Brand Analytics Tips – How Old is Your Brand?
Text Analytics Tips Answers, How Old Is Your Brand? – Using OdinText on Brand Mention Type Comment Data
By Tom H. C. Anderson
[METHODOLOGICAL NOTES (If you’re not a researcher feel free to skip down to ‘Brands & Age’ section below): In our first official Text Analytics Tips I’ve started with exploring one of the arguably simplest types of unstructured/text data there is, the unaided top-of-mind ‘brand mention’ open-ended survey question. These kinds of questions are especially important to brand positioning, brand equity, brand loyalty and advertising effectiveness research. In this case we’ve allowed for more than one brand mention. The questions reads “Q. When you think of brand names, what company’s product or service brand names first come to mind? [Please name at least 5]”. The question was fielded to n=1,089 US Gen Pop Representative survey respondents in the CriticalMix Panel in December of 2015. The confidence interval is +/-2.9% at the 95% confidence level]
Making Good Use Comment Data Can Be Easy and Insightful
An interesting and rather unique way to look at your brand is to understand for whom it is most likely to be top-of-mind.
Unfortunately, though they have proven more accurate than structured choice or Likert scale rating questions in predicting actual behavior, free form (open end) survey questions are rare due to the assumed difficulty in analyzing results. Even when they are included in a survey and analyzed, results are rarely expressed in anything more useful than a simple frequency ranked table (or worse, a word cloud). Thanks to the unique patented approach to unstructured and structured data in OdinText, analyzing this type of data is both fast and easy, and insights are only limited to the savviness of the analyst.
The core question asked here is rather simple i.e. “When you think of brand names, what company’s product or service brand names first come to mind?”. However, asking this question to over a thousand people, because of the share volume of brands that will be mentioned (in our case well over 500), even this ‘small data’ can seem overwhelming in volume.
The purpose of this post is to show you just how easy/fast yet insightful analysis of even more specific and technically more basic comment data can be using Next Generation Text AnalyticsTM.
After uploading the data into OdinText, there are numerous ways to look at this comment data, not only the somewhat more obvious frequency counts, but also several other statistics including any interesting relationships to available structured data. Today we will be looking at how brand mentions are related to just one such variable, the age of the respondent. [Come back tomorrow and we may take a look at a few other statistics and variable relationships.]
Brands by Age
Below is a sortable list of the most frequently mentioned brands ranked by the average age of those mentioning said brand. This is a direct export from OdinText. The best way to think about lists like these is comparatively (i.e. how old is my brand vs. other brands?). If showing a table such as this in a presentation I would highly recommend color coding which can be done either in OdinText (depending on your version), or in excel using the conditional formatting tool.
[NOTE: For additional analytics notes and visualizations please scroll to the bottom of the table below]
|Brand Name||Average Age|
|Banquet (Frozen Dinners)||57.5|
|Johnson & Johnson||48.3|
|Arm & Hammer||46.4|
|Black & Decker||44.2|
A few interesting points from the longer list of brands are:
The oldest brand, “Maxwell House Coffee”, has an average age of 66. (If anything, this mean age is actually conservative, as the age question gets coded as 66 for anyone answering that they are “65 or older”). This is a typical technique in OdinText, choosing the mid-point to calculate the mean if the data are in numeric ranges, as is often the case with survey or customer entry form based data.
The Youngest brand on the list, “Urban Outfitters”, with an average age of 24 also probably skews even younger in actuality for the same reason (as is standard in studies representative of the US General Population, typically only adults aged 18+ are included in the research).
Dr Pepper is in the exact middle of our list (46 years old). Brands like Dr. Pepper which are in the middle (with an average age close to the upper range of Generation X) are of course popular not just among those 46 years old, but are likely to be popular across a wider range of ages. A good example, Coca-Cola also near the middle, mentioned by 156 people with an average age of 45, is pulling from both young and old. The most interesting thing then, as is usual in almost any research, is comparative analysis. Where is Pepsi relative to Coke for instance? As you might suspect, Pepsi does skew younger, but only somewhat younger on average, mentioned by 107 consumers yielding an average for the brand of 43. As is the case with most data, relative differences are often more valuable than specific values.
If there are any high level category trends here related to age, they seem to be that Clothing brands like Urban Outfitters and Versace (both with the youngest average age of 24), Aeropostale (26), and Forever 21 (Ironically with an average age of 26), and several others in the clothing retail category tend to skew very young. Snack Food especially drinks like Arizona Ice Tea (age 25), and Naked Juice (29), as well as web properties (Bing and YouTube both 29), and electronics (obviously PlayStation 29 and slightly older Nintendo 31 being examples), are associated with a younger demographic on average.
In the middle age group, other than products with a wide user base like major soda brands, anything related to the home, either entertainment like Time Warner Cable or even Hulu (both 45), or major retailers like Wegmans and Wal-Mart (also both 45), are likely to skew more middle age.
The scariest position for a brand manager is probably at the top of the list, with average age for Maxwell House, and Hunts (both 66), Stouffers and Marie Callender’s (both 64), the question has got to be, who will replace my customer base when they die? What we see by looking at the data are in fact that a slight negative correlation between age and number of mentions.
Again, it’s often the comparative differences that are interesting to look at, and of course the variance. Take Coca-Cola VS Pepsi for instance, while their mean ages are surprisingly close to each other at 45 and 43 respectively, looking at the variance associated with each gives us the spread (i.e. which brand is pulling from a broader demographic). Coca-Cola with a standard deviation of 14.5 years for instance is pulling from a wider demographic than Pepsi which as a standard deviation of 12.9 years. There are several ways to visualize these data and questions in OdinText, though some of our clients also like to use OdinText output in visualization software like Tableau which can have more visualization options, but little to no text analytics capabilities.
Co-Occurrence (aka Market Basket Analysis)
Last but not least, looking at which brands are often mentioned together, either because they are head to head competitors going after the exact same customers or because there may be complimentary (market basket analysis type opportunities if you will) can also certainly be interesting to look at. Brands that co-occur frequently (are mentioned by the same customers), and are not competitors may in fact represent interesting opportunities for ‘co-opetition’. You may have noticed more cross category partnering on advertising recently as marketers seem to be catching on to the value of joining forces in this manner. Below is one such visualization created using OdinText with just the Top 20 brand mentions visualized in an x-y plot using multi-dimensional scaling (MDS) to plot co-occurrence of brand names.
Hope you enjoyed today’s discussion of a very simple text question and what can be done with it in OdinText. Come back again soon as we will be giving more tips and mini analysis on interesting mixed data. In fact, if there is significant interest in today’s post we could look at one or two other variables and how they relate to brand awareness comment data tomorrow.
Of course if you aren’t already using OdinText, please feel free to request a demo here.