I’ve often said that the best use of Twitter is as a broadcast medium. You should be creating a ton of interesting content and sharing it with your followers. To that end I’ve done a bunch of research on how to optimize the clickthrough rate (CTR) of the links you’re tweeting.

For the purposes of this data, I’ve calculated CTR as the number of clicks on a tweeted link divided by the number of followers the account had when it tweeted that link.

Below is an infographic presenting some of my past findings as well as some entirely new data I’ve found about increasing CTRs on Twitter.

If you missed my webinar The Science of Social Media check out the on-demand recording now for more social media science like this. And don’t forget to take this quick survey to tell me what other social media data you want to see.

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One of the most popular webinars I’ve ever worked on, The Science of Timing was also one of my favorite. If you haven’t seen it, go check out the on-demand recording now.

In it, I present data I’ve collected over three years about the effect timing has on a variety of online marketing activities, including blogging. Below is an infographic that represents a collection of the three most important stats I have about when to publish blog posts.

In the webinar, I also make the point that my results are based on huge aggregates of thousands, millions, or even in some cases, billions of lines of data. They might not represent the exact best times for your industry. What they represent is a set of times to experiment with, using your own audience.

As in the medical science field, researchers conduct experiments with hundreds or thousands of subjects and find best-practice courses of treatment. In individual cases, doctors start with these best-practices and if they work, great. If they don’t work, they experiment with the next course of treatment. Marketing science is like this. Use my data as a starting point for your own experimentation.

Since most Americans live in the eastern time zone, the times below are ET.

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I’ve studied the relationship between Twitter success and politics before, and since this year is a big year in elections, I figured I’d do it again.

This time I looked at how the 4 front-running republican presidential candidates are doing on Twitter. I analyzed the obvious numbers, followers, retweets and mentions, but I also looked at a few of my favorite, deeper metrics: retweets-per-follower, link-percentage and reply-percentage. I also looked at Twitter “penetration” in two upcoming primary states: South Carolina and Florida. Notice anything that surprises you in this data?

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One of the most interesting patterns I’ve found while studying social media and marketing data is what I call in my book “contra-competitive timing.” I’ve found in numerous cases that the most successful times and days to publish new content are off-peak times.

It’s like when you’re at a noisy party and it’s hard to hear the person talking to you 2 feet away, but suddenly you say something awkward and the room quiets down. Now everyone can hear you. The same is true with the internet. When there is less other noise to compete with (ie fewer Tweets, emails, blog posts, etc) your content can gain attention more easily.

The infographic below showcases some of the best examples of this phenomenon I’ve identified.

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If you like myth busting social media data like this, be sure to buy my newest book, Zarrella’s Hierarchy of Contagiousness.

One of the social media questions I’m most interested in is about whether Twitter is best suited for conversation or broadcasting. To many people’s surprise, I generally find myself on the broadcast side, and most of the data I’ve analyzed seems to back me up.

Just yesterday, I started a little informal Twitter poll and found that respondents were pretty evenly split between broadcasting and conversation.

This time, I looked at more than 100k randomly chosen active Twitter accounts and their Tweets. I analyzed the percentage of their Tweets than contain a link as well as the percentage that began with an @. I also measured the percentage of their last 100 Tweets that were ReTweeted.

I found that accounts that had high link-percentages between 60% and 80% had the most ReTweets and accounts with low reply-percentages between 0% and 10% had the most ReTweets.

If you’re interested in ReTweets, broadcasting lots of interesting content works much better than “engaging in the conversation.”

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2011 was a fun year for me, I moved to Las Vegas, set a Guinness World Record and released my third book: “Zarrella’s Hierarchy of Contagiousness.”

Also during 2011 I published some of my favorite data. As the year wraps up and we get ready to celebrate the coming of 2012, here’s a list of my 10 most popular posts.





New Twitter Data: Optimal Link Placement for Clicks
A heatmap showing where to place links in your tweets to get the most clicks.

[Infographic] 5 Scientifically Proven Ways to Get More ReTweets
An infographic-style collection of my top data about how to get your tweets ReTweeted.

10 Words That Will Increase (or Decrease) Your Twitter CTR
A list of words correlated with higher (or lower) clickthroughs on links in Tweets.

New Data: 33% of Facebook Posting is Mobile
Data showing that a sizeable percentage of all Facebook content creation is done from mobile devices.

[Infographic] 5 Scientifically Proven Ways to Get More Followers
Another infographic-style collection, this time with data on how to get more followers.

Infographic: 5 Questions and Answers about Facebook Marketing
Data-based answers to five important questions about Facebook marketing.

New Data: Tweet Lots of Links to Get Followers
Statistics showing a positive correlation between the number of links a Twitter accounts tweets and the number of followers it has.

New Data: Post to Your Facebook Page Every Other Day for the Most Likes
Data showing the optimum Facebook wall posting frequency.

New Data: “Engage in the Conversation” May Not Actually Work
My most controversial post of 2011, this data challenges the popular “engage in the conversation” myth.

Infographic: The 20 Most (and Least) Connected Words on LinkedIn
The words correlated most closely with connections (both positive and negative) on LinkedIn profiles.

What was your favorite post of 2011?

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If you like social media data and science like this, buy my latest book: “Zarrella’s Hierarchy of Contagiousness.

Science makes everything better. Seriously, it’s a proven fact. So of course I did some analysis about Christmas and found some surprising insights. Don’t get fooled by the unicorns-and-rainbows myths about the holidays anymore. Here’s the science. Enjoy and Merry Christmas and Happy New Years!

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The recent launch of Marketing Grader is a boon for me because it gathers a ton more data than its predecessor, allowing me to analyze all sorts of stuff I couldn’t before.

One such area of exploration, is the relationship between a brand’s Klout score and the successfulness of its website. Among its 38 metrics, Marketing Grader analyzes the Klout score of a brand’s Twitter account, the number of domains linking to the brand’s website (as reported by SEOmoz) as well as the number of unique visitors it gets (as reported by Compete).

When I analyzed the relationship of these numbers I was surprised at what I found.

Brands with higher Klout scores tended to get more visitors and more links. And when they have really high Klout scores (like above 70) they get far more traffic and links than other sites. Of course this isn’t causation, but correlation. It means that Klout may be a surprisingly effective measure of overall online marketing success.

The data tells us, not only only does Klout-measured social media influence translate into more traditional measures of web marketing effectiveness, but also that the web is a very winner-take-all kind of place. There are essentially two kinds of brands online, those with a ton of influence, traffic and links and those with very little.

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If you like social media data and science like this, my latest book “Zarrella’s Hierarchy of Contagiousness” is now only $1.99 in Kindle Edition (which will work on any computer or device). Buy it now!

After compiling a dataset of more than 200,000 link-containing Tweets to generate the CTR heatmap, I decided to dig into what words, phrases and characters correlate with higher (or lower CTR). Below are my findings. And yes, I know correlation isn’t causation, but to quote Edward Tufte: “it sure is a hint.”

The first “word” I analyzed was the hashtag. I found that there is very little difference in the CTR of a link whether or not it is accompanied by a hashtag. They don’t hurt or help.

Perhaps the most surprising thing I found is that Tweets that contained the words “daily is out” (which is the signature of the paper.li automated Tweets) had much higher CTRs than Tweets that did not. It turns out, paper.li works, at least for driving clicks.

I also found that Tweets using the word “via” as a method of ReTweeting had a higher CTR.

Somewhat unsurprisingly, Tweets specifically directed at a person tend to have a higher CTR since I’m much more likely to pay attention to a Tweet if it’s sent to me. This is probably the reason the paper.li Tweets do so well, they tend to contain 3 @-mentions.

Like the “via” ReTweeting syntax, Tweets with “RT” in them had a higher CTR than those that did not.

My data seems to indicate that when you politely ask people to do something, like a nice social call-to-action, they’re likely to do it.

Again, specifically asking readers to check out a link seems to result in a higher CTR than not doing that.

Now we get to the words that correlated with lowered CTRs. The first one I found was “@addthis” which is a signature for the Tweets that come from the automated AddThis sharing functionality.

Tweets that mention the word marketing have a lower CTR than Tweets that don’t mention it. Most marketers won’t be surprised by this.

The automated Tweets that come from the GetGlue system have a lower CTR than Tweets that don’t come from that system.

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If you like social media data and science like this, my latest book “Zarrella’s Hierarchy of Contagiousness” is now only $1.99 in Kindle Edition (which will work on any computer or device). Buy it now!

I’ve studied data from a variety of social media platforms, but never LinkedIn. So a few days ago I got around to playing with it a little bit and found a few interesting things.

First, I looked at the words in the title and summary of more than 40,000 randomly selected profiles and how they correlated with the number of connections those profiles have. I found that there are a handful of highly connected positions (unsurprisingly investors and recruiters) as well as under-connected titles (religion and healthcare).

The infographic below shows the 20 most connected common words (and the 20 least connected words) found in titles and summaries. The vertical axis is the percentage more (or fewer) connections profiles using those words had compared to the average in my dataset.

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If you like social media data and science like this, my latest book “Zarrella’s Hierarchy of Contagiousness” is now only $1.99 in Kindle Edition (which will work on any computer or device). Buy it now!

One of the questions that I’ve been asked the most in the years I’ve been doing Twitter data analysis is where in a Tweet is the best position (beginning, middle or end) to include a link to get the most clicks. I had always assumed the end was the best, so I never thought much about the question.

But about a month ago, I decided to actually look at the data about it and test my assumption. Over the course of the next few weeks I gathered 200,000, random, bit.ly-link-containing Tweets. I used the bit.ly API to calculate a click through rate (clicks on a link divided by number of followers of tweeter). And then I analyzed the relationship of the link’s position inside the Tweet and it’s CTR. I figured the best way to visualize this would be through a heat map.

The entire heat map symbolizes a Tweet, with areas to the left in the beginning of the Tweet and areas to the right at the end. Dark red bars represent a position with a high CTR and light-red or white bars show a position with a very low CTR.

It turns out that the best area for clicks is about 25% of the way through the Tweet. Do these findings match your experience? Will you be experimenting with this placement?

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Over the past few days, we moved to Las Vegas. Yes that’s right, I actually live here now.

Among many things, one of the parts of the city I admire the most is the wealth and depth of marketing knowledge and science. So to do my part and try to add to it, I did a little analysis of Vegas-based ReTweeting activity.

I studied more than 400,000 random Tweets that contained the word “vegas” and among them, found more than 60,000 ReTweets. On that data set, I did the analysis of ReTweet activity by hour and the most (and least) ReTweetable words. You’ll notice that activity tends to be much later in the day that in other parts of the country, people are waking and staying up later here. And looking at the most/least ReTweetable words, you’ll see that the words at the the top of the list are future and present heavy and talk about shows, parties and birthdays. While words at the bottom of the list are “salesy” words and include a lot about leaving Vegas and going home.

I also analyzed the official Twitter accounts of the 24 major hotel casinos on the strip and nearby and ranked them by “ReTweets-per-Tweet” based on their last 200 updates.

I’ll be doing more Las Vegas centric social media research in the coming weeks, so let me know if there’s anything specific you’d like to see me look at.

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My book is up for sale now! You can buy either the Kindle version or the hardcover. There are also bulk 5 and 52 copy packs. Either way, go grab a copy now.

The book is based on the years of research I conducted into contagious ideas and how to engineer them. It grew out of my flagship presentation: “The Science of Social Media” which I just delivered as a Guinness World Record setting largest webinar ever.

It’s a quick read and has lots of graphs and real data to help you combat the unicorns and rainbows of bad social media marketing myth.

So go check it out and let me know what you think!

And, if you like any of my work, or my wife Alison’s work, vote for our SXSW panel too!

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In preperation for my upcoming webinar, The Science of Social Media (register now!), I’ve developed a new tool: WhoReTweetedMe.com.

Simply enter the URL of a recent (between 1 day and 2 weeks old) blog post, click the button and wait a moment. You’ll see a report containing the timeline of Tweets to that URL, statistics about potential reach and average follower count of retweeters as well as a list of the 20 most influential users to tweet the link.

There’s also a bookmarklet you can drag to your bookmarks bar. Navigate to the page you want to analyze and click on the bookmark to see the WhoReTweetedMe.com report.

The tool is still very beta, so don’t be surprised if you see some errors, but I think it’s valuable enough to release now.

Let me know what you think.

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For more data, statistics and social media science, don’t forget to register for my Science of Social Media webinar.

Over the years, I’ve done a ton of research on Retweets. I’ve found 5 specific points that are the most powerful ways to get more ReTweets, so I rolled them up into one simple infographic. If you’re curious where this data came from, check out the two links above.

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For more mythbusting and social media science, don’t forget to register for my Science of Social Media webinar.

Over the past few years I’ve developed two models of contagious content: R₀ (pronounced: R Naught) and Zarrella’s Hierarchy of Contagiousness. Because of the way these two systems work, I put a lot of emphasis on reach building activities (getting lots of followers, likes and views) and metrics (number of followers and likes). Based on the reaction I’ve seen to some of my recent work challenging the hegemony of “engaging in the conversation,” I’ve come to understand that I may not have fully explained why number of followers is such a key metric.

Before an individual can share a piece of your content, three things have to happen. First, they have to be exposed to it (following you on Twitter or a fan of your page on Facebook). Second, they have to actually become aware of that content (I follow something like 8,000 people on Twitter and don’t see anywhere near everything they Tweet). Third, and finally, they must be motivated by something in that content to share it. Together, these three elements make up my hierarchy of contagiousness.

The conversion rate at each part of the hierarchy can be expressed as a percentage, the number of people who will complete that part of the process. And the three rates taken together represent R₀. Here, R₀ stands for reproduction rate, a concept taken from the study of infectious diseases called epidemiology. It is the number of new cases of an infection that a single infection will cause. If I have a cold and I give that cold to two people and each of them give it to two more people, the R₀ of that cold is two (or 200%). The R₀ of most biological pathogens is above one, but I’ve never found an idea with a sustained R₀ above one. Given a large enough population and a long enough time, the R₀ of every idea falls below one and the idea stops spreading.

Below is a simple graphic explaining my viral math with my hierarchy of contagiousness, the accompanying formula and some example numbers alongside.

If you have 10,000 followers on Twitter and you Tweet to all of them, you are exposing 100% of them to your content. (The exposure rate is 100% for most platforms, but for some, like email, you can segment your audience and only send to a small percentage). Let us assume 1% of them actually read your Tweet (an educated guess at an awareness rate based on observed click through, ReTweet and reply rates). That means your Tweet has the chance to motivate 100 people to ReTweet it. Assuming it succeeds with 1% of those users (another educated guess), your content will get one ReTweet.

Obviously, these percentages are made up and vary wildly, but try plugging a few of your recent pieces of content in and do the math backwards to estimate your R₀. If you have 20,000 followers and a Tweet of yours got 16 ReTweets, you have a 0.08% R₀. You can then assume that for every 1,250 new followers you ad, you would have gotten one more ReTweet.

As marketers, we can optimize at each step of this process, by building reach (number of followers) for “exposure,” using tactics like contra-competitive timing and personalization for the “awareness” step, and learning about viral triggers for the last “motivation” stage. Awareness and motivation tend to be the trickiest to optimize for, but they’re certainly worth it. However, the easiest is reach. Get more followers or likes, and you’ll get more shares (and in turn, more followers and likes).

I’ve found that some users are more influential than others, and this is generally means they themselves have a larger reach. But there aren’t very many things you can do as a marketer to attract a huge number of highly followed influencers to your content beyond the same tactics that you would use to attract a huge number of “normal” users. And I’ve also found that some users (like lebolukewarm in this example) can have influence larger than their reach would suggest, but it’s impossible to target these one-off, contextually influential users. You can only increase your sheer follower numbers and thereby increase the likelihood that you have users like lebolukewarm following you.

The number of followers (or Facebook likes, or blog subscribers) you have is the best measure of your social media reach. It’s elegant and easy to compare to your competitors. There are other, more complex (and expensive) ways to measure reach, but they aren’t much more useful than follower count. Your goal with reach metrics is not to measure every single possible edge case, but rather to measure most of your reach and monitor trends in it. The question you need to answer is simple, are you building your reach, or losing it?

And don’t get fooled by fake, hypothetical straw men arguments. If someone tells you that they’d rather have 100 “highly engaged” followers than 10,000 less “engaged” followers, ignore them. In reality, if you have 10,000 followers you’ll have a decent number of “engaged” listeners. (Unless you’re doing something really shady to get those followers.) In the vast majority of real cases, someone with 10,000 followers has more reach and influence than someone with 100.

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For more mythbusting and social media science, don’t forget to register for my Science of Social Media webinar.

My effort to analytically study the unicorns-and-rainbows myth of “engaging in the conversation” started by looking at the relationship between the number of followers a user has and the percentage of their Tweets that are “@” replies. I found that highly followed users are less conversational than those with few followers.

This time, I looked at the relationship between follower count and the percentage of a user’s tweets that contain links and are not replies. Using a sample of random, recently active Twitter accounts, I found a strong correlation. As the amount of links a user tweets increases the number of followers they have also increases. And users with more than 1,000 followers tend to tweet many more links than users with fewer than 1,000 followers.

The data is starting to mount to suggest that “engaging in the conversation” is a waste of time from a marketing and reach-building perspective, especially when compared to sharing content.

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