Hashtag Analytics: Comparison of Communities

Every community is different.

Take #measure and #analytics ... two seemingly similar communities, one focusing on Web Analytics, one focusing on ... wait for it ... analytics!!!

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.




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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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Hashtag Analytics: Seeing The Future In The #Measure Community

Take a look at the social media measurement tools that exist, and you're not likely to see many people focusing on the future.

Some of the those in the Web Analytics Pantheon and Social Media world create spectacular tools that do a very credible job of looking back, slicing and dicing user behavior in any number of ways.  They identify those who influences the conversation.  They describe the type of conversations folks have.  They map the network a person belongs to.  We're blessed to have such interesting tools.


I'm not here to compete with that stuff ... you can't compete with it.  I'm here to help folks see what the future holds.  This, after all, is where all of the actionable strategies happen.  Think about it ... you don't care that the weather was sunny and 62 degrees two days ago, you need to plan for the weekend, so you need to know what the weather will be ... not what it was.

Take a look at the #measure community, for instance.  I looked at the community recently, taking data from a prior four week period to forecast the next week, then using the data to create a five week future view of the community.  Click on the image below to see the forecast.

What you see is a decrease in the level of participation within the community.  Now, this does not mean that the community is in decline, not at all.  It simply means that for the small time-frame analyzed, the community was less engaged than it needed to be to continue to grow in a healthy way.  In fact, because of Christmas / New Year's, the declines were much more significant than this, I had to adjust for the seasonality of Christmas / New Year's.  When I run this analysis pre-Thanksgiving, I get a different answer, a more positive answer.  When I run this analysis at the end of the month ... well, then we'll truly know where this community is heading.  

At this time, this is a medium-sized community that is very, very hard to break in to ... with plus/minus sixty folks who have joined the conversation.

Now, in every e-commerce company, somebody is responsible for forecasting sales for the next twelve months, by day.  So it makes logical sense that any community manager would want to know what the future of his/her community is, right?  This is something you don't find in any of the popular Twitter-based analytics tools.  This is my focus.  This is what I love doing, it's completely actionable, and it's an area of analysis not being explored!

Next week, we'll do something neat --- we'll remove one important user from the community, and we'll see if the absence of the individual harms or helps the future trajectory of the community.  If you are an active participant in the #measure community, please send me a user_id that you'd like to see removed in the forecast ... I'll run an example for the individual who gets the most votes.

And in two weeks, we'll compare the #measure community to the #analytics community ... competing communities doing similar work ... which community is forecast to have a stronger future?


If you agree that forecasting the future health of a community is important, drop me a line or leave a comment.  If you want to hire me to create a forecast for your community, contact me.  If you want to buy the booklet, called "Hillstrom's Hashtag Analytics", download it via Kindle, or via print from Amazon.

Hashtag Analytics: Forecasting Engagement

Over the next few weeks, we're going to dig into some of the dynamics within the #measure web analytics community.

I retrieved data for an eight week period from early November to early January.  In the analysis, I looked at four weeks of history, seeing if there were trends that helped me understand who would participate again in the next week.

I created a segmentation scheme ... thirteen existing segments that describe user behavior. This will get a teeny-bit geeky for some of you, so feel free to fast forward to the results section of the discussion if you wish.

• Recency = 1 Week, 1 Tweet Past Four Weeks.
• Recency = 1 Week, 2+ Tweets Past Four Weeks, Participate Only 1-2 Weeks of Past 4.
• Recency = 1 Week, Participated in 3 of Past 4 Weeks.
• Recency = 1 Week, Participated in 4/4 Weeks, Skew = Broadcasting.
• Recency = 1 Week, Participated in 4/4 Weeks, Skew = Neutral.
• Recency = 1 Week, Participated in 4/4 Weeks, Skew = Being Re-Tweeted
• Recency = 2 Weeks, 1 Tweet Past Four Weeks.
• Recency = 2 Weeks, 2+ Tweets Past Four Weeks, Participate Only 1-2 Weeks of Past 4.
• Recency = 2 Weeks, Participated in 3 of Past 4 Weeks.
• Recency = 3 Weeks, 1 Tweet Past Four Weeks.
• Recency = 3 Weeks, 2+ Tweets Past Four Weeks.
• Recency = 4 Weeks, 1 Tweet Past Four Weeks.
• Recency = 4 Weeks, 2+ Tweets Past Four Weeks.

Results:


The following table (click on the image to enlarge it) illustrates how the community "engaged" in a subsequent week.

Within this community, there are significant differences in subsequent engagement.

• Those who just pop-off one tweet to broadcast something are not likely to come back.
• The most engaged are obvious ... those who participated in four of the past four weeks.
• Those who are frequently re-tweeted are the ones most likely to be engaged next week, a "duh", but interesting to see, nonetheless.
• If a user goes more than two weeks without participating, it begins to become unlikely that the user will jump back in.
• A new user only has a 10% chance of being re-tweeted.
• Amazingly, new users made up 50% of those who were active in the following week.
• New users, however, only yielded 21% of all tweets & re-tweets.
• 25% of all activity (tweets + re-tweets) within the #measure community in this one week came from just 23 individuals (6%).  These folks participated in four of the prior four weeks, and skewed to individuals who are likely to be re-tweeted.

So What?


Yup, that's a common response from folks.  Well, here's what we're going to illustrate in the next two weeks.

1. Next week, I will present a forecast model that shows me how the #measure community is likely to grow and thrive in the future.  I'll show how important it is to nurture the community, to encourage folks to participate.  In addition, I'll demonstrate the overwhelming importance of new participants.
2. In two weeks, I will illustrate what happens to the forecasted trajectory of this community when one influential individual drops out of the community.

If you can forecast where your community is headed in the future, and you can understand the importance of new/valuable users, you can grow your community accordingly.
Interested in the forecasting aspect of Hillstrom's Hashtag Analytics?  Buy the book!

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Hashtag Analytics: A Twitter "Storm"

The weather folks in Portland, OR use #pdxtst to share information about weather-related events.  So whenever a "storm" is coming, you'll observe a "Twitter Storm"!

In late November, there was talk of a big storm coming to the Pacific Northwest.  In the four weeks prior to talk of a big storm, there wasn't much activity in the community:

Recency	Cases	Engage	# Engage
1 Week	26	34.4%	9
2 Weeks	20	20.1%	4
3 Weeks	15	13.3%	2
4 Weeks	8	29.9%	2
2 Months	38	10.8%	4
3 Months	47	4.2%	2
4-6 Mo.	254	2.5%	6
7-9 Mo.	108	1.2%	1
10-12 Mo.	20	1.4%	0
Newbies	8	100.0%	8
Percent Newbies =		19.4%

Basically, there isn't much to talk about, and this is a closed community, without many new folks participating.

And then, the weather changes.  Take a look at how the metric change as a result!

Recency	Cases	Engage	# Engage
1 Week	45	71.1%	32
2 Weeks	5	80.0%	4
3 Weeks	15	53.3%	8
4 Weeks	40	52.5%	21
2 Months	18	38.9%	7
3 Months	39	33.3%	13
4-6 Mo.	221	19.0%	42
7-9 Mo.	140	20.7%	29
10-12 Mo.	27	7.4%	2
Newbies	292	100.0%	292
Percent Newbies =		64.9%

Pow!!

Notice that engagement rates go bonkers, regardless of recency since last tweet.  The entire population is buzzing about this event!  Even more important, nearly two out of every three "tweeters" are new to the #pdxtst.


Now, we need to see what happens the following week.  Take a look at engagement rates.

Recency	Cases	Engage	# Engage
1 Week	450	6.9%	31
2 Weeks	13	0.0%	0
3 Weeks	1	0.0%	0
4 Weeks	7	0.0%	0
2 Months	21	0.0%	0
3 Months	32	0.0%	0
4-6 Mo.	137	0.0%	0
7-9 Mo.	150	0.7%	1
10-12 Mo.	31	0.0%	0
Newbies	5	100.0%	5
Percent Newbies =		13.5%

Clearly, there wasn't anything worth talking about, so the community shut down.


The following week, there's a bit more buzz, but only among recent participants.

Recency	Cases	Engage	# Engage
1 Week	37	35.1%	13
2 Weeks	419	2.6%	11
3 Weeks	13	0.0%	0
4 Weeks	1	0.0%	0
2 Months	28	0.0%	0
3 Months	28	7.1%	2
4-6 Mo.	133	0.8%	1
7-9 Mo.	154	0.0%	0
10-12 Mo.	34	0.0%	0
Newbies	8	100.0%	8
Percent Newbies =		22.8%

I'm going to show you what happens for one more week.  Pay attention to the glut of folks who participated during the winter weather event ... they've dropped down to three weeks of recency.

Recency	Cases	Engage	# Engage
1 Week	35	54.3%	19
2 Weeks	24	37.5%	9
3 Weeks	408	15.9%	65
4 Weeks	13	15.4%	2
2 Months	27	11.1%	3
3 Months	21	4.8%	1
4-6 Mo.	107	7.5%	8
7-9 Mo.	175	1.7%	3
10-12 Mo.	45	4.4%	2
Newbies	58	100.0%	58
Percent Newbies =		34.1%

Ok, there's weather to talk about this week, and the audience comes back --- but most important, there's the glut of people with three weeks of recency ... those folks engage at a normal rate, but there are so many people in this cohort that they fuel the conversation ... sixty-five people in this band engage about lousy weather.


What's the point of this analysis?


There are two key takeaways:

1. When something goes viral, you end up with a large cohort of individuals who have the potential to fuel a conversation in the future.
2. For there to be a conversation, there has to be something to talk about.  Notice how this community comes to life when there is something to talk about!

In other words, a significant weather event "awakened" this community, fueling high engagement rates, and fueling a ginormous increase in new users.  But a ton of new users/followers/fans doesn't mean anything unless this audience has something to talk about.  Counts and numbers have very little meaning ... having something to talk about is important.

Want to learn more about Hashtag Analytics?  Give Hillstrom's Hashtag Analytics a read!

• Print at Amazon.com:  $7.95.
• Kindle at Amazon.com:  $2.99.
• Nook at Barnes & Noble:  $2.99.
• Hire me for a project, click here!

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.

Nordstrom: A Hashtag Analytics Example

Let's look at an example of Hashtag Analytics in action.

Today, we'll focus on Nordstrom, a former employer of mine, a company that many admire for their focus on customer service.

I pulled data for five recent weeks.  I used four weeks to segment users, and then I used one week to see if I could predict if there are folks out there who simply cannot help themselves by mentioning #nordstrom or Nordstrom or by re-tweeting messages from @nordstrom!

I identified 6,121 individuals who said something about #nordstrom/nordstrom/@nordstrom in my four week "pre" period.

• 85% only issued one tweet during the four week analysis period.
• 10% issued just two tweets during the four week analysis period.

In other words, this isn't a highly engaged audience.

Recall, I created a segment, called "Mega Participants".  These are folks who tweeted in the last week, and tweeted in 3 or 4 of the past four weeks.

• 73 out of 6,121 participants were classified as "Mega Participants".
• 1.2% of the audience can be called "Mega Participants".

And as one might expect, Mega Participants are likely to "engage" next week:

• 41.1% of Mega Participants engaged the following week.
• 3.1% of all other participants engaged the following week.
• 3.5% of all participants engaged the following week.

Recall that I created eight "Digital Profiles" to describe Twitter community behavior.  Here's the distribution of the Nordstrom community during the four week "pre" period.

• 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.

More than half of the audience is in the "Dipping A Toe" Digital Profile, with very low re-engagement rates in the next week.

What does a "Dipping A Toe" participant look like?  Here's one example from @amandalustbuser:

• John Mayer is playing in Nordstrom. This is a sign. I should buy everything.

Here's another example from @alyssafrazier:

• Having a shoe shopping black pump dilemma @nordstrom.

Regardless, the participant is truly "Dipping A Toe", the participant isn't terribly engaged.  It shouldn't be a huge surprise that more than half of the audience exhibits this type of behavior, and isn't terribly likely to tweet again in the near future.


"May Be Interested" is a more engaged Digital Profile.  Take a peek at a tweet that is somewhat representative of this audience, from a participant, and you'll see why.

• @nordstrom always carry a mirror.  I've been to several cocktail parties where women have lipstick on their teeth.

Those who are "Shaping The Conversation" are the most likely to be engaged next week. Take a peek at a tweet that is somewhat representative of this audience, from a participant:

• I got the @nordstrom job! I start my training for the lingerie dept on Monday.

Yup, it shouldn't be a surprise that this person was "engaged"!

It can be fun to review individual participants by Digital Profile.  Let's profile a few of the participants.

First, here's @nordstrom, the corporate presence.  During this four week stretch:

• 25 tweets.
• 4 statements.
• 8 retweets.
• 13 amplifications.
• 0 conversations (responses).
• 13 links.
• Re-tweeted 150 times by others.
• Answered 215 times by others.
• Digital Profile = Spreading The Word.

Contrast that with @nordstrombeauty:

• 19 tweets.
• 8 statements.
• 4 retweets.
• 4 amplifications.
• 3 conversations (responses).
• 10 links.
• Re-tweeted 72 times by others.
• Answered 4 times by others.
• Digital Profile = Shaping The Conversation.

And here is @nordstrombvue, the store manager from the Bellevue, WA store.

• 18 tweets.
• 6 statements.
• 9 retweets.
• 1 amplification.
• 3 conversations (responses).
• 6 links.
• Re-tweeted 45 times by others.
• Answered 14 times by others.
• Digital Profile = Joining The Conversation

The store manager in Bellevue is more likely to re-tweet content from other folks.  The Nordstrom Beauty twitter presence is more likely to be directive, to tell the audience what to think.  The Nordstrom corporate presence is more likely to tell folks what's going on.


There are participants who are highly engaged.  There's @daliamacphee, for instance, a participant who is actively selling her merchandise, merchandise offered in Nordstrom stores.

• 158 tweets.
• 142 statements.
• 1 retweet.
• 8 amplifications.
• 7 conversations (responses).
• 150 links.
• Re-tweeted 12 times by others.
• Answered 5 times by others.
• Digital Profile = Making A Statement

And there are folks who are in the top Digital Profile, called "Shaping The Conversation", like aka_kristin.  She tweeted her audience every time she was in a Nordstrom store, and every time she was in a unique department at Nordstrom.

• 24 tweets.
• 22 statements.
• 0 retweets.
• 0 amplifications.
• 2 conversations (responses).
• 12 links.
• Re-tweeted 0 times by others.
• Answered 1 times by others.
• Digital Profile = Making A Statement

How Does This Become Actionable?


By using Digital Profiles and by identifying Mega Participants, I can predict which participants are likely to be "engaged" next week.  I simply maintain a database of all Twitter members engaged with @nordstrom, and i predict which participants are likely to be "engaged" next week. I feed my predictions back to you, the marketer, and you then tweet your message to your heart's content to the audience most likely to be engaged in the future.

In the case of Nordstrom, I identified more than 100 Twitter users who evangelize the brand.  If I were at Nordstrom, I would communicate directly to this audience, as if they were part of my e-mail marketing list (to draw a parallel).


I realize this is big-company type work, and companies like Nordstrom are probably already compiling databases of Twitter evangelists, but it is worth sharing so that you can start thinking about how you apply Twitter to your Database Marketing initiatives.

Hashtag Analytics: Free Spreadsheets And A Booklet!

You knew it was coming!  You wanted a concise methodology for forecasting the future of your social media community.  And now you have it.

Hillstrom's Hashtag Analytics is a soup-to-nuts methodology for forecasting the future of a social media community on Twitter.

What do you get?

• A FREE dataset (in .csv format) that contains eight weeks of participant behavior in the #blogchat community, summarized at a participant/week level.

• A FREE spreadsheet that allows you to forecast the future trajectory of your social media community (you will have to write the programming code to get your data into the spreadsheet).

• 44 pages of text that outline the thought process behind forecasting the future trajectory of a social media community on Twitter.

You are unlikely to find anything of this nature from social media analytics experts, and if you do find something that allows you to forecast the future of a social media community on Twitter, you're going to pay an agency a hundred thousand dollars for the right to do the forecasting!

This booklet is available in three formats.

• Available in print from Amazon.com for $7.95.
• Available via Amazon Kindle for $2.99.
• Available via Barnes & Noble Nook for $2.99.

If, after you buy the book, you find that you want an expert to run a forecast for your social media community on Twitter, give me a holler, I'll be happy to perform the analysis for you!

Hashtag Analytics: Part 10 = Four Month Forecast

We know the probability of a #blogchat participant engaging again in the next four weeks.

We know the Digital Profile the #blogchat participant will migrate to if the participant engages.

We know how many new participants we'll have in the next four weeks, by Digital Profile.

This allows us to create a simulation, illustrating how the community will evolve over time!

Well, we have good news here ... the community was at 2,193 monthly participants, and is forecast to increase to 2,403 participants, then 2,485 participants, then 2,518 participants, then 2,532 participants over the next four months.  Remember, growth isn't coming from engagement rates ... growth is instead coming from new participants!


We can also forecast where key metrics are headed.  We know that the #blogchat community will grow by 16%, what will happen to tweets and other key metrics?

Well, this is a positive story!


What's happening is that the participants who are engaged are moving into more valuable Digital Profiles, Digital Profiles where participants are more likely to tweet and participate at high levels!




What Did We Learn?


We learned that the #blogchat community is a vibrant and successful community.


We learned that engagement rates are generally low, and that is perfectly acceptable.


We learned that a small number of participants generate most of the "oxygen" for this community ... we called them "Mega Participants".

We learned that kindness matters!!!  We learned that the simple act of thanking a first-time participant who retweets content yields an engagement rate that is up to ten times greater than observed when a first time participant is not acknowledged for a retweet of content.

We learned about Digital Profiles, descriptions of various participants that have predictive ability.

We learned that there is a common path that a participant takes as the participant goes from a first tweet to "Making A Statement" and participating at a high level.


We learned that participant growth will come from new participants, and that is perfectly acceptable (and is congruent with most of the e-commerce, retail, and catalog work I do).

We learned that the #blogchat community is growing at a 16% rate over four months.

We learned that total tweets within the #blogchat community is growing at a 40% rate, because many participants are moving into high-value Digital Profiles!!!!

We learned that, overall, Mack Collier and his #blogchat community is thriving and succeeding, a good thing!!!

We learned that we can predict the future ... not many social media analytics experts have a methodology for predicting tweet volume and participant volume ... we, however, have a methodology for doing this!!!


What's Next?

Tomorrow, we conclude our series with the introduction of a booklet that teaches us how to predict the future of a social media community on Twitter!

Hashtag Analytics: Part 9 = Seeing The Future

The beautiful thing about Digital Profiles is that we can see where participants are likely to migrate to, given the Digital Profile they reside in.

Let's take the Digital Profile a participant belonged in last month, and measure the probability of a participant engaging again in the next month.

As we've talked about all throughout this series, engagement rates are not high, and that's not a bad thing ... the #blogchat community does a sensational job of recruiting new participants!

If a participant engages again, we can look at the Digital Profiles that the participant is likely to migrate to.  This table illustrates counts by Digital Profile:

There is a logical path that a participant navigates ... the participant usually migrates to "Joining The Conversation" and then to "Shaping The Conversation".  Sometimes, the user migrates to "Making A Statement" and then to "Shaping The Conversation".

Regardless, participation and migration paths make sense.

If we know what engagement rates are, if we know what migration patterns are, and if we know what new participant counts are, we can create a simulation that allows us to forecast the future trajectory of the #blogchat community.

We'll finish our series tomorrow with a four-month forecast for the #blogchat community.

Hashtag Analytics: Part 8 = Digital Profiles

I'm going to spare you the geeky details behind Digital Profiles, there's a dozen or more posts on the topic on this blog.

Instead, we'll focus on the outcome of a Digital Profile project.  I looked at a four week period of time, using that data to classify each #blogchat user into one of eight Digital Profiles.  Each profile describes a subset of participants who possess similar behavior.

Here is the description of each of eight Digital Profiles.  Next, we'll analyze how each Digital Profile behaves in a future four week period of time.

Profile #1 = Shaping The Conversation:  These individuals are usually the most active within the #blogchat community.  What sets these users apart from other active participants is their preference for starting conversations.  When these folks participate, they average 9.4 tweets per week, and offer the most statements per week (3.4) of any other Digital Profile.  One might think of this audience as being the "thought leaders" of a #blogchat event.

Profile #2 = May Be Interested:  A subset of individuals who only participate in one of four events, but have the potential for being "conversationalists".  When they do participate, their questions are answered, they make statements, and they participate in conversations.

Profile #3 = Making A Statement:  These are active participants who generally don't participate in conversations.  Instead, these participants make statements, they have something to say, and they are comfortable sharing their point of view.

Profile #4 = Dipping A Toe:  This is an inactive Digital Profile.  Basically, these users issue one statement, and then disappear, they are “dipping a toe”!

Profile #5 = Joining The Conversation:  An active Digital Profile that likes to respond to conversations.  These individuals are likely to issue re-tweets, to amplify tweets, and to respond to tweets.

Profile #6 = One Topic Experts:  This Digital Profile is unique, in that users seem to participate in only one event during the course of the month.  When they participate, they tend to respond to statements, and they are very likely to re-tweet content from others.  When you have a topic where you are the expert, you want these folks to participate, because these folks will re-tweet your messages and offer oxygen to any conversation!

Profile #7 = Spreading The Word:  You want these folks in your #blogchat, because their sole purpose is to spread the word!  This profile represents users who are likely to re-tweet content.  These users are also likely to re-tweet content with links embedded in the tweet.

Profile #8 = The Ignored:  This is the saddest of all eight Digital Profiles.  These participants tend to issue one tweet, and most often, it is a re-tweet of content from somebody else.  Unfortunately, their re-tweet is not acknowledged, seemingly shutting down subsequent participation.
 

Hashtag Analytics: Part 7 = Why Newbies Matter

The number one complaint I get from just about anybody I discuss my work with is this:

• You only care about newbies.  Loyalty is where it is at, Kevin.

Alright.

Let's look at the #blogchat community.

From mid-September to mid-October, 1,631 participants yielded at least one tweet.


From mid-October to mid-November, only 32.6% of that audience tweeted at least once again using the #blogchat hashtag.

Do you understand the importance of that metric?


This is so eerily similar to the e-commerce, retail, and catalog data I analyze.  Those businesses retain about 38% of their twelve-month buyers, year-over-year, on average.

This metric is important, because the #blogchat community, on a monthly basis, loses 67.4% of participants.  The #blogchat community, then, must recruit 1,099 new participants over the next month, in order to keep the community at the same level of participation as last month.

1,631 participants, of which only 532 continue, requiring 1,099 new participants to fill the gap.

How did the #blogchat community do?


Try 1,924 new participants!!

This is the secret to success, folks.  It isn't about loyalty and extracting another three tweets out of mega participants.


No, it is all about finding new participants, newbies who can be developed to eventually become mega participants.

The #blogchat community has figured this out.  Good for them!