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Followerwonk Partners with Buffer To Optimize Tweet Scheduling

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Posted by @petebray

Today, we’re happy to announce a partnership between Followerwonk and Buffer to help you optimize your tweeting. We’re really excited to be teaming up with such a great product and company, and the combination of our apps really does advance the cause of our customers.

Before I dig into specifics of that relationship, I want to lay some groundwork to explain why we formed this partnership.

It all comes down to this little pearl:

Tweets are delicate

Tweets have a half-life of a mere 18 minutes. Poof! and their utility to reach new customers, drive traffic, and extend your reach is pretty much gone.

But it gets worse for our little tweets.

First, most of us have a heck of a job consistently coming up with good content to tweet day after day. It’s kinda like going to the gym: we start out strong, but most of us quickly fade to where we spend the entire time in the sauna.

Second, even if we do come up with lots of good content, we risk undermining our own performance.

I want to talk with you about ways to squeeze the most out of the content we do come up with. How can we maximally schedule our tweets to perform?

Cultivate your current audience

Given the gossamer-like nature of tweets, a simple first step is to schedule most of your tweets when your followers are most active.

This is where our relationship with Buffer helps.

First, go to Followerwonk and complete an Analysis of your followers. Once you’re finished, we’ll present a chart of their most active hours. (Mouse over each hour to view your local time.)

By itself, this insight is extraordinarily useful. There is significant variation from one person to the next in terms of when their followers are most active, and we can now take advantage of this data with the Buffer button integrated into Followerwonk.

When you click this new button, it will create a schedule on Buffer with as many times as you specify (you can specify from 1 to 99 times, currently). And when you do, you’ll find a new schedule on Buffer like this:

(Of course, if you don’t yet have a free account on Buffer, definitely grab one.)

We use a weighted, random distribution to divvy up the times you specify. This means that we don’t ignore off-hours; we just assign times to them less frequently. Fine-tune the schedule we create for you in Buffer.

Also, make sure you install the Buffer Chrome extension. Once you do, you’ll be presented with a Buffer alternative when you tweet on Twitter.

With this, you can take a moment before each tweet and consider: is this one of the top hours for my audience? If not, hit the Buffer button and rest assured the tweet will be queued up to go out at a more optimal time.

Aim for your future audience

Your tweets should not be solely determined by your current followers. A couple of reasons why:

  • You may have a lot of spam followers or who people who aren’t important to your business.
  • Since they are already followers, you’ve already “converted” them. Part of your goal on Twitter should be to find new potential converts.
  • There’s potentially a lot of “low hanging fruit” in other hours that you might not typically reach.

This is where the Followerwonk integration with Buffer really excites me! You can now create schedules based not just on the most active hours of your followers, but of any other person’s followers.

I’ll explain how to do this in a moment. But first, I want to address what is central to this approach: whether or not tweets can reach out beyond your current followers.

Yes, tweets can extend out of your network

I analyzed 4,757 active Twitter accounts pulled from a sub-set of Followerwonk users. (This isn’t a random sample, but it’s far easier for us to analyze these users, as we track all of their data internally.)

Here’s the breakdown of their activity:

Note the last one.

A huge number of users are retweeting content from those they don’t follow. That’s really revealing, as many folks assume that engagement is limited to your existing social network. But it’s not. And it’s this sort of boundary-breaking activity that’s golden.

I then took those approximately 5,000 users and crawled 610,779 of their tweets and retweets. Of those items, here’s the breakdown:

I love retweets. (In fact, we based an entire influence metric around them.) And here we see that they’re an important component of most users’ activities! They’re almost as important as @mentions, in fact.

Let’s zoom in further and look just at retweets.

Here, we see that roughly 27% of retweeted tweets are of users the retweeter doesn’t follow. That’s big! And it means that there’s serious penetration of content into new networks.

Without doing an actual survey, it’s impossible to say exactly how users get their content retweeted by non-followers. But I have a few ideas.

Notice that retweets of non-followers have a larger number of @mentions. This is useful! It suggests that, in part, this breakout strategy is due to @mentioning others (and the recipient retweeting them).

If we consider retweets as a proxy for readership, we see that tweets can and do frequently extend beyond one’s current followers. This ability for a tweet to transcend a social network is likely due to any number of factors, including the “discovery” tab on Twitter, retweets of third parties, search, @engagement or #hashtag components of the tweet, and so on.

I draw out this point to highlight that you shouldn’t feel restricted to just tweet when your followers are online. Certainly, that’s an important consideration for any basic Twitter strategy, but keep in mind that your tweeting during certain hours has assembled an audience whose activity probably closely matches your current schedule. Time to break that mold? 

The benefits of off-hour targeting

At any given time on Twitter, here’s what your potential audience probably looks like:

Here’s the thing: your current followers are likely a large percent of those “attentive and eager” readers who you’ve captured during your active hours. And while you certainly need to continue to cultivate that crowd, it’s perhaps less easy for you to find new prospects. You’ve already gotten a lot of the “low hanging fruit.”

But just think: there are active and eager fans in other hours, and they’ve probably never seen a single tweet from you. With the right content, you can probably do a terrific job at reaching them.

Let’s discuss how.

First, find a competitor or affinity brand on the other side of the world. Do an analysis and notice the active hours for her followers. Quite different than yours, eh?

Look at the full report to better understand the characteristics of their followers. The word clouds might reveal biographic details that might be of use. Look, too, at her most influential followers. These folks can help you spread your message.

Now think about content. Since few of your current followers are online during this user’s followers’ most active hours, you can think of a content strategy designed exclusively to this audience of far-flung prospects:

  • Tweet in a different language
  • Include responsible and constructive @mentions as in-roads into this audience
  • Use our comparison reports to find relationship overlaps between you and this competitor; DM or otherwise engage with shared followers who are in the off-hour timezone
  • Target content to the audience (for example, if you’re and SEO targeting France, you might focus on Francophone search engines)
Once you’ve tailored the content, queue them up in Buffer with a schedule based on the analysis of this far-off competitor.

Smart scheduling = lots of opportunities

Unlike Google+ and other social networks, you can’t separately target people on Twitter. Theoretically, all your tweets reach all your followers.

But, of course, they really don’t.

As I’ve highlighted above, you can use schedules as a basic means to separately target different swaths of your current and future audience. For most people, off-hour targeting affords a unique means to reach new territory.

But it’s not always of course: if you’re a local business, it probably won’t work well to be targeting folks in France! However, we think that there are a lot of other creative ways you can utilize analyses of competitors, suppliers, and local experts to fine-tune a scheduling and content strategy.

We’re extremely excited about our new relationship with Buffer. There are lots of ingenious ways to combine activity analysis like these and targeting scheduling.

We look forward to extending this partnership with great new things in the future!

(And so I don’t have to go all off-hours, why not follow me on Twitter right now?)

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Introducing New Followerwonk Engagement Metrics for Twitter

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Posted by @petebray

At Followerwonk, we're all about helping our customers find, engage, and optimize their Twitter audience. We're relentlessly focused on letting you dig into your followers, do advanced searches to help plumb the depths of Twitter, track your social graph, and more.

We're excited for you to explore some of the new metrics we've rolled out today! This new data goes beyond "simple" (yet useful!) items like follower count, friend count, and so on (things that are easily available for our app to process). Our new metrics require us to deeply crawl Twitter users' timelines (that is, their actual tweets and retweets). With this, we're able to surface data that offers new ways for you to better understand your audience, competitors, and prospects.
So, without further ado, let's go to the screenshot…
As you can see, we now tell you an overall engagement percentage for users. Essentially, this tells you how much that user interacts with others on Twitter. For example, users with 82% engagement means that 82% of sampled items from their timeline are @mentions or retweets of other people.
Indeed, this engagement metric is composed of two underlying scores that we also surface for you. @Contact is the percentage of a user's timeline that consists of tweets that directly mention another person (you know, a tweet that begins with @name). And Retweets is the percentage of retweets in their timeline.
Finally, in the Tweets with URLs metric, we'll tell you how often their tweets contain links.
There are a lot of different actionable strategies you can use this data for, and I want to walk you through a few different scenarios that'll also show you where we're surfacing this data right now. (We plan for more exhaustive ways to bubble this data up to you in the new year.)
A few caveats before I begin: this data is "expensive." It requires a lot of API calls, storage, and analysis. As such, we currently only provide these metrics on select Twitter users: namely, those with more than 2,500 followers and, of course, all our Pro members' Twitter accounts. (We do plan to continually expand these metrics to more and more of the Twittersphere.) We also feel that this data is valuable, and so it's exclusively available to PRO users. (If you aren't already a subscriber, this might be just the reason to bring you into the fold!)
Listeners vs Broadcasters
The bottom-line is that you ideally want to find people who will be receptive to @mentions, and who, if they follow you, are consuming your tweets. There's nothing necessarily wrong with "broadcasters" (those who never engage, and simply tweet URLs and observations). In fact, these accounts are often extremely popular simply because they've honed a particular message strategy that works for them.
But the real gold in terms of social media is to find an audience who listens to you: that is, they're likely to engage with you, consume your tweets, and retweet your message to their audience. Ultimately, the real sweet spot is to find this receptive audience among highly influential users (those who, when they retweet you, echo your message far and wide).
With this in mind, we can use Followerwonk's bio search to search for users with, say, "SEO" in their bio. The results come back sorted by follower count (a good proxy for influence, but we can also order by influence), and we can then rollover each user to find their engagement rate to better understand their likelihood of returning an @mention of them.
By looking at the percentage of their tweets that contain URLs, we can also find those accounts that may have limited value (and who may be spammers).
Finding your most receptive followers
Of course, while trawling through random Twitter users may be useful, it is perhaps less productive than digging into who follows you right now. There are several ways to do that. For example, in the advanced search options in bio search, you can limit results to just your followers. This will let you search for "SEO" users among only those who follow  you.
Of course, we already have special features to more capably examine all of your followers. That's the Analyze feature.
And we've now included a few new graphs that surface these new metrics for you.
Here, examining @followerwonk's followers, we can view a breakdown of our followers by their engagement. You can run these reports for your own account (or on competitors, friends, customers, and so on). That way, you can click on any of the segments and receive overlays of users in that segment. And, on mouseover, we tell you more details on their engagement:
Of course, you're not limited to this interface. Click the download button and you'll have an Excel (or CSV) report of all of your followers on your desktop in minutes. With that, you can do all sorts of goodness.
Here, I can sort this data to find all those users who tweet 100% URLs. This is a strong spam signal (but not always, of course). There's some thinking that followers of yours who are "spammy" might decrease your overall influence or network reach. In some ways, this is similar to incoming links to your Web site from "bad neighborhood" sites. What to do? Here's where you can possibly optimize your followers (admit you thought that was a strange expression when I said it above!). Use these spreadsheets to assemble a list of possible spam accounts, do further diligence on them (looking at their actual tweets, for example), and consider forcing them to unfollow you. How? Block 'em.
Finding a competitive sweet spot
Let's say you're an startup soda company. (Is there such a thing anymore?)
You want to aggressively court those customers who are going to really take your message of corn syrupy goodness far and wide. Here's how you can use Followerwonk to help. In this example, we're analyzing 3 of the big boys among soda companies:
Note that we can compare these companies' engagement. This helps us plan our social media strategy: it might be useful to match their engagement level  Or, maybe not: you might want to run a bit of a contrary course.
Since we've done a deep analysis of these accounts social graphs, we can dig deeper:
You can probably assume that those people who follow all 3 of these soda companies are serious soda-heads. (Is that a thing?) A quick click, and we have another overlay of these users, and a mouseover will tell us all about them, in terms of those most likely to be receptive to our great new soda.
And, yes, you can download these reports into Excel/CSV, too.
Laying the groundwork
As I mentioned, we're expanding these stats across a wider swath of Twitter users, and we're working on other ways to surface them to you.
These new engagement stats are the start of a lot of great new features we have in store for you. In fact, the reason we have these metrics at all is because we need them for something even cooler! You'll just have to wait and see what we've got in store for you…
Meanwhile, please do let us know what you think. Don't forget to find me on Twitter and say hello!

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