Posts tagged structured data
What Your Customer Satisfaction Research Isn’t Telling You and Why You Should Care

Why most customer experience management surveys aren’t very useful

 

Most of your customers, hopefully, are not unhappy with you. But if you’re relying on traditional customer satisfaction research—or Customer Experience Management (CXM) as it’s come to be known—to track your performance in the eyes of your customers, you’re almost guaranteed not to learn much that will enable you to make a meaningful change that will impact your business.Why Are Your Customers Mad At You-revise v2

That’s because the vast majority of companies are almost exclusively listening to happy customers. And this is a BIG problem.

Customer Satisfaction Distribution - Misconception: Most Customer Feedback is Negative

To understand what’s going on here, we first need to recognize that the notion that most customer feedback is negative is a widespread myth. Most of us assume incorrectly that unhappy customers are proportionately far more likely than satisfied customers to give feedback.

... the vast majority of companies are almost exclusively listening to happy customers. And this is a BIG problem.

In fact, the opposite is true. The distribution of satisfied to dissatisfied customers in the results of the average customer satisfaction survey typically follows a very different distribution. Indeed, most customers who respond in a customer feedback program are actually likely to be very happy with the company.

Generally speaking, for OdinText users that conduct research using conventional customer satisfaction scales and the accompanying comments, about 70-80% of the scores from their customers land in the Top 2 or 3 boxes. In other words, on a 10-point satisfaction scale or 11-point likeliness-to-recommend scale (i.e. Net Promoter Score), customers are giving either a perfect or very good rating.

That leaves only 20% or so of customers, of which about half are neutral and half are very dissatisfied.

So My Survey Says Most of My Customers Are Pretty Satisfied. What’s the Problem?

Our careful analyses of both structured (Likert scale) satisfaction data and unstructured (text comment) data have revealed a couple of important findings that most companies and customer experience management consultancies seem to have missed.

We first identified these issues when we analyzed almost one million Shell Oil customers using OdinText over a two-year period  (view the video or download the case study here), and since then we have seen the same trends again and again, which frankly left us wondering how we could have missed these patterns in earlier work.

1.  Structured/Likert scale data is duplicative and nearly meaningless

We’ve seen that there is very little real variance in structured customer experience data. Variance is what companies should really be looking for.

The goal, of course, is to better understand where to prioritize scarce resources to maximize ROI, and to use multivariate statistics to tease out more complex relationships. Yet we hardly ever tie this data to real behavior or revenue. If we did, we would probably discover that it usually does NOT predict real behavior. Why?

2.  Satisficing: Everything gets answered the same way

The problem is that customers look at surveys very differently than we do. We hope our careful choice of which attributes to measure is going to tell us something meaningful. But the respondent has either had the pleasant experience she expected with you  OR in some (hopefully) rare instances a not-so-pleasant experience.

The problem is that customers look at surveys very differently than we do. We hope our careful choice of which attributes to measure is going to tell us something meaningful.

In the former case her outlook will be generally positive. This outlook will carry over to just about every structured question you ask her. Consider the typical set of customer sat survey questions…

  • Q. How satisfied were you with your overall experience?
  • Q. How likely to recommend the company are you?
  • Q. How satisfied were you with the time it took?
  • Q. How knowledgeable were the employees?
  • Q. How friendly were the employees? Etc…

Jane's Experience: Jane, who had a positive experience, answers the first two or three questions with some modicum of thought, but they really ask the same thing in a slightly different way, and therefore they get very similar ratings. Very soon the questions—none of which is especially relevant to Jane—dissolve into one single, increasingly boring exercise.

But since Jane did have a positive experience and she is a diligent and conscientious person who usually finishes what she starts, she quickly completes the survey with minimal thought giving you the same Top 1, 2 or 3 box scores across all attributes.

John's Experience: Next is John, who belongs to the fewer than 10% of customers who had a dissatisfying experience. He basically straightlines the survey like Jane did; only he checks the lower boxes. But he really wishes he could just tell you in a few seconds what irritated him and how you could improve.

Instead, he is subjected to a battery of 20 or 30 largely irrelevant questions until he finally gets an opportunity to tell you his problem in the single text question at the end. If he gets that far and has any patience left, he’ll tell you what you need to know right there.

Sadly, many companies won’t do much if anything with this last bit of crucial information. Instead they’ll focus on the responses from the Likert scale questions, all of which Jane and John answered with a similar lack of thought and differentiation between the questions.

3.  Text Comments Tell You How to Improve

So, structured data—that is, again, the aggregated responses from Likert-scale-type survey questions—won’t tell you how to improve. For example, a restaurant customer sat survey may help you identify a general problem area—food quality, service, value for the money, cleanliness, etc.—but the only thing that data will tell you is that you need to conduct more research.

For those who really do want to improve their business results, no other variable in the data can be used to predict actual customer behavior (and ultimately revenue) better than the free-form text response to the right open-ended question, because text comments enable customers to tell you exactly what they feel you need to hear.

4.  Why Most Customer Satisfaction or NPS Open-End Comment Questions Fail

Let’s assume your company appreciates the importance of customer experience management and you’ve invested in the latest text analytics software and sentiment tools. You’ve even shortened your survey because you recognize that the be Overall Satisfaction (OSAT) and most predictive answers come from text questions and not from the structured data.

You’re all set, right? Wrong.

Unfortunately, we see a lot of clients make one final, common mistake that can be easily remedied. Specifically, they ask the recommended Net Promoter Score (NPS) or Overall Satisfaction (OSAT) open-end follow-up question: “Why did you give that rating?” And they ask only this question.

There’s nothing ostensibly wrong with this question, except that you get back what you ask. So when you ask the 80% of customers who just gave you a positive rating why they gave you that rating, you will at best get a short positive about your business. Those fewer than 10% who slammed you will give you  problem area certainly, but this gives you very little to work with other than a few pronounced problems that you probably knew were important anyway.

What you really need is information that you didn’t know and that will enable you to improve in a way that matters to customers and offers a competitive advantage.

An Easy Fix

The solution is actually quite simple: Ask a follow-up probe question like, “What, if anything, could we do better?”

This can then be text analyzed separately, or better yet, combined with the original text comment which as mentioned earlier usually reads Q. “Why did you give the satisfaction score you gave? And due to the Possion distribution in customer satisfaction yields almost only positive comments with few ideas for improvement.  This one-two question combination when text analyzed together fives a far more complete picture to the question about how customer view your company and how you can improve.

Final Tip: Make the comment question mandatory. Everyone should be able to answer this question, even if it means typing an “NA” in some rare cases.

Good luck!

Ps. To learn more about how OdinText can help you learn what really matters to your customers and predict real 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 ]

Text analysis answers: Is the Quran really more violent than the Bible? (3of3)

Text analysis answers: Is the Quran really more violent than the Bible?by Tom H. C. Anderson

Text Analytics Bible Q

Part III: The Verdict

To recap…

President Obama in his State of the Union last week urged Congress and Americans to “reject any politics that target people because of race or religion”—clearly a rebuke of presidential candidate Donald Trump’s call for a ban on Muslims entering the United States.

This exchange, if you will, reflects a deeper and more controversial debate that has wended its way into not only mainstream politics but the national discourse: Is there something inherently and uniquely violent about Islam as a religion?

It’s an unpleasant discussion at best; nonetheless, it is occurring in living rooms, coffee shops, places of worship and academic institutions across the country and elsewhere in the world.

Academics of many stripes have interrogated the texts of the great religions and no doubt we’ll see more such endeavors in the service of one side or the other in this debate moving forward.

We thought it would be an interesting exercise to subject the primary books of these religions—arguably the core of their philosophy and tenets—to comparison using the advanced data mining technology that Fortune 500 corporations, government agencies and other institutions routinely use to comb through large sets of unstructured text to identify patterns and uncover insights.

So, we’ve conducted a surface-level comparative analysis of the Quran and the Old and New Testaments using OdinText to uncover with as little bias as possible the extent to which any of these texts is qualitatively and/or quantitatively distinct from the others using metrics associated with violence, love and so on.

Again, some qualifiers…

First, I want to make very clear that we have not set out to prove or disprove that Islam is more violent than other religions.

Moreover, we realize that the Old and New Testaments and the Quran are neither the only literature in Islam, Christianity and Judaism, nor do they constitute the sum of these religions’ teachings and protocols.

I must also reemphasize that this analysis is superficial and the findings are by no means intended to be conclusive. Ours is a 30,000-ft, cursory view of three texts: the Quran and the Old and New Testaments, respectively.

Lastly, we recognize that this is a deeply sensitive topic and hope that no one is offended by this exercise.

 

Analysis Step: Similarities and Dissimilarities

Author’s note: For more details about the data sources and methodology, please see Part I of this series.

In Part II of the series, I shared the results of our initial text analysis for sentiment—positive and negative—and then broke that down further across eight primary human emotion categories: Joy, Anticipation, Anger, Disgust, Sadness, Surprise, Fear/Anxiety and Trust.

The analysis determined that of the three texts, the Old Testament was the “angriest,” which obviously does not appear to support an argument that the Quran is an especially violent text relative to the others.

The next step was to, again, staying at a very high level, look at the terms frequently mentioned in the texts to see what if anything these three texts share and where they differ.

Similarity Plot

Text Analytics Similarity Plot 2

This is yet another iterative way to explore the data from a Bottom-Up data-driven approach and identify key areas for more in-depth text analysis.

For instance—and not surprisingly—“Jesus” is the most unique and frequently mentioned term in the New Testament, and when he is mentioned, he is mentioned positively (color coding represents sentiment).

“Jesus” is also mentioned a few times in the Quran, and, for obvious reasons, not mentioned at all in the Old Testament. But when “Jesus” is mentioned in the New Testament, terms that are more common in the Old Testament—such as “God” and “Lord”—often appear with his name; therefore the placement of “Jesus” on the map above, though definitely most closely associated with the New Testament, is still more closely related to the Old Testament than the Quran because these terms appear more often in the former.

Similarly, it may be surprising to some that “Israel” is mentioned more often in the Quran than the New Testament, and so the Quran and the Old Testament are more textually similar in this respect.

So…Is the Quran really more violent than the Old and New Testaments?

Old Testament is Most Violent

A look into the verbatim text suggests that the content in the Quran is not more violent than its Judeo-Christian counterparts. In fact, of the three texts, the content in the Old Testament appears to be the most violent.

Killing and destruction are referenced slightly more often in the New Testament than in the Quran (2.8% vs. 2.1%), but the Old Testament clearly leads—more than twice that of the Quran—in mentions of destruction and killing (5.3%).

New Testament Highest in ‘Love’, Quran Highest in ‘Mercy’

The concept of ‘Love’ is more often mentioned in the New Testament (3.0%) than either the Old Testament (1.9%) or the Quran (1.26%).

But the concept of ‘Forgiveness/Grace’ actually occurs more often in the Quran (6.3%) than the New Testament (2.9%) or the Old Testament (0.7%). This is partly because references to “Allah” in the Quran are frequently accompanied by “The Merciful.” Some might dismiss this as a tag or title, but we believe it’s meaningful because mercy was chosen above other attributes like “Almighty” that are arguably more closely associated with deities.

Text Analytics Plot 3

‘Belief/ Faith’, ‘Non-Members’ and ‘Enemies’

A key difference emerged immediately among the three texts around the concept of ‘Faith/Belief’.

Here the Quran leads with references to ‘believing’ (7.6%), followed by the New Testament (4.8%) and the Old Testament a distant third (0.2%).

Taken a step further, OdinText uncovered what appears to be a significant difference with regard to the extent to which the texts distinguish between ‘members’ and ‘non-members’.

Both the Old and New Testaments use the term “gentile” to signify those who are not Jewish, but the Quran is somewhat distinct in referencing the concept of the ‘Unbeliever’ (e.g.,“disbelievers,” “disbelieve,” “unbeliever,” “rejectors,” etc.).

And in two instances, the ‘Unbeliever’ is mentioned together with the term “enemy”:

“And when you journey in the earth, there is no blame on you if you shorten the prayer, if you fear that those who disbelieve will give you trouble. Surely the disbelievers are an open enemy to you

 An-Nisa 4:101

“If they overcome you, they will be your enemies, and will stretch forth their hands and their tongues towards you with evil, and they desire that you may disbelieve

Al-Mumtahina 60:2

That said, the concept of “Enemies” actually appears most often in the Old Testament (1.8%).

And while the concept of “Enemies” occurs more often in the Quran than in the New Testament (0.7% vs 0.5%, respectively), there is extremely little difference in how they are discussed (i.e., who and how to deal with them) with one exception: the Quran is slightly more likely than the New Testament to mention “the Devil” or “evil” as being an enemy (.2% vs 0.1%).

Conclusion

While A LOT MORE can be done with text analytics than what we’ve accomplished here, it appears safe to conclude that some commonly-held assumptions about and perceptions of these texts may not necessarily hold true.

Those who have not read or are not fairly familiar with the content of all three texts may be surprised to learn that no, the Quran is not really more violent than its Judeo-Christian counterparts.

Personally, I’ll admit that I was a bit surprised that the concept of ‘Mercy’ was most prevalent in the Quran; I expected that the New Testament would rank highest there, as it did in the concept of ‘Love’.

Overall, the three texts rated similarly in terms of positive and negative sentiment, as well, but from an emotional read, the Quran and the New Testament also appear more similar to one another than either of them is to the significantly “angrier” Old Testament.

Of course, we’ve only scratched the surface here. A deep analysis of unstructured data of this complexity requires contextual knowledge, and, of course, some higher level judgment and interpretation.

That being said, I think this exercise demonstrates how advanced text analytics and data mining technology may be applied to answer questions or make inquiries objectively and consistently outside of the sphere of conventional business intelligence for which our clients rely on OdinText.

I hope you found this project as interesting as I did and I welcome your thoughts.

Yours fondly,

Tom @OdinText

TOM DEC 300X250

 

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

Text Analytics Tips

Text Analytics Tips, with your Hosts Tom & Gosia: Introductory Post Today, we’re blogging to let you know about a new series of posts starting in January 2016 called ‘Text Analytics Tips’. This will be an ongoing series and our main goal is to help marketers understand text analytics better.

We realize Text Analytics is a subject with incredibly high awareness, yet sadly also a subject with many misconceptions.

The first generation of text analytics vendors over hyped the importance of sentiment as a tool, as well as ‘social media’ as a data source, often preferring to use the even vaguer term ‘Big Data’ (usually just referring to tweets). They offered no evidence of the value of either, and have usually ignored the much richer techniques and sources of data for text analysis. Little to no information or training is offered on how to actually gain useful insights via text analytics.

What are some of the biggest misconceptions in text analytics?

  1. “Text Analytics is Qualitative Research”

FALSE – Text Analytics IS NOT qualitative. Text Analytics = Text Mining = Data Mining = Pattern Recognition = Math/Stats/Quant Research

  1. It’s Automatic (artificial intelligence), you just press a button and look at the report / wordcloud

FALSE – Text Analytics is a powerful technique made possible thanks to tremendous processing power. It can be easy if using the right tool, but just like any other powerful analytical tools, it is limited by the quality of your data and the resourcefulness and skill of the analyst.

  1. Text Analytics is a Luxury (i.e. structured data analysis is of primary importance and unstructured data is an extra)

FALSE – Nothing could be further from the truth. In our experience, usually when there is text data available, it almost always outperforms standard available quant data in terms of explaining and/or predicting the outcome of interest!

There are several other text analytics misconceptions of course and we hope to cover many of them as well.

While various OdinText employees and clients may be posting in the ‘Text Analytics Tips’ series over time, Senior Data Scientist, Gosia, and our Founder, Tom, have volunteered to post on a more regular basis…well, not so much volunteered as drawing the shortest straw (our developers made it clear that “Engineers don’t do blog posts!”).

Kidding aside, we really value education at OdinText, and it is our goal to make sure OdinText users become proficient in text analytics.

Though Text Analytics, and OdinText in particular, are very powerful tools, we will aim to keep these posts light, fun yet interesting and insightful. If you’ve just started using OdinText or are interested in applied text analytics in general, these posts are certainly a good start for you.

During this long running series we’ll be posting tips, interviews, and various fun short analysis. Please come back in January for our first post which will deal with analysis of a very simple unstructured survey question.

Of course, if you’re interested in more info on OdinText, no need to wait, just fill out our short Request Info form.

Happy New Year!

Your friends @OdinText

Text Analytiics Tips T G

[NOTE: Tom is Founder and CEO of OdinText Inc.. A long time champion of text mining, in 2005 he founded Anderson Analytics LLC, the first consumer insights/marketing research consultancy focused on text analytics. He is a frequent speaker and data science guest lecturer at university and research industry events.

Gosia is a Senior Data Scientist at OdinText Inc.. A PhD. with extensive experience in content analytics, especially psychological content analysis (i.e. sentiment analysis and emotion in text), as well as predictive analytics using unstructured data, she is fluent in German, Polish and Spanish.]

 

Teaching Text Analytics

Turning Market Researchers into Data Scientists with Text Analytics (This Thursday Join Paul Kirch and OdinText CEO Tom H. C. Anderson as they discuss Next Generation Text Analytics and the challenges with educating an industry and your clients on new techniques.)

Educating clients and colleagues on the value of new techniques methodologies is important. It can also be challenging, yet it’s something we have to do as an industry, and as individual researchers in order to stay competitive.

Text Analytics Education

This Thursday, Join Paul Kirch from Boss Academy Radio and OdinText CEO Tom H. C. Anderson tomorrow as they discuss Next Generation Text Analytics and the challenges with educating an industry and your clients on new techniques. This is a live video cast and seats are limited to first come first serve.

Tom Anderson has spent the last decade as a champion of Text Analytics, telling marketing researchers about this new technique before it became vogue (before FB, Twitter and LinkedIn)! In May 2015, Tom Founded OdinText Inc. which is a powerful Next Generation approach to Text Analytics.

According to Tom, “A lot of people have now heard about text analytics thanks to a lot of first generation text analytics software marketing. Many of these early companies overpromised and under delivered”. Join Paul and Tom as they discuss how OdinText is overcoming these preconceptions (some good and some bad), and how one can educate an entire industry on the hidden value of unstructured data.

OdinText Wins 2015 CASRO Research Award

CASRO Honors OdinText’s Innovative Next Generation Text Analytics Software at 40th Annual Conference OdinText, a provider of cloud-based analytics software, today announced that its Next Generation Text Analytics software-as-a-service (SaaS) product, has been awarded the Research Entrepreneur of the Year award by CASRO, an organization that represents more than 300 companies and market research operations.

The award honors organizations that—through the excellence of their work, professionalism of their practice, and integrity of their conduct— exemplify the best work in the research industry. The award also acknowledges an organization that has introduced a new direction or service to its research business portfolio and provides leading-edge and innovative services that expand traditional market, opinion, and social research.

Recognized for its patented SaaS technology, OdinText allows companies to analyze large amounts of unstructured and mixed data. OdinText can be used across various types of data including but not limited to survey research, email and telephone data, discussion board ratings, and news articles.

“At OdinText, we don’t see a difference between structured and unstructured data - text mining and data mining – they are far more meaningful together,” said Tom H. C. Anderson, CEO of OdinText. “We are honored to be recognized by CASRO, an organization that has such a long history of championing innovative and sound research techniques.”

In addition to exploring patterns in the data and allowing users to confirm hypothesis, OdinText suggests key relationships in the data that may be overlooked by the user. The software also allows for one-step simulation and predictive analytics.

“Marketing research is evolving, getting both broader and deeper in terms of skill sets needed to succeed,” said Jim DeMarco, vice president of business intelligence and analytics at FreshDirect. “OdinText provides researchers with the capability to access more advanced analysis quicker and helps the business they work on gain an information advantage. This is exactly the kind of innovation our industry needs right now.”

The Coca-Cola Company as well as online grocer, FreshDirect sponsored OdinText’s nomination and the company received the award at CASRO’s 40th Annual Conference, in addition to the $5,000 prize.

“The work of OdinText is indicative of the exciting new methodologies and technologies which are having an increased influence on our changing industry,” said Diane Bowers, president of CASRO. “Acknowledgement of this type of work and the financial support that accompanied this honor highlights our role as a leader in the future of our industry.”

 

About OdinText Inc. OdinText’s Next Generation Text AnalyticsTM turns market researchers into data scientists. The powerful cloud-based software helps users discover patterns and trends in complex unstructured text data. Visit www.odintext.com to learn more or schedule a demo. Backed by Connecticut Innovations and private investors, OdinText is a privately-held company based in Stamford, Conn. Request more information here.

Pre-IIEX Text Analytics Q&A

Tom H. C. Anderson on Next Generation Text Analytics(Interview Re-Posted from Insight Innovation Exchange IIEX2015)

What are you going to be talking about at IIeX?

THCA: Next Generation Text Analytics of course! Over the past year we’ve seen so many interesting uses of our text analytics software OdinText (http://odintext.com). I’m just hoping to share a little bit of what our users have been teaching us.

What are some takeaways from your session?

THCA: Text Analytics has moved beyond a nice to have and is now a need to have. My own opinion of text analytics has changed radically within just the past two years. I now believe we are wasting a lot of time and money with much of the traditional structured research questions just getting the same answers over and over again. Text Analytics and thus an understanding of unstructured data, when implemented correctly, can improve insights far more than anything that can be done with structured data. There really isn’t any more value that can be squeezed out of a likert scale.

Tell us a little bit about yourself.

THCA: I’m fanatical about text analytics. I wasn’t always this bad, but the more I learn the more I love what I do. Happy to tell you more about it!

What are you hoping to get out of IIeX?

THCA: IIeX is a great place to meet like minded researchers, people who are excited about the insights process and are looking for ways to do things a little differently, better, faster, cheaper – SMARTER!

What are you most looking forward to at the event?

THCA: I’m pretty active on social media, Twitter (@TomHCAnderson), LinkedIn(https://www.linkedin.com/in/tomhcanderson) and Facebook (https://www.facebook.com/pages/Tom-H-C-Anderson), and so get to meet a lot of researchers online. But once in a while it’s good to do things the old school way, and IIeX is one of if not the best event to meet up and network with other Next Gen Market Researchers.

Learn more about Tom and his sessions at IIeX.

Coca-Cola Social Media Team Chooses OdinText Text Analytics Software and Wins 2015 ARF Award

Coca-Cola Leverages OdinText analytics to develop new approach to social listening and wins ARF’s 2015 RE:Think Your Future “Make Your Mark” Award!

At the Advertising Research Foundation’s (ARF) annual RE:Think Conference this week?

Please join OdinText and The Coca-Cola Company this coming week at the 2015 Advertising Research Foundation’s annual RE:THINK event to learn more about the future of big data and text analytics.

 

On Monday in SuttTomCokeTextAnalyticson South, 2nd Floor at 3:30, OdinText Founder and CEO Tom H. C. Anderson will be joined by several other industry leaders in what promises to be a very interesting panel entitled “Applying Mobile Big Data at the Intersection of Content, Context & User Analytics”. As unstructured data is the key to the future of mobile, text analytics promises to be a key part of the discussion.

 

 

On Wednesday afternoon in the Grand Ballroom, The Coca-Cola Company’s Digital Anthropologist, Allison Barnes and Global Media Insights Director, Justin De Graaf, are being honored for their very interesting work and award winning paper entitled “Digital Anthropology: Researching Audiences Online”.

CokeAllisonTextAnalytics

CokeJustinTextAnalytics

Allison and Justin will briefly discuss how Coca-Cola is leveraging OdinText to understand specific consumer groups, developing an innovative new approach that works across the Coca-Cola system. An approach that is scalable, utilizes existing tools, works across all markets and adapts to custom requirements.

“The new technique moves beyond word clouds [and other first generation social listening features] which represent the old way, and towards true analytics, which we’re getting from OdinText”

As Allison points out in their paper, great text analytics is critical for The Coca-Cola Company since “Social is a new avenue our consumers use to share their lives, and tapping in to those shared thoughts allows us to become more closely tied to the things that matter most to them. Our ability to continue innovating communications, products and happiness is at the heart of our future and harnessing social allows us to do exactly that”.

Congratulations to Justin, Allison and the rest of the social media team at Coca-Cola for a job well done!

We look forward to seeing you at The ARF Monday or Wednesday. And as always we welcome your questions about OdinText and text analytics anytime!

New to Text Analytics?

6 lessons for CIOs and CMOs who are new to text analytics(Free White Paper)

Unstructured data is the most prevalent form of information on the planet. It also underpins much of our communication. It exists in our e-mails, surveys, social media accounts, call center logs, etc. With a strong text analytics strategy in place, companies can get critical information from this data to drive better business decisions.

You may recall my blog interviews with several client side analytics managers ahead of this years’ Useful Business Analytics Summit.

Data Driven Business has compiled a free white paper which focuses on the business benefits (and challenges!) of text analytics from the perspectives of 4 of these experts from Highmark Health, Toyota, Mozilla, and Visa (All who are slated to speak at the 13th Annual Text Analytics Summit West on November 4-5 in San Francisco.

You can download the free whitepaper here: http://goo.gl/hxZg80

Some of the issues covered include:

· 6 lessons for CIOs and CMOs who are new to text analytics

· Identifying KPIs for your text analytics initiatives

· Barriers to adoption and how to overcome them

Big thanks to the following contributors:

Mark Pitts, Vice President, Enterprise Informatics, Data & Analytics at Highmark Health

Farouk Ferchichi, Executive Director at Toyota Financial Services

Matthew P.T. Ruttley, Manager of Data Science at Mozilla Corporation

Ramkumar Ravichandran, Director, Analytics at Visa

@TomHCAnderson

[Full Disclosure: Tom H. C. Anderson is Managing Partner of Anderson Analytics, developers of patented Next Generation Text Analytics™software platform OdinText. For more information and to inquire about software licensing visit ODINTEXT INFO REQUEST]