Audience Building on A Case Study

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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

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