I Analyzed Seven Factors that Affect My Medium Stats. This Is What I Found.

Medium provides us with three stats, number of views, reads, and fans for each post. The read ratio doesn’t seem to provide any extra information on top of these.

You can do a lot with these three stats. You can learn a lot of lessons from them. I’ve spent analyzing these stats for the last ten days. I published a series of posts on lessons I learned from them. This is the tenth post in this series.

My interpretation of views and fans is as follows. The number of views shows how successful a post is in pulling in readers. The number of fans shows how successful a post is in satisfying the readers.

I see the number of reads as a bridge between views and fans. The number of reads deserves its own analysis. I’m going to do that in the future and I’m going to publish my lessons. This post will be about views and fans.

In order to analyze my stats, I downloaded them with this script (Third party link and script. Please use it on your own risk).

I did some extra manual work, such as adding the publications and tags to the posts. I wrote a small program to avoid some manual work. If you can’t program, you can do the same work manually, by copying and pasting in Excel.

To give you an idea, I had more than 140 posts in my Medium profile at the time of this analysis.

Which Factors Impact Medium Stats?

In order to optimize my Medium stats, I need to know which factors impact them. Here is the list of the factors that I analyzed. If you think that there might be other factors affecting Medium stats, please let me know in the comments.

  • Publication the post is published in
  • The title of the post (*)
  • Post length (*)
  • The likability of the post, including its readability
  • Day of the week the post is published on (*)
  • Tags of the post (*)
  • Featured picture of the post (*)

My posts published in the Startup publication received 10x more views compared to the rest. In order to subtract the impact of that factor, I analyzed only the posts that are published in the Startup publication when analyzing the factors marked with (*).

Publication the Post Is Published In

According to my stats, getting published in a major Medium publication, the Startup, made the biggest change in my views and fans. It multiplied my views by 10 and my fans by 4.

The Title of the Post

Getting published in a major Medium publication gives a huge boost to your views and fans, but why stop there?

You can still optimize your stats by coming up with strong and counterintuitive titles. The title of a post has a huge impact on the views. I published three posts about writing impactful titles.

The Likability of the Post

I measured the likability of the post as the ratio of fans to views. The factors that make a post less popular are the following:

  • Incongruent title and content
  • Unbelievable claims
  • Arguments unpopular with the target audience
  • Low readability

I explained the details of each factor and gave several examples in the post This Is What I Learned from My Most Hated Blog Posts.

I use the Hemingway App to measure and improve the readability of my posts. You can see the details and an example screenshot in this post.

Featured Picture of the Post

Believe it or not, I checked my top 10 and bottom 10 posts for the pictures. I liked most of those pictures equally. I think they play a role, but I can’t measure it in my posts.

I’m convinced that each blog post has to have a relevant, beautiful picture at the top. Make sure this is at the top. Otherwise, Medium doesn’t display it in the preview card. This happened to me once.

You can find free pictures on Pixabay. Make sure you give credits to those photographers via a link.

Day of the Week the Post is Published On

This is one of the more interesting results I came across. Monday seems to be the prime time to publish a post, but that doesn’t mean you have to ignore the remaining days.

The posts published on Monday seem to get the 30% of the total views and fans. There is no reason to assume that the posts published in the weekend underperform the rest.

Fig. 1. Views and Fans vs Day of the Week

I came up with two conclusions from this data.

  • Publish my best content on Monday.
  • Publish on every day of the week.

Post Length

When I look at Fig. 2, posts that are six minutes long received the most views and fans. However, the fan to view ratio is the maximum when the post was seven minutes long.

Fig. 2. Views and Fans vs Post Length

To be honest, I didn’t normalize these values for other factors such as day of the week. Maybe, I’m just publishing longer posts on Mondays, because I have more time to write them in the weekend. This is something I have to look into as future work.

Until then, I’m going to stick with posts that are six to seven minutes long.

Tags of the Post

This was a difficult one to analyze. First, I had to fill the tags one by one. Then, I had to write a program to process these numbers. Finally, I realized that my more recent posts didn’t reach their full potential in views and fans yet.

As a result, I’m not 100% sure that the results of this analysis are accurate. I include them as reference for future work.

Fig. 3. Views and Fans vs Tags

The tags are sorted according the number of fans, the orange columns. The blue columns refer to the number of views.

People interested in startups don’t seem to click much on my posts, but the ones they do tend to like it more than the people that are interested in management.

In this analysis, I only used the tags that had more than ten posts. There are other tags with less than ten posts. I will use some of those tags in the future, such as leadership. I’m curious how they will perform.

The results are not clear in this one. I’m going to make a similar analysis in the future.

Factors that I Haven’t Analyzed

There are some factors that I haven’t analyzed in this study. I don’t know how to analyze them at the moment, such as the number of followers when a post was published.

I have no impact on some of them, such as getting a high number of initial claps from the editors of the publication.

I’m not going to experiment with some of them, such as consistency of publishing posts or interactions with the readers via responses. I’m not going to ignore reader comments just to measure the impact.

  • Timing of acceptance to the publication
  • Number of followers of the writer
  • Getting a high number of initial claps from the editors of the publication
  • Interactions with the readers via responses
  • Consistency of publishing posts, including tags


Here are the lessons I learned after all the analysis I have done.

  • Do my best to get published regularly in the Startup publication.
  • Come up with strong and counterintuitive titles.
  • Aim for posts around 6 – 7 minutes.
  • Pay attention to the likability of the post, including its readability.
  • Publish my best content on Monday, but keep publishing on every day of the week.
  • Pay attention to the tags.
  • Experiment with tags that I don’t use often.
  • Pay attention to the picture of the post.
  • Respond to user comments.
  • Keep publishing a post per day, focusing on the better performing tags.

When looking at these results, please take into account that they are based on my posts and my audience. Maybe your posts and your audience like 3 minute posts more than 6 minutes posts. Or they’d click your posts about startups more than management.

So, please use these results as information only. Do your own analysis if you can and please let me know if you publish it.

Future Work

  • Normalize the stats such as length vs day of the week.
  • Analyze the read stats.
  • Download the stats every week and do a week over week analysis.
  • Try to find a way to use the number of followers when a post is published in the analysis.


I thank my mastermind partner David Nowak for giving me the idea to look at my Medium stats.

Your Turn

Please let me know what you think about this analysis and conclusions.

  • Are there other factors that would play a role in Medium stats?
  • Did you do a similar analysis yourself?
  • If so, what are the lessons you’ve learned?
  • If not, are you inspired to do a similar analysis yourself?