Thanks to @frogmonkee we have some new data to track how BanklessDAO’s Twitter presence is growing.
This builds off a previous post, that examined Twitter analytics for the month of May.
This posts adds June, specifically accounting for date range: May 8th - July 2nd (so we’ll have a better picture of July in the next post).
Here are the Twitter impressions:
But engagement is the name of the game:
A density map shows most of the daily engagement hovering around 500 and < 25000 impressions:
Overall, at a glance, it appears twitter engagement in June is slightly lower than May, but let’s break it down:
This area chart suggests that BanklessDAO Twitter excels at:
- Media Views
- Media Engagements
Said differently, the bulk of BanklessDAO’s twitter comes from media views and media engagement. This includes gifs, pictures, videos (media views) that would compel people to click on those assets (media engagement).
Conversely, the following do not contribute as much to overall engagement:
- url clicks
- user profile clicks
A percentage, stacked bar chart could provide clearer breakdown:
This gives a clearer indication that we could rank the contributors to BanklessDAO twitter engagement (in descending order):
- Media Views & Media Engagement (lumping together gifs, video, pictures)
- Detail Expands (threads that compel readers to click and scroll down)
These are probably the (current) bread and butter of BanklessDAO twitter.
Next we have:
- URL clicks (links to external sites)
- User Profile Clicks (people visiting the BanklessDAO twitter profile)
Then rounding out with traditional metrics we’re familiar with:
*Note: I’m not a social media expert, but here are my interpretations:
To boost engagement, we might sustain efforts around media views, media engagement and detail expands (threads), while finding ways to encourage more likes, retweets and replies. If an external URL link is to be inserted, perhaps save that for the bottom of a thread (note: I’ve heard twitter algo punishes external links).
Interestingly the correlation between Daily Engagement and Daily Number of Tweets came out to roughly r = 0.70, which is fairly high.
If we wanted to keep it simple, we might say:
Engagement is a numbers game, the more we Tweet, the more engagement there is.
Finally, I filtered the data by days with highest engagement:
and saved all tweets from those days in a separate csv file incase the social media team wanted to examine specific Tweets that contributed to high engagement [see raw data titled “tweets_on_days_with_highest_engagement.csv” (link).
Exploratory scripts here.
Thanks for reading and let me know what questions you have