Vox and Outbrain: A Tale of Two Publishing Worlds


The value in publishing, as illustrated by Ben Thompson. (Image copyright Ben Thompson, used with permission).

The value in publishing, as illustrated by Ben Thompson. (Image copyright Ben Thompson, used with permission).

It’s a tale of two publishing worlds.

Last week, Vox, the publisher of sites including The Verge and SB Nation, landed $46.5M in funding at a valuation of $380 million. It’s just the latest in a series of new publishers who have convinced investors that there’s a profitable future in online media, something that seemed once impossible, given the economic drubbing that online publishing experienced over the last ten years, when the news around publishing seemed to be a never-ending string of announcements of layoffs, buyouts, and closures.

Also last week, Outbrain, an advertising company that specializes in disguised ads at the bottom of news stories, reportedly filed preliminary paperwork for an estimated $1 billion IPO on NASDAQ.

While both might seem like a win for online publishing, it’s not a pairing. It’s a juxtaposition that illustrates the bifurcation underway with large news sites in the U.S.

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Make Your Content Go Evergreen

(Note: These are the prepared notes of a lightning talk I gave at the January 15, 2015 Hacks/Hackers Meetup in S.F. There are a few additional notes here, and it is not verbatim.)

Hi I’m Ryan Singel, one of the co-founders of Contextly. Contextly is an engagement service that helps publishers build their audience in the age of drive-by readers. One of the ways we do that is through a set of recommendations that show up at the end of a piece of content. These include related and exploratory links that let a reader dive deeply into a subject or explore widely.

We think a lot about evergreens. That’s because one of our strategies is to algorithmically identify evergreen content and include them in our Explore section. This keeps good stories alive long after they’ve fallen off the homepage – extending their life — and getting the best of a site to readers who haven’t seen them before.

This has turned out to be a very effective strategy that is good for publications and readers.

So here are the 3 things I want you to believe by the end of this talk: Evergreens are more valuable than you thought they were when you walked in; they are worth identifying and analyzing; and publications need to have a Evergreen plan.


First some hard data: Here’s a breakdown of the pageviews on one of our publishers from 2014, broken down by date — as indicated by url structure in Google Analytics. Only about half of the pageviews of this site in 2014 were to posts published in 2014. That’s amazing and a great situation to be in.


Now some anecdata: Before founding Contextly, I was an editor at Wired in charge of a section called Threat Level.


In 2011, we published this 1,100 word story about Silk Road, the Bit-Coin/Tor enabled online drug marketplace. (Note: This was written by Adrien Chen from Gawker, published on Wired via a content-sharing agreement.)

It did really well, and it kept doing well.


Yesterday I searched my inbox for the title of that story. This is a snippet of the returns in my inbox from October of 2013, more than two years later. There’s an entry every day of that month, because I got an email about the top ten stories in that section. Which is another way of saying that Silk Road story was a top ten story every day that month, two years later. That’s an incredibly strong signal that Silk Road was of deep interest to our readers.

Not only that, the percentage of readers of that story who went on to read related content was consistently over 11%. (Note: this kind of recirculation rate is about triple industry average.)

That doesn’t just happen on Wired.

Remember Mother Jones breaking the 47% Romney video story? Of course you do. David Corn landed a huge scoop, changing the presidential election. That story was shared on FB 183,000 times and had 76,000 retweets.

Well, a year earlier, Mother Jones published a post with 11 charts explaining income inequality in the US. That piece was tweeted far less, only 4600 times.


But FB shares? It was shared nearly 250,000 times.


And that was spread out over time. (Note: Robert Wise, MJ’s Online Technology Director was in the audience at this talk and confirmed this was the case.)

The point? An Evergreen story can be as popular and influential as the biggest scoop.

So what do we mean by evergreen: Our definition is any content that continues to provide value to readers long after its publication date.


In identifying these for our clients, we’ve seen a few types of evergreen content, including these three:

Seasonal – Valentines and Halloween seem like no-brainers, but we’ve even seen Irish pound cake recipes become evergreen ahead of St. Patrick’s day.


Reviews and How-Tos


Definitive/Factual: Great stories, obituaries, explainers


Publications need to identify their evergreen stories. The most popular are the easiest to see in analytics. Identifying others may be time consuming. Try using tools like Google Analytics’s Content drilldown. Have a long view.

Look in Google Webmaster tools to see what search terms are bringing people to your site.

Ask your editorial team about their favorites. Make evergreens part of your editorial meetings.


(Note: Since this lightning talk was *not* a sales pitch, I did not push an algorithmic approach. Having your editorial team select some posts they think are evergreen is an easy thing to do.

However, this approach does not scale. Story archives can be very large. They can span decades and contain hundreds of thousands of posts. Traffic to each of those posts changes over time according to readers’ shifting interests as well as external influences such as events and seasons.

Ideally, evergreen detection algorithms (*Cough* Contextly) will be implemented to monitor the traffic to your archive, resurfacing content that is starting to become interesting to readers.)

Once you’ve IDed some of your evergreens, don’t just try to figure out how to exploit them.

Learn from them. Evergreens are one way your community tells you what they find valuable. I’ll repeat that: evergreens are one way your community communicates what is valuable to them.


What I should have learned from the Silk Road story’s evergreen behavior was to put a reporter on that beat — not just that we should tweet that story out over the weekend every once in a while.

Finally, create an evergreen strategy: There is no one right answer, but there’re likely many good answers. Experiment.


Here’s some ideas:

Wired spent several years writing a date-pegged story to every day of the year in a section called This Day In Tech. These would get assigned to different writers, and stories would go on the homepage once a year. Eventually, Wired even turned this into a book called Mad Science (Note: It’s even got 5 stars on Amazon.)


Business Insider took the sneaky route and republished an old story that had done well as if it were new, with no indication of its original pub date. It was effective, if unethical.


NPR began a strategy of promoting evergreen stories back to Facebook, timed to publish between 3 and 6 a.m. Here’s a Terry Gross interview with Maureice Sendak from 2011, that was then put on Facebook two years after the initial airing. It got more than 500 shares and nearly 3K Likes.


Finally, Vox has a strategy of creating “explainers” that are intended to be evergreen. Their CMS is customized for this, but that’s not really necessary. You can create FAQs and explainers using any CMS.

(NOTE: The same day I gave this presentation, Vox wrote up an experiment about silently updating their evergreens and the effect that had on traffic/readers).


Oh look, it’s an evergreen slide: So the three things I hope you took away: Evergreens are really valuable; publications should identify and learn from them; and publications should have an evergreen strategy that includes how to make use of evergreens and create more of them.


Finally, if you want to learn more about Contextly’s research into evergreens, as well as our algorithms to detect them, check out our blog at http://blog.contextly.com or drop us a note at info@contextly.com.


Thank you.

Top 15 Stories Published from the 1940s-50s that Did Well on Hacker News

Pinecone in an evergreen treeSome of what’s old is new again.

Below you’ll find a list of the top 15 stories published from the 1940s-1950s, spanning World War II to the beginning of modern computing, that have interested the Hacker News community over the last seven years. At Contextly, a content recommendation service for publishers, we call these stories “evergreen,” as they continue to be valuable long after their publishing date.

The list ranges from a healthy selection of George Orwell to a classic treatise on the possibility of using links to organize the world’s knowledge to the pitch deck for Disneyland. Oh, there’s Einstein dropping a bomb on capitalism, too.

A few themes emerged as I read through these.

Being first to predict something transformative in the future is intrinsically interesting, including what was missed in that prediction. Classics still resonate beyond the classroom, and the memory hole has not swallowed Orwell, which is doubleplusgood.

Formerly unpublished works from well-knonw authors catches attention, even when the new work isn’t particularly good. And, finally, secret government documents are very interesting — perhaps even more so for formerly having been secret.
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Some Analysis of All Hacker News Evergreen Stories


At Contextly, we build engagement tools that help publishers build high-value, loyal audiences. One of the ways we provide value to a publisher is by automatically detecting older stories that are still valuable to readers and including these stories in our recommendations. We call these stories “evergreens”.

Although, we can detect and surface such stories, describing the value of these stories in terms of page views leaves something to be desired.

We would like to describe the value of evergreen stories in a more compelling way. A better description would be one that moves us closer to understanding the economic value of stories, especially the economic value to publishers and readers.

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All Hacker News Evergreen Stories Ordered by Score

This resource contains all evergreen stories posted to Hacker News through November 7th, 2014. Up to that time, 1,544,661 stories were submitted to Hacker News. Of those stories, 6,826 have been identified as evergreen. They are posted here ordered by score. They are posted here in chronological order.

Conceptually, an evergreen story is a story that provides value to readers well after its publication date. For the purpose of this project:

An evergreen story is any story where the difference between the submission date of the story and the publication date of the story is two years or more. The publication date of the story is indicated in the story’s title by using the annotation “(YYYY)”, e.g. “The WorldWideWeb application is now available as an alpha release (1991)” by Tim Berners-Lee.

If you are interested in Some Analysis of All Hacker News Evergreen Stories.

TITLE: Forgotten Employee (2002)
SCORE: 746

TITLE: There’s no speed limit (2009)
SCORE: 699

TITLE: Fucking Sue Me (2011)
SCORE: 663

TITLE: Tron Legacy (2010)
SCORE: 657

TITLE: Why I Quit Being So Accommodating (1922)
SCORE: 650

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All Hacker News Evergreen Stories in Chronological Order

This resource contains all evergreen stories posted to Hacker News through November 7th, 2014. Up to that time, 1,544,661 stories were submitted to Hacker News. Of those stories, 6,826 have been identified as evergreen. They are posted here in chronological order. They are posted here ordered by score.

Conceptually, an evergreen story is a story that provides value to readers well after its publication date. For the purpose of this project:

An evergreen story is any story where the difference between the submission date of the story and the publication date of the story is two years or more. The publication date of the story is indicated in the story’s title by using the annotation “(YYYY)”, e.g. “The WorldWideWeb application is now available as an alpha release (1991)” by Tim Berners-Lee.

If you are interested in Some Analysis of All Hacker News Evergreen Stories.

TITLE: Equatorie of the Planetis (1393)

TITLE: Leonardo da Vinci’s Handwritten Resume (1482)

TITLE: The Very First Written Use of the F Word in English (1528)

TITLE: Munster’s Map of the New World (1550)

TITLE: De Re Metallica (1556)

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Is That Free WordPress Plugin Actually Free?

Free Beer Sign with Caveat Tomorrow Only

Photo by Tom Morris. CC-licensed.

Our content recommendation service Contextly recently ran into an issue with a client who was using a fairly popular WordPress plugin called Social Sharing Toolkit, which is intended to make it easy for readers of a site to share a post on a wide range of social networks.

The plugin seemed to be blocking our service from showing our content recommendations to readers.

We installed it locally to discover what the problem was and and how to work around it. (For the technically minded, the blocking problem was that this plugin loaded an old version of jQuery after jQuery had already been loaded and used by our plugin.)

I then ran our test blog through tools.pingdom.com to check some speed changes and HOLY MOLY, the difference was staggering.

On our test post without the “Social Sharing Toolkit,” the post had 53 requests, downloading a total of 521.3 kb. The page loaded in 659 ms, under three quarters of a second. It was in the top 5% of when it comes to speed of loading.

After turning on “Social Sharing Toolkit,” the post had 612 requests. It doubled the page size to 1.2 MB. And the page loaded in 4.73 seconds. And now the blog loads slower than 67% of sites on the net!

Screen Shot 2014-11-06 at 11.00.43 AM

What happened? The plugin loaded tracking bugs and scripts from BlueKai, TubeMogul, Reson8, Casalemedia, Mathtag, AdAdvisor, 360yield, Sonobi and a whole slew of others. These scripts put cookies on readers’ browsers so that third parties can tie together data about your readers from around the web.

Screen Shot 2014-11-06 at 10.57.57 AM

Not only is this plugin compromising your readers’ privacy and giving away your valuable data, it slows your site down. That’s a bad experience for readers and slow-loading counts against your Google ranking.

This is *not* to say that all free plugins in WordPress do this. The large majority do not.

But if you are using a plugin for some kind of service, especially one that uses external servers to do work on your sites behalf, you should check what their privacy policies are and if the plugin is inserting tracking cookies and scripts. One way to do this is to use a webpage analyzer like tools.pingdom.com or Google’s PageSpeed Insights and look at the list of calls made by your webpage. If you don’t recognize any of them, try removing plugins one by one until you identify what plugin is responsible for the call.

There are such things as free plugins, that’s the beauty of WordPress’s community. But there are also such things as “free” plugins that cost you a lot.

This might be a bargain that you are willing to pay in order to get the service; but it’s a bargain you should *know* you are striking.

If you install a plugin, run a speedtest before and after you turn it on. And run a check every once in awhile because sometimes popular plugins get bought by shady outfits which then include this kind of stuff in the next “upgrade”.

And, if you are wondering, Contextly does include a user cookie, which we exclusively use to personalize recommendations for readers. We host our JavaScript, CSS and image files on our servers or commercial grade CDNs to speed up loading for our clients. As we make clear in our privacy policy, we do not sell or rent reader or publisher info, and we do not load any other company’s tracking scripts.

Breaking Down Content Silos by Integrating Videos and Products into Recommendations

grain silos

These aren’t content silos, but you get the idea.

Today’s publications do more than just publish text stories.

For instance, a huge number of publications also produce videos, and a growing number are combining content with commerce.

Too often, these content types exist in separate silos inside a publication. The closest these content types get to one another is cuddling on a little used navigation bar, where a user who clicks on Videos gets taken to a special portion of the site or off to YouTube.

Since our goal is to help sites build engagement and loyalty by getting the right content to the right reader, we wanted to solve the “siloed content” problem.

So, we’re proud to announce that we have extended our recommendation technology to include video and product recommendations in the related section when they are actually related.

A publisher might have hundreds of thousands of YouTube subscribers, and millions of views on their videos.

But when a reader clicks a link from Facebook or Twitter to visit an individual story on the publisher’s site, the publisher has no clear way for that reader to even know the site creates videos — let alone show off one related to the current story. This is especially true on mobile.

In May, when we officially launched Contextly, we announced a new way of powering content recommendations for publications of all sizes. That approach marries curation tools to wickedly smart data analysis.

We’ve now been able to use that same smart data analysis and recommendation system to provide a far more comprehensive set of recommendations for publishers that make videos and sell items.

Here’s a few screenshots of what that silo-breaking looks like on one of our publishers, Adafruit, which is a DIY site catering to the Maker generation:

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Exploring Algorithmically Identified Evergreen Stories


Much of digital publishing developed under the influence of traditional publishing. The home page is an manifestation of the front page.

Stories are published to the home page, but are soon pushed off the home page by a never-ending stream of new stories.

This is similar to the consumption habits by readers of newspapers. A newspaper serves its purpose for a day or two before joining the recyclables, making room for the most recent newspaper.

It seems the practical matter of real estate is lowering the lifetime value of many stories. Publishing new stories necessarily means older stories get buried. But some stories have the potential to provide value to readers well past the five-day mark. We call these stories “Evergreens”.

The lifetime of a story is determined by the value it provides to readers. For example, this story about the Giants and Dodgers game provided most of its value to readers during the first few hours. However, this story about Jean-Paul Sartre’s refusal of the Nobel Peace Prize in 1964 is still being read and discussed fifty years later.

At Contextly, we see great potential in stories that are relevant well past their publication date. That is why we developed algorithms to automatically identify and surface evergreen stories.

This moves us in the direction of maximizing the lifetime value of a story.

What Makes a Story Evergreen?

We have reviewed a number of stories that have been identified as evergreen by our algorithms. There are some notable patterns in these stories. They can be described as Seasonal, How-Tos, Reviews, and Factual.

Seasonal evergreens peak in value around the same time every year.

The leaked New York Times Innovation Report cited this story as a specific example of a successful experiment with evergreen stories. The story was about ‘love’ and ran on Valentine’s day, emphasizing the importance of showing the right story at the right time. They marveled, “…even old content can generate significant traffic without ever appearing on the home page.”

Handmade Charlotte is a client of ours. A number of their Halloween-themed stories have recently been identified as evergreen by our algorithms. This one shows kids how to make awesome Lucha Libre masks for Halloween:LucheLibreMasks

How-Tos and Reviews are often identified as evergreens by our algorithms. Good examples include How To Build a Worm Farm by Modern Farmer, A Short Guide to Tequila and Making a Great Margarita by KQED: Bay Area Bites and Adafruit‘s comparison of popular microcontrollers.

Readers’ needs for historical context or background information can result in Factual stories being identified as evergreens. Good examples include Jean-Paul Sartre’s refusal of the Nobel Peace Prize in 1964 and Nelson Mandela’s Obituary.

Our client, CFO.com, has a story from 2008 that describes the difference between corporate dissolution vs corporate liquidation, another example of a factual story having evergreen qualities.

It is interesting to note that the examples of factual stories used here have a Wikipedia-like quality to them; they probably satisfy a similar information need as Wikipedia entries.

Algorithmically identifying and surfacing evergreen stories increases the lifetime value of stories. This benefits readers, writers and publishers.

Readers gain access to more high-quality content at times when it is most relevant to them. Writers are made more productive because the lifetime value of some of their highest-quality stories increase. Publishers benefit because the total value of their stories increase.

Contextly’s mission to help publishers build high-value, loyal audiences drives the development of technology like evergreen story detection algorithms.

If you would like to talk more about evergreen stories and algorithms, I would love to hear from you!: ben@contextly.com


Are You Making the Most of Your Content Marketing or Just Wasting Money?

Portrait If you are paying to get someone to visit your site, the last thing you want is for that visitor to bounce off your site. That’s just money wasted.

Depending on your organization’s business goals, you want that visitor to sign up for a product demo, join your e-mail list or simply share the story they read on Facebook.

Regardless, you need to think about two things: One, what does this person want and Two, how can I meet that need, while also promoting my business objectives. If you don’t do this, you are going to waste precious paid media dollars you are spending on “promoted” or “sponsored” content.

Contently, a company that helps companies create great content for their sites and marketing campaigns, runs a fantastic content marketing blog with practical tips for their customers and thought-leader pieces to raise awareness of their company generally.

Contently does two very smart things on their blog.

They have a very useful e-book called “The Beginner’s Guide to Blogging and Content Strategy” that’s very good – and you can get it simply by giving them your e-mail address. And there’s a clear and easy way to get it. At the end of every post, there’s a box that offers it to readers.

Screen Shot 2014-09-01 at 5.28.00 PM

The second smart engagement thing they do is use Contextly to surface relevant, engaging and personalized content recommendations. This is a bet that they can entice readers to dive deeper, find more great content and become a loyal reader.

It’s a notion that’s backed up with research. In January 2014, Chartbeat released a report that found the single most important factor in whether a Google, Facebook or Twitter visitor to a publication would return was whether they read more than one story. That’s another way of saying that reducing your bounce rate will increase your return readership.

Contrast that with AARP’s sites. I don’t know what the long-term value of an AARP member is, but dues are $16 per year and they have a magazine packed with ads targeting a lucrative demographic.

Like Contently, AARP buys traffic into their site and they have a few calls to action in their content.

But at the end of their stories, they include Taboola ads that take readers off of the site. So on a story about “10 Great Small Cities for Retirement“, AARP is showing ads for “Here’s the Navy’s awesome new stealth-fighter drone in action” that takes the reader off the site.

That’s not just not relevant; it’s nuts. AARP has a reader who is interested in the best places in the U.S. to retire — and AARP is a group that helps retired people — and someone there decided it made sense to make ten cents sending that reader off their site to a story about stealth fighters, instead of showing that reader their best content for retirees or those about to retire?

Taboola is what publishers use on their own site if they have a large amount of traffic, no concern for their brand and no way to make enough advertising money. (And yes, I’ve heard the argument that publishers use the money from sending people off their site to bring new ones in. It’s a great sales pitch, but it’s economically ridiculous. One does not buy high and sell low from the same arbitrager. Only one party wins there and it’s not the publisher.)

If you are a content marketer, you *might* want to buy traffic from Twitter, Facebook, Outbrain or Taboola to see if you can profitably acquire users, but you shouldn’t be selling traffic.

Jay Baer, who runs Convince and Convert, does some smart state to engage readers on his content marketing blog, as well.

He uses a smart “Hello Bar” that is currently promoting an upcoming conference his company runs. His header promotes his newsletter tied to his book “Youtility, while the top of his blog post suggests that readers ought to buy the book, with links to Amazon and Barnes and Noble to make that easy.

Screen Shot 2014-09-01 at 5.45.11 PM

And finally, he also uses Contextly — in a different configuration than Contently, to show off his relevant and popular and evergreen content (he’s got a *lot* of evergreen content.

If you are a content marketer, you want to convert your readers into a loyalist and eventually an evangelist.

This is especially true with paid promotion of your content. Don’t waste your marketing budget by not taking advantage of every opportunity to provide value for the readers you paid to get to your site.

There’s a large continuum of loyalty when it comes to readers.

It could start as simple as someone who will recognize your company name the next time your content shows up on their Facebook page or in their Twitter stream.
That means they might remember that the last time they read something on your site it was good, so they don’t hesitate to click.

And on the far end, you get to someone who uses your service, who visits your site regularly – who recommends your site or product to friends and colleagues – and searches out your content to share because it makes them look smarter.