About a month ago (March 2020), before the worldwide COVID19 lockdown began, I set myself a target of writing a Medium article every single day. I wanted to find out whether a living could be made out of writing on Medium and how long it would take to get there.
I’m a Blockchain and Software Developer, so most of my articles have been about learning the basics of Blockchain Development, intermediate and advanced articles, and general tips and cheatsheets so readers can learn as much and as quickly as possible.
I’m aware that this isn’t a huge market right now, and not the most newsworthy, but that’s where my passions lie. It’s where I’m able to write almost without really thinking about it.
Medium provides a nice stats page for stories, but it is quite primitive. There’s a browser extension called Enhanced Stats which add more information to the stats page. Still, I wanted to go deeper. Since I started, I have been recording daily stats and charting graphs to track progress.
Initially, I wanted to visualise milestones to hit so that progress didn’t feel so slow. Using total earnings as a measure, I set out a scale like this: $1, $2.50, $5, $10, $25, $50, $100, $250… etc to $10,000.
Total earnings can be calculated by adding each month’s total earnings on the Medium Partner Program page.
This table was super helpful in breaking down what initially felt like a monumental task. I didn’t (and still don’t) expect to reach $10,000 soon, but stranger things have happened (and it is total earnings after all, so likelihood is it will be reached eventually). Watching the red turn to green over the past month has been a cool progress-bar incentive to push for more.
As time went by the tables evolved into a more detailed dataset of views, reads, averages, totals, scatter graphs and line graphs.
If I was keeping track of total earnings, why not go a little further and start tracking earnings with views and reads over that same period? This screenshot shows the headings of a new table I created:
- Total Earnings — The accumulated amount of all money earnt from Medium
- Increase — Amount changed since the previous day
- Daily Income — “Increase” offset by one for graph purposes
- Views — Views that day
- Reads — Reads that day
- Average Views Per Day (30 days) — Medium shows the total number of views over 30 days. This value is that number divided by 30)
- Average Reads Per Day (30 days) — Same as above but for reads
- Total Views — A sum of all daily views
- Total Reads — A sum of all daily reads
Visualising The Data
One of the great things about this data set is that it can correlate earnings against views, reads and averages. That means it can provide evidence as to which has more of an effect on earnings. Which produces the strongest trend line for the most earnings? Is it reads or views? With this knowledge, I can change my tactics accordingly.
For example, If views were more important for earnings, then more effort must be made to draw readers in, like clickbaity titles and fancy images/gifs. If reads were more important, I would need to spend more time writing super engaging articles which enticed the reader to keep reading to the end.
Figure 1 and figure 2 shows earnings against views, and earnings against reads in scatter graphs respectively. At first glance, the trend lines are pretty obvious, with a lot of room for manoeuvre. In both graphs, there is a general trend, but actual earnings can deviate a lot. It seems like there could be a slightly stronger correlation to reads, but nothing convinces me to commit to any hypothesis just from these two graphs.
Total views, reads and earnings over time.
Figure 3 shows the graph for totals over time. I’m not sure this one provides much information. It’s great to see the steady progress, and I would hope to see this rise look more exponential in future, but at the moment it’s fairly useless to make any predictions about tactics.
You could say that there’s a slightly greater similarity between earnings and views here, but that may be due to the proximity of the blue and yellow lines, and since they use different axis (blue = left, yellow = right) it doesn’t mean much.
I’ll keep an eye on this one for long term trends.
Figure 3 is about daily stats, but what about the averages? Do reads and views over an extended period bare weight on how much earnings the writer gets day-to-day?
Again, this seems like a bit of a vanity metric and doesn’t say much. Keep an eye on this over the long term.
What is the direct relationship between the views and reads per day and the earnings for that day?
This is my favourite of the lot. Figure 5 graphs the views, reads and earnings of each day, spread over the last month.
From tracing the peaks and troughs of the yellow line (daily earnings) it seems as though the peaks are slightly more correlated to peaks in the blue lines (views). There are several occasions where only the blue and yellow lines peak, and the red line shows no real movement. This is where views have spiked, but the articles haven’t been as engaging as to be read through to the end. The titles drew readers in but the content didn’t make them stay till the end.
This would suggest that earnings are slightly more correlated to views than they are to reads.
An interesting takeaway from this graph is that it seems as though the floor for the minimum earnings I receive in a single day has increased since the start of April. Take the yellow earnings line as a trend, and notice how that each trough increases value over time, and subsequent troughs don’t fall beneath previous ones. This suggests that a new floor has been set for some reason. Maybe that my general reach and presence on the site is being taken into account in the calculations?
There are a few metrics that I’ve failed to record and that may have more of a bearing on the earnings than I initially thought.
Medium’s Stats page has a “Fans” section, which details the stats of how many readers liked your posts. I haven’t recorded these but may add them to the spreadsheet.
I also haven’t taken into account my growing follower list, which has pretty much double over the last three weeks. This may be contributing to the daily floor in earnings that is shown in figure 5.
It’s also worth mentioning that I didn’t get one article out per day strictly. I started with that. However, to get the most reach from my articles I began submitting them to large publications. The most effective one I’ve been writing for is Better Programming. The issue with being published through a publication you do not edit yourself is that you don’t have control over when your article gets published, that’s up to the editors.
Often, I’d write a big article for a publication, then sit around and wait a day or two to be published. Initially, I tried to write a smaller self-published one as well as this to keep the 1 article per day minimum going, but I found I was burning out and not always writing quality articles. Instead, I settled for consistently outputting at least one good article per day for submission, and if 2 days went by without anything being released and then 3 were published on one day, so be it.
Getting articles curated was, and still is, a huge boon for an article. I’m only writing articles which I think might get curated into specific categories now. I noticed that this is way more likely if you’re publishing with a large publication like Better Programming than if you self-publish or publish with smaller publications.
It’s unclear whether views or reads contribute more to earnings at this time. One month isn’t enough data, and I’m not yet reaching the monthly targets I ideally would like to be at. That being said, it is growing and I am optimistic about moving forward.
The most important thing is producing quality content as often and as regularly as possible, and hoping something flies. That’s a horrible conclusion because it helps no one other than those willing to put in the graft. It is the truth though.
Read month two, with more data and better conclusions:
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