Linden Lab has released a Second Life End-of-Year Update, outlining some of the achievements and events of 2018, and things are looking pretty good overall.
They shared some statistics on user concurrency, which show that it has remained steady over the past two years. It’s not increasing, but it’s not going down, either, which is encouraging:
As the year comes to a close, we’ve rounded up some interesting statistics to share insight into how the Second Life community is spending its time and money.
One thing is clear: Second Lifers were a busy bunch in 2018. You spent an estimated 336 million hours inworld in the past year alone! And there are 50 million+ chat messages daily.
Our daily concurrency rates remain stable, too. Take a look at this chart, which shows the overall traffic trends on logged in Second Life users over the past two years.
Pictured: Second Life concurrency rates from early 2017 to late 2018.
Also, They shared some stats about sales this year, and they’re pretty good too:
This active population helped keep the Second Life economy healthy in 2018. Approximately $65 million was paid out to Residents in the past year for a variety of items and services. On the Marketplace, there are currently over 5 million virtual goods for sale. Since we lowered prices on the Mainland and maintenance fees on Private Estates, we’ve seen some growth in the overall land market as well. For example, we saw increases across the board in land ownership – more Region owners, more parcel owners, more group-owned land, more Regions on the Grid. As many owners traded up from Openspaces and Homesteads to full Regions to take full advantage of the lower pricing, we saw growth in overall SQM owned by Residents.
Back in 2011, ReadWrite reported that Second Life made almost $100 million in revenue a year, so sales appear to have gone down, but I still think that $65 million is a pretty impressive figure. It’s clear that Second Life is still a cash cow for Linden Lab, the profits of which are funding not only SL development but also the company’s work on Sansar.
Gindipple posted the following pie chart to the official Sansar Discord channel (click on the picture to go to Flickr and see it in a larger size):
Gindipple explains that the high figures for one experience, Fnatic (the green slice in the pie chart), were due to people coming into that experience to get a prize key for another game. Which might be the very first occurrence of somebody gaming the traffic figures in Sansar (a long-standing practice in Second Life).
Top five most popular Sansar experiences in the past 7 days were:
So, go explore! There has been a definite increase in the number of new people visiting Sansar since it was launched on Steam this past Wednesday. And Friday night at the TurnupVR-sponsored pub crawl was amazing! Over 32 people showed up at Solas’ bar and danced, laughed and broke bottles. It was absolute chaos at times, with everybody excitedly talking over each other, and it reminded me of the early days of VRChat. I think we can say that Sansar has definitely arrived!
Now that Linden Lab has launched on Steam, we have quite a few different statistics available, some of which may which may contradict each other. Gindipple recently shared some rather encouraging statistics on the official Sansar Discord, which show an overall increasing trend in the average number of daily and monthly Sansar users:
Gindipple’s and Galen’s statistics will differ because they take samples of the user data at different times, using a publicly available API. One may sample the data more often than another; I don’t know how often Gindipple samples the data, but Galen says he takes a sample approximately every 10 minutes.
Steam tracks [people] logged in via Steam. Gindipple]/Galen log people in [Sansar] experiences that are public. We [Linden Lab] count them all – regardless how they logged in, where they are or what they do. 3 different numbers where ours will always be the bigger, sum of all, number.
As far as I am aware, Linden Lab does not publish their statistics, which are internal to the company. (If this is incorrect, then could somebody from Linden Lab let me know, and then I will update this blogpost accordingly, thank you!)
Interestingly, please notice that the latter Steam graph gives a different 24-hour peak usage than the former (the top one says the peak usage in the past 24 hours is 65 users, while the bottom one says it is 75).
So now we have a wealth of different data showing us just how much Sansar is being used! This is a vast improvement over the early days in Sansar, where most of the time we had to guess how many people were using the platform.
There has been much discussion and sometimes heated debate, both here and on other blogs, about the actual level of usage of Sansar, how to measure that usage best, and what those statistics mean. Galen, who is a very talented programmer who builds and sells scripts under the brand name Metaverse Machines, has put together a useful and informative new webpage gathering together various Sansar statistics. Let’s take a look at what he has given us.
First is a list of current statistics:
The number of Sansar experiences listed in the Atlas
How many people are in how many experiences right now
The peak (and average) number of people in Sansar today
How many Sansar experiences have been visited in the past 3 hours
How many experiences were visited today
How many experiences were visited this month
Galen also lists the four most active Sansar experiences right now, with some statistics for each:
Finally, he presents charts of daily, weekly, and monthly public visitors to Sansar, showing both peak and average numbers, as well as a chart of the number of Sansar experiences built over time:
Galen explains the data in the charts:
The following charts come from data collected using a publicly available API. We take a snapshot of all currently listed experiences and how many people are in them approximately every 10 minutes. The “peak” lines represent the highest concurrent head-count across all experiences measured in those snapshots across the whole day (or week). The averages are computed by adding up the total head-count measures for each day (or week) and dividing by the number of snapshots. The gaps between each snapshot make this data imperfect but very solid. To avoid visual confusion, today’s data is excluded.
