SIMILAR ANALYSES COULD BE HELPFUL FOR YOU
Crunching story stats over time
To understand how my writing, my readers, and the platform interact, I devoted a whole day to analyze my statistics. I here distil some important conclusions to help me and you define what works best in this platform.
I recently analyzed various metrics individually for all stories I published in 2021, my first year around here. By computing correlations I could confirm that although not the strict parameter defining gains, it is critical to improve the numbers of views. And on carrying out that analysis, I could qualitatively find which of the topics I write about perform best:
Given the relevance of the overall number of views, I’m now interested in seeing how my writing and views evolved over time and how they relate to my production along various topics. I took my numbers of views day by day since I entered the platform, and then looked at it by publication. This is the raw data as provided by the platform, extracted with this online digitization program and replotted:
There are some clear peaks that I know roughly correspond to my articles in TDS Editors. But can I see this more objectively? Yes, I can. Let me present the next plot, where I did two things:
- I smoothed the number of views over 3-day windows.
- I added flags representing the dates when I published my articles, color- and shape-coded by the kind of publication (see figure caption)
So what?
The graph above shows me two things; one was expected, the other not that much -at least to me.
1) The first thing I see, and this is expected, is that my most viewed articles are those in Towards Data Science. By far. It’s super clear how after each publication there, I have a strong peak in numbers of views. One exception is my article about why users cannot expect to gain much by using the Brave browser, on December 13th 2021. You can see a burst in views right after the first light-blue rhombus:
A very interesting point regarding that article, is that it’s basically a full re-write of this other article written in short format and published in a venue specialized for shortforms:
Contrary to the one above, this one got almost no views. To date, it made just 6 cents while the other article made 13 USD. This is something I see repeatedly: shortforms do not give me any substantial gain, even when their content is similar to that of other articles. Their numbers of views are just too low! Hence, if you are here for the money, shortform is not convenient.
2) The second thing I see, and this I didn’t expect (although I have an explanation now, see next), is that my baseline number of views increases after each burst by a “viral” article. Let me show you again the number of views averaged over 3-days windows, now also annotated with some guides to the eyes:
Before July 17th I had just a couple of visits per week (almost literally). Then after my two first “viral” articles in Towards Data Science (fostered by the hype on Deepmind’s AlphaFold) my number of views stayed kind of stable at around 50 views per day. After the peak in mid-august, the new average was around 100 views per day. This next went to around 200, then 400, and it’s now around 500 (there was a strong drop around Dec 31st and Jan 1st, but that’s understandable as many readers, myself included, were off!).
My guess is that viral articles, or more humbly, articles that have a good reach, result in an audience grow. This audience is then presented with my new articles, perhaps also with some old ones, thus sustaining the shift up. Two conclusions would then be (i) that viral articles matter, and (ii) that sustained production is important -because if you build up an audience but don’t keep it engaged, they will hardly contribute to your development.
I hope all my analysis has been of help to you. Maybe you didn’t really learn anything that’s totally new to you, but at least you may have obtained solid backup for your qualitative observations. One way or the other, I’ll be happy to know what you think and what your experience is, either qualitatively or quantitatively.
If you liked this analysis, don’t miss this other story:
I am a nature, science, technology, programming, and DIY enthusiast. Biotechnologist and chemist, in the wet lab and with computers. I write about everything that lies within my broad sphere of interests. Check out my lists for more stories. Become a Medium member to access all its stories and subscribe to get my new stories by email (original affiliate links of the platform for which I get small revenues without special costs to you). Donate here through various means. Contact me here for any kind of inquiry.
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