What factors go into determining how many Twitter followers you gain (and lose) each day?
I was driven in part by Rand Fishkin's recent "mad scientist" experimentation that he touched on at MozCon. There, he noted that his tweets with images resulted in significant follower losses.
Do they? And what other behaviors result in more (or fewer) followers?
I've found some interesting gems.
Of course, it's worth noting that aggregate, general trends don't necessarily speak to your specific situation. In fact, as you'll see, they're often exactly the opposite! To that end, I want you to play along at home...
You've got new data!
If you're a Moz subscriber who has had their Twitter account connected to Followerwonk for three or more months, then chances are you'll find a new complimentary report there. (I also only computed these reports for those who have more than 50 Twitter followers, and who tweeted in at least 10% of the days analyzed.)
Once you've downloaded the report, please clean up the data. Look for any days with zero gains/losses that look wonky (i.e. something should be there but isn't). These are either Twitter or Followerwonk outages. Delete them AND the day immediately following outage. This is important, as the day following outages usually has outsized gains to make up for the missing date. It can heavily skew any statistical analyses.
If you're not a customer, no worries; this blog post highlights some pretty interesting general Twitter growth metrics.
(I am going to repeat this offer again in a few months—in fact, we may build it into Followerwonk. So subscribe now to ensure that you have plenty of social graph history for analysis. Please tweet me to let me know if you find this data useful. We may build it permanently into the product if so!)
Followerwonk has unique data for deep mining
We track social graph changes for thousands of users, and we compute new and lost followers on a daily basis. We're one of the only companies that to do this (maybe the only one).
Sure, lots of sites compute net changes; but we track gains and losses, and we track who your new followers (or unfollowers) are. This is a huge set of data to explore to look for significant trends, to get hints as to what causes follower growth, and more.
This post is an introduction to that exploration. We'll cover a lot more in future posts (including analyzing the types of users that you gain after specific Twitter or offline activity).
Let's take a look.
I deeply analyzed Twitter content and compared it to follower growth (and loss)
I created a day-by-day summary of new and lost followers. My data set included roughly 800,000 "days" for over 4,000 users, and requiring analysis of millions of tweets.
The result was a large spreadsheet with a lot of content metrics.
For example, I determined the # of tweets with images, those with URLs, those that are "broadcasting" vs those that are @mentioning someone, and so on.
I did this because my hypothesis is that follower growth (and loss) is significantly impacted by the content that one tweets.
Let's break out Excel
For all of my analyses, I use that old Microsoft stand-by: Excel.
I'd typically recommend R: It has a lot richer analytic capability. But it has a much steeper learning curve, and I wanted this blog post to be a bit of a tutorial, so Excel fits the bill.
If you're following along at home, you'll want to first enable Excel's "Analysis ToolPak." Dunno why, but Microsoft chooses to turn it "off" by default. This add-on allows you to easily perform correlations, linear regression, and more.
Mean, median, mode, mangos...
As a first step, I like to get a lay of the land via basic descriptive statistics.
To do this in Excel, find the Data Analysis tool, and select Descriptive Statistics. Check the box labeled "Summary statistics," then select all of the columns with numeric data, and you will get a summary table.
(Of course, sometimes scientific notation is hard to read at a glance. To remedy, I highlight all of the numeric cells, right click, and select "Format Cells." Then I change it to "Number" with 4 decimal places.)
Remember, this is analyzing 800,000 days across several thousand Twitter users. We see that the average daily account growth in new followers is about 0.2%, while the average daily account loss is 0.1%.
By the way, it's worth pointing out that this isn't necessarily a representative sample. It's an aggregate of mostly Moz/Followerwonk customers. And it spans the range from very big Twitter accounts, to very small ones (where getting a few new followers will result in outsize daily % gains).
What correlates with what?
I select Data Analysis and choose "correlation." I select all of the numeric columns as the input range.
I get a nice table of results!
There's some interesting stuff here:
- Weekends correlate slightly with fewer tweets and activity across the board. That makes sense.
- Broadcast tweets (that is, those that don't begin with an @mention) correlate highly with tweets with hashtags. Approximately 45% of broadcast tweets in our sample contain hashtags.
- Tweets with images correlate moderately with tweets with hashtags and with URLs. And, in turn, tweets with hashtags correlate moderately with tweets with URLs. This also makes sense. In many ways, images, hashtags, and URLs are all facets of marketing. When a user employs one, he is likely to employ the other two.
Of course, the relationships between tweets with URLs and tweets with hashtags is fairly simple.
It's a lot harder to understand, for example, what variables predict follower growth (or follower loss). After all, there are a ton of different factors at play. And, as we see from the correlation chart, only a few things stand out.
First, pay attention to the percentage daily growth of followers compared to follower loss.