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Recently, there’s been a big push for social over search – the idea has emerged that social channels are the main, if not only, sources that consumers turn to to get their news. While it’s true that social promotion is an important part of anyone’s content strategy – we know Facebook isn’t going away anytime soon – Google still drives more traffic than any other referrer. This is especially true during big news events. So how should this affect your strategy during extraordinary news events?

 

Search vs. Social Traffic During Election Day

We’ve written before about how search leads social in the early hours of major events. Take, for example, our analysis of Brexit, where we note that in the hours leading up to polls closing in the UK, search overtook social traffic. We concluded that this behavior happens overwhelmingly in big news events as people proactively seek out news, instead of passively ingesting information from their Facebook newsfeed.

Following these conclusions, we decided to delve a little more into this behavior using the US Presidential election as a case study. Like Brexit, the US election represents a special type of news event: one where publishers have prior knowledge of the event and can prepare ahead of time to optimize their content strategy.

The graph below shows the breakdown of publisher traffic coming from Google versus Facebook between 12AM on Election Day (November 8th) and 12AM on the 10th (Eastern Standard Time), as compared to the trends we see on an average day (denoted by the dotted lines).

Search vs Social Traffic During Election Day

On an average weekday, we tend to see about 36% of referred traffic in our network coming from Facebook, while 41% comes from Google. As we can see in the graph above, Google traffic throughout Election Day was already performing higher than expected. From midnight on November 7th until polls started closing on the 8th we saw an approximate four point increase in Google traffic and six point decrease in Facebook traffic.

As the first US states started closing their polls at 6PM, there was an even surge in traffic from Google as readers became more and more entranced by the final results. This trend continued until slightly after the last polls closed in Alaska at 1AM EST. During this period, Google traffic shot up by an average 14 percentage points across our network. This uptick in Google referrals corresponds to readers proactively looking to Google for information about election results.

After the race was called at 3AM, we see a very noticeable swing to Facebook as readers flocked to share the news of the election results, read opinions of others in their peer network, and consume the overwhelming amount of post-election commentary.

 

Takeaways For Publishers

So what does this tell us about reader trends during Election Day, and how they relate back to trends during major breaking news events? We see three main takeaways:

  1. Concentrating on SEO strategy before big news events is critical to maximizing traffic during the event. The majority of referred traffic in the first few hours of an event will inevitably be coming from search.
  2. The most successful stories on Facebook tend to have an emotional versus strictly informative lens, as seen with the shift of traffic to Facebook after the election was called. So while search traffic is important to harness during breaking news events, keep in mind that social traffic picks up again in the aftermath.
  3. Despite the growing commentary on social media contributing to “filter bubbles” in the news people seek out and ultimately engage with, during large impactful news events readers don’t settle for what materializes on their Facebook feeds. We still see major trends in readers proactively scouring the web to stay up to date and informed on the progress of events as they unfold.

Stay tuned for more election trends, or get in touch with any questions.

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.

twitter-percent-returning-users

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.

twitter-visits-per-returning-user

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.

engagement-twitter

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.

ad-inventory-viewability

ad-inventory-average-exposure

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.

value-of-ad-inventory