It's been quite a while since I looked into user retention on Hive. This is a topic I've written about many times over the years, you can find examples of my past posts here. Please note that my statistics focus on authors, for the simple reason that it is easier to verify that most are 'real individual users' compared to alternative measurements.
Brand New User Retention Collapsed In Recent Years
Actually, it seems fairly unfortunate that my last look at overall new user retention was all the way back in 2023, because it appears in the meantime I missed this:
In mid 2023 monthly brand new user retention dropped sharply from a rate that had been consistently above 35%, to an all time low of 16.4% in December 2024. Since then, it appears to have been recovering, but is still well below our healthiest days.
Cohorts
Another way to look at user retention is through the lens of cohorts. I first wrote about Cohorts on Hive in 2022 and revisited the topic in 2023.
The following chart shows active authors on Hive split into cohorts based on when they created their account. Pre-2020 accounts are in yearly cohorts, all accounts created from 2020 on are in quarterly cohorts.
Next is posts per month, split by the same cohorts.
Post rewards split by cohorts:
The proportion of author rewards going to each cohort:
You can see above that the pre-2020 cohorts are collectively receiving over 40% of author rewards despite being a smaller group now. The 2016 cohort gets an especially disproportionate portion of rewards, they are now a group of only about 160 active authors.
Circling back to the original topic, another way of measuring new user retention, by comparing the monthly change in number of users in the newest cohort, shows the same collapse in new user retention in 2023/2024.
Conclusion
Brand new user retention took a severe dip starting from mid 2023, reaching its lowest point in December 2024. It has partially recovered in 2025.
I plan to follow up and investigate what might have caused the drop in user retention, the recovery, and what the impact was on user retention overall, but I have not determined that yet.
Cohorts are such a great way of representing retention. We used a similar methodology to determine how many customers churned away from the business I worked for. The next step is always determining the root cause of cohorts leaving.
I feel like the most valuable of these charts is the cohort vs reward, but the grouping gets a bit busy when we look at quarters compared to years for the earlier users. Makes it difficult to do direct comparisons.
Excellent analysis. :)
I split the more recent years into quarters to be able to see things in a more granular fashion for newer users. I don't think there's as much value in doing the same for pre-fork users, as I still have a fairly manual process and not much new is happening with the older users being hardcore users continuing to stick around long term.
If you want I can give you access to the Google Sheet I used to collate the data and create the graphs. It would be fairly straightforward to join the quarterly cohorts together and make a cleaner graph. You can message me on stingchat if you'd like access.
Thank you for the offer, but at this stage, I do not have a lot of time. I have a lot of things to clean up before I start my new job the 2nd week of November - way too much stuff! :D
Ouch, that author retention dip hurts! 📉 Hope we can lure some of those newbies to the races. 🐎
When I think user retention my mind always go to prices...which shouldn't be if we want to ultimately retain more users.
As for the dip, it really baffles me sometimes as I see the effort made by various groups towards onboarding and user retention
Looks like they vote between each other, exactly the thing most of them criticize to smaller users. 🙃