Posts tagged psychographics
Can Text Analytics Shed Light On Trump's Appeal?

From tacit to more explicit insights, text analytics helps answer the why’s in voting

Because of the interest in yesterday’s post I decided to continue on the topics of politics today.  As a marketing researcher and data scientist though I found yesterday’s analysis a bit more interesting. Not because of the findings per se, but because we were able to use text analytics to accurately predict real attitudes and behavior by not just ‘reading between the lines’ but extrapolating a relationship between seemingly non related attitudes and opinions, which of course are related and predictive when you look more closely.

Of course text analytics can be interesting when used on more explicit data as well. So today I’ll take a look at two more open ended comment questions two different surveys.

In case you're wondering, the benefit of a text answer rather than asking several structured survey questions with rating scales is that unaided text questions give a much truer measure of what issues are actually important to a respondent. Rating scale questions force respondents to have an opinion on issues even when there is none, and thus structured survey questions (even the popular ones like Net Promoter Score) are usually far less effective in predicting actual behavior than text data in our experience.

Reason for Political Affiliation

Immediately after the self-description exercise in yesterday’s analysis we obviously needed to ask what the respondents political affiliation was (so that we could understand what relationship, if any, there is between how we view ourselves and political affiliation).

Respondents were able to designate which party if any they were affiliated with, whether they considered themselves Independent, Tea Party, Green, or something else, and why?





The ability to get a good quantitative relative measure to a why question is something unique to text analytics. Perhaps surprisingly there were rather few mentions of specific campaign issues. Instead the tendency was to use far more general reasons to explain why one votes a certain way.

While Republicans and Democrats are equally unlikely to mention “Conservatism’ and “Liberalism” when describing themselves (from yesterday's post), Republicans are about twice as likely to say they are affiliated with the Republican party because of their “Conservative” values (11% VS 5% “liberal” for Democrats).

Democrats say they vote the way they do because the Democratic party is “For the People”, “Cares about the Poor” and “the Middle [and] working class”.

Republicans on the other hand say they vote Republican because of “values” especially the belief that “you have to work for what you get”. Many also mention “God” and/or their “Christian” Faith as the reason. The desire for smaller/less government and greater Military/Defense spending are also significant reasons for Republicans.

Of course we could have probed deeper in the OE comments with a second question if we had wanted to. Still it is telling that specific issues like Healthcare, Education, Gay Rights and Taxes are less top-of-mind among voters than these more general attitudes about which party is right for them.

Describe Your Ideal President

As mentioned earlier we are looking toward social media to understand and build models. Therefore, we also recently asked a separate sample of n=1000 Americans, all who are active on Twitter, what qualities they felt the President of the United States (POTUS) should have.





The chart above is divided by those who said they tend to vote or at least typically skew toward that respective party.

The findings do help explain the current political climate a bit. Both Democrats and Republicans were most likely to mention “honesty” as a quality they look for, perhaps indicating a greater frustration with politics in general. The idea of “honesty” though is more important to voters who skew toward the GOP.

Those who favor the Democratic party are significantly more likely to value traits like Intelligence, Compassion/Empathy, skill, educational attainment of the candidate and open-mindedness.

Those who lean Republican however are significantly more likely to value a candidate who is perceived both as a strong leader in general, but also more specifically is strongly for America. Rather than educational attainment, softer more tacit skills are valued by this group, for instance Republican voters put greater emphasis on experience and “know how”. Not surprisingly, based on yesterday’s data on how voters view themselves, Republican voters also value Family values and Christian faith in their ideal POTUS.

Research has shown that people prefer leaders similar to themselves. Looking back to some of the self descriptions in yesterday's data we definitely see a few similarities in the above...

Thanks for all the feedback on yesterday’s post. Please join me week after next when I plan on sharing some more interesting survey findings not related to politics, but of course to text analytics.




Tom H.C. Anderson

Tom H.C. Anderson

To learn more about how OdinText can help you understand what really matters to your customers and predict actual behavior,  please contact us or request a Free Software Demo here >

[NOTE: Tom H. C. Anderson is Founder of Next Generation Text Analytics software firm OdinText Inc. Click here for more Text Analytics Tips]

Text Analysis Predicts Your Politics Without Asking

How What You Say Says Way More Than What You Said

Pretend for a moment that you had a pen pal overseas and they asked you to describe yourself. What would you tell them? What makes you “you”?

It turns out that which traits, characteristics and aspects of your identity you choose to focus on may say more than you realize.

For instance, they can be used to predict whether you are a Democrat or a Republican.

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 affiliation.

So we posed the question above verbatim to a nationally representative sample of just over n=1000 (sourced via CriticalMix) and ran the responses through OdinText.

Not surprisingly, responses to this open-ended question were as varied as the people who provided them, but OdinText was nevertheless able to identify several striking and statistically significant differences between the way Republicans and Democrats described themselves.

NOT About Demographics

Let me emphasize that this exercise had nothing to do with demographics. We’re all aware of the statistical demographic differences between Republicans and Democrats.

For our purposes, what if any 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, we found 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 it when describing yourself if you are a Republican than if you are a Democrat.

Predicting Politics with Text Analytics

Predicting Politics with Text Analytics

Self-Image: Significant Differences

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

  • Republicans were 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, were significantly more likely to describe themselves in terms of friendships, work ethic and the quality of their smile.

Interestingly, we turned up 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 I can assume with 90% accuracy that you vote Republican.

Again, this does not mean that Christians who are married are more than 90% 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?

While this exercise was exploratory and the results should not be taken as such, it demonstrates that text analytics make it entirely possible to read between the lines and determine far more about you than one would think possible.

Obviously, there is a simpler, more direct way to determine a respondent’s political affiliation: just ask them. We did. That’s how we were able to run this analysis. But it’s hardly the point.

The point is we don’t necessarily have to ask.

In fact, we’ve already built predictive models around social media profiles and Twitter feeds that eliminate the need to pose questions—demographic, or more importantly, psychographic.

Could a political campaign put this capability to work segmenting likely voters and targeting messages? Absolutely.

But the application obviously extends well beyond politics. With an exponentially-increasing flood of Customer Experience FeedbackCRM and consumer-generated text online, marketers could predicatively model all manner of behavior with important business implications.

One final thought relating to politics: What about Donald Trump, whose supporters it has been widely noted do not all fit neatly into the conventional Republican profile? It would be pretty easy to build a predictive model for them, too! And that could be useful given the widespread reports that a significant number of people who plan to vote for him are reluctant to say so.

Look Who’s Talking, Part 1: Who Are the Most Frequently Mentioned Research Panels?

Survey Takers Average Two Panel Memberships and Name Names

Who exactly is taking your survey?

It’s an important question beyond the obvious reasons and odds are your screener isn’t providing all of the answers.

Today’s blog post will be the first in a series previewing some key findings from a new study exploring the characteristics of survey research panelists.

The study was designed and conducted by Kerry Hecht, Director of Research at Ramius. OdinText was enlisted to analyze the text responses to the open-ended questions in the survey.

Today I’ll be sharing an OdinText analysis of results from one simple but important question: Which research companies are you signed up with?

Note: The full findings of this rather elaborate study will be released in June in a special workshop at IIEX North America (Insight Innovation Exchange) in Atlanta, GA. The workshop will be led by Kerry Hecht, Jessica Broome and yours truly. For more information, click here.

About the Data

The dataset we’ve used OdinText to analyze today is a survey of research panel members with just over 1,500 completes.

The sample was sourced in three equal parts from leading research panel providers Critical Mix and Schlesinger Associates and from third-party loyalty reward site Swagbucks, respectively.

The study’s author opted to use an open-ended question (“Which research companies are you signed up with?”) instead of a “select all that apply” variation for a couple of reasons, not the least of which being that the latter would’ve needed to list more than a thousand possible panel choices.

Only those panels that were mentioned by at least five respondents (0.3%) were included in the analysis. As it turned out, respondents identified more than 50 panels by name.

How Many Panels Does the Average Panelist Belong To?

The overwhelming majority of respondents—approx. 80%—indicated they belong to only one or two panels. (The average number of panels mentioned among those who could recall specific panel names was 2.3.)

Less than 2% told us they were members of 10 or more panels.

Finally, even fewer respondents told us they were members of as many as 20+ panels; others could not recall the name of a single panel when asked. Some declined to answer the question.

Naming Names…Here’s Who

Caption: To see the data more closely, please click this screenshot for an Excel file. 

In Figure 1 we have the 50 most frequently mentioned panel companies by respondents in this survey.

It is interesting to note that even though every respondent was signed up with at least one of the three companies from which we sourced the sample, a third of respondents failed to name that company.

Who Else? Average Number of Other Panels Mentioned

Caption: To see the data more closely, please click this screenshot for an Excel file.

As expected—and, again, taking the fact that the sample comes from each of just three firms we mentioned earlier—larger panels are more likely than smaller, niche panels to contain respondents who belong to other panels (Figure 2).

Panel Overlap/Correlation

Finally, we correlate the mentions of panels (Figure 3) and see that while there is some overlap everywhere, it looks to be relatively evenly distributed.

Caption: To see the data more closely, please click this screenshot for an Excel file.

Finally, we correlate the mentions of panels (Figure 3) and see that while there is some overlap everywhere, it looks to be relatively evenly distributed. In a few cases where correlation ishigher, it may be that these panels tend to recruit in the same place online or that there is a relationship between the companies.

What’s Next?

Again, all of the data provided above are the result of analyzing just a single, short open-ended question using OdinText.

In subsequent posts, we will look into what motivates these panelists to participate in research, as well as what they like and don’t like about the research process. We’ll also look more closely at demographics and psychographics.

You can also look forward to deeper insights from a qualitative leg provided by Kerry Hecht and her team in the workshop at IIEX in June.

Thank you for your readership. As always, I encourage your feedback and look forward to your comments!

@TomHCanderson @OdinText

Tom H.C. Anderson

PS. Just a reminder that OdinText is participating in the IIEX 2016 Insight Innovation Competition!

Voting ends Today! Please visit MAKE DATA ACCESSIBLE and VOTE OdinText!


[If you would like to attend IIEX feel free to use our Speaker discount code ODINTEXT]

To learn more about how OdinText can help you understand what really matters to your customers and predict actual behavior,  please contact us or request a Free Demo here >

[NOTE: Tom H. C. Anderson is Founder of Next Generation Text Analytics software firm OdinText Inc. Click here for more Text Analytics Tips ]