Martha Stewart: A Hashtag Analytics Example

You cannot deny @marthastewart ... a global media empire and 2.0 million followers on Twitter, to boot.

But what about her community ... the folks who respond to @marthastewart or include #marthastewart in their tweets?

Let's use the magic of Hashtag Analytics to explore her community over a recent four week period of time!

• 7,168 users communicated via@marthastewart or #marthastewart.
• 5,419 users tweeted just one message.
• 1,045 users tweeted twice.
• 704 users tweeted 3+ times.
• 198 users (2.8%) were classified as "Mega Participants", with a tweet in the past week and tweets in 3+ of the past four weeks.
• Only 24% of Mega Participants created content that was re-tweeted.  Obviously, these folks are active because the love Martha Stewart, not because of the rewards of having their content shared.
• Even among Mega Participants, the median number of tweets over this four week period of time is just five.

Let's classify Martha's active community via Digital Profiles.  Remember, we have eight Digital Profiles that describe how participants use Twitter within a community.  Here we go (the analysis window is pushed back one week so that I can analyze engagement rates).

• Shaping The Conversation:  187 participants, 19.3% re-engagement rate.
• May Be Interested:  92 participants, 9.8% re-engagement rate.
• Making A Statement:  338 participants, 24.6% re-engagement rate.
• Dipping A Toe:  1,359 participants, 5.1% re-engagement rate.
• Joining The Conversation:  1,321 participants, 10.1% re-engagement rate.
• One Topic Experts:  2,309 participants, 5.9% re-engagement rate.
• Spreading The Word:  210 participants, 22.4% re-engagement rate.
• The Ignored: 892 participants, 4.5% re-engagement rate.

Remember, yesterday, we analyzed the @nordstrom community.  Here's what their data looked like:

• Shaping The Conversation:  132 participants, 19.7% re-engagement rate.
• May Be Interested:  103 participants, 3.9% re-engagement rate.
• Making A Statement:  503 participants, 12.7% re-engagement rate.
• Dipping A Toe:  3,443 participants, 2.2% re-engagement rate.
• Joining The Conversation:  694 participants, 2.5% re-engagement rate.
• One Topic Experts:  806 participants, 2.0% re-engagement rate.
• Spreading The Word:  111 participants, 7.2% re-engagement rate.
• The Ignored: 329 participants, 1.8% re-engagement rate.

Clearly, these are two very different communities, with two different user bases.  Martha Stewart's community is about twice as likely to engage next week as is the Nordstrom community.  This isn't good or bad, it's simply a different community.  For Martha Stewart, those in "Shaping The Conversation", "Making A Statement", and "Spreading The Word" Digital Profiles are the most valuable, in terms of subsequent engagement.


Engagement rates (probability of tweeting next week using #marthastewart or @marthastewart) by weeks since last tweet look shockingly like classic e-commerce, retail, or catalog trends:

• Recency = 1 Week:    14.8% Re-Engagement Rate.
• Recency = 2 Weeks:    7.1% Re-Engagement Rate.
• Recency = 3 Weeks:    5.4% Re-Engagement Rate.
• Recency = 4 Weeks:    4.0% Re-Engagement Rate.

Here's another great tidbit.  Remember that early in our analysis series, we noted that those who were "loved" by the #blogchat community experienced far greater engagement rates than those who were not loved.  We looked at those who had values of recency = 1 and weeks = 1 and tweets = 1 ... if these folks were simply acknowledged in some way for their single tweet, they were 10x more likely to engage in the future.


Within the Martha Stewart community, we observe a similar trend for recency = 1 / weeks = 1 / tweets = 1:

• Those who were loved had a 14.8% re-engagement rate.
• Those who were "not loved" had a 5.9% re-engagement rate.

Granted, these are small numbers for a small snapshot in time.  But I see the same trends in every analysis I run, so there is something to this.  If a community "loves" those who participate in the community, especially newbies, the community thrives.


Let's see if the same thing holds true for Mega Participants:

• Those who were loved had a 66.7% re-engagement rate.
• Those who were "not loved" had a 52.4% re-engagement rate.

A little love matters, even to Mega Participants!


Make This Actionable!

Ok, I'll make this information actionable!  I created a model that predicts next week's engagement rate by participant.  If you like math, then you'll enjoy the Logistic Regression equation ... otherwise, skip ahead!

• Logit = -3.399 - 0.670*SQRT(Recency) + 1.046*(Weeks Participated In Past Four Weeks) + 0.235*(Average Tweets Per Week) - 0.148*(Shaping The Conversation) + 0.457*(May Be Interested) + 0.678*(Making A Statement) + 0.150*(Dipping A Toe) + 0.165*(Joining The Conversation) + 0.271*(One Topic Experts) + 0.426*(Spreading The Word) + 0.000*(The Ignored).
• Logit = EXP(Logit) / (1 + EXP(Logit)).

Now that we are past the geeky math, we can move forward!

It turns out that there are 163 of more than 7,000 participants who are forecast to have a 40% or greater chance of using @marthastewart or #marthastewart next week.  That's a whopper of a percentage, don't you think!

These folks were hyper-active in the past month, and are likely to be active next week.  They averaged 11.4 tweets in the past four weeks, vs. 1.37 for the remainder of the population. They were re-tweeted 1.06 times in the past four weeks after issuing a statement via @marthastewart or #marthastewart vs. 0.10 times re-tweeted for the rest of the audience.  They re-tweet other comments 2.67 times vs. an average of 0.39 for everybody else.


In other words, these folks are influential!


If you are responsible for the Martha Stewart community, you use this process to identify these highly valuable community members.  My model identified 163 folks who are likely to engage next week, and are likely to spread the message to others.  Each week, I can compile a list of users possessing these characteristics.  And if I am part of Martha Stewart's marketing team, this is the audience that I'm going to communicate to ... I'm going to give them insider information and I'm going to make them feel special for their unwavering kindness.


In essence, we use Hashtag Analytics to create a database of Twitter users who evangelize our message.  It's classic Database Marketing, folks, applied to Twitter.