Author Archive

As you’ve all but certainly heard, Facebook had a major outage midday on Friday. Overall traffic on news sites dropped by 3%, thousands took to Twitter to voice their frustration, and, apparently, a select few called the LA Sheriff’s Department. Most interestingly for us, the Facebook outage provided a natural experiment to look at what the world of web traffic looks like without Facebook. Here, I’ll delve into two issues that are particularly interesting to look at through the lens of the outage.

Facebook and dark social

So-called “dark social” traffic — traffic to articles that lacks a referrer because it comes via HTTPS or apps — is subject to endless speculation. What portion of it comes from emailed links? From links sent via instant messaging? From standard social sources like Facebook and Twitter but with the referrer obscured? From search sites that use HTTPS? By virtue of the fact that no explicit referrer is sent, it’s impossible to tell for sure. Since Facebook makes up a huge portion of non-dark traffic, one might guess that a big chunk of dark traffic is actually Facebook traffic in disguise.

Of course, during the outage virtually all Facebook traffic was stopped, so we can use that data to ask how much dark traffic was definitely not coming from Facebook. The answer? Very little of it was coming from Facebook directly. Take a look at the graph below.


Facebook referrals dropped by almost 70% during the outage (note that traffic didn’t drop to 0, presumably because some number of people had Facebook pages open before the outage). There’s certainly a drop in dark social, but it’s not nearly as stark, and dark social traffic just before the outage was only 11% higher than at its low point during the outage. Since 70% of Facebook traffic dropped off, that would imply that at most 16% (11% / 70%) of traffic could’ve been directly attributable to Facebook.

Now, of course, we’d expect some other social sharing might be negatively impacted — if people aren’t discovering articles on Facebook, they might not be sharing them in other ways. So, that doesn’t mean that 16% of dark social traffic is from Facebook, but it does provide strong evidence that 84% of dark social traffic is something other than Facebook traffic in disguise.

Where people go in an outage

As I discussed in my last post, a huge percentage of mobile traffic comes from Facebook. Given that, we’d probably expect mobile traffic to be hardest hit during the outage. And, indeed, entrances to sites on mobile devices were down 8.5%, when comparing the minute before the outage to the lowest point while Facebook was down.

Interestingly, though, we see the opposite effect on desktops: a 3.5% overall increase in desktop traffic after the beginning of the outage. That increase was largely fueled by a 9% increase in homepage direct traffic on sites with loyal homepage followings. We saw no increases in traffic via other referrers, including Twitter and Google News, during the outage. While we certainly can’t claim that the outage was the cause of that uptick in desktop traffic, the timing is certainly notable.


In short, then: our brief world without Facebook looked a bit different, albeit in predictable ways. Significantly less news was consumed on phones, slightly more homepages were visited on desktops, and 30 minutes later, when Facebook came back online, traffic returned to normal.

In the much-circulated New York Times Innovation Report, perhaps the most discussed graph was this one, showing a roughly 40% decline in homepage audience over the past three years.


With that graph, innumerable articles announcing the “death of the homepage” were written, in The Atlantic, Poynter, and on numerous blogs. Most hinged on the relationship between the rise of social traffic and the decrease in homepage traffic. One thing that isn’t mentioned in most of these articles, though, is that the rise in social traffic was contemporaneous with a rise in mobile traffic, and that mobile is as much a principal part of the story as social is. Here, I’d like to explore the three-way interaction between mobile traffic, social, and homepage visitation.

Social traffic and mobile devices

The importance of social sharing on mobile devices is much discussed. (Take for example, the recent ShareThis report, which reported that 63% of Twitter activity and 44% of Facebook activity happens on mobile.) People aren’t just using social media on mobile to share articles, of course, they’re also clicking to those articles. Below, we break down the share of traffic coming from Facebook and Twitter by device across a random sample of our sites. (Note: We specifically chose sites without separate mobile sites and without mobile apps, to ensure that we’re making fair comparisons across devices.)


Facebook’s share of overall mobile referrals is nearly 2.7x larger than its share on desktop. Twitter’s share is 2.5x larger on mobile than on desktop. And, if anything, those numbers likely undercount the significance of social referrals, since many apps don’t forward referrer information and get thrown into the bucket of “dark social.” In some sense, then, it’s fair to say that—for most sites—mobile traffic more-or-less is social traffic.

Mobile and homepage traffic

Setting aside where visitors come from, mobile visitors are substantially less likely to interact with a site’s homepage. Below we plot, for the same collection of sites as above, the fraction of visitors that have visited any landing page (e.g. the homepage, a section front) over a month.


What we see is dramatic: Desktop visitors are over 4x more likely to visit landing pages than those on phones.

Is that because mobile visitors come from social sources, and social visitors are less likely to visit landing pages—a fact that’s often cited when discussing the state of homepage traffic? Or is it not an issue of referrer at all—are mobile visitors intrinsically less likely to visit landing pages? To move toward an answer, we can control for referrer and ask the same question. Below, we plot the fraction of visitors who come to the site from Facebook and then and during the same month (but not necessarily on the same visit) visit a landing page.


Comparing this graph to the previous one, three things are clear:

  1. As discussed above, mobile visitors are significantly less likely to ever visit landing pages than desktop and tablet visitors.
  2. Similarly, visitors who come from Facebook are significantly less likely to ever visit landing pages than those who come from other sources. On average, only 6% of visitors who come from Facebook ever visit a landing page, compared to nearly 14% of overall visitors.
  3. These two phenomena are to some degree independent—desktop-based Facebook visitors are half as likely to visit landing pages as other desktop-based visitors, while mobile Facebook visitors are one-third as likely to visit homepages as other mobile visitors.

It’s also worth a quick note that, in all of these respects, tablet traffic is much closer to desktop traffic than it is to mobile traffic.

Overall, this seems to be cause for substantial concern to publishers—increases in social and mobile traffic are the two most significant traffic trends of the past few years, and both are strongly associated with drops in homepage traffic. Since, as we’ve seen before, homepage visitors are typically a site’s most loyal audience, potential drops in homepage visitors should be concerning. In the short term, it’s safe to assume that a successful mobile strategy will hinge upon a steady stream of social links—that visitors won’t return unless we reach out to them directly. In the longer term, there’s a lot of work for all of us in determining how best to build an audience in a post-desktop (and potentially post-homepage) world.

Revisiting Return Rates

July 14th, 2014 by Josh

Starting today, we’ve updated our definition of return rate in both our Weekly Perspectives and in the Chartbeat Publishing dashboard. Consequently, you’re likely to see a shift in the numbers in your dashboard — so we wanted to write a quick note explaining the change, why we made it, and what you can expect to see.

Defining return rate

Return rate, if you’re not familiar with it, is a metric designed to capture the quality of traffic that typically comes from a referrer. It measures the fraction of visitors coming from a given referrer who return to a site later — if 1,000 people come to a site from, say, Facebook, should we expect 10 of them to come back or 500? Depending on the answer, we might interpret and respond to a spike from Facebook quite differently.

While the intuition behind return rate is straightforward, the actual formula used to calculate it is a bit more up for grabs. Up until now, we’ve calculated return rates using the following formula:

CodeCogsEqn (3)

That formula roughly captures a notion of “how likely is it, for a given visit from Facebook, that that visit will be ‘converted’ into a return?”


As we’ve talked through that definition over the past year, we’ve come to realize that it’s more natural to phrase returns in terms of people, not visits — to ask “how likely is it, for a given visitor from Facebook, that that person will be ‘converted’ into a return?” Hence, we’re now using the following calculation:

CodeCogsEqn (4)

So, rather than speaking in units of “visits,” this definition speaks in units of “visitors” — a seemingly small (but significant) change.

In addition, we’re now only counting a return if it’s at least an hour after the initial entrance, which corrects for a pattern we sometimes see where visitors enter a site and then re-enter a few minutes later.



What’s changing?

It’s likely that the return rate numbers in your dashboard and Weekly Perspectives will drop under this new definition. To help you sort out whether your numbers are trending up or down, we’ve gone back and recalculated reports using the new methodology, going back to the beginning of June.

We hope that the transition to the new definition is painless, but if you have any questions, feel free to comment or get in touch with me at

On March 31, the Media Rating Council (MRC) announced it was lifting its advisory on viewable impressions for display advertising, bringing the industry one step closer to transacting on viewability for the first time. The point at which publishers are asked to deliver highly viewable campaigns is rapidly approaching. If you haven’t started to develop a strategy to maximize the viewability of your ads, I’d wager that in the next three months, you will.

There are many tactics that can be applied to improve your ads’ view ability: ensuring fast ad loads; lazy-loading advertisements; and redesigning a website to feature always in-view units.

One issue has gotten surprisingly little discussion, though: Ads are much more viewable on pages that people actually want to read. Take a look at the following figure, which was computed across a sample of a billion ad impressions across the month of May 2014.

Screen Shot 2014-06-19 at 12.00.22 PM

We see there’s a strong relationship between what fraction of ads are seen and how long a person spends reading the page: as Engaged Time increases from 15 seconds to one minute, viewability goes up by over half, from 37% to 57%. Visitors who read for more than 75 seconds see more than 60% of advertisements.

This isn’t too surprising. Of course, people who read pages more deeply see more of the ads on the page, but it’s still worth taking note. We’ve argued for years that articles with higher average Engaged Time should be promoted because they represent the articles your audience is most interested in, and—in the days where viewability is more critical than ever—promoting your most deeply read articles makes good business sense, too.

Want more? Download the Chartbeat Quarterly.

Want to know more about traffic sources how they can help you understand your audience’s behavior? Download our guide.

Over the past year, we’ve published extensive research on how to use data to understand and build your audience — everything from the effects of Engaged Time to scrolling behaviors and traffic sources driving traffic to the sites in our network. All of the data in those pieces are combined from a set of customers who allow us to use their data in anonymous, aggregated form. Looking at statistics aggregated from across a wide swath of sites is interesting because it lets us identify network-wide facts.

But, subtle patterns often get averaged out, so it’s hard to tell a nuanced story using aggregated data. Today, in partnership with New York Magazine and Rick Edmonds and Sam Kirkland of Poynter, we’re excited to present something different: a deep look into the data for one site, New York Magazine’s, about what factors drive visitor loyalty. (A quick note: This data is presented with the consent of New York Magazine and Chartbeat never shares customer-specific data.)

If you’re going to read one piece, I’d highly encourage you to click over and read  the Poynter team’s piece, which contains much of the data given below, as well as extensive feedback from the Vulture team. But, we also wanted to present our own take on the data, which you’ll find below. Our goal is less to provide answers than to get you thinking about what questions you might ask of your own site.


How We Define “Loyalty” and Why It’s Important to Measure

Before we can look at how visitors become loyal to a site, the first thing to do is define loyalty. Informally, by “loyal” we mean something like “a person who is highly likely to continue to return to the site across time.” For instance, a person might be loyal to the site of their daily newspaper. One way of getting toward a specific definition using the data is by asking how many times a person must visit before we’re nearly certain they’ll continue to return. In the figure above, we plot the probability that a person will return to, given the number of times they’ve already been to the site.

There are perhaps three things worth noting on this plot:

  1. Visitors who have come once so far in a month are just over 20% likely to return.

  2. That rate of return climbs rapidly until we reach visitors who have visited five or six times. Once a person has come five or six times in a month, we can be highly confident that they’ll continue to return.

  3. The downward slope on the right side of the graph is a windowing effect because we’re looking at one month of data: people are unlikely to come every single day in a month, so once a visitor has come more than about 22 times their probability of returning more times begins to decrease.

Based on this, a reasonable definition of a “loyal” visitor is one who visits at least five times in a month — after a person has come five times, we have a strong belief that they’ll continue to come back.

The Relationship Between Time of Day and Return Rate

After asking if visitors returned to the site, the next question was when visitors returned. One of the most striking data points we found was that visitors are far more likely to return at the same time of day as that of their initial visit — those who first visit the site today at noon are most likely to come back to the site tomorrow at noon, and so on. While that pattern is significant throughout the day, for Vulture it’s substantially stronger for visitors who come in the afternoon and evening, as demonstrated in the figure below.


In this figure, we’re comparing two sets of visitors: those who first arrive on a Wednesday between 10:00 a.m. and 10:59 a.m. and those who arrive on the same day, but between 6:00 p.m. and 6:59 p.m. The red lines show what hours of the day the 10 a.m. visitors return to the site throughout the rest of the month, and the blue lines represent the same statistics for the 6 p.m. visitors. For both audiences, the vast majority of time spent on other days of the week is at the same time of day — for instance, the 10 a.m. audience is most likely to return on Tuesday, Wednesday, or Thursday at about 10 a.m. What’s striking, though, is that the 6 p.m. audience spends dramatically more time on site throughout the week when compared to the 10 a.m. crowd. It’s worth noting that, though we’re showing traffic from Wednesday morning and evening, the basic pattern holds for those who arrive at other hours on other days.

One theory might be that this variation is caused by a difference in topics consume — perhaps, for instance, readers are engaging with Vulture’s TV coverage during the afternoon and evening. Interestingly, we saw no evidence that this is the case: the breakdown of traffic by topic is roughly constant throughout the day. On the other hand, this variation in return times lines up extraordinarily well with device usage. In the early daytime, when traffic is less likely to return, upwards of 40% of traffic is mobile. In the evening, when traffic is much more predictable and more likely to return, mobile falls to only 22% of overall traffic.

This data raises more questions than it answers: What can be done to get the morning audience to come back more frequently? How can editors take advantage of the daily patterns of their evening readers? Answering those questions is out of the scope of this article, but the upshot here is that there is a hugely interesting opportunity in understanding behavior as it relates to time of day.

Improving Return Rates of New Visitors

Obviously, one key challenge for any publication is in getting new, incidental visitors to move down the funnel toward loyalty. We saw three factors that exhibited significant influence over a new visitor’s probability of returning: how they arrived at the site, the type of content they landed on, and how much time they spent reading.

Vulture’s top referrers are similar to what we see across the internet, as are their relative rates of return. Unsuprisingly, new visitors coming from its sister site are most likely to return (22%), followed those from Twitter (16%) and Buzzfeed (10%). Perhaps surprisingly, the length of an article proved to be a strong predictor of likelihood to return, as shown below.


Stepping through this graph from left to right:

  1. Visitors who land on the shortest articles are extremely unlikely to return, but their probability of return rapidly increases from there.

  2. Those who view the Vulture homepage, forming the first peak at about 3900 pixels, are substantially more likely to return than those who view average-length articles — this article, for example — which are 4000-4500 pixels high.

  3. However, those who visit longer articles — this article, for example — are substantially more likely to return.

We see similar trends when we look at the time that a visitor spends reading whatever page they land on.


Visitors who spend substantial time reading on the first page they land on are also much more likely to return to the site. Overall, this confirmed an editorial hunch the Vulture team had, that they were better off moving away from extremely short pieces of content.

But that’s the Vulture team specifically; shorter posts may work best for your site. We dove into this study with precisely because every site is different: the content is different, the people visiting are different, the goals and metrics are different. I hope you and your team will see this data as a starting point for everything you can be looking at and acting on. There’s a lot more richness to your site’s data than purely traffic numbers. If you need help getting started and knowing what to look for — Chartbeat or not — just send me an email at