Zarrella’s Hierarchy of Contagiousness

If you’re familiar with Maslow’s hierarchy of needs, the title of this post will ring a bell.

I first began to formulate this framework as a model for understanding how ReTweets work (If you’re interested in my Science of ReTweets study, check out my live webinar Friday). But I think the concept extends far beond just Twitter in fact, it is a framework for understanding criteria required for an individual to share any kind of content. Each of these criteria has a corresponding action we as marketers can take to increase the contagiousness of our content and ideas.

As you can see there are three criteria and together they form a funnel of decreasing volumes, like a sales conversion rate funnel.

  1. A person must be exposed to your content to ever have a chance of spreading it. This means they have to be following you on Twitter, fans of your page on Facebook, on your email list etc.
  2. The person must become aware of your specific piece of content before they can spread it. They have to read your Tweet or open your email.
  3. That person must be motivated by something (generally in the content itself) to want to share it with their contacts.

Every piece of content, social network and campaign has vastly different conversion rates at each step of this process but, to understand the scales involved, it helps to visualize a hypothetical set of percentages. The gray boxes on the left of the graphic above represent assumed numbers: if you email 900 people and 20% of them notice and open the email and then 10% of those readers are forward it to a friend, your email was shared 18 times.

At each step we can change the numbers in our favor:

  1. Increase the number of people exposed to your content by building your reach. Get more email subscribers or Twitter followers.
  2. Create attention grabbing content. Do lots of testing on your subject lines to increase open rates.
  3. Include powerful viral calls to action.
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Data Shows Articles with Digits May be Shared More on Facebook Than Those Without

More new Facebook data, continuing this series.

The next Facebook sharing data point I analyzed is the presence of numbers (in digit form, 1 through 9) in titles. In a wide range of marketing arenas digits have been shown to perform very well. They tend to help conversion rates in the form of prices and on social news sites like Digg “Top 10” style posts have always done well.

The difference isn’t huge but according to my data, articles with digits in their titles tend to be shared more on Facebook than stories without digits. I found that most articles in my data set didn’t use numbers in their titles, and you can see the scale of difference in volumes in the gray bars at the bottom of the chart.

For details on my methodology start with this post, then read

Data Shows: Articles Published on the Weekend are Shared on Facebook More

When I started posting my new series of Facebook data points, one of the most requested graphs was the days of the week (and times of day, which is coming soon) that are best to publish on to get lots of Facebook shares. What I found when I looked at days of the week is at first a little unexpected, but upon further thought fairly logical.

While I found less articles posted on the weekends (notice the gray bars at the bottom of the graph which indicate volume of URLs analyzed for each day), those stories that were published on the weekends tended to be shared on Facebook more, on average, than stories that were published during the week. The reasons for this probably include the fact that more than half of companies in the US block Facebook, so people can only use the social network at home, on the weekends. Additionally, the mainstream Facebook audience does not use Facebook for work.

The takeaway? If you want your article to be shared on Facebook by your readers, try posting it over the weekend.

For information on my methodology, start with this page. For this data point I’m using over 5000 stories and “average” is the interquartile mean which is less sensitive to outliers. The 0% line indicates the average number of “shares” stories from each site in my study get, when the line is above 0% it means that stories on that day are shared more than the average, and when it is below, they’re shared less. If you’re curious why it appears most of the stories in the data set are above average, this is because of the difference in the volume of published stories on various days.

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Data Shows that Facebook is Better for Video Marketing Than Twitter

Continuing my series of Facebook sharing data (if you’re curious about my methodology, read the first post), I looked at articles that had the word “video” in their titles.

It turns out that those stories that indicated they contained videos were shared more than the average story on Facebook, while they were actually shared less than the average story on Twitter. This is likely because the Facebook platform makes it easy to embed multimedia content into updates while Twitter does not.

The takeaway here? Facebook may be a better platform for your videos to go viral than Twitter.

And again, if you have any other datapoints you’d like to see, please let me know. I’m really excited about my new Facebook analysis capabilities and I’ve got a ton more stuff planned for you.

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Data Shows: “Twitter”-Centric Stories are Not Heavily Shared on Facebook

A couple of weeks ago, I started collecting a new dataset and I’m really excited about it because it’s the first time I’m collecting data from the mother-of-all social media sites: Facebook.

I’ve begun by capturing links posted to social media sites from 10 extremely popular news outlets. Some of the top blogs, both mainstream and geeky, as well as a handful of the most web-enabled newspapers of record. Then I’m counting the number of times those links are shared on Facebook (in three different ways) and on Twitter (through good old ReTweets). I then find the average number of “shares” for links posted to each site and compare the individual stories to the average in percent form and then combine those numbers to get a percent “effect” as a positive or negative number away from the average.

At this point I’ve got well over a thousand links and counting with full information stored. I’m also getting better at retrieving the data I want faster and more reliably.

I’ve already got a bunch awesome of things to show you, so keep your eyes out for more, but first lets talk about “meta mentions.” A meta mention is when someone on a given site, say Facebook talks about Facebook, or when someone Tweets about Twitter. Typically with ReTweet data I’ve seen that talking about Twitter gets you a lot of ReTweets, and this is to be expected since most people on Twitter are into talking about Twitter. Of course with older technologies like email, people aren’t really “into” email so much as they just use it to get stuff done.

So far my data shows that while articles that use the word “Facebook” in their title get shared more often than the average story on both Facebook and Twitter, stories that mention “Twitter” actually get shared less on Facebook. My assumption here is that Facebook is less of the early adopter crowd that wants to sit around all day and talk about Twitter, while Twitter users are more likely to be social media geeks.

The key takeaway is to know your audience. If you want to go viral on Facebook, don’t talk about Twitter.

And since I’m just starting to get into Facebook data like this, what kind of stuff would you guys like to see?

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