To really prove this out I would want to get my hands on the Dev Google Analytics/Big Query data. It's so much more reliable than people entering whatever they fancy into a free text field.
To add some more complexity to this there is a feature which recommends more active users as 'Devs to follow' when new users sign up. So potentially the Devs who bubble up to the top of the list will have more followers who could interact with their posts.
Software Engineer and jack-of-all-trades, mostly working with machine learning and AWS.
Interested in the trends in tech and working out how we can use them!
Very true, be interesting to find out, that said I've limited exposure to using it, bar short access to my company's GA, I've never really got to take a look at a properly data-rich page!
Ohh I've definitely seen the suggested follow in effect, biggest follower gain I've seen probably came from this, but noticed some users commenting on a lot of their followers are very inactive... longer term I think it would lead to an increase but probably less than that from some of the other factors we mentioned?
There was a discussion in another post about those inactive accounts being potential spam/bot accounts. However, what I've found (more 'gutfeel' stuff) is that the 90:9:1 rule rings true. 90% will lurk and just view, 9% will comment or add reactions some of the time, 1% are active in the comments and posts. I was a lurker with no info in my profile for well over a year before posting anything.
Software Engineer and jack-of-all-trades, mostly working with machine learning and AWS.
Interested in the trends in tech and working out how we can use them!
Very good point - pretty sure I did the same, lurked then moved up to comments/reacts, then posts, be interesting to know what the average conversation rate/time is..?
Could try:
User endpoint -> capture dataset
Use articles data set + Users -> associate posts w/ user
Use 'date_joined' compared to article post dates to work out the conversion
Bonus points: Get comments for each post too -> compare too
Probably deserving of an IP ban at some point through the user capture though I imagine! :D
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To really prove this out I would want to get my hands on the Dev Google Analytics/Big Query data. It's so much more reliable than people entering whatever they fancy into a free text field.
To add some more complexity to this there is a feature which recommends more active users as 'Devs to follow' when new users sign up. So potentially the Devs who bubble up to the top of the list will have more followers who could interact with their posts.
Changelog: Suggested follows on onboarding!
Ben Halpern ・ Mar 27 '18 ・ 2 min read
Very true, be interesting to find out, that said I've limited exposure to using it, bar short access to my company's GA, I've never really got to take a look at a properly data-rich page!
Ohh I've definitely seen the suggested follow in effect, biggest follower gain I've seen probably came from this, but noticed some users commenting on a lot of their followers are very inactive... longer term I think it would lead to an increase but probably less than that from some of the other factors we mentioned?
There was a discussion in another post about those inactive accounts being potential spam/bot accounts. However, what I've found (more 'gutfeel' stuff) is that the 90:9:1 rule rings true. 90% will lurk and just view, 9% will comment or add reactions some of the time, 1% are active in the comments and posts. I was a lurker with no info in my profile for well over a year before posting anything.
Very good point - pretty sure I did the same, lurked then moved up to comments/reacts, then posts, be interesting to know what the average conversation rate/time is..?
Could try:
Probably deserving of an IP ban at some point through the user capture though I imagine! :D