The early days: launch and initial app adoption

From day one Rosta started getting organic search impressions and downloads on the App Store. Also, daily active users have been slowly but consistently growing.
Let's observe the app daily sessions (bimonthly average) evolution since the app was launched back in August 2017:

By the end of 2019 daily active users surpassed the 1k for the first time. And by early 2020 it was even hitting peaks of 1.3k+ DAUs.
The slowly growing trend can be clearly seen: there wasn't a single period where the average daily sessions didn't grow!

COVID-19 effect: free-falling

Actually, that last "it only grows" statement held until the Jan-Feb 2020 bimonthly period. Then, it happened what we all know by now: the COVID-19 global pandemic. We are at the moment living one of the biggest crisis as a direct consequence. A huge worldwide economic collapse.
Among other things, this caused workers to temporarily stay at home due to lockdowns and quarantines. Even worse: workers are being laid off all over the globe.

— And what all of this has to do with your silly utility app?
Well, if you think that it wouldn't impact on a Shift Workers app usage just take a look at the plot below:

By March user activity was literally free-falling. Daily sessions were all of a sudden down by 50%. All the previous year's growth was just gone in a matter of a couple of weeks. A real bummer!

Not everything seems to be lost.

It seems that the big fall hit its lowest point by the end of March/beginning of April. By mid-May, the daily sessions started to slowly grow again. In the last few weeks, it seems to be settling once more around the 1k active sessions.

Will Rosta outlive COVID-19?

I can't say for sure if the worst has already passed or is yet to come. I can try to do my best to get Rosta back on track, though.
Users still reach out frequently with awesome feedback and features suggestions. To me, that's the best indicator that encourages me to keep working on the app as soon as I can. And I definitely will!

How did I plot the data?

I implemented a quick and dirty Python script to produce Ad-Hoc pygal charts from App Store analytics CSV.
You can find the source code here