still with us and seems to still catch us off guard.
Enough is enough.

What I intend to show you throughout this piece is that the original Panda questionnaire still remains a powerful predictive tool to wield in defense of what can be a painful organic traffic loss. By analyzing the winner/loser reports of Panda 4.1 using standard Panda surveys, we can determine whether Google’s choices are still in line with their original vision. So let’s dive in.

The process

The first thing we need to do is acquire a winners and losers list. I picked this excellent
one from SearchMetrics although any list would do as long as it is accurate. Second, I proceeded to run a Panda questionnaire with 10 questions on random pages from each of the sites (both the winners and losers). You can run your own Panda survey by following Distilled and Moz’s instructions here or just use PandaRisk like I did. After completing these analyses, we simply compare the scores across the board to determine whether they continue to reflect what we would expect given the original goals of the Panda algorithm.

The aggregate results

I actually want to do this a little bit backwards to drive home a point. Normally we would build to the aggregate results, starting with the details and leaving you with the big picture. But Panda
is a big-picture kind of algorithmic update. It is specially focused on the intersection of myriad features, the sum is greater than the parts. While breaking down these features can give us some insight, at the end of the day we need to stay acutely aware that unless we do well across the board, we are at risk.

Below is a graph of the average cumulative scores across the winners and losers. The top row are winners, the bottom row are losers. The left and right red circles indicate the lowest and highest scores within those categories, and the blue circle represents the average. There is something very important that I want to point out on this graph.
The highest individual average score of all the losers is less than the lowest average score of the winners. This means that in our randomly selected data set, not a single loser averaged as high a score as the worst winner. When we aggregate the data together, even with a crude system of averages rather than the far more sophisticated machine learning techniques employed by Google, there is a clear disparity between the sites that survive Panda and those that do not.

It is also worth pointing out here that there is no
positive Panda algorithm to our knowledge. Sites that perform well on Panda do not see boosts because they are being given ranking preference by Google, rather their competitors have seen rankings loss or their own previous Panda penalties have been lifted. In either scenario, we should remember that performing well on Panda assessments isn’t going to necessarily increase your rankings, but it should help you sustain them.

Now, let’s move on to some of the individual questions. We are going to start with the least correlated questions and move to those which most strongly correlate with performance in Panda 4.1. While all of the questions had positive correlations, a few lacked statistical significance.


Insignificant correlation

The first question which was not statistically significant in its correlation with Panda performance was “This page has visible errors on it”. The scores have been inverted here so that the higher the score, the fewer the number of people who reported that the page has errors. You can see that while more respondents did say that the winners had no visible errors, the difference was very slight. In fact, there was only a 5.35% difference between the two. I will save comment on this until after we discuss the next question.

The second question which was not statistically significant in its correlation with Panda performance was “This page has too many ads”. The scores have once again been inverted here so that the higher the score, the fewer the number of people who reported that the page has too many ads. This was even closer. The winners performed only 2.3% better than the losers in Panda 4.1.

I think there is a clear takeaway from these two questions. Nearly everyone gets the easy stuff right, but that isn’t enough. First, a lot of pages just have no ads whatsoever because that isn’t their business model. Even those that do have ads have caught on for the most part and optimized their pages accordingly, especially given that Google has other layout algorithms in place aside from Panda. Moreover, content inaccuracy is more likely to impact scrapers and content spinners than most sites, so it is unsurprising that few if any reported that the pages were filled with errors. If you score poorly on either of these, you have only begun to scratch the surface, because most websites get these right enough.


Moderate correlation

A number of Panda questions drew statistically significant difference in means but there was still substantial crossover between the winners and losers.
Whenever the average of the losers was greater than the lowest of the winners, I considered it only a moderate correlation. While the difference between means remained strong, there was still a good deal of variance in the scores. 

The first of these to consider was the question as to whether the content was “trustworthy”. You will notice a trend in a lot of these questions that there is a great deal of subjective human opinion. This subjectivity plays itself out quite a bit when the topics of the site might deal with very different categories of knowledge. For example, a celebrity fact site might be very trustworthy (although the site might be ad-laden) and an opinion piece in the New Yorker on the same celebrity might not be seen as trustworthy – even though it is plainly labeled as opinion. The trustworthy question ties back to the “does this page have errors” question quite nicely, drawing attention to the difference between a subjective and objective question and the way it can spread the means out nicely when you ask a respondent to give more of a personal opinion. This might seem unfair, but in the real world your site and Google itself is being judged by that subjective opinion, so it is understandable why Google wants to get at it algorithmically. Nevertheless, there was a strong difference in means between winners and losers of 12.57%, more than double the difference we saw between winners and losers on the question of Errors.

Original content has long been a known requirement of organic search success, so no one was surprised when it made its way into the Panda questionnaire. It still remains an influential piece of the puzzle with a difference in mean of nearly 20%. It was barely ruled out from being a heavily correlated feature due to one loser edging out a loss against the losers’ average mean. Notice though that one of the winners scored a perfect 100% on the survey. This perfect score was received despite hundreds of respondents.
It can be done.

As you can imagine, perception on what is and is not an authority is very subjective. This question is powerful because it pulls in all kinds of assumptions and presuppositions about brand, subject matter, content quality, design, justification, citations, etc. This likely explains why this question is beleaguered by one of the highest variances on the survey. Nevertheless, there was a 13.42% difference in means. And, on the other side of the scale, we did see what it is like to have a site that is clearly not an authority, scoring the worst possible 0% on this question. This is what happens when you include highly irrelevant content on your site just for the purpose of picking up either links or traffic. Be wary.

Everyone hates the credit card question, and luckily there is huge variance in answers. At least one site survived Panda despite scoring 5% on this question. Notice that there is a huge overlap between the lowest winner and the average of the losing sites. Also, if you notice by the placement of the mean (blue circle) in the winners category, the average wasn’t skewed to the right indicating just one outlier. There was strong variance in the responses across the board. The same was true of the losers. However, with a +15% difference in means, there was a clear average differentiation between the performance of winners and losers. Once again, though, we are drawn back to that aggregate score at the top, where we see how Google can use all these questions together to build a much clearer picture of site and content quality. For example, it is possible that Google pays more attention to this question when it is analyzing a site that has other features like the words “shopping cart” or “check out” on the homepage. 

I must admit that the bookmarking question surprised me. I always considered it to be the most subjective of the bunch. It seemed unfair that a site might be judged because it has material that simply doesn’t appeal to the masses. The survey just didn’t bear this out though. There was a clear difference in means, but after comparing the sites that were from similar content categories, there just wasn’t any reason to believe that a bias was created by subject matter. The 14.64% difference seemed to be, editorially speaking, related more to the construction of the page and the quality of the content, not the topic being discussed. Perhaps a better way to think about this question is:
would you be embarrassed if your friends knew THIS was the site you were getting your information from rather than another.

This wraps up the 5 questions that had good correlations but substantial enough variance that it was possible for the highest loser to beat out the average winner. I think one clear takeaway from this section is that these questions, while harder to improve upon than the Low Ads and No Errors questions before, are completely within the webmaster’s grasp. Making your content and site appear original, trustworthy, authoritative, and worthy of bookmarking aren’t terribly difficult. Sure, it takes some time and effort, but these goals, unlike the next, don’t appear that far out of reach.


Heavy correlation

The final three questions that seemed to distinguish the most between the winners and losers of Panda 4.1 all had high difference-in-means and, more importantly, had little to no crossover between the highest loser and lowest winner. In my opinion, these questions are also the hardest for the webmaster to address. They require thoughtful design, high quality content, and real, expert human authors.

The first question that met this classification was “could this content could appear in print”. With a difference in mean of 22.62%, the winners thoroughly trounced the losers in this category. Their sites and content were just better designed and better written. They showed the kind of editorial oversight you would expect in a print publication. The content wasn’t trite and unimportant, it was thorough and timely. 

The next heavily correlated question was whether the page was written by experts. With over a 34% difference in means between the winners and losers, and
literally no overlap at all between the winners’ and losers’ individual averages, it was clearly the strongest question. You can see why Google would want to look into things like authorship when they knew that expertise was such a powerful distinguisher between Panda winners and losers. This really begs the question – who is writing your content and do your readers know it?

Finally, insightful analysis had a huge difference in means of +32% between winners and losers. It is worth noting that the highest loser is an outlier, which is typified by the skewed mean (blue circle) being closer to the bottom that the top. Most of the answers were closer to the lower score than the top. Thus, the overlap is exaggerated a bit. But once again, this just draws us back to the original conclusion – that the devil is not in the details, the devil is in the aggregate. You might be able to score highly on one or two of the questions, but it won’t be enough to carry you through.


The takeaways

OK, so hopefully it is clear that Panda really hasn’t changed all that much. The same questions we looked at for Panda 1.0 still matter. In fact, I would argue that Google is just getting better at algorithmically answering those same questions, not changing them. They are still the right way to judge a site in Google’s eyes. So how should you respond?

The first and most obvious thing is you should run a Panda survey on your (or your clients’) sites. Select a random sample of pages from the site. The easiest way to do this is get an export of all of the pages of your site, perhaps from Open Site Explorer, put them in Excel and shuffle them. Then choose the top 10 that come up.  You can follow the Moz instructions I linked to above, do it at PandaRisk, or just survey your employees, friends, colleagues, etc. While the latter probably will be positively biased, it is still better than nothing. Go ahead and get yourself a benchmark.

The next step is to start pushing those scores up one at a time. I
give some solid examples on the Panda 4.0 release article about improving press release sites, but there is another better resource that just came out as well. Josh Bachynski released an amazing set of known Panda factors over at his website The Moral Concept. It is well worth a thorough read. There is a lot to take in, but there are tons of easy-to-implement improvements that could help you out quite a bit. Once you have knocked out a few for each of your low-scoring questions, run the exact same survey again and see how you improve. Keep iterating this process until you beat out each of the question averages for winners. At that point, you can rest assured that your site is safe from the Panda by beating the devil in the aggregate. 

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