Posts tagged Text Analytics
Behind every comment is an experience that matters

TL;DR Creating well-defined topics is an essential step so that you can get the information you need from comments to inform better decisions.

Contained within each comment provided by customers are essential topics impacting your business.  Do you know what they are?

Direct to consumer or two-sided market companies find it hard to organize the high volumes of customer comments. The TOPICS that your customers bring up are the ones that are most important to them and a valuable source of insights for you. Understanding what your customers' are talking about at scale gives you a business advantage that only the best have put into operations.  

Defining the topics that are important to your business is a process but not a difficult one. To get started, it helps to list the steps in the customer journey. Doing this will give you a big list of Topics. Now we need to define those topics with the words and phrases used when talking about the subject. 

Below is an example of a topic and how it is defined.

Topic: Exchanges and Returns

Topic Definition: exchange exchanged exchanges exchanging refund refundable refunded refunding refunds returnable "non-refundable" "return policy" "return policies" "return it" "return them" "returns it" "returns them" "returning it" "returning them" "returned it" "returned them" 

A collection of topics with their definitions, we call a dictionary. Managing and updating dictionaries is important in on-going analysis. PROTIP: Dictionaries are essential in the study but knowing where to route findings is critical to turning insights to actions. A best practice is also defining what department and WHO in that department will be the liaison. Leading companies have established insight channels to provide actionable insights to the relevant department. 

Contained within each comment provided by customers are essential topics impacting your business. Let's find out what they are?

Top 5 Text Analytics Tips of The Year

Happy 2019 & Top Posts of the Year

Thank you all for your readership in 2018. We’re starting out the New Year with some changes to our website, so please bear with us as we migrate our older blog posts over and get things updated.

Our first post of 2019 will be our annual post on the changes in popular slang, a favorite among trend watchers and those following Millennials and Gen Y.

In the meantime in case you missed it, here are the top 5 posts of this past year ranked by popularity.

#1 A New Trend in Qualitative Research

Almost Half of Market Researchers are doing Market Research Wrong! - Interview with the QRCA (And a Quiet New Trend - Science Based Qualitative).


#2 Trend Watching +OdinText

How Your Customers Speak - OdinText Indexes Top Slang and Buzz Words for 2018 


#3 What You Need to Know Before Buying AI/Machine Learning

7 Things to Know About AI/Machine Learning (Boiled Down to two Cliff Notes that are even more important).


#4 Advertising Effectiveness +OdinText

Ad Testing +OdinText, a Review of the 2018 Super Bowl Ads

#5 The State of Marketing Research Innovation

What You Missed at IIEX 2018 – 3 Takeaways


Closely tied for 5th place were Market Research CEO’s Summarized and Text Analyzed (via the Insights Association CEO Summit), and Trump’s Brand Positioning One Tear In (Political Polling + OdinText)

Wishing you an exciting and prosperous 2019!

Your friends @OdinText

2018 Next Gen Market Research Award Winners

2018 Next Gen Market Research (NGMR) Disruptive Innovation Award Winners Announced

Celebrating creativity and hard work in our industry is a joyful duty. Over the years the NGMR nominations have maintained, if not increased in quality. This year once again the judging committee had several difficult choices to make, including one of our past winners who yet again this year proved very deserving of another award. We also received an exciting mix of Client and Supplier nominations, and thus may in the future consider these separate categories.

For those of you attending the TMRE, in Scottsdale AZ this year, after the award ceremony at 8:30 AM this morning there will be the Winners Panel where we open the discussion around innovation to the winners and the audience at 11:00. I urge you to attend a lively and exciting conversation.

Without further ado, here are this years’ deserving winners

Outstanding Disruptive Start-Up

Opinion Economy (CEO Ted Waz accepting)

Data fraud in marketing research sample has been estimated to be a $1 Billon a year problem. Sadly, researchers have come to expect that on average at least 15-30% of their data is fraudulent. This fraud drives up costs and worse, undermines the credibility of market research in general.

Opinion Economy has tackled this ongoing problem in a new way. The partners of the research firm 20|20 Research created a blockchain technology solution tied to a SaaS technology platform.  Their solution is a new system creating a market economy whereby incentives are aligned to reward quality, not volume.

The Opinion Economy platform will not only drive costs lower but ensures that both respondents, as well as buyers, are incentivized to provide quality rather than quantity.  Respondents with higher reputation scores can set a higher price for participation; sample buyers with higher reputation scores can more easily obtain a sample.  Each is incentivized to be a fair and truthful actor in survey and payment transactions.

The net effect promises to be a real, dynamic marketplace for research sample where quality is rewarded for both the respondent and the researcher.


Industry Change Agent of the Year

ThinkNow Co-Founder & Principal, Mario X. Carrasco

Be it penning perspective on what advertisers can learn from Drake or unveiling disruptive technologies that transform cultural conversations, Mario X. Carrasco, Co-Founder and Principal of ThinkNow, an award-winning technology-driven cultural insights agency, has helped elevate market research from mere data points to conduits of soul-baring insights on the most sought after yet misunderstood audiences—multicultural consumers.

Carrasco, a proud Mexican-American, approaches multicultural marketing from a place of authentic concern for how these audiences are portrayed in mainstream media. Under his co-leadership, ThinkNow is one of the few independent firms researching and sharing expert commentary on multicultural populations in the U.S., purposefully pursuing hot topics like virtual reality, cryptocurrency, and other conversations that often exclude the multicultural perspective.

Mario has worked to make sure multicultural is a lifestyle at ThinkNow, not a division, and to make sure these opinions are heard, and help address some of the disparities in multicultural marketing.

As NGMR Award Judge Kristin Luck put it, “Mario embodies the attributes of what we believe makes a truly next-gen market researcher. He’s been a leader in developing innovative marketing and research solutions that drive deep multi-cultural understanding and integrate mobile intelligence, first-party data, and panel profile insights to create a more holistic view of today’s complex consumer.”


Most Innovative Research Method

Align – Susan Ferrari

(Quant+Text Data Analysis of B2B ‘Data Lake’ to Predict KPI’s)

The Most Innovative Research Method category is always among the most competitive, and this year was no exception. This years winner is Susan Ferrari of Align, as exemplified by her work with a large financial institution.

Sue’s case and methodology is one that she has been working toward and perfecting over time. It involves both structured and unstructured (text) data analysis as well as predictive analytics. It is useful to both practitioners of big data and small, and for researchers working with either B2C or B2B data.

The truly innovative approach that few people have thought about, and fewer are trying involves a certain amount of risk, as does anything genuinely new and innovative. Spending resources on a project where outcomes are unknown can be scary. She was counseled not to do it by one of her vendors (full transparency, Sue and her team used the OdinText platform for much of the analysis).

However, Sue pushed on, showing true grit in first building what has recently been referred to as a ‘Data Lake,’ and then spending a lot of time and effort prepping and standardizing these disparate data sources. The work was, in fact, a characteristic of a Big Data type project, made up of much smaller individual data sets. Smaller than what we see in some B2C data lakes, but not in the total economic opportunity represented (As this was B2B data, each record/customer represented millions of dollars).

Sue effectively married disparate survey sources, with real behavior KPI’s (revenue, return behavior etc.), with unstructured comment data (text), and in large part because of the value in free form customer text comments, was able to predict and understand several business units critical KPI’s.

Senior management was extremely impressed with the insights and strategic prioritization the analysis provided.

As NGMR Awards Judge Michael Gadd pointed out “The methodology is very interesting – I have long thought that there are opportunities to marry different types or survey data for analysis and predict outcomes but increasingly we are having issues with the quality of survey data.  Interestingly however, we find generally speaking with proper professional probing from qualitative data we get deeper, more accurate and reliable insight. This is a truly remarkable application of both!”

Please join me in congratulating this years’ winners!

Big thank you also to the Judging Committee:

Mike Gadd, CEO Gadd Research

Kristin Luck, Luck Collective

Tom De Ruyke, Insites Consulting

Steve August, Poinyent/August & Wonder

Scott Upham, Valient Market Research


And finally also a big thank you to all the NGMR Group members who nominated the many talented researchers and companies, and The Market Research Event (TMRE) for their assistance in getting the word out about this year’s call for nominations, and to VoxPopMe which together with OdinText supported this year's awards.


A New Trend in Qualitative Research

Almost Half of Market Researchers are doing Market Research Wrong! - My Interview with the QRCA (And a Quiet New Trend - Science Based Qualitative).

Two years ago I shared some research on research about how market researchers view Quantitative and Qualitative research. I stated that almost half of researchers don’t understand what good data is. Some ‘Quallies’ tend to rely and work almost exclusively with comment data from extremely small samples (about 25% of market researchers surveyed), conversely there is a large group of ‘Quant Jockey’s’ who while working with larger more representative sample sizes, purposefully avoid any unstructured data such as open ended comments because they don’t want to deal with coding and analyzing it or don’t believe in it’s accuracy and ability to add to the research objectives. In my opinion both researcher groups have it totally wrong, and are doing a tremendous disservice to their companies and clients.  Today, I’ll be focusing on just the first group above, those who tend to rely primarily on qualitative research for decisions.

Note that today’s blog post is related to a recent interview, which I was asked to take part in by the QRCA’s (Qualitative Research Consultant’s Association) Views Magazine. When they contacted me I told them that in most cases (with some exceptions), Text Analytics really isn’t a good fit for Qualitative Researchers, and asked if they were sure they wanted to include someone with that opinion in their magazine? I was told that yes, they were ok with sharing different viewpoints.

I’ll share a link to the full interview in the online version of the magazine at the bottom of this post. But before that, a few thoughts to explain my issues with qualitative data and how it’s often applied as well as some of my recent experiences with qualitative researchers licensing our text analytics software, OdinText.

The Problem with Qualitative Research

IF Qual research was really used in the way it’s often positioned, ‘as a way to inform quant research’, that would be ok. The fact of the matter is though, Qual often isn’t being used that way, but instead as an end in and of itself. Let me explain.

First, there is one exception to this rule of only using Qual as pilot feedback for Quant. If you had a product for instance which was specifically made only for US State Governors, then your total population is only N=50. And of course it is highly unlikely that you would ever get all the Governors of each and every US State to participate in any research (which would be a census of all governors), and so if you were fortunate enough to have a group of say 5 Governors whom were willing to give you feedback on your product or service, you would and should obviously hang on to and over analyze every single comment they gave you.

IF however you have even a slightly more common mainstream product, I’ll take a very common product like hamburgers as an example, and you are relying on 5-10 focus groups of n=12 to determine how different parts of the USA (North East, Mid-West, South and West) like their burgers, and rather than feeding  directly into some quantitative research instrument with a greater sample, you issue a ‘Report’ that you share with management; well then you’ve probably just wasted a lot of time and money for some extremely inaccurate and dangerous findings. Yet surprisingly, this happens far more often than one would imagine.

Cognitive Dissonance Among Qual Researchers when Using OdinText

How do I know this you may ask? Good Text Analytics software is really about data mining and pattern recognition. When I first launched OdinText we had a lot of inquiries from Qualitative researchers who wanted some way to make their lives easier. After all, they had “a lot” of unstructured/text comment data which was time consuming for them to process, read, organize and analyze. Certainly, software made to “Analyze Text” must therefore be the answer to their problems.

The problem was that the majority of Qual researchers work with tiny projects/sample, interviews and groups between n=1 and n=12. Even if they do a couple of groups like in the hamburger example I gave above, we’re still taking about a total of just around n=100 representing four or more regional groups of interest, and therefore fewer than n=25 per group. It is impossible to get meaningful/statistically comparable findings and identify real patterns between the key groups of interest in this case.

The Little Noticed Trend In Qual (Qual Data is Getting Bigger)

However, slowly across the past couple of years or so, for the first time I’ve seen a movement of some ‘Qualitative’ shops and researchers, toward Quant. They have started working with larger data sets than before. In some cases, it has been because they have been pulled in to manage larger ongoing community/boards, in some cases larger social media projects, and in others, they have started using survey data mixed with qual, or even better, employing qualitative techniques in quant research (think better open-ends in survey research).

For this reason, we now have a small but growing group of ‘former’ Qual researchers using OdinText. These researchers aren’t our typical mixed data or quantitative researchers, but qualitative researchers that are working with larger samples.

And guess what, “Qualitative” has nothing to do with whether data is in text or numeric format, instead it has everything to so with sample size. And so perhaps unknowingly, these ‘Qualitative Researchers’ have taken the step across the line into Quantitative territory, where often for the first time in their career, statistics can actually be used. – And it can be shocking!

My Experience with ‘Qualitative’ Researchers going Quant/using Text Analytics

Let me explain what I mean. Recently several researchers that come from a clear ‘Qual’ background have become users of our software OdinText. The reason is that the amount of data they had was quickly getting “bigger than they were able to handle”. They believe they are still dealing with “Qualitative” data because most of it is text based, but actually because of the volume, they are now Quant researchers whether they know it or not (text or numeric data is irrelevant).

Ironically, for this reason, we also see much smaller data sizes/projects than ever before being uploaded to the OdinText servers. No, not typically single focus groups with n=12 respondents, but still projects that are often right on the line between quant and qual (n=100+).

The discussions we’re having with these researchers as they begin to understand the quantitative implications of what they have been doing for years are interesting.

Let me preface this with the fact that I have a great amount of respect for the ‘Qualitative’ researchers that begin using OdinText. Ironically, the simple fact that we have mutually determined that an OdinText license is appropriate for them means that they are no longer ‘Qualitative’ researchers (as I explained earlier). They are in fact crossing the line into Quant territory, often for the first time in their careers.

The data may be primarily text based, though usually mixed, but there’s no doubt in their mind nor ours, that one of the most valuable aspects of the data is the customer commentary in the text, and this can be a strength

The challenge lies in getting them to quickly accept and come to terms with quantitative/statistical analysis, and thereby also the importance of sample size.

What do you mean my sample is too small?

When you have licensed OdinText you can upload pretty much any data set you have. So even though they may have initially licensed OdinText to analyze some projects with say 3,000+ comments, there’s nothing to stop them from uploading that survey or set of focus groups with just n=150 or so.

Here’s where it sometimes gets interesting. A sample size of n=150 is right on the borderline. It depends on what you are trying to do with it of course. If half of your respondents are doctors (n=75) and half are nurses (n=75), then you may indeed be able to see some meaningful differences between these two groups in your data.

But what if these n=150 respondents are hamburger customers, and your objective was to understand the difference between the 4 US regions in the I referenced earlier? Then you have about n=37 in each subgroup of interest, and you are likely to have very few, IF ANY, meaningful patterns or differences.

Here’s where that cognitive dissonance can happen --- and the breakthroughs if we are lucky.

A former ‘Qual Researcher’ who has spent the last 15 years of their career making ‘management level recommendations’ on how to market burgers differently in different regions based on data like this, for the first time is looking at software which says that there are maybe just two to 3 small differences, or even worse, NO MEANINGFUL PATTERNS OR DIFFERENCES WHATSOEVER, in their data, may be in shock!

How can this be? They’ve analyzed data like this many times before, and they were always able to write a good report with lots of rich detailed examples of how North Eastern Hamburger consumers preferred this or that because of this and that. And here we are, looking at the same kind of data, and we realize, there is very little here other than completely subjective thoughts and quotes.

Opportunity for Change

This is where, to their credit, most of our users start to understand the quantitative nature of data analysis. They, unlike the few ‘Quant Only Jockie’s’ I referenced at the beginning of the article already understand that many of the best insights come from text data in free form unaided, non-leading, yet creative questions.

They only need to start thinking about their sample sizes before fielding a project. To understand the quantitative nature of sampling. To think about the handful of structured data points that they perhaps hadn’t thought much about in previous projects and how they can be leveraged together with the unstructured data. They realize they need to start thinking about this first, before the data has all been collected and the project is nearly over and ready for the most important step, the analysis, where rubber hits the road and garbage in really should mean garbage out.

If we’re lucky, they quickly understand, its not about Quant and Qual any more. It’s about Mixed Data, it’s about having the right data, it’s about having enough data to generate robust findings and then superior insights!

Final Thoughts on the Two Meaningless Nearly Terms of ‘Quant and Qual’

As I’ve said many times before here and on the NGMR blog, the terms “Qualitative” and “Quantitative” at least the way they are commonly used in marketing research, is already passé.

The future is Mixed Data. I’ve known this to be true for years, and almost all our patent claims involve this important concept. Our research shows time and time again, that when we use both structured and unstructured data in our analysis, models and predictions, the results are far more accurate.

For this reason we’ve been hard at work developing the first ever truly Mixed Data Analytics Platform, we’ll be officially launching it three months from now, but many of our current customers already have access. [For those who are interested in learning more or would like early access you can inquire here:].

In the meantime, if you’re wondering whether you have enough data to warrant advanced mixed data and text annalysis, check out the online version of article in QRCA Views magazine here. Robin Wedewer at QRCA really did an excellent job in asking some really pointed questions that forced me too answer more honestly and clearly than I might otherwise have.

I realize not everyone will agree with today’s post nor my interview with QRCA, and I welcome your comments here. I just please ask that you read both the post above, as well as the interview in QRCA before commenting solely based on the title of this post.

Thank you for reading. As always, I welcome questions publicly in post below or privately via LinkedIn or our Inquiry form.


Artificial Intelligence in Consumer Insights

A Q&A session with ESOMAR’s Research World on Artificial Intelligence, Machine Learning, and implications in Marketing Research  [As part of an ESOMAR Research World article on Artificial Intelligence OdinText Founder Tom H. C. Anderson was recently took part in a Q&A style interview with ESOMAR’s Annelies Verheghe. For more thoughts on AI check out other recent posts on the topic including Why Machine Learning is Meaningless, and Of Tears and Text Analytics. We look forward to your thoughts or questions via email or in the comments section.]


ESOMAR: What is your experience with Artificial Intelligence & Machine Learning (AI)? Would you describe yourself as a user of AI or a person with an interest in the matter but with no or limited experience?

TomHCA: I would describe myself as both a user of Artificial Intelligence as well as a person with a strong interest in the matter even though I have limited mathematical/algorithmic experience with AI. However, I have colleagues here at OdinText who have PhD's in Computer Science and are extremely knowledgeable as they studied AI extensively in school and used it elsewhere before joining us. We continue to evaluate, experiment, and add AI into our application as it makes sense.

ESOMAR: For many people in the research industry, AI is still unknown. How would you define AI? What types of AI do you know?

TomHCA: Defining AI is a very difficult thing to do because people, whether they are researchers, data scientists, in sales, or customers, they will each have a different definition. A generic definition of AI is a set of processes (whether they are hardware, software, mathematical formulas, algorithms, or something else) that give anthropomorphically cognitive abilities to machines. This is evidently a wide-ranging definition. A more specific definition of AI pertaining to Market Research, is a set of knowledge representation, learning, and natural language processing tools that simplifies, speeds up, and improves the extraction of meaningful data.

The most important type of AI for Market Research is Natural Language Processing. While extracting meaningful information from numerical and categorical data (e.g., whether there is a correlation between gender and brand fidelity) is essentially an easy and now-solved problem, doing the same with text data is much more difficult and still an open research question studied by PhDs in the field of AI and machine learning. At OdinText, we have used AI to solve various problems such as Language Detection, Sentence Detection, Tokenizing, Part of Speech Tagging, Stemming/Lemmatization, Dimensionality Reduction, Feature Selection, and Sentence/Paragraph Categorization. The specific AI and machine learning algorithms that we have used, tested, and investigated range a wide spectrum from Multinomial Logit to Principal Component Analysis, Principal Component Regression, Random Forests, Minimum Redundancy Maximum Relevance, Joint Mutual Information, Support Vector Machines, Neural Networks, and Maximum Entropy Modeling.

AI isn’t necessarily something everyone needs to know a whole lot about. I blogged recently, how I felt it was almost comical how many were mentioning AI and machine learning at MR conferences I was speaking at without seemingly any idea what it means.

In my opinion, a little AI has already found its way into a few of the applications out there, and more will certainly come. But, if it will be successful, it won’t be called AI for too long. If it’s any good it will just be a seamless integration helping to make certain processes faster and easier for the user.

ESOMAR: What concepts should people that are interested in the matter look into?

TomHCA: Unless you are an Engineer/Developer with a PhD in Computer Science, or someone working closely with someone like that on a specific application, I’m not all that sure how much sense it makes for you to be ‘learning about AI’. Ultimately, in our applications, they are algorithms/code running on our servers to quickly find patterns and reduce data.

Furthermore, as we test various algorithms from academia, and develop our own to test, we certainly don’t plan to share any specifics about this with anyone else. Once we deem something useful, it will be incorporated as seamlessly as possible into our software so it will benefit our users. We’ll be explaining to them what these features do in layman’s terms as clearly as possible.

I don’t really see a need for your typical marketing researcher to know too much more than this in most cases. Some of the algorithms themselves are rather complex to explain and require strong mathematical and computer science backgrounds at the graduate level.

ESOMAR: Which AI applications do you consider relevant for the market research industry? For which task can AI add value?

TomHCA: We are looking at AI in areas of Natural Language Processing (which includes many problem subsets such as Part of Speech Tagging, Sentence Detection, Document Categorization, Tokenization, and Stemming/Lemmatization), Feature Selection, Data Reduction (i.e., Dimensionality Reduction) and Prediction. But we've gone well beyond that. As a simple example, take key driver analysis. If we have a large number of potential predictors, which are the most important in driving a KPI like customer satisfaction?

ESOMAR: Can you share any inspirational examples from this industry or related industries (advertisement, customer service)  that can illustrate these opportunities

TomHCA: As one quick example, a user of OdinText I recently spoke to used the software to investigate what text comments were most likely to drive belonging into either of several predefined important segments. The nice thing about AI is that it can be very fast. The not so nice thing is that sometimes at first glance some of the items identified, the output, can either be too obvious, or on the other extreme, not make any sense whatsoever.  The gold is in the items somewhere in the middle. The trick is to find a way for the human to interact with the output which gives them confidence and understanding of the results.

a human is not capable of correctly analyzing thousands, 100s of thousands, or even millions of comments/datapoints, whereas AI will do it correctly in a few seconds. The downside of AI is that some outcomes are correct but not humanly insightful or actionable. It’s easier for me to give examples when it didn’t work so well since its hard for me to share info on how are clients are using it. But for instance recently AI found that people mentioning ‘good’ 3 times in their comments was the best driver of NPS score – this is evidently correct but not useful to a human.

In another project a new AI approach we were testing reported that one of the most frequently discussed topics was “Colons”. But this wasn’t medical data! Turns out the plural of Colon is Cola, I didn’t know that. Anyway, people were discussing Coca-Cola, and AI read that as Colons…  This is exactly the part of AI that needs work to be more prevalent in Market Research.”

Since I can’t talk about too much about how our clients use our software on their data, In a way it’s easier for me to give a non-MR example. Imagine getting into a totally autonomous car (notice I didn’t have to use the word AI to describe that). Anyway, you know it’s going to be traveling 65mph down the highway, changing lanes, accelerating and stopping along with other vehicles etc.

How comfortable would you be in stepping into that car today if we had painted all the windows black so you couldn’t see what was going on?  Chances are you wouldn’t want to do it. You would worry too much at every turn that you might be a casualty of oncoming traffic or a tree.  I think partly that’s what AI is like right now in analytics. Even if we’ll be able to perfect the output to be 100 or 99% correct, without knowing what/how we got there, it will make you feel a bit uncomfortable.  Yet showing you exactly what was done by the algorithm to arrive at the solution is very difficult.

Anyway, the upside is that in a few years perhaps (not without some significant trial and error and testing), we’ll all just be comfortable enough to trust these things to AI. In my car example, you’d be perfectly fine getting into an Autonomous car and never looking at the road, but instead doing something else like working on your pc or watching a movie.

The same could be true of a marketing research question. Ultimately the end goal would be to ask the computer a business question in natural language, written or spoken, and the computer deciding what information was already available, what needed to be gathered, gathering it, analyzing it, and presenting the best actionable recommendation possible.

ESOMAR: There are many stories on how smart or stupid AI is. What would be your take on how smart AI Is nowadays. What kind of research tasks can it perform well? Which tasks are hard to take over by bots?

TomHCA: You know I guess I think speed rather than smart. In many cases I can apply a series of other statistical techniques to arrive at a similar conclusion. But it will take A LOT more time. With AI, you can arrive at the same place within milliseconds, even with very big and complex data.

And again, the fact that we choose the technique based on which one takes a few milliseconds less to run, without losing significant accuracy or information really blows my mind.

I tell my colleagues working on this that hey, this can be cool, I bet a user would be willing to wait several minutes to get a result like this. But of course, we need to think about larger and more complex data, and possibly adding other processes to the mix. And of course, in the future, what someone is perfectly happy waiting for several minutes today (because it would have taken hours or days before), is going to be virtually instant tomorrow.

ESOMAR: According to an Oxford study, there is a 61% chance that the market research analyst job will be replaced by robots in the next 20 years. Do you agree or disagree? Why?

TomHCA: Hmm. 20 years is a long time. I’d probably have to agree in some ways. A lot of things are very easy to automate, others not so much.

We’re certainly going to have researchers, but there may be fewer of them, and they will be doing slightly different things.

Going back to my example of autonomous cars for a minute again. I think it will take time for us to learn, improve and trust more in automation. At first autonomous cars will have human capability to take over at any time. It will be like cruise control is now. An accessory at first. Then we will move more and more toward trusting less and less in the individual human actors and we may even decide to take the ability for humans to intervene in driving the car away as a safety measure. Once we’ve got enough statistics on computers being safe. They would have to reach a level of safety way beyond humans for this to happen though, probably 99.99% or more.

Unlike cars though, marketing research usually can’t kill you. So, we may well be comfortable with a far lower accuracy rate with AI here.  Anyway, it’s a nice problem to have I think.

ESOMAR: How do you think research participants will react towards bot researchers?

TomHCA: Theoretically they could work well. Realistically I’m a bit pessimistic. It seems the ability to use bots for spam, phishing and fraud in a global online wild west (it cracks me up how certain countries think they can control the web and make it safer), well it’s a problem no government or trade organization will be able to prevent from being used the wrong way.

I’m not too happy when I get a phone call or email about a survey now. But with the slower more human aspect, it seems it’s a little less dangerous, you have more time to feel comfortable with it. I guess I’m playing devil’s advocate here, but I think we already have so many ways to get various interesting data, I think I have time to wait RE bots. If they truly are going to be very useful and accepted, it will be proven in other industries way before marketing research.

But yes, theoretically it could work well. But then again, almost anything can look good in theory.

ESOMAR: How do you think clients will feel about the AI revolution in our industry?

TomHCA: So, we were recently asked to use OdinText to visualize what the 3,000 marketing research suppliers and clients thought about why certain companies were innovative or not in the 2017 GRIT Report. One of the analysis/visualizations we ran which I thought was most interesting visualized the differences between why clients claimed a supplier was innovative VS why a supplier said these firms were innovative.

I published the chart on the NGMR blog for those who are interested [ ], and the differences couldn’t have been starker. Suppliers kept on using buzzwords like “technology”, “mobile” etc. whereas clients used real end result terms like “know how”, "speed" etc.

So I’d expect to see the same thing here. And certainly, as AI is applied as I said above, and is implemented, we’ll stop thinking about it as a buzz word, and just go back to talking about the end goal. Something will be faster and better and get you something extra, how it gets there doesn’t matter.

Most people have no idea how a gasoline engine works today. They just want a car that will look nice and get them there with comfort, reliability and speed.

After that it’s all marketing and brand positioning.


[Thanks for reading today. We’re very interested to hear your thoughts on AI as well. Feel free to leave questions or thoughts below, request info on OdinText here, or Tweet to us @OdinText]

Text Analytics Reveals the Average American Thanksgiving Menu

American’s Recount the Items That Make Up Their Annual Thanksgiving Dinner That most American Holiday, Thanksgiving, is here. But other than Turkey, what does the typical thanksgiving dinner consist of?

Last year for Thanksgiving we used the OdinText Analytics platform to understand what Americans are most thankful for. This year we were curious which items other than Turkey grace the Thanksgiving table.

Without further ado, here are the 50 most frequently mentioned items.

50 Thanksgiving Items Text Analytics

There were some differences by various demographic cuts from gender, age and geography. Below we look at the four major US geographies across 7 items with some of the biggest differences.

Regional Thanksgiving Differences Text Analytics

It is in fact possible to predict where someone lives with relative accuracy based on how they describe their Thanksgiving table.

For instance, Northeasterners are far more likely to expect Squash and Brussel Sprouts, and far less likely to have deviled eggs.

Midwesterners are more likely to mention Noodles, Deviled Eggs, and anything “creamy”, and less likely to mention Squash or Brussel Sprouts.

In the West potatoes, olives and wine are more likely to be mentioned, while Brussel Sprouts, Corn, Squash and Dressing are less popular.

Southerners like Deviled Eggs, cheese and broccoli and less likely to mention olives or noodles

While text analytics is a very quantitative science, it is worth pointing out that drilling into comments for a qualitative feel of descriptors is often worthwhile. For example, something as simple as ‘Dressing’ or ‘Green Bean Casserole’ can often be described with high emotion and specific reference to family members who typically make the dish very well, i.e. “And of Course, Grandma’s Dressing” or “Uncle Joe’s delicious Green Beans, I have no idea what he put’s in there, it’s just awesome…”

What are your favorite dishes?

Happy Thanksgiving!



[Note: This Text Analytics Poll was conducted among n=1,500 Gen Pop Americans ages 18-65 November 19-21, 2017 and text analyzed with OdinText. For more information on OdinText see Info Request]

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We believe market researchers make the best data scientists. That is one of the reasons why I have decided to encourage as many market researchers as possible to take the plunge, and start using text analytics in 2018.

In order to encourage my colleagues to pull the trigger before the new year, we are offering free training and use of OdinText for the remainder of 2017, for anyone who begins their license in January of 2018.

Have you reached an insight plateau in your organization?   Growing research departments and organizations ensure that they are properly leveraging ALL of their data.

OdinText is different than other “text analytics software”. OdinText has 10 patent claims on mixed data analysis. In other words, you can upload your entire data files with ALL the variables in your data file into OdinText (comments, Likert scales, demographics, behaviors). Our easy interface, AI, visualizations and Predictive Analytics guide you to the important insights.

You can quickly begin to understand critical issues such as:

  • Which concept is performing best and why?
  • How can we decrease customer churn?
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  • What are the top-5 priorities driving satisfaction?
  • How can I increase return rate?

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PS. We haven’t forgotten about you our valued users. Look out for a special emails from us this quarter as we share our appreciation with you and more information on upcoming features.

Americans Resigned to Gun Violence?

New Text Analytics Poll Measures American’s Desire for Change After Vegas Shooting 

In the wake of the awful tragedy in Las Vegas Monday I like many others viewed the news with shock, sadness, disgust and disbelief. And I believe also with a feeling of helplessness. At least that’s what I prefer to think of it as rather than apathy. The number shot and killed was horrific, but not any more horrific than the Sandy Hook shootings which occurred less than an hour away from us here in Connecticut.

What is the tipping point I wondered Monday morning? How bad does it have to get before people demand change, and what does change look like. Certainly for many an outright ban on all guns would be desired I thought, or would it? In the minds of a sizeable group it seems the second Amendment is as important as freedom itself.  But to affect real change we need some consensus if not a majority.

I took the question to representative sample of 1,600 Americans fielded Tuesday - Thursday. Rather than simply asking whether they agreed or disagreed with more gun control, the NRA, second Amendment or some such topic I simply stated “ Gun control is a difficult issue in the US. Reasonably what things do you think could be done about it? [Please as specific as possible]

The kinds of insights possible with open unstructured questions are impossible to get with forced/multiple choice type questions. The comments represented below represent peoples initial thoughts and responses in their own words off the top of their heads without any influence or suggestions.

Most Popular Answer – “Nothing”

Below is a visualization of the free form answers to the question. Frequency of topic answers on the Y axis, and speed of answering on the X axis.

What could be done about gun control poll

The most frequent answer to this question among US adults after the worst shooting in US history is “Nothing”. Those answering nothing gave the question exactly 49 seconds of thought on average before answering. Let that sink in for a minute.

Looking toward the right of the plot, we can see those answers which were given after slightly more thought. For instance, the suggestion of “One Gun Per Person”, while given by extremely few people, was given after slightly longer deliberation. Certainly, the second most popular answer “Background Checks” doesn’t seem to be cutting it.

Relative Frequency of Suggested Solutions

Taking a closer look at some of the more popular suggestions, the chart below tells us a bit about where the population is in understanding the issue.

Gun Violence 2

Summarizing this chart, I would say the average American still doesn’t seem willing to take radical steps to curtail gun access and violence.

Almost none of the suggestions would have stopped the Vegas shooter. Hardly anyone suggested strict gun laws such as those in countries like Japan where guns are no longer a problem. “One Gun Per Person” is an interesting but rare suggestion that would also have helped in the Vegas shooting.

However, many of the most popular suggestions, like “Background Checks”, “Banning Automatic Weapons”, “Mental Health Detection” would not have been helpful. Yet others like “Eliminate Semi-automatics” and “Regulate Caliber of Guns” show a real lack of understanding about guns.

An unexpected suggestion picked up by our software was the banning of “Bump/Slide Fire Stocks”. This was mentioned by almost 2% of those giving suggestions. While this answer does show strong understanding of guns and the ‘hack’ the shooter used to fire as if his guns were fully automatic, it is perhaps one of the scariest answers in the chart, and one that even the NRA seems to be willing to accept.

Banning Bump stocks is not likely to make any serious impact at all on future gun violence and is just a distraction and a scape goat. This is unfortunately likely to become a big talking point on the Hill.

Non- Gun Owners who don’t understand fire arms enough to make distinction between jargon and real improvements to our safety owe it to themselves to get educated enough to debate these issues and rally for real change.

Don’t be apathetic, don’t be helpless, make change happen!


Leif Erikson + Text Analytics: What’s the Connection?

What Data Explorers and Researchers Can Learn from Leif Erikson and Norse Mythology You may or may not be aware that Leif Erikson Day will be observed in the U.S. this October 9th. The holiday commemorates the discovery of North America by the Norwegian explorer for whom it’s named (long before Christopher Columbus took his voyage).

In Leif’s honor and the spirit of exploration, I thought this would be a good opportunity to indulge in two subjects near and dear to my heart: Norse mythology and text analytics.

Now you’re probably wondering what possible connection Norse mythology could have with text analytics. The answer lies in the name OdinText.

To my dismay, I’ve come to realize that very few people outside of Scandinavia and Germany share my enthusiasm for Norse mythology. And not infrequently I get asked what our name is all about.

So today I’m going to explain the connection for the edification and enlightenment of all!


Odin: The One-Eyed God of Wisdom

OdinText derives its name from the Norse god, Odin, a figure similar in respects to the Greek god Zeus in that he’s something of a patriarch to a brood of other ancient deities.

Like his Greek counterpart, Odin is associated with specific human attributes and phenomena, chiefly battle and wisdom. It’s the latter quality that inspired us to adopt Odin’s name.

Legend has it that Odin—who is always depicted as missing an eye—sacrificed his eye in exchange for the “Wisdom of Ages.”

He is also said to have been attended by two ravens—Huginn (translation: “thought”) and Muninn (“memory” or “mind”)—who scoured the world each day for news and information and then reported it back to Odin each night.

Finally, Odin is credited with creating the runic alphabet, which until it was supplanted by Latin around 700 AD, was the source of text for many Germanic languages.


Two Ravens and Breakthrough Insights

The notion of Odin’s two intelligence-gathering ravens appeals to me as a metaphor for what I believe is the ultimate application of text analytics: to answer questions we don’t know to ask.

Of course, text analytics are perfectly suited to answer any questions we can think to pose, but in today’s uncertain and rapidly changing environment, insight that leads to true competitive advantage often lies in the questions we don’t know to ask.

You may be familiar with the term “dark data,” for example, which was originally defined by Gartner as all of the data organizations collect in their daily operations that goes unexploited. That definition has now been expanded to include the ocean of data being generated by people every day, more than 80% of which is text-based.

Today, thanks to software like OdinText, we have the opportunity to scour and mine these oceans of text data for what we don’t know we don’t know. I’m talking about genuine, breakthrough insights, the sort that are discovered and not the product of a precision hunt.

So in celebration of Leif Erikson, the explorer, I urge you to join me in pursuit of discovering the unknown. You already have the data. Let’s put it to use.

You’ll be happy to know that unlike our friend, Odin, you won’t need to give an eye to acquire this knowledge. Just an hour of training.

Contact us for an OdinText demo today!

Yours faithfully,


TOM DEC 300X250 Tom H. C. Anderson OdinText Inc. 888.891.3115 x 701

ABOUT ODINTEXT OdinText is a patented SaaS (software-as-a-service) platform for natural language processing and advanced text analysis. Fortune 500 companies such as Disney and Shell Oil use OdinText to mine insights from complex, unstructured text data. The technology is available through the venture-backed Stamford, CT firm of the same name founded by CEO Tom H. C. Anderson, a recognized authority and pioneer in the field of text analytics with more than two decades of experience in market research. Anderson is the recipient of numerous awards for innovation from industry associations such as ESOMAR, CASRO, the ARF and the American Marketing Association. He tweets under the handle @tomhcanderson.

1 Analyst + 15K Comments in 8 languages + 2 hours = Awesome Insights!

1 Analyst + 15K Comments in 8 languages + 2 hours = Awesome Insights! Please join us on September 14th for this free live webinar co-hosted by TMRE.

Spaces limited/first come first serve, please register here.

We’ll be covering our extremely well received multi country, multi lingual analysis case study. I think you’ll be surprised at the implications and amazed that this kind of global research can now be done quickly and inexpensively by anyone.

Look forward to seeing you there!