Posts in Text Analytics
Predicting Return Behavior and Sales with CX Ratings or NPS

A Customer Experience Case Study Utilizing OdinTexts’ Text and Predictive Analytics (Predicting Actual Return Behavior and Sales with CX Ratings or NPS)

We were honored today to have one of our case studies featured by Greenbook. Though we have several other similar cases like it, it remains one of our favorite uses of Customer Satisfaction/Customer Experience data (whether NPS or any other rating scales are used). The final analysis involved close to a million customers over a two-year period.

In the case study which features Jiffy Lube, we found that contrary to what Bain Consulting had been claiming in Harvard Business Review for over a decade, Customer Satisfaction Ratings (whether NPS, OSAT or any thing else), these metrics have very little correlation with actual return behavior/repurchase, and absolutely NO correlation with sales/revenue (business growth).

The solution to better understanding and modeling both return behavior and sales lies in leveraging both the structured and unstructured text data, something OdinText is uniquely built to do.

You can read the abbreviated case study on Greenbooks’ site here.

Feel free to contact us with any questions or for a slightly more in-depth write up.

OdinTexts’ software has recently been updated and is now even more powerful, in terms of easily handling predictive analytics related to any customer experience metric whether OSAT, NPS or any other metric. You may request information, as well as early access, to our upcoming release here.

Thank you for reading, and thank you to Greenbook for selecting and sharing this interesting case study.

@TomHCAnderson

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: OdinText.com/Predict-What-Matters].

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.

@TomHCAnderson

Best 10 Text Analytics Tips Posts of The Year

Our Top 10 Most Read Data and Text Mining Posts of 2017

Thank you for reading our blog this year. The OdinText blog has quickly become even more popular than the Next Gen Market Research blog, and I really appreciate the thoughtful feedback we’ve gotten here on the blog, via Twitter, and email.

In case you’re curious, here are the most popular posts of the year:

#10 NFL Players Taking a Knee is More Complex and Polarizing Than We Think If a Topic is Worth Quantifying – It’s Also Worth Understanding The Why’s Behind It

#9 Text Analytics Picks The 10 Strongest Super Bowl Ads New Text Analytics Poll Shows Which Super Bowl Ads Really Performed Best

#8 Why Your HR Survey is a Lie and How to Get The Truth OdinText Discovers Job Satisfaction Drivers in Anonymous Employee Data

#7 Of Tears & Text Analytics (An OdinText User Story – Text Analytics Guest Post (AI Meets VOC))

#6 65 CEO’s Share Thoughts on Insights (Insights Associations Inaugural CEO Summit – A Future Tied to Collaboration and Technology)

#5 Why Machine Learning is Meaningless (Beware of Buzzwords! The Truth about ‘Machine Learning’ and ‘Artificial Intelligence’)

#4 Do You Speak Teen? OdinText Announces 2nd Annual List of Top 10 Slang Terms (How Text Analytics Can Help Marketers Move at the Speed of Slang)

#3 Text Analysis Reveals Potential French Election French Election Upset (Text Analytics Poll Showed How Close Le Pen Came to ‘Trumping’ Macron)

#2 Text Analytics Poll: Why We Unfriend on Facebook (You Can’t Handle The Truth (And Other Top Reasons Why We Unfriend on Facebook)

#1 What Americans Really Think About Trump’s Immigration Ban and Why (Text Analysis of What People Say in Their Own Words Reveals More Than Multi-Choice Surveys)

 

I thought I’d also check what our top 5 posts were from last year as well, here they are in case you missed them:

Top Posts From 2016

#1 Text Analysis Answers Is The Quran Really More Violent Than The Bible (3 Parts)

#2 Attensity Sold – What Does it Mean?

#3 Customer Satisfaction Surveys: What do Satisfied VS Dissatisfied Customers Talk About?

#4 What’s Really Wrong With Polling?

#5 What Your Customer Satisfaction Research Isn’t Telling You

Thanks again for reading and commenting. As always I welcome your thoughts and questions via LinkedIn, or feel free to request info on anything you’ve read above here.

Happy New Year!

@TomHCAnderson

Predicting Your KPI’s Has Never Been Easier & 29 Other Case Studies

As you may recall this year OdinText was honored to be voted Most Innovative in North America, and Third Most Innovative Globally in the Greenbook Research Industry Trends (GRIT) Report. This year Greenbook offered something special, each of the Top-50 Marketing Research Firms were invited to submit a case study for inclusion in an e-book showcasing the best of the best in consumer insights.

The e-book was released yesterday with submissions from 29 of the top 50 market research firms contributing a brief case study. We’d be happy to share the entire e-book with you, you can request it here.

The case we submitted is one of my favorites where Shell Oil was able to use OdinText to predict three business performance indicators;  Stated Satisfaction/WOM (i.e. NPS),  Actual Return Behavior and Sales. Something they had never been able to do before.

While that case was run using very big data, several of our users have conducted the same powerful analysis with much smaller survey data.

Again, we’d be happy to send you the entire booklet with all the companies listed, and if you’re interested in something similar we’d be happy to schedule a brief informational call to discuss, and/or a demo with your own data.

Again Thank You to Greenbook and everyone who selected us for the honor, and congratulations to all the other great companies listed in the report!

Tim Lynch VP OdinText

Happy Hanukkah and Hottest Gift Ideas for 2017
Text Analytics Poll Reveals Hottest Holiday Gift Ideas

Tomorrow marks the beginning of Hanukkah, and Christmas is just around the corner. So, I thought what better time to use OdinText to investigate ideas for gift giving.

Top 10 Hottest Gifts Ideas for 2017

If you’re out of ideas on what to get your loved ones you may want to consider these popular ideas

  1. Mobile Phones iphonex

Well iPhone X to be exact. While smart phones in general are by far the most popular gift this year, iPhones specifically were mentioned twice as often as any cell phones in general, and no other specific brand even came close in mentions.

  1. Gift Cards Gift Cards

They’re a more popular choice than one would think. Sure, it’s fun to receive the perfect gift – Like an iPhone X – but in lieu of that gift cards are a cheaper more popular option among both gifters and receivers.

  1. Fingerlings Fingerlings

For younger friends and family, there are several choices that popped, especially these AI enabled tiny creatures. They're cute and they react to voice and touch. These little guys were mentioned about as often as Gift Cards!

  1. Amazon Echo or Google Home 

Amazon Echo being far more often mentioned than Google Home, both are hot this years. [ While various Apple accessories were popular in our data (including Apple Watch), Apple HomePod was not.]

  1. Fidget Spinners 

Yes, these darned things are still around, and by the sound of it are likely to make it into a lot of stockings

  1. Drones 

A bit on the more expensive side, drones are still a very hot gift and there are many options and prices.

  1. Lol Dolls 

For younger girls these small collectible dolls like a lot of products these days are very much about the packaging. They and their accessories come in small palls that are cracked open. Part of the fun and ‘cuteness’ (a trend called kawaii that’s been on it’s way here from Japan for some time and is now arriving)

  1. Hatchimals 

Similar to LoL Dolls, but a step beyond with an interesting twist. These creatures come in eggs, and hatch in weird ways. The more expensive ones have AI that interacts with the child. The creature responds to stroking/heating of the egg, and then hatches.

  1. Tickle Me Elmo 

This little guy never went away. Still a safe bet for the little Guy or Gal in your life.

  1. PopSockets 

At 10th place is also the cheapest option of the bunch. If you opt for # 1 above, you may as well go all out and get one of these little smart phone accessories too. They won’t break the bank!

Hopefully these gave you some ideas. If you have any for us we’d love to hear them below.

@TomHCAnderson

 

[Note, OdinText analyzed well over 5,000 comments related to the holidays and gift giving, above is just a small sample of the insights available in the data. I’ll may share a bit more on this before Christmas as time permits.]

PS. There's still time for you to get OdinText for 2018 and to instantly be able to analyze any kind of data. Take advantage of our year end offer and get trained and start analyzing data free in 2017.

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. http://odintext.com/blog/machine-learning-and-artificial-intelligence-in-marketing-research/

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 [ http://nextgenmr.com/grit-2017 ], 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!

@TomHCAnderson

 

[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.

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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.

Brandtrust Uses OdinText to Quantify Qual at Scale and Unearth Dormant Brand Equities

Editor’s note: Today’s post was contributed by Brandtrust, an OdinText client, as part of a new ongoing series from our users. We felt Brandtrust was an outstanding candidate for a use case because they already have a set of sophisticated, proprietary methodologies, which were made even more valuable by easily incorporating the OdinText platform. Case Study: Realizing the Untapped Potential of Stories

A long-time client asked us to help determine the equity of an overlooked legacy sub-brand, as they were interested in how that sub-brand relates to the parent brand, both from the perspective of legacy sub-brand consumers and younger prospective consumers. They wondered if there was untapped potential in this neglected property.

Utilizing our Narrative Inquiry approach — text analytics with a unique take on unearthing human truth — Brandtrust asked legacy and prospective consumers to share their memories and experiences with the sub-brand and parent brand via an open-ended survey tool. We exposed prospective customers, who by definition lack experience with the sub-brand, to representative sub-brand stimulus, and then had them reflect on their exposure experience.

By tapping into stories around actual experiences, our team was able to elicit language around the relationships consumers had or have with the brand and sub-brand. Utilizing OdinText to analyze the unstructured data (a.k.a. stories) we received, we looked for narrative and emotional patterns across and between the legacy and prospective consumers.

Dormant Brand Equities at the Intersection

Our client’s operating hypothesis was that the perceptions and emotions of the two targets would vary dramatically.  As it turned out, legacy consumers did express more nostalgia with the sub-brand and recalled their past experiences with it fondly, associating the sub-brand with family connection and memorable special events. Prospective consumers, not surprisingly, expressed a greater sense of trepidation related to the unfamiliar-but-established sub-brand.

Interestingly, and most useful to our client, however, there were important areas of intersection between the two consumer groups, both in perception and experience of the sub-brand and the parent brand.

The sub-brand and parent brand elicited joyful emotions and communicated the concept of care, a key tenant of the parent brand. Additionally, the sub-brand reflections of both consumer groups contained elements of enjoyable education (think “learning is fun!”) and heartwarming interactions — equities that were well aligned with recent parent brand initiatives.

All in all, the client was pleased with the outcome and benefited greatly from the knowledge obtained: their quest to determine next steps in this endeavor were finally realized.

Methodological Review

The development and execution of this branch of methods at Brandtrust could have been daunting, but with OdinText at our fingertips, it was far less manual and labor-intensive than it would have been in the days of building code frames, buckets, and nets.  And yet, there is still a great deal of merit in the means by which text analysis was initially derived.

At this point in technological advancement machines cannot, and likely never fully will, replace humans; which is why Brandtrust employs a distinctive approach called Lateral Pattern Analysis — with parallel machine and human analysis, and a combined synthesis between the two, to determine the final outcome of our Narrative Inquiry studies.

Narrative Inquiry questionnaires are built on Brandtrust’s key research pillars, including grounded theory, phased dialogue, behavioral framing, narrative pattern identification, and priming reduction. Reliance on these key elements ensures that our Narrative Inquiry respondents — through a process of recall and reflection — can share with us the rational and emotional makeup of their perceptions and behaviors.

OdinText’s built-in emotional framework assists our team with the “machine” side of processing a vital but squishy element of human understanding through story: Emotion. Brandtrust draws from years of experience in processing story and emotion qualitatively, and OdinText’s features have helped us extend the reach and statistical certainty of that expertise.