Every community is different.
Look at this table, a table that reviews the segment users from each community belong to. In the table, users had to participate in their community in the past week ... the data is then captured for the past four weeks:
| Recency = 1 Week | |||
| #measure | #analytics | change | |
| Tweets = 1, Past 4 Weeks | 973 | 1,716 | -743 |
| Tweets = 2+ Past 4 Weeks | 283 | 406 | -123 |
| Tweets = 3 of Past 4 Weeks | 87 | 94 | -7 |
| Tweets = 4 of Past 4 Weeks, Statements | 24 | 35 | -11 |
| Tweets = 4 of Past 4 Weeks, Neutral | 22 | 16 | 6 |
| Tweets = 4 of Past 4 Weeks, Re-Tweeted | 22 | 10 | 12 |
| Totals | 1,411 | 2,277 | -866 |
The difference in the communities is like night and day.
The #measure community has 68 of 1,411 users that are "highly active".
The #analytics community has 61 of 2,277 users that are "highly active".
The #analytics community is not much of a community, in reality, with 1,716 of 2,277 participants tweeting only one time. This is a "broadcast community".
Sometimes, simple segmentation schemes help us understand the dynamics surrounding a community. In both communities, there is a very small audience of core members producing all of the content, combined with a large population of broadcasters. Proportionately, the #measure community has more loyal users than does the #analytics community.
Hillstrom's Hashtag Analytics:
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