While the number of followers is not the end-all-be-all of blog success indicators, to me it’s a fairly important metric and getting more followers always makes me happy – especially when I check their blog and find their interests relate to mine. After all, if you don’t have content that interests people you’re not likely to ever get in the hundreds, or even thousands of followers.

There are many articles on the net about how to get more followers, and this one from WordPress itself captures a good summary of them. Of course quality of content is paramount here, but one thing that always got me curious is how time factors in to the number of followers. In other words, if you write articles like crazy will you get a corresponding number of followers, or do you have to wait months or even years for people to find your blog?

I realized this was a good opportunity to do a mini experiment by gathering statistics from various blogs and doing some calculations. My primary data points are the number of posts, followers, and number of months active (i.e. the time the blog was started or just “age”), taken from 22 blogs. I had to use a trick or two to get some of these numbers, but I’ll leave those details to another post.

My goal of the research was to determine how the number of posts and age correlate to the number of followers of a given blog, and which of these two factors is more predominant.

First, I’ll discuss the most important results, which are the two correlations of interest for the main set of data, and two subsets of that.

All blogs in my data set (22)

**CORREL (Age, Followers) = .53****CORREL (Posts, Followers) = .73**

Blogs with 100 or less followers (10)

**CORREL (Age, Followers) = .20****CORREL (Posts, Followers) = .16**

Blogs with 100 or more followers (12)

**CORREL (Age, Followers) = .41****CORREL (Posts, Followers) = .68**

(Here “CORREL” refers to the statistical correlation function in Excel, whose formula is documented here)

I value the last two figures the most, which represent the correlations for blogs with 100 or more followers. After all, those blogs with more followers are those whose statistics are more meaningful. These blogs show a relatively strong correlation between the number of posts and overall followers (.68), and a significantly weaker one between age and number of followers (.41). The correlations for blogs under 100 followers are very low (.20, .16) which is consistent with a higher randomness due to smaller post count.

What do these correlation figures really mean? A value of 0.0 or one close to it means there is little or no correlation, or connection between the two variables. One moves independently of the other. A value of 1.0, on the other hand, means a perfect positive correlation so that the two variables are tightly connected. Though correlation to number of posts is significantly higher than that of age of the blog, it may be hard to grasp what a “correlation of .68” really means, even if you go and research the mathematical equation that is used.

Generally speaking, in order to understand correlation it’s a good idea to look at a scatter graph of the two variables so you can visual inspect how they are related. The feature image for this post contains exactly that – a scatter graph of number of blog posts vs followers. Although there is one outlier where there is over 3000 followers for only ~250 posts (this blog is called “The Sophomore Slump”), the rest of the data points generally lie on a curve where the posts increase with the followers (or vice versa). It’s interesting to note that removing this blog from the data set makes the overall correlation value jump to 0.80.

One additional correlation which was surprising was that for the rate of [Followers / Posts] vs the total number of followers. This was** -.39** which indicates that the less followers per post, the more total followers, and a spot check of tend data is consistent with that. For example, one of the blogs has 6014 followers, but 6700 posts, with ratio of followers/post of .90. Conversely, another blog had a ratio of 9.3, but only 512 followers. However you shouldn’t read too much into this since -.39 is a very weak negative correlation (-1.0 would be a perfect negative one), and you can interpret this as meaning there isn’t much connection between these two metrics.

Besides staring at a lot of numbers, what did I really learn from this exercise? Though content is still king, and there are blogs with many posts who only have a small amount of followers, in general there is a strong relationship between the amount of posts and the number of followers, and a much weaker relationship between the age of the blog and the followers. If followers are what you desire, keep chugging along and with good enough content you’ll surely get them.

Because of the tedious aspect of mining statistics for blogs, I used only 22, but ideally I’d like to run a follow-up study with a much bigger sample set, say around 100 blogs. Also it would be nice to include the number of likes, views, and comments, but those numbers are either difficult to mine effectively or not available publicly.

The full list of blogs I used is at the bottom of this post.

**References**

http://en.support.wordpress.com/getting-more-views-and-traffic/

http://office.microsoft.com/en-us/excel-help/correl-HP005209023.aspx

**Blogs whose statistics were used in this study**

http://saoriistinwien.wordpress.com

http://1994sunshine.wordpress.com

http://sammc5773.wordpress.com

http://trivialistic.wordpress.com

http://adampastryguy.wordpress.com

http://pocketfulofsurprises.wordpress.com

http://aikidonosekai.wordpress.com

http://atouchofjapanese.wordpress.com

http://bakewithmeblog.wordpress.com

http://slightlychilledporcupine.wordpress.com

http://dlmayfield.wordpress.com

http://moviewriternyu.wordpress.com

Interesting find! At the end of the day, posting and blogging is just a hobby, regardless of how many followers I get (although it doesn’t hurt to have them either). Keep up the good work!

Thanks for the comment. Glad to know someone appreciated my research (: