Brand Analytics – Branding and Gender

Tom H. C. Anderson
January 6th, 2016

Text Analytics Tips - Branding

Text Analytics Tips: Branding Analytics – 500 Major Brands and Gender- by Tom H. C. Anderson
(Continuation from yesterday’s Brand Analytics post)

Thank you everyone who contacted us for more information about OdinText yesterday. As a result, I’ve decided to dig into the same branding question a bit deeper taking a look at one or two additional variables today and tomorrow. Of course, if anyone is interested in seeing just how easy and powerful OdinText is feel free to request info or a demo.

Yesterday we looked at Brand Awareness by Age quite a bit. Today, I thought we’d look at gender. But before that, I’ve posted another visualization from the Brand vs. Age data below.

Though popular, we’ve found at OdinText that typical word clouds are almost completely useless. Yesterday, I showed a co-occurrence plot of the data which certainly is more meaningful than a word cloud, as unlike a word cloud, position is used to tell you something about the data (those terms mentioned most frequently together appear closer to each other).

Brand Analytics Unstructured VisualizationBrand Analytics Unstructured Visualization

In the chart above, we are plotting two variables from yesterday’s data, Average Age on the x-axis and frequency of mentions (or popularity) on the y-axis. Visualizations like this are a great way to very quickly explore and understand unstructured data. Even without getting into the detail, often just the overall shape of a chart will tell you something about your data. In the case above, we have a triangle type shape where the most popular brands, such as Samsung, Sony and Coca-Cola, tend to appear in the middle. Two outliers are Apple and Nike who are not only our most popular brands, but also skew a bit younger.

But let’s leave Age and take a look at Gender. Though a nominal variable because gender typically only has two values (male and female), it is also a dichotomous variable and thus lends itself nicely to more advanced visualization. Basically, any dichotomous variable including Yes or No (present vs. not present) can be very useful in OdinText patented text analytics process. What can we tell about brands when we look at gender?

Brand Anallytics and Gender 600by300 Text Analytics Tips

Brands by Gender

There are certain stereotypes that seem validated. You’re far more likely to be a guy than a gal if, when you think of brands, the first things that pop into your mind are Software and Electronics such as Microsoft (15% vs. 6%) and Sony (17% vs. 11%), or if you think of an auto brands like Ford (11% vs. 7%) or a McDonalds (4% vs. 1%).

Text Analytics Gender by Brand with OdinText

Perhaps as expected, women are far more likely to think of consumer packaged goods brands like Kraft (14% vs. 7%), Johnson & Johnson (6% vs. 2%), Kellogg’s (5% vs. 0.4%), General Mills (6% vs. 2%), Dove (4% vs. 0.2%), and P&G (4% vs. 1%).

Interestingly the list doesn’t stop there though, women tend to be able to mention several more brands than men including many less frequently mentioned brands such as Coach (3% vs. 0.3%), Gap (3% vs. 1%), Colgate (3% vs. 0.5%), Tide (2.6% vs. 0.4%), Victoria’s Secret (2.5% vs. 0.2%), Michael Kors (2.8% vs. 0.7%), Kleenex (2.5% vs. 0.5%), Tommy Hilfiger (2$ vs. 0.2%), Huggies (1.7% vs. 0%), Olay (1.7% vs. 0%), Hershey’s (1.7% vs. 0.2%), Mattel (1.5% vs. 0%) and Bath and Body Works (1.3% vs. 0%).

That’s it for today but come back tomorrow and we’ll look at one last data point related to this one unstructured branding question we’ve been looking at to see whether brands can have a political skew.

Of course in the meantime, please feel free to request more information on how you too can become a data scientist with OdinText. Text Analytics can be a great tool for brand analytics including answering brand positioning, brand loyalty and brand equity questions.

-Tom @OdinText

 

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

5 thoughts on “Brand Analytics – Branding and Gender”

  1. The spatial maps are excellent tools. The awareness, top-of-mind, and usage data are perfect inputs. The possibilities for other data are limited only by the analyst’s imagination.

  2. Thank You Terry! Yes, agree completely. That’s what many don’t seem to ‘get’ when it comes to text analytics software. You need to have good software that is powerful and easy to use, but there is no replacement for an analyst that is genuinely interested in and understands the application of the data. (Word clouds don’t cut it…)

  3. We liked the brand mapping. We found the gender by brand aware analysis of particular relevance. We had just been discussing internally the pros and cons of mixed gender focus groups.

    Once upon a time, 30 years ago or more, it was the belief of many qualitative researchers and clients that mixed groups were a no no. Books on gender differences speak to how women and men communicate differently – hence all the mix-ups and sometimes conflict. One hypothesis says women speak more of relationships in women only groups and men more of activities. Mixed, women are more likely to speak to activities as well. Not sure if this is totally true based on the tons of groups, mixed and gender specific, we observe. Sometimes though the men only groups can be likened to the living dead.

    It doesn’t surprise us based on your analysis that women mention more brands. It may always have been the case. Women have been more involved with the activity of purchase even of large ticket items such as cars, perhaps less so trucks. They would seem to enjoy shopping more whereas many men simply wish to get the process over as quickly as possible. It may also be the case for online studies where men, especially young men who are difficult to engage in surveys in the first place, wish to get the process over faster and as a result mention fewer brands.

    We were surprised to see the difference in McDonald’s awareness. I guess men are far more frequent visitors of hamburger restaurants than women or/and it may be a more engaging experience for them.

    Anyway, these posts have given us food for thought and reminded us of some good brand mapping practices.

    Thanks and the best for 2016.

    Cheers,

    Mike

  4. Thanks Tom,the article posted by you is very helpful.The topics you shared related to brand equity is effective.Do you know In 2004, the Dove brand released the Real Beauty Campaign to spark discussion about beauty. The ad might have seemed unconventional to some, but the controversial questions sure made people reevaluate their own opinion of what beautiful is. The ad asked the public whether the women presented in photos were “Oversized or Outstanding” and “Wrinkled or Wonderful?” Viewers were asked to then vote at RealBeautyCampaign.com.This is an example,more such effective details at http://www.smstudy.com/Article/Beauty-and-the-Beast-of-Unrealistic-Expectations

  5. @William, of course we do. Unilever is a client, and we conducted text analytics on that campaign. We even have a case study we’e allowed too share. Feel free to contact us if interested

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