Pretend for a moment that you have a pen pal overseas and she asked you to describe yourself. What would you tell her? What makes you “you”? It turns out that the traits, characteristics, and aspects of your identity you choose to focus on may say more than you realize.

In fact, text analytics and profile analysis are sophisticated techniques that can be used to identify patterns and convert unstructured data into actionable insights, allowing the creation of buyer personas for highly targeted and effective individualized marketing campaigns.

Building buyer personas can be tricky – it’s important to have the right balance between quantitative and qualitative data to get an accurate and actionable profile. And that’s where text analytics techniques come into play. With an accurate buyer persona, marketers are able to target their message to subsets of customers with common needs and interests.

Translating Patterns into Personas

With the U.S. Presidential race underway in earnest, I thought it would be interesting to explore what, if any, patterns in the way people describe themselves could be used to identify their “political personas”. For instance, are there characteristics that might predict whether you are a Democrat or a Republican?

Our friends at OdinText used a text analytics application to identify several striking and statistically significant differences between the way Republicans and Democrats describe themselves.

Guess what? It’s not about demographics!

Let me emphasize that this exercise had nothing to do with demographics: gender, age, ethnicity, income, etc. We’re all aware of the statistical demographic differences between Republicans and Democrats. Specific demographic information people shared in describing themselves was only pertinent to the extent that it constituted a broader response pattern that could predict political affiliation. For example, it turns out that Republicans were significantly more likely than Democrats to say they have blonde hair. Of course, this does not necessarily mean that someone with blonde hair is significantly more likely to be a Republican; rather, it simply means that if you have blonde hair, you are significantly more likely to feel it noteworthy to mention when describing yourself, if you are a Republican than if you are a Democrat.

When it comes to self-image, there are significant differences

OdinText’s analysis turned up several predictors for party affiliation, here are 15 examples indexed below:

  • Republicans are far more likely to include their marital status, religion, ethnicity and education level in describing themselves, and to mention that they are charitable/generous.
  • Democrats, on the other hand, are significantly more likely to describe themselves in terms of friendships, work ethic and the quality of their smile.


Interestingly, they identified quite a few more predictors for Republicans than Democrats, suggesting that the former may be more homogeneous in terms of which aspects of their identities matter most. This translates to a somewhat higher level of confidence in predicting affinity with the Republican Party.

As an example, if you describe yourself as both “Christian” and “married,” without knowing anything else about you, it can be assumed with 90 percent accuracy that you vote Republican.

Again, this does not mean that Christians who are married are more than 90 percent likely to be Republicans, but it does mean that people who mention these two things when asked to tell a stranger about themselves are extremely likely to be Republicans.


So what?

Could a political campaign put this capability to work segmenting likely voters and targeting messages? Absolutely! But the application obviously extends well beyond politics.

While this exercise was exploratory and the results should not be taken as definitive, it demonstrates that text analytics tools make it entirely possible to read between the lines and determine far more about customers than marketers previously thought was possible.

Consumers are increasingly expecting brands to deliver real-time, relevant messages. As a result, the discipline of marketing is rapidly evolving to Individualized Marketing, requiring marketers to understand the customer as an individual, quickly adapt to each customer’s changing needs, and execute marketing initiatives at a personal level.

“Individualized Marketing is not about sales.
It’s about building a relationship with the customer.”

– Product Manager, Convidera GmbH

According to Teradata, 80 percent of marketers agree that Individualized Marketing is a top priority. However, only 43 percent of marketers say that they are delivering individualized experiences for their customers. These sophisticated predictive model techniques help close the gap between the strategy and execution of an effective individualized marketing campaign. With an exponentially-increasing flood of customer satisfaction data, customer experience feedback, CRM data, and consumer-generated social media text, marketers can now predictively model all manner of customer behaviors to help them #MoveTopRight.

However, before you can focus on personalization, your brand needs to have the right Story, the right Strategy, and the right Systems in place. Learn how to integrate TopRight’s 3S approach to bring simplicity, clarity, and alignment to your marketing efforts. Download our latest eBook Transformational Marketing: Moving to the TopRight.