A New Way to Segment Your Twitter Followers With Analytics

The Twitter list feature has made it possible to create groups of people and follow their twitter stream independently. It’s a new twist on a classic marketing technique – segmentation. In the case of Twitter, it’s an opportunity to listen to a group of followers with a common set of interests and learn what’s on their mind.

If you have a personal Twitter account, it’s easy enough to create the lists that might be most meaningful to you. For example, you might organize them by family, friends, golfers, wine enthusiasts, etc. When it comes to business, however, it’s a little less straightforward how to go about segmenting your followers. And if your business has thousands of followers, it can get pretty tedious trying to segment them manually.

Using Text Mining and Clustering to Segment Your Followers

There are several approaches to segmenting your followers on Twitter. The powerful approach I’ll illustrate here is to use classic text mining and clustering techniques to let the data you have regarding your followers organize itself into the most appropriate segments. For instance, you could use each follower’s Twitter bio as the data to be used in creating segments. I recently did this for a client that had over 3,200 followers on Twitter. An illumnating visual aid to use in this example is a word cloud. Here is the word cloud that resulted when combining all 3,200 bios.

As you can see, there are certain words that seem to stand out and suggest different segments, such as storage, marketing, virtualization, media, technology, etc. That seems straightforward enough, but not all of the key words I just listed necessarily appear only once in a bio.

For instance, how do you go about classifying the following bios?

• Solutions for data storage, protection, availability, virtualization and collaboration.
• I tweet about Internet marketing, social networking, business development, and technology.
• Strategy, positioning, PR and Social Media for tech, B2B, consumer. Focus on clean, semi, storage and start-ups.

Each of the examples includes more than one of the most frequently used words listed above. You can see that assigning a follower to a segment on the basis of a single world is not a simple matter. If you were trying to attempt the segmentation of these followers manually, you could easily spend a lot of time trying to decide the most appropriate segment in which to place them.

That’s where the power of text mining can save the day. Using text mining and clustering algorithms, it’s possible to classify the bios into segments not just based on the appearance of a single word, but on the frequency of the appearance of all the words in the bio, and their tendency to appear together in the same bio. It’s the same principle used to find relevant documents when you use a search engine. In that way, all of the information in a bio is used to create the segments of followers who are most alike.

As an example, let’s take a look at the resulting word cloud for the storage segment.

As you can see, the bios in this segment are indeed dominated by a single word: storage, and secondarily, data. The text mining and clustering algorithms combine to create a very pure segment of followers that are focused on storage issues. The beauty of the algorithms is that they are not limited to the presence of a single word. For instance, consider the following segment:

Once you see this segment, it makes perfect sense. While the words social and media appeared separately in the word cloud of all bios, it turns out, not surprisingly, that the two words appear very frequently together, and create another very pure and distinct segment of followers. While you might be saying, well, that one was obvious, would either of the following two segments be obvious from the original word cloud?

Putting it All Together to Listen to Your Customers

The algorithms are finding patterns in the data that may not be intuitively apparent. This is a great illustration of the ability of text mining and analytics – in this case clustering – providing considerable added value in finding unexpected and fruitful patterns in data.

And here’s something that’s a real time saver: once the segments have been defined, as new followers are added, a classification algorithm can be used to place each new follower in the best segment, making the process of determining which list to place a new follower automatic.

Now let’s get back to the original reason for placing your Twitter followers on a list: to listen to what a group of people with similar charcteristics are saying. So here’s the icing on the cake: after placing your followers on a list, you can then collect their tweets over a period of time and use the same methodology: text mining and clustering, to classify their tweets into common areas of interest or concern. When you consider that you can process thousands of tweets this way, and find novel and unexpected patterns  in their comments, you have the ultimate opportunity to really listen to your customers.

Summary

• The Twitter list feature has made it possible to listen to a group of followers with a common set of interests and learn what’s on their mind.
• It’s possible to use text mining and clustering algorithms to let the data you have regarding your followers organize itself into the most appropriate segments.
• The algorithms are finding patterns in the data that are not be intuitively apparent, providing considerable added value in finding unexpected and fruitful insights into the mindset of your customers.
• As new followers are added, a classification algorithm can be used to automatically place each new follower in the best segment.
• Text mining and clustering can then classify the tweets of individuals on the different lists into common areas of interest or concern, and find novel patterns  in their comments, providing the ultimate tool for listening to your customers.