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Posted by ChadPollitt

This post was originally in YouMoz, and was promoted to the main blog because it provides great value and interest to our community. The author's views are entirely his or her own and may not reflect the views of Moz, Inc.

Many of the traditional channels for online content discovery are thoroughly understood and their adoption rates are high.

The readily accepted channels—from SEO and PPC, to email and social media broadcasting—can deliver the best content to the right people at the right time.

Today, however, the Internet is experiencing a deluge of content, and many channels for content discovery are bloated. Estimates say that more than 2.73 million blog posts are written and published daily. Many industries are experiencing a content surplus, making it even more challenging for marketers to get their content seen.

Social media networks like Facebook and Twitter are adjusting their algorithms to ensure the least amount of organic visibility for brands, too. Traditional paid media, such as banner advertising, is becoming less effective year-over-year because banner blindness runs rampant. According to Solve Media, you're more likely to survive a plane crash than click on a banner ad.

That sounds farfetched until you look at the results from the Nielsen Norman Groups 2007 eyetracking study (shown below).

Red areas indicate where users looked the most; yellow areas indicate fewer views; areas colored blue depict the least-viewed portions of the page; gray areas didn't attract any views/actions; and the green boxes are used to highlight advertisements.

As a result, new techniques, tactics and tools are cropping up and being used by marketers of all stripes to maximize the visibility of their content. There's now an entire content promotion ecosystem. From influencer marketing to native advertising, brands are experimenting in new ways.

Many brands are sponsoring articles on blogs or other online publications with large preexisting audiences. An interesting stat we just included in our own " Content Promotion Manifesto" is that brands spent, on average, 6.7 percent of their content marketing budgets on sponsored content in 2013. It's trending upwards, too. From the The New York Times to Forbes' Brand Voice, there's no shortage of famous examples.

While advertorials have been around for decades, this top-of-the-funnel sponsored article channel is relatively new for many content marketers. Over the last year, we have received many questions from clients about sponsored content—questions about pricing, scale, value and strategy. We struggled to answer most of them; there wasn't anywhere to get answers.

Because of this, we decided to reach out to 550 online publications to gather as much information about their sponsored content programs as possible. We wanted to find out the following:

• An agreed upon definition for sponsored articles
• The current state of sponsored articles as a channel
• Examples of sponsored articles
• Sponsored article pricing and value
• A media buying strategy for sponsored articles
• Tools and platforms for sponsored articles

We quickly learned that sponsored content on blogs and other online publications, when viewed as a marketing channel, is very immature. Pricing doesn't have much rhyme or reason, either. However, after collecting and interpreting data on 550 online properties, and dissecting countless native advertising studies, we hope to shine a light on a little known content marketing channel.

The results of the study are outlined below.

Note: The complete Media Buyers Guide to Sponsored Content study is available for download here.

Defining Sponsored Articles

With content marketing adoption rates so high, many brands are looking to native advertising to promote their content. The Interactive Advertising Bureau (IAB) defines native advertising as "paid ads that are so cohesive with the page content, assimilated into the design, and consistent with the platform behavior that the viewer simply feels that they belong." According to the IAB, native advertising contains six different types of ad units: in-feed, promoted listings, in-ad with native element, paid search, recommendation widgets, and custom.

Sponsored articles fall into the in-feed subgroup. However, so does promoted content on Facebook, LinkedIn and Twitter. Bcause they appear within the normal content feed of the publisher, it doesn't matter if the publisher is Facebook or BuzzFeed.

In other words, sponsored articles amount to advertising on a media outlet in the form of editorial content that looks like it's supposed to be there. Brands value this because association with a publication and exposure to its audience can drive awareness, traffic, conversions, and leads.

The Current State of Sponsored Articles

We uncovered lots of fascinating information about sponsored articles while conducting our research. They are actually an evolved version of what many marketers call advertorials, which have been around for decades. The biggest difference between the two is where the content resides in the customer buying journey. Advertorials are middle to bottom-of-the-funnel content.

An example of a magazine advertorial

On the other hand, sponsored articles strictly reside at the top of the funnel. Their purpose is to be helpful, entertaining, or both. Top-of-the-funnel content doesn't appear to be salesy and brand-centric to the reader. It's the rise of content marketing that helped move advertorials up the funnel. This helps brands become not just purveyors of goods and services, but a producer of ideas and a distributor of knowledge.

Controversy

Sponsored articles have received pushback from some publishers, brands, and consumers—and even government regulators who are concerned because the articles resemble editorial content. This can damage the editorial integrity of a publication, as well as a brand's image.

Both publishers and marketers have a vested interest in not appearing to mislead consumers. Native advertising in general is misunderstood by many consumers and marketers. (That's partly why we conducted this study.)

In the video below, John Oliver does a good job of articulating many consumers' concerns regarding sponsored articles.

Copyblogger's 2014 State of Native Advertising Report surveyed over 2,000 marketers and discovered that 73 percent were either completely unfamiliar with or hardly familiar with native advertising.

Thirty-eight percent of the marketers could identify forms of native advertising from a checklist, and only three percent claimed to be very knowledgeable.

Earlier this year, Contently surveyed 542 U.S. Internet users to determine what they thought about sponsored articles. Only 48 percent of the respondents believed sponsored content that was labeled as such was paid for by an advertiser that had influenced the content produced. The rest thought the label meant something else.

Just over 66 percent of the respondents reported they are not likely to click on an article sponsored by a brand and 33 percent said they're just as likely to click on a sponsored article as they are to click on (unsponsored) editorial content.

There is also contradictory evidence surrounding the overall effectiveness of sponsored articles. Research from Chartbeat shows that only 24 percent of visitors scroll past the fold when visiting a sponsored article—compared with 71 percent for editorial content.

However, The New York Times claims readers spend the same amount of time on sponsored articles as traditional news stories. This is backed up by a study from Sharethrough and IPG Media Labs. They found that consumers actually look at sponsored articles more than typical editorial articles (26 percent vs. 24 percent) and spend a similar amount of time on each (1 minute vs. 1.2 minutes).

Not all publishers offer sponsored article opportunities to marketers. During our research, some respondents told us that protecting editorial integrity and preserving audience trust were a higher priorities. On the other hand, many big name publishers like Forbes, The New York Times, Business Insider, The Atlantic, Washington Post and The Wall Street Journal have all embraced sponsored articles as a revenue source.

BuzzFeed's entire business model is built around what it calls sponsored "listicles," a.k.a. sponsored articles. While some publishers are averse to adopting this native form of advertising, it doesn't seem to be causing any damage to the publishers who are using native ads.

The U.S. Federal Trade Commission (FTC) hasn't quite figured out how to regulate native advertising. The FTC has delayed handing down regulations around disclosure requirements, language and graphic separation. Until that happens, the display of native advertising will remain at the discretion of publishers.

With that said, the IAB has set native advertising guidelines for its members. The IAB reports that clarity and prominence of paid native ad unit disclosure are vital, regardless of native advertising type.

Their two criteria are straightforward:

• Use language that conveys the advertising has been paid for, thus making it an advertising unit, even if that unit does not contain traditional promotional advertising messages.
• Be large and visible enough for a consumer to notice it in the context of a given page and/or relative to the device the ad is being viewed on.

In the case of sponsored articles, a reasonable consumer should be able to distinguish between editorial content from the publisher and paid advertising.

Growth

A 2013 survey conducted by Hexagram and Spada revealed that 62 percent of publishers had embraced sponsored articles, with another 16 percent planning to go this route by the end of 2014. Comparable research from eMarketer showed that only 10 percent of digital publishers didn't have and weren't considering native advertising on their sites.

The 2014 Native Advertising Roundup revealed that 73 percent of media buyers use native advertising, and 93 percent expect to spend the same or more in the future. Native advertising spending in the U.S. is expected to increase from $1.3 billion in 2013 to $9.4 billion in 2018. A full 40 percent of publishers expect native advertising to drive a quarter or more of their digital revenue this year.

Native advertising, when compared to traditional display ads, have been found to be more effective. 25 percent more consumers looked at sponsored articles than display ad units. Native ads produced an 18 percent lift in purchase intent and a nine percent lift for brand affinity responses. BIA/Kelsey released a study which shows brands are planning on spending more on native advertising, and publishers stand to benefit as long as they can preserve the trust and interest of their audience.

Examples of Sponsored Articles

Since look, feel, design, language and requirements of sponsored content are left up to the discretion of publishers, the presentation of sponsored content on different sites varies widely. Some publications provide brands with what can be described as a virtual microsite within the site itself. Others are more streamlined, using an article that appears as a piece of featured content but is labeled as sponsored.

Sponsored Article Pricing

There are no real standards for pricing in the digital world with regard to sponsored content. This makes budgeting for the channel very tough to do. It also makes long-term strategic execution at scale and across multiple publications a near impossibility.

While the value of sponsoring content is clearly understood by many brands, how to execute it and who to talk to in order to get it done is generally unclear. The study set out to add rhyme and reason to this burgeoning channel by exploring costs and comparing them across a broad spectrum of online publications and blogs.

This is valuable information for marketers and media buyers wishing to negotiate with online publications. It can even be used by publications that have not yet offered sponsored content opportunities to establish fair pricing.

Since publishers completely control their own pricing and standards, they maintain their own criteria for validating costs associated with sponsored articles. In today's analytics-driven marketing culture, where channels are often compared and returns are measured, sponsoring content across several different publications can't be so easily consolidated into a single "sponsored content" channel since each one has a unique value proposition.

This study is the industry's first attempt to scientifically justify, quantify, and predict current going-rate prices of sponsored articles using explicit data points that can be measured for each online publication. Our goal was to create the first-ever quantitatively supported pricing standard for sponsored articles.

We hope our research puts an end to these challenges and empowers marketers with the ability to budget, negotiate, and ultimately scale the deployment of sponsored articles within their channel mix.

Research

In total, the research for this study was conducted over a five-month period of time earlier this year. It included manual outreach via email and phone to over 1,000 media outlets and blogs. The outreach resulted in responses from 550 publishers that sold sponsored article units.

The study took an unbiased approach to data inclusion and included a representative sample set. It collected data on globally-recognized publications, one-person blogs, and everything in between.

Publications were classified using the following criteria:

• Content is created by more than five writers/contributors/columnists, and:
• The website already utilizes traditional display advertising (e.g., banner ads)

Everything that didn't meet the above criteria was classified as a blog.

Each price collected in the study was the minimum charge for getting a sponsored article published, regardless of other pricing factors. A total of 17 factors were cited as justification for pricing schemes from the 550 publishers.

1. Word count: The number of words in a sponsored article
2. User time on page: The amount of time a typical reader spends on a web page
3. Links: Specifications regarding whether or not links would be provided, and if so, how many, where and whether or not they would be "nofollow" links
4. Lead capture: For publishers that provide links to gated assets, many charge on a per-lead basis
5. Impressions (CPM): Cost per thousand impressions based on historic data
6. Time and effort required from publication's editorial staff
7. Monthly website traffic
8. PageRank: Often used by publishers to justify relative pricing when they run more than one media outlet
9. Domain Authority: Often used for publishers to justify relative pricing when they own more than one publication
10. Page-level engagement: A metric that is measured by how far readers scroll down the page and the amount of time spent on a given article
11. Social media promotion: Often an optional add-on that would increase price (may come as part of a package deal)
12. Email promotion: Often an optional add-on that would increase price (may come as part of a package deal)
13. Display advertising: Often an optional add-on that would increase price (may come as part of a package deal)
14. Number of articles: How many sponsored articles you are buying at a time
15. Visibility time: The amount of time an article stays live on the site
16. Verticals: For large publications that cover many verticals or subject areas, some verticals are more expensive than others
17. Pay-per-click: Another engagement-level metric that is measured by the number of click-throughs to an intended landing page

In order to do a quantitative analysis, explicit data was collected from all of the publications to calculate predictor variables. Those variables included:

• Domain Authority: A ranking score from Moz, on a 100-point scale, that uses more than 40 signals to calculate how well a website will perform in the search engine results pages (SERPs). The higher the score, the more authoritative the website is viewed as being.
• Page Authority: Another ranking score from Moz, on a 100-point scale, that calculates how well a given webpage is likely to rank in the SERPs. In the case of this study, the publication's home pages were used.
• PageRank: A ranking metric from Google that calculates the relevance of a webpage. This score analyzes the number of incoming links and the quality of the referring webpages to generate a measurement between 0 (low relevance) and 10 (high relevance).
• AlexaRank: A ranking score from Alexa.com that is based on traffic data from users over a rolling three-month period. A site's ranking is based on a combined measure of unique visitors and page views. The site with the greatest combination of these is ranked No. 1, and higher number rankings correlate with lower traffic data.
• Facebook Following: The number of fans (or "likes") a publication's Facebook page has.
• Twitter Following: The number of followers a publication's or a blogger's Twitter account has. For publications with multiple accounts and/or contributing authors, only the account with the largest following was used.
• Pinterest Following: The number of followers a publication's or a blogger's Pinterest account has.

Assumptions

It is assumed that the data set in this study is a representative sample of the entire ecosystem of blogs and other online publications because the results closely mirror Moz's distribution of Page Authority that analyzed more than 10,000 SERPs and 200,000 unique pages. This regression model had a mean (average) Page Authority of 40.8 and standard deviation of 15.1. The distribution can be seen below.

The regression model in this study had a mean of 47.1 and a standard deviation of 15.5. The sample set of blogs and publications had a slightly higher Page Authority than the Moz study. This was expected because the study only measured root domains and not long-tail pages within those domains.

Aside from that slight disparity, the distribution curves are nearly identical. For those readers who are number junkies, the descriptive statistics of the Page Authority data in the study are below.

Limitations

Variations in sponsored content offerings – The study established the pricing baseline based on the cost of one sponsored article. Since some publications only offered long-term commitments to marketers that could include other benefits (banners, email, social promotion, etc.), some publications' unit pricing could be inflated. As a result, the regression model may not be an accurate price predictor in all scenarios.

Social account data – Not all online publications have accounts on Facebook, Twitter and Pinterest. In these cases, the number zero was used to quantify followers. Also, for publications with multiple accounts on the same network, the study measured the account with the most followers.

Alexa Rank Inaccuracies – Alexa admits publicly that there are limits to making judgments from its data. Sites with relatively low traffic may not be accurately measured by Alexa.

Analysis

The graph below seeks to show the exact methodology we used to conduct the sponsored content pricing study. It's purpose is to give readers confidence in our pricing models so they feel comfortable in adapting the formulas.

When all prices are graphed, bloat appears on each end of the pricing spectrum. In order to reconcile the dense areas, the study broke down the pricing data and regression models for blogs and publications separately.

Blog Pricing Analysis

The graph below represents the distribution of prices for all 474 blogs in the study.

Because of the wide range and low frequency of prices recorded in the "more" area, we decided to label these data points as outliers. By removing the outliers (approximately 3.8 percent of the sample) from the analysis, the variance decreased by 87 percent, making for a more accurate predictive model. All descriptive statistics for the blog data sample before and after removing the outliers were laid out in the study.

With the remaining 456 cases, a multi-variable regression test for price against all of the predictor variables was run, after which the insignificant variables were removed to formulate the pricing regression model for blogs, as shown below.

The end result confidently determined the fair market price formula for a sponsored article on a blog:

Publication Pricing Analysis

The graph below represents the distribution of pricing for all 76 publications recorded in the study.

The outliers were kept in this regression model because of the range in quality and size of online publications is large. The descriptive statistics are available in the actual study.

Following the same methods as the blog analysis, the study ran a multi-variable regression test to construct a predictive model for publication pricing. After removing the insignificant variables the output looks like this:

The end result confidently determines the fair market price formula for a sponsored article on a publication:

What All This Math Really Boils Down To

With the formulas below, marketers now have a way to assign value when purchasing or negotiating for sponsored articles on blogs or publications.

Prior to this study marketers had no way of knowing if they were getting a fair deal or not using this emerging channel.

• Blog Price Formula = -60.5 + 5.97(DA) + 0.978(thousand Fb fans) + 15.1(PR) – 0.000007(AR)
  
• Publication Price Formua = -37000 + 314(DA) + 20.9(thousand Fb fans) + 5152(PR) – 46.6(thousand Pinterest followers)

That said, media buyers should also note that many top-tier publications package their sponsored content offering in different ways. Keep this in mind when using the formulas above. Below are examples of some variation in sponsored article packages.

Networks and Tools for Sponsored Articles

While conducting research, several tools and networks kept coming up. Some networks set up for the sole purpose of connecting marketers with publishers for sponsored content. Even HubSpot has built an informal ad hoc network for its partner agencies to connect with its publishing customers. Content measurement tools, including Nudge, which was built to measure sponsored content, are starting to crop up, too.

A few other networks and tools worth noting:

• Adproval: A media outlet marketplace for connecting publishers and advertisers
• BlogHer: A blog and social media influencer community focused on social media coverage of women
• Blogsvertise: A blog marketplace for connecting publishers and advertisers
• Buysellads: A media outlet marketplace for connecting publishers and advertisers
• Cision: The brand's Content Marketing Database includes a searchable database of over 2,000 sponsored opportunities with thousands of U.S. publications
• GroupHigh: Blogger outreach marketing software that helps companies find bloggers, in addition to managing and tracking relationships, and measuring results
• Izea: A sponsorship marketplace that connects social media influencers with brands
• Markerly: A brand amplification platform that connects brands with bloggers
• Sway Group: Connects brands and agencies with the largest network of female bloggers on the Web
• The Syndicate: A brand storytelling partner and blog sponsorship network.

With the growth of online content showing no signs of slowing, the use of sponsored content as a marketing channel will undoubtedly continue to grow as well. Besides, it's a proven revenue stream for publishers who have often struggled to make money on the Internet.

However, as the popularity of sponsored content grows, so does the likelihood of it being regulated by governments. Until then, consider this post your definitive guide to sponsored content. The study can be downloaded here.

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Posted by randfish

Today I'm going to make a crazy claim—that in SEO today, there are times, situations, and types of analyses where correlation is actually MORE interesting and useful than causality. I know that sounds insane, but stick with me until the end and at least give the argument a chance. And for those of you who like visuals, our friend AJ Ghergich and his intrepid team of designers created some nifty graphics to accompany the piece.

Once upon a time, SEO professionals had a reasonable sense of many (or perhaps even most) of the inputs into the search engine's ranking systems. We leveraged our knowledge of how Google interpreted various modifications to keywords, links, content, and technical aspects to hammer on the signals that produced results.

But today, there can be little argument—Google's ranking algorithm has become so incredibly complex, nuanced, powerful, and full-featured, that modern SEOs have all but given up on hammering away at individual signals. Instead, we're becoming more complete marketers, with greater influence on all of the elements of our organizations' online presence.

Web marketers operate in a world where Google:

• Uses machine learning to identify editorial endorsements vs. spam (e.g. Penguin)
• Measures and rewards engagement (e.g. pogo-sticking)
• Rewards signals that correlate with brands (and attempts to remove/punish non-brand entities)
• Applies thousands of immensely powerful and surprisingly accurate ways to analyze content (e.g. Hummingbird)
• Punishes sites that produce mediocre content (intentionally or accidentally) even if the site has good content, too (e.g. Panda)
• Rapidly recognizes and accounts for patterns of queries and clicks as rank boosting signals (e.g. this recent test)
• Makes 600+ algorithmic updates each year, the vast majority of which are neither announced nor known by the marketing/SEO community

Given this frenetic ecosystem, the best path forward isn't to exclusively build to the signals that are recognized and accepted as having a direct impact on rankings (keyword-matching, links, etc). Those who've previously pursued such a strategy have mostly failed to deliver on long-term results. Many have found their sites in serious trouble due to penalization, more future-focused competitors, and/or a devaluing of their tactics.

Instead, successful marketers have been engaging in the tactics that Google's own algorithms are chasing—popularity, relevance, trust, and a great overall experience for visitors. Very frequently, that means looking at correlation rather than causation.

[Via Moz's 2013 Ranking Factors - the new 2015 version is coming this summer!]

We'll engage in a thought experiment to help highlight the issue:

Let's say you discover, as a signal of quality, Google directly measures the time a given searcher spends on a page visited from the SERPs. Sites with pages searchers spend more time on get a rankings boost, while those with quick abandonment find their pages falling in the rankings. You decide to press your advantage with this knowledge by using some clever hacks to keep visitors on your page longer and to make clicking the back button more difficult. Sure, it may suck for some visitors, but those are the ones you would have lost anyway (and they would have hurt your rankings!), so you figure they're not worth worrying about. You've identified a metric that directly impacts Google's algorithm, and you're going to make the most of it.

Meanwhile, your competitor (who has no idea about the algorithmic impact of this factor) has been working on a new design that makes their website content easier, faster, and more pleasurable to consume. When the new design launches, they initially see a fall in rankings, and don't understand why. But you're pretty sure you know what's happened. Google's use of the time-on-site metric is hurting them because visitors are now getting the information they want from your competitor's new design faster than before, and thus, they're leaving more quickly, hurting the site's rankings. You cackle with delight as your fortune swells.

But what happens long term? Google's quality testers see diminished happiness among searchers. They rework their algorithms to reward sites that successfully deliver great experiences more quickly. At the same time, competitors gain more links, amplification, social sharing, and word of mouth because real users are deriving more positive experiences from their site than yours. You found an algorithmic loophole and exploited it briefly, but by playing the "where's Google weak?" game rather than the "where's Google going?" game, you've ultimately lost.

Over the last decade, in case after case of marketers optimizing for the causal elements of Google's algorithm, this pattern of short-term gain leading to long-term loss continually occurs. That's why, today, I suggest marketers think about what correlates with rankings as much as what actually causes them.

If many high-ranking sites in your field are offering mobile apps for Android and iOS, you may be tempted to think there's no point to considering an app-strategy just for SEO because, obviously, having an app doesn't make Google rank your site any higher. But what if those mobile apps are leading to more press coverage for those competitors, and more links to their site, and more direct visits to their webpages from those apps, and more search queries that include their brand names, and a hundred other things that Google maybe IS counting directly in their algorithm?

And, if many high ranking sites in your field engage in TV ads, you may be tempted to think that it's useless to investigate TV as a channel because there's no way Google would reward advertising as a signal for SEO. But what if those TV ads drive searches and clicks, which could lead directly to rankings? What if those TV ads create brand-biasing behaviors through psychological nudges that lead to greater recognition and a higher likelihood of searchers click on, link to, share, talk about, write about, buy from, etc. your TV-advertising competitor?

Thousands of hard-to-identify, individual signals, mashed together through machine learning, are most likely directly responsible for your competitor's website outranking yours on a particular search query. But even if you had a list of the potential inputs and the mathematical formulas Google's process considers most valuable for that query's ranking evaluation, you'd be little closer to competently beating them. You may feel smugly satisfied that your own SEO knowledge exceeded that of your competitor, or of their SEO consultants, but smug satisfaction does not raise rankings. In fact, I think some of the SEO field's historic obsession with knowing precisely how Google works and which signals matter is, at times, costing us a broader, deeper understanding of big-picture marketing*.

Time and again, I've seen SEO professionals whom I admire, respect, and find to be brilliant analysts of Google's algorithms lose out to less-hyper-SEO-aware marketers who combine that big picture knowledge with more-basic/fundamental SEO tactics. While I certainly wouldn't advise anyone to learn less about their field nor give up their investigation of Google's inner workings, I am and will continue to strongly advise marketers of all specialties to think about all the elements that might have a second-order or purely correlated effect on Google's rankings, rather than just concentrate on what we know to be directly causal.

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* No one's guiltier than I am of obsessing over discovering and sharing Google's operations. And I'll probably keep being that way because that's how obsession works. But, I'm trying to recognize that this obsession isn't necessarily connected to being the most successful marketer or SEO I can be.

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