Archive for the ‘Data Science’ Category

ICYMI: Last week, our Chartcorps and Data Science teams hosted a Q&A webinar covering the latest mobile research from our spring Quarterly issue. You can check out a full recording of the webinar (or read the Cliff Notes version) here.

You asked, we answered.

Attendees asked lots of great questions during last week’s mobile research findings webinar—some of which we didn’t have a chance to answer during the live Q&A session:

What are some strategies for increasing pageviews per user on mobile devices?

Whether you’re promoting your site on social media or deciding which articles to put on a landing page, consider sending traffic to pages with high mobile recirculation, which tend to drive readers further into your site.

Filter on the “mobile” platform in the Chartbeat Publishing Dashboard and sort your Top Pages section by Recirculation to quickly see which articles are doing the best at driving your audience deeper into your site.

To increase mobile recirculation on a particular page, take a look at where mobile visitors scroll to before leaving, either in real-time with the Heads Up Display or historically with Report Builder.

How can digital publishers tailor content better for mobile platforms?

With mobile platforms, the best way to optimize for your audience is to make sure that the most viewable part of the page—just below the digital fold—is set up for success. Are you giving your audience a chance to move on to additional content before that first big drop off? And if they do drop off after that point, are they at least leaving with a strong takeaway from the article?

If you’re constantly tracking your mobile audience, you’ve probably also noticed trends in terms of which referrers typically send traffic via mobile devices, and even what time of day you usually see the highest amount of mobile traffic. These insights can all inform you on when and where to promote your mobile stories.

Track the second-by-second, pixel-by-pixel attention of your audience with our Editorial Dashboard.

Have you noticed a difference in mobile consumption with the increased popularity of “phablets” — larger screen phones like the iPhone 6 plus or the Galaxy Note 4?

Right now we break down devices into three categories—desktop, mobile, and tablet—so we haven’t looked at any trends yet in the various kinds of mobile devices, but we’ll keep it in mind.

How do you measure scroll depth in the case of these studies?

To measure maximum scroll depth, we look at the the furthest point a user scrolled on the page, as tracked by our pinger, a piece of Chartbeat javascript that runs in your browser. Note that maximum scroll depth isn’t necessarily the point at which you left the page, although the two are often the same number.

To get the maximum scroll depth data for the Data Science Quarterly, we used the same data store that feeds our Report Builder.

What kind of correlation have you seen between scroll depth on landing pages and pageviews per visit?

We haven’t done a study on this correlation yet, but if you’re using the Report Builder tool you can build recurring reports tracking pageviews and unique visitors for a specific page.

Set metrics to “pageviews” and “unique cookies” and set a page path filter equal to “/” — the path of your homepage — and filter on device type equals mobile. That way you can keep track of the numbers for your own homepage day in and day out and discover any trends that might be unique to your own audience. (For more tips and tricks check out Report Builder 101).

Learn more about our suite of editorial tools here. Questions? Shoot us a note or check out our Chartcorps Support Site.

Did you miss last week’s webinar on mobile findings from the new Data Science Quarterly? Saw it and want to review the fundamentals? No fear. You can watch Andy and Chris here.

Want the quick scoop? Here’s a recap of their discussion:

During the webinar Chris from Chartcorps and Andy from our Data Science team walked through the latest research from our new Data Science Quarterly to help you better understand the data and leverage the insights we’ve found. Particularly, they talked about viewability and user behavior in a mobile context. Some questions they considered:

  • What parts of the browser page are most viewable?
  • What is the relationship between scroll depth and viewability?
  • How are mobile and desktop viewing experiences different?


    The most viewable part of the page for mobile browsing is just below the digital fold, but for desktop browsing it’s above the fold. Nevertheless, the majority of users exit near the fold on both platforms.

    For mobile, we see a lot more scroll depth (vs. desktop). At certain points on the page, there are pronounced peaks in active users. Andy explained this incongruity in terms of scrolling methods: on phones, users tend to scroll in more discrete chunks, whereas on desktops, scrolling is much smoother. As a result, mobile users tend to scroll much further down the page.

    Because of the unique nature of mobile scrolling, ads that must be reached by scrolling a few times see an uptick in viewability.

    That said, it’s important to note that scroll depth is a proxy to viewability—a user might scroll to the bottom of the page so quickly that the ads do not qualify as viewable.


    When you understand how your audience consumes content, you can better adapt to create a more engaging experience. If, for example, you know where on the page reader engagement is coming a halt, you can adjust elements on the page or rework your actual content to keep your audience actively reading.

    These are the kinds of insights and tips we prioritized when building the new Heads Up Display. Chris closed the webinar with a sneak-peak of the new Heads Up Display and all its features—many of which are mobile-focused. In real time, you can analyze what mobile users see, how they interact with the page, and, where they are spending time on the page.

    By pairing real-time metrics like Engaged Time or scroll depth with historical data from our Report Builder tool, you can instantly act on opportunities—and learn from those actions to build out future strategy.


    Since you know that scroll depth is strongly linked to viewability on mobile devices, you can build historical reports to evaluate which mobile pages correlate with those metrics. Using Report Builder, you can see a ranking of your most scrollable articles: create a one-time report using a “device type equals mobile” filter, select “scroll starts” and “average scroll” as your metrics, and group by “page path”.

    You could also investigate a specific article’s mobile performance by setting its page path as a filter. That way you can track it day in and day out to see if any changes you’re making to the article’s layout are affecting users’ scroll depth. (For more sample reports check out our Report Builder 101 post).

    For the whole shebang, including more historical reports, watch the full webinar.

    We also had some great follow up questions from our webinar attendees. Check out the answers from Andy and Chris here.

    Questions about the data? Shoot Andy an email. Want to know more about our Heads Up Display or Report Builder Tools? Get in touch.

    Regardless of your newsroom’s size or how many articles you publish every day, chances are you’ve got a Twitter account.

    What’s more, you’ve likely tried, to greater or lesser success, to leverage the social network for the distribution and promotion of your content. But once your thought-provoking, 140-characters-or-less message is dispatched, what happens next? Will the time and effort you spent pitching your editor pay off? Will you draw in readers who will actively engage with the content? Will you manage to convince readers to explore additional articles? Could you even convince users to come back over and over again? Or, is all that effort lost in the Twitterverse, drawing in a few readers who come and leave, never to be seen again?

    These are just a few of the questions we’ve been trying to answer here at Chartbeat. But rather than placing all visitors who come from Twitter into a single class and making the assumption that they all behave the same way, we decided to take a deeper dive with an eye toward nuance. We examined the behavior of readers who come from tweets published by content owners (first parties) versus those coming from independent agents (third parties).

    To test our assumption and measure different forms of engagement, we decided on four metrics: average Engaged Time; average number of other pages a viewer visits on a site within two hours of their first visit; percent of users who return after initially visiting; and of those users who return, the number of times on average they will return in the next 30 days. Roughly broken down, this gives us two metrics to look at short-term reader value (engagement and redirects), and two to look at long-term reader value (percent retained and rate of user return).

    From previous experience and assumptions we made, it seemed to us that readers coming directly from a content owners’ tweet would probably already be a member of that publisher’s loyal audience. It would therefore seem logical that these users show qualities similar to that of loyal readers—chiefly that they exhibit a higher than average return rate, and read more pages when visiting.

    So it wasn’t surprising that when observing the percent of users that came back, readers from first-party sources showed returning rates about 15% higher than readers from third parties. During their initial visit, readers coming from Twitter also tend to stick around longer, with first-party consumers reading on average three pages during a visit, compared to non-social traffic’s one page. The difference between first-party and third-party social consumers, however, does not differ significantly.


    The number of times returning users came back however was surprising. Of those users who came back at all, users coming from a first party returned on average 8 to 10 times. A similar user, though, who came from a third party came back 11 to 13 times. This may suggest that after passing that retention barrier and convincing a reader to come back, the users you receive turn out to be much more valuable, as they return more often, and help bolster your current loyal population.


    When looking at the time a reader engaged with a page, we found that readers from third parties actually engaged with content significantly longer than first-party readers. While first-party consumers engaged with content about the same amount as any other user, regardless of where they came from (averaging between 37 and 39 seconds), third-party readers engaged on average between 42 to 45 seconds. (Calculated with a p-value of p < 0.01) These differences, while seeming small, can lead to practical differences in engagement from a few seconds to nearly a 40% difference in Engaged Time.


    Though there are many reasons that these differences may be occurring, one possible conclusion lies in what attracts readers to engage with content. Users who follow publishers on Twitter are apt to know more about the publisher’s content; consequently having a greater sense of what type of content they want to read. Loyal readers, as opposed to new readers, may therefore be skimming through content, knowing they will come back later for follow-up stories, or to learn more. Non-loyal readers, however, who are generally the readers coming from third-party tweets, come due to the referral of a friend. These readers may engage deeply with content due to the personal connection with the recommender of that content.

    So, to answer the initial questions of whether your tweets actually matter to the health of your site: Of course they do—you already knew that. Your tweets are vital to your loyal audience, and bring in readers who return more often and consume higher quantities of content than readers coming from anywhere else. Don’t forget about the importance of making content people want to tweet about, though! Because it turns out people actually listen to the recommendations of their friends, deeply engage in the content before them, and if enamored enough to return in the future, turn into fiercely loyal members of your site’s virtual population.

    Note: This post was co-authored by Kris Harbold and Andy Chen.

    By looking at the amount of time visitors are exposed to ads on different parts of a web page, we can get a sense of how much value your ad inventory generates for advertisers. And the results might not be what you expect.

    It turns out, traditional advertising heuristics about which parts of the page are most valuable are wrong. Let’s look at figures 1 and 2, which show us the relationship between where the middle of an ad is positioned and how it’s viewed. We look at how ads are viewed through two metrics: viewability and average active exposure time. An ad is viewable if at least 50% of the area is in view for at least one continuous second. Of those visitors that have the opportunity to view the ad, average active exposure time is how many seconds they’ve spent actively browsing the page while that ad is in view.



    As we might expect, both viewability and average active exposure time eventually trend downward. But this broad downward trend isn’t without a few blips. In particular, let’s consider the conspicuous dip in both viewability and average active exposure in the topmost 500 pixels of the page. Notice that viewability and average active exposure drop to a level that isn’t reached until 1,500 and 2,500 pixels down, respectively. This can be explained by the fact that visitors tend to start scrolling down the page right after they arrive, sometimes even before the page fully loads. Then, visitors settle on engaging content further down the page, which results in the subsequent increase in our metrics.

    So what does this mean for your inventory? We know that the value of an ad position depends not only on its viewability but also on average active exposure. It follows that the most valuable ad real estate isn’t at the very top of the page, but rather just below the top, where both our metrics peak out. And while both metrics decrease below the fold, we see that average active exposure is remarkably resilient: a visitor that sees an ad 2,500 pixels below the top will still spend an average of 10 seconds of engaged time with the ad in view. That’s plenty of time for the ad to have an effect on the viewer.


    When we look at data from across the Chartbeat Universe, one thing that stands out is the difference in how people from around the world engage with content—how long they spend reading, how far they scroll down the page, and which devices they use. Here’s a broad look at how visitors from different regions of the world spend their time and give their attention.