Posts tagged branding
Key Driver Analysis: Top-down & Bottom-up Approach

Text Analytics Tips - Branding Get a complete picture of your data: The ‘Top-Down and Bottom-Up Approach’

At OdinText we’ve found that the best way to identify all key drivers in any analysis really, especially in customer experience management (including but not limited to KPI’s such as OSAT, Net Promoter Score, Likelihood to Return or other real behavior) is through a dual process combining a theory-driven (aka “top-down”) and a data-exploratory or data-driven approach (aka “bottom-up”):

Top-Down

This approach requires you to identify important concepts or themes before even starting to explore and analyze your data. In customer satisfaction or brand equity research you can often start by identifying these key concepts by reviewing the strengths and weaknesses associated with your brand or product, or by listing the advantages and challenges that you believe may be prevalent (e.g., good customer service, poor management, professionalism etc.). This is an a priori approach where the user/analyst identifies a few things that they believe may be important.

Bottom-Up

This approach requires you to use a more advanced text analytics software, like OdinText, to mark and extract concepts or themes that are most frequently mentioned in customers’ text comments found in your dataset and that are relevant to your brand or product evaluation (e.g., high cost, unresponsiveness, love). Better analytics software should be able to automatically identify important things that the user/analyst didn’t know to look for.

Top-down vs. Bottom-up

The top-down approach does not reflect the content of your data, whereas the bottom-up approach while being purely based on the data can fail to include important concepts or themes that occur in your data less frequently or is abstracted in some way. For instance, in a recent customer satisfaction analysis, very few customer comments explicitly mentioned problems associated with management of the local branches (therefore, “management” was not mentioned frequently enough to be identified as a key driver by the software using the bottom-up approach).

However as the analyst had hypothesized that management might be an important issue, more subtle mentions associated with the concept of management were included in the analysis. Subsequently predictive analytics revealed that “poor management” was in fact a major driver of customer dissatisfaction. This key driver was only “discovered” due to the fact that the analyst had also used a top-down approach in their text analysis.

It may be that some of the concepts or themes identified using the two approaches overlap but this will only ensure that the most important concepts are included.

Remember, that only when combining these two very different approaches can you confidently identify a complete range of key drivers of satisfaction or other important metrics.

I hope you found today’s Text Analytics Tip useful.

Please check back in the next few days as we plan to post a new interesting analysis similar to, but even more exciting than last week’s Brand Analysis.

-Gosia

Text Analytics Tips with Gosi

 

[NOTE: Gosia is a Data Scientist at OdinText Inc. Experienced in text mining and predictive analytics, she is a Ph.D. with extensive research experience in mass media’s influence on cognition, emotions, and behavior.  Please feel free to request additional information or an OdinText demo here.]

Brand Analytics Tips – Branding and Politics

Text Analytics Tips - Branding Ford = Donald Trump and Adidas = Bernie Sanders! Text Analysis of 500 Brands by Political Affiliation - by Tom H. C. Anderson (Final part of this week’s Brand Analytics post)

During the last two days we’ve analyzed a very simple open-ended/comment survey question, namely Q. When you think of brand names, what company’s product or service brand names first come to mind?”. While OdinText can handle this question easily right out of the box without any customization, doing the types of analysis we’ve been doing either by human coding method, or any other text analytics software, or scripting in R and Python etc. would have been difficult to impossible.

While we could certainly continue to analyze this brand awareness question, which can be very useful in brand equity tracking or advertising effectiveness , I’m going to end the analysis of this question today by looking at a variable I thought would be interesting, namely political affiliation.

OdinTextAnalyticsPolitics2

Politics of Brands

Are major brand names politically affiliated? There has been some previous research into this question by looking at how corporations tend to make political donations. In fact, there’s even an iPhone app called, you guessed it, BuyPartisan which compiles campaign finance data from the top Fortune 500 companies and matches it with their products. But do Republicans and Democrats have different brand consideration sets?

Today we’ll look at our recent random sample of over a thousand Americans to see if there is a significant link between unaided top of mind brand awareness and political affiliation in hopes of understanding which liquid soap if any Donald Trump supporters might be more likely to buy.

Political Polling Democrat Republican Text Analytics Chart

Though there isn’t a statistically significant differences between Democrats and Republicans across the majority of the 500+ brands mentioned in our study, there are a few notable exceptions.

The most Republican brand out there is Dawn. For some reason this liquid soap is TEN times more likely to be mentioned by Republicans as Democrats (3% vs. 0.3%)!

Five other popular brands that skew significantly more Republican than Democrat are Ford (12% vs. 6%), Kellogg’s (4% vs. 1%), Palmolive and Wells Fargo (both 1.4% vs. 0%).

Conversely, brands that are more likely to be Top of mind among Democrats are Air Jordan (2.3% vs. 0%), Target (7% vs. 3%), Adidas (5% vs. 2%), and Bose (1.4% vs. 0%).

Why some of these differences? Your guess is as good as mine. In some cases like Dawn, perhaps it has to do with National distribution channels and more ‘Red States’ getting stocked with this brand? Interestingly though, according to BuyPartisan, “buying Dawn dish soap will support the Republican party”, so perhaps there is more to some of these categories. In other cases like Air Jordan and Adidas (the latter which is German), these two brands at least are perhaps more likely to be seen in larger urban settings and therefore more ‘blue’?

Blogging about brands for the past three days was more fun than I thought. Several people reached out to us with further questions. Of course if anyone would like additional information on OdinText please fill out our simple text analytics demo request.

Please come back next week for more Text Analytics Tips as we plan to explore a very different data set with different insights.

Tom @OdinText

 

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

Brand Analytics – Branding and Gender

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.]

Brand Analytics Tips – How Old is Your Brand?

Text Analytics Tips 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.]

Text Analytics Tips Age OdinText

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
Maxwell House 66
Hunts 66
Aspirin 66
Chrysler 64.6
Stouffers 63.7
Marie Callender's 63.7
Walgreen 63.7
Cooper (Mini) 63.7
Bayer 62.6
USAA 62.5
Epson 62.5
Brother 61.3
Aol 61.3
Comet 61.3
Snapple 61.3
Lowes 61.2
Marriott 60.3
Ritz 60.3
Hellman's 60.3
Ikea 60.3
Belk 60.3
State Farm 60.3
Oscar Mayer 60
Folgers 59.8
Libby's 59.8
Hormel 59.2
Depot 59.2
Heinz 59.2
Electric 59.2
Bordens 59.2
Nestles 59
Green Giant 59
Sargento 58.3
Del Monte 58
Prego 58
Kashi 58
Westinghouse 58
Stouffer 58
Taylor 58
Home Depot 57.6
Publix 57.5
Banquet (Frozen Dinners) 57.5
Buick 57
Krogers 57
Hellman's 57
Safeway 56.5
Purex 56.4
Hewlett 56.4
Unilever 56.1
RCA 56.1
Post 56.1
P&G 55.9
Budweiser 55.9
Yoplait 55.8
Chobani 55.7
Ragu 55.7
Campbell's 55.5
Wells Fargo 55.2
Hershey 55.1
Betty Crocker 55
Sharp 55
Hines 55
Trader Joe's 55
Palmolive 54.9
Kia 54.7
Lexus 54.7
Life 54.7
Hotpoint 54.7
Campbells 54.6
Oscar Mayer 54.5
Dial 54.4
Nissan 54.4
Hillshire Farms 54.3
Motorola 54.1
Keebler 54
CVS 53.8
Canon 53.8
Lakes 53.7
Pillsbury 53.3
Hilton 53.3
Faded Glory 53.3
Friskies 53.3
Duncan Hines 53.3
Puffs 53.3
Olay 52.8
Sketchers 52.5
Fred Meyer 52.5
Delta 52.5
Hunt 52.3
Bose 52.3
Ocean Spray 52.3
Ivory 52.3
Swanson 52.3
Dewalt 52.3
Firestone 51.8
Estee Lauder 51.5
Miller 51.5
Tide 51.4
Honda 51.3
Meijer 51.3
Perdue 51.3
Jeep 51.3
Head 51.3
Lee Jeans 51.3
Pantene 51
Chevrolet 51
Cannon 50.8
Chef Boyardee 50.8
Frito Lay 50.6
Avon 50.5
Motors 50.4
Kodak 50.4
General Mills 50.2
BMW 50
Lipton 49.8
Kohl's 49.8
Goodyear 49.7
Kraft 49.6
Craftsman 49.5
Sunbeam 49.4
IBM 49.3
Frigidare 49.1
Sears 49.1
Ford 49.1
Walgreens 49.1
Dole 49.1
Chevy 49
Wonder (Bread) 49
Dannon 49
JVC 49
Hyundai 49
Clinique 49
Marlboro 49
Mercedes 49
Gerber 49
Acme 49
Kleenex 48.8
Kelloggs 48.7
JC Penney 48.6
Louis Vuitton 48.5
Calvin 48.4
LL Bean 48.4
Gillette 48.4
Johnson & Johnson 48.3
Shell 48.3
Kenmore 48.1
Dawn 48
Hanes 48
Macdonalds 48
Tylenol 48
Colgate 47.5
Wrangler (Jeans) 47.3
Burger King 47.3
Whirlpool 47.1
GMC 47
Yahoo 46.9
Dish Network 46.8
Verizon 46.7
Hersheys 46.6
Whole Foods 46.5
Sara Lee 46.5
Hostess 46.5
Mazda 46.5
Toyota 46.4
Arm & Hammer 46.4
Nabisco 46.3
Tyson 46.1
Starbucks 46
Wal-Mart 45.9
Western Family 45.8
Wegmans 45.8
Dr Pepper 45.7
Hulu 45.7
Time Warner 45.7
Maybelline 45.7
MLB 45.7
Iams 45.7
Cox 45.7
Country Crock 45.7
Compaq 45.7
Sonoma 45.7
Quaker Oats 45.7
Nordstrom 45.4
Coca 45.3
Champion 45.3
Bass 45
Chrome 44.7
Coors 44.7
iPhone 44.6
Bounty 44.5
Dodge 44.4
Maytag 44.3
Black & Decker 44.2
Pfizer 44.2
Suave 44.2
HP 44
Scott 44
Subway 44
Skechers 44
Geico 44
Panasonic 43.9
Lays 43.8
KFC 43.8
Charmin 43.8
Dell 43.8
Polo 43.8
Windex 43.7
Burts Bees 43.5
Purina 43.5
Clorox 43.5
Columbia 43.3
Ralph Lauren 43.2
Visa 43.2
Pepsi 43
Crest 43
NFL 43
Sanyo 43
Dove 42.9
Intel 42.9
Wendy's 42.8
Kroger 42.8
Remington 42.3
Phillips 42.3
Mars 42.3
Cover Girl 42.3
Heb 42.3
Twitter 42.3
Amazon 42
Body Works 42
Best Buy 41.8
Costco 41.8
Banana Republic 41.8
Disney 41.7
Amway 41.7
Levi 41.5
Sony 41.4
Samsung 41.4
Macy's 41.1
Glade 41.1
Boost 41
Boost Mobile 41
Toshiba 40.8
Ebay 40.8
Comcast 40.7
Facebook 40.6
Walmart 40.5
Microsoft 40.5
Google 40.4
Kitchen 40.4
Nestle 39.8
Mcdonalds 39.5
Gucci 39.5
Vons 39.3
Philip Morris 39.3
Loreal 39.3
Mattel 39.1
Apple 39
Pepperidge Farm 39
Vizio 39
Lysol 39
Ugg 39
Tropicana 39
Sure 39
Fila 39
Tmobile 39
Coach 38.9
Acer 38.8
Tommy Hilfiger 38.6
Nike 38.1
Target 38
Old Navy 37.9
Chase 37.8
Michael Kors 37.7
K-Mart 37.5
Lenovo 37.5
Equate 37.2
Hoover 36.8
Under Armour 36.6
Windows 36.5
Asics 36.5
Kitchenaid 36.5
Victoria's Secret 36.2
Mac 36.1
Reebok 36.1
Android 36
Direct TV 36
Sprint 36
Netflix 35.9
Adidas 35.7
Citizen 35.7
New Balance 35.6
Guess 35.4
Bic 35.2
Great Value 35.2
Pizza Hut 35
Puma 34.9
Asus 34.4
Fox 34.3
Justice 34.3
North Face 34.1
Xbox 33.6
Gap 33.4
Doritos 33.4
HTC 33.4
Converse 33.3
Sprite 33.2
Febreeze 33
Axe 33
Kay 32.7
Glad 32.7
Mary Kay 32.7
Viva 32.7
Reese's 31.8
Lego 31.7
Amazon Prime 31.5
Nintendo 31.2
Vans 31.2
Taco Bell 31
Fisher Price 30.4
Chanel 29.7
Old Spice 29.7
Playstation 29.4
Eagle 29.4
Hamilton Beach 29.3
Footlocker 29.3
Pink 29.3
Swiffer 29.3
Timberlands 29.3
Naked Juice 29
Youtube 29
Bing 29
Air Jordans 28.4
Huggies 28.2
Aeropostale 27.7
Hollister 27.3
Prada 27.3
Carters 26.8
Kirkland 26.3
Forever 26.3
Aeropostle 26.3
Arizona 25.6
Pampers 24.5
Versace 24.5
Urban Outfitters 24.5

 

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.

Text Analytics of Brands with OdinText

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.

@TomHCAnderson