January 24, 2011

Hashtag Analytics: Removing a Member of the Community

The social media community and online analytics community do a great job of measuring things.  They can tell you, for instance, that @michelehinojosa (a popular individual in the #measure community), is influential or not influential, they can score her based on all sorts of criteria.

For some reason, nobody seems to want to answer a different question ... "What would happen if you removed @michelehinojosa from the community?"

That's what we're going to explore today.

Recall, this is a five-week forecast for the #measure community.

Now, let's have some fun.  We'll remove any activity associated with @michelehinojosa from the community.  Then, we re-segment all users, and we produce a forecast for the next five weeks, without @michelehinojosa participating in the community.

Here's the forecast!

This user has a profound influence on the community.  Let's look at the base week, week zero.
  • Two users other than @michelehinojosa would not exist in the community without her input.
  • Times users are re-tweeted with her = 253, without her = 233.
  • Times users are answered with her = 64, without her = 60.
  • Total tweets = 747 with her, 703 without her.
In other words, this user provides about 7% of the "oxygen" that carries this community.  And as we forecast her influence into the future, we see the same thing ... 5% to 6% of the "oxygen" is provided by this user, going forward ... the influence diminishes a bit as new users enter the community.

Let's look at another user ... @immeria.

This fine young man has a different type of impact on the community.  He impacts more users ... without his participation, about 2% of the community no longer participates.  He does not impact the total oxygen of the community as much, in other words, he doesn't impact the number of tweets or number of conversations.  But he does bring along 2% of the community.  And his impact lasts through the forecast cycle, meaning he impacts new participants as well.

This exercise can be run for every user in a community.  We can easily forecast what impact each user has on the overall future of a community.  By looking forward, we get to see what might happen, and we can take steps to change the future.  When we simply look back into the past, we only measure what happened in the past.

In this simple example, when we remove just two users from a community of about four hundred weekly participants, we lose close to 8% of all future activity in this community.  In spite of a ton of new users, these two folks, @michelehinojosa and @immeria, foster a wonderful and vibrant community.  That's a decent measure of influence, don't you think?

If you want to learn more about Hillstrom's Hashtag Analytics, give the booklet a try.  It's one of the top forty direct marketing books currently available on the Kindle platform!!

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