Posts in Marketing Research
Analitica de Texto En Español

Analitica de Texto En Español – Spanish Text Analysis

Analitica de Texto En Español, I didn’t write that, it is machine translation of "Text Analytics in Spanish"

Mathematics has often been called the Universal Language, but in an age of instant machine translation, any text, or text data, is as understandable as math.

That’s one of the reasons I was very happy to take part in a special series of interviews in celebration of the Spanish Association of Market Research’s 50th Anniversary.

Several of our clients are analyzing non English text with OdinText, but in some ways a single mono lingual analyst being able to instantly analyze the comments of millions of customers speaking multiple foreign languages is even more exciting. And this isn’t science fiction, many of our global clients have been doing this for some time now.

The current issue of AEDMO’s Magazine (Asociación Española de Estudios de Mercado, Marketing y Opinión) celebrates technology in the world of research, and several prominent researchers have been invited to write on their core issues of expertise. I was honored to give an interview on text analytics.

If you don’t get their magazine you can read our Q&A on their blog here in Spanish or English.

Their Editor Xavier Moraño asked some very interesting and pertinent questions.

I’d love to hear your thoughts and questions.

Tom H. C. Anderson Chief Research Officer @OdinText

Advertising Effecitveness +OdinText

ad testing +OdinText [Authors note. As I am writing this blog post early Monday morning after the Super Bowl, I have already completed the initial ad testing analysis. It’s the case where modern AI and analytics software (OdinText) is faster than the data collection process/vendor we’re relying on. We’ve asked an open ended comment question among n=3,000 respondents about which super bowl ads they like/dislike and why. Eager to have the analysis complete as soon as possible, the analysis is already done, and blog written based on n=1,011 initial responses received. But since 1,998 more are expected I’m painfully waiting to publish results until the rest of the fielding comes in. The bad part is waiting for the sample. The good part is knowing that now repeating the analysis will literally take less than 1 minute. Just uploading the data into OdinText, and then the brand names and advertisement likes and dislikes will automatically be coded, analyzed and charted in seconds. I just have to review if anything has changed materially and make small updates in my copy below in such case. As it turned out, more data did change findings, and so I did have to change my blog copy. Ah, the joys of modern analytics!]

 

The Advertising Pundits weighed in on which ads were best and worst even before the Super Bowl aired. We tend to do things a little differently at OdinText and allow data, not opinion to drive.

Of course, for "best" and "worst" not to be subjective, we need some definition of desired outcomes. Last year we looked at a simple formula to evaluate efficacy consisting of Awareness + Positive Sentiment/Liking of the ads.

For instance, you may not remember this because of the low sentiment, but last year 85 Lumber was one of the companies with the highest Awareness after the super bowl. However, because it also had low sentiment and relevancy (as it dealt with the explosive issue of immigration/Trump's wall in a somewhat ambiguous way). It's probably the case that it ended up doing better among its core customer segments than among the general population, but since Super Bowl Ads are expensive, I argued that all things equal, a strategy with a broader target in mind, which aims to leave a positive impact among this broader group, should provide a better ROI. Looking at it another way, to have the most significant positive impact we want to maximize both awareness and sentiment almost equally.

With those assumptions and comments from over 3,000 respondents, OdinText's AI predicted which of the Super Bowl Ads were successful, and which were not. Below I've shown 10 Brands/ads, the best performing 6 and the worst 4.

OdinText Ad Ratings

10 superbowl ads rated

THE WINNERS

#1. THE NFL

https://www.youtube.com/watch?v=KUoD-gPDahw

 

In a year where there has been a lot of controversy surrounding NFL players taking a knee, and with a few of our respondents explicitly stating that they had boycotted the Super Bowl this year, it interesting to see the NFL advertising, and doing it so well. The NFL's Dirty Dancing with Manning and Beckham performed best, I believe in part because of its high relevance to the audience, but also for garnering high awareness together with very high positive sentiment/liking. In fact, only one other ad came close in sentiment.

#2 AMAZON ALEXA

https://www.youtube.com/watch?v=J6-8DQALGt4

 

That second most well-liked ad was Amazon's Alexa. Not as much because of its awareness (which was rather low in comparison), but because of its extremely high sentiment. The audience loved the various famous actors playing the voice of Alexa at least as much as they enjoyed NFL players Dirty Dancing.

#3 TIDE

https://www.youtube.com/watch?time_continue=1&v=doP7xKdGOKs

In 3rd place we have Tide. They earned the spot less so for sentiment (though viewers did like the ad). The reason Tide did so well was primarily due to the awareness it garnered. Tide had THE HIGHEST awareness of any Super Bowl Ad. However contrary to some of the Advertising Pundits opinions, it just wasn't quite as consistently well-liked by viewers as Dirty Dancing, and Alexa.

If you are in the camp who believe Awareness is everything, then Tide should have an even higher spot.

#3 DORITOS (& MOUNTAIN DEW ICE)

https://www.youtube.com/watch?time_continue=10&v=4eKYR_iL5eU

Doritos + Mountain Dew Ice was so close in our model, that I’m going to give them a tie for 3rd. Not awareness like Tide, but for balancing both positive sentiment and awareness perfectly. It's mix of awareness and liking was in the same proportions as NFL Dirty Dancing, just at a slightly smaller scale.

Obviously considering the audience and occasion, just like the NFL ad, Doritos especially is a highly relevant product, and as importantly the humorous approach with two extremely popular yet not commonly seen together stars (Namely Morgan Freeman and Peter Dinglage/Tyron from Game of Thrones) succeeded in the unique Combo messaging of Fire & Ice.

#5 BUD-LIGHT (NOT BUDWEISER)

https://www.youtube.com/watch?v=CxGUmtRLm5g

https://www.youtube.com/watch?v=lvTE_5c7buk

 

Budweiser is almost expected to do well. So, in a way, it may be surprising to see it doesn’t make it into our analysis (Beer, in general, did poorly especially Miller Ultra). It really should be so easy for Budweiser though. Here's a case where the occasion is more than just relevant, it's almost as if the brand has a historic Super Bowl halo effect. That said, their performance was less than impressive.

While the idea of stopping the Budweiser line to make water in an emergency could be touching for some, reminding consumers you have a good fun product may be a safer strategy than asking for kudos for merely being a good corporate citizen?

And that’s where Bud-Light’s Knight did better. Beer should be about fun…

#6 TOYOTA

https://www.youtube.com/watch?v=QwF3ipuNyfc

Here's a case where awareness was quite low, but the ad was still more liked than average compared to the other brands. Toyota and our #8 brand just barely made the list. While the setting was right "The Super Bowl," in the end perhaps the ‘Priest, an Imam, a Monk and a Rabbi' may have felt a bit less like a joke, and more like preaching…

THE LOSERS

#1 COCA-COLA

https://www.youtube.com/watch?v=-R-EEdvDrUU

Like Budweiser, we expect a lot from Coca-Cola when it comes to advertising. They’ve been pushing the diversity message for a few years now. It may be that pulling at heart strings is far harder to do than making people laugh. Coca-Cola had lower than average sentiment coupled with relatively low awareness. Not a winning combination.

#2 DODGE RAM

https://www.youtube.com/watch?v=SlbY1tGARUA

 

Dodge Ram did better than Coca-Cola at least, especially on awareness, but even concerning sentiment/liking.

The negative aspect of course in large part was the appropriateness of Martin Luther King's message at the beginning.

When it comes to ads like these though, I think we must assume, as was the case for 85 Lumber last year, that perhaps the brand knows what it's doing. They aren't there to please everyone (as you would hope is the goal of Pepsi and Coca-Cola), but to message their core audience with a ‘We Get You – Even if Everyone Else Doesn't'. And so, awareness wise, Ram did better than Amazon, Bud-Light, and Pepsi. But on an overall basis, they get dinged by the overall sentiment due to the some would say clumsy ‘MLK + Patriotic' messaging. Only time and sales will tell…

#3 T-MOBILE

https://www.youtube.com/watch?v=C-rumHvmqCA

T-Mobile was less well liked than you’d think, who doesn’t like babies right? Turns out people are getting tired of the “social responsibility ads” in their entertainment, at least that’s what they told us.

#4 DIET COKE (TWISTED MANGO)

https://www.youtube.com/watch?v=T6T6YrPA4aM

The Booby Prize. Ok, so here is a bad ad. In PR they used to say, any PR is good PR. But Diet Coke didn't do too well on either of our metrics. It had low awareness combined with even lower sentiment/liking. Diet Coke Mango, because, just no…

 

HALF TIME

A WORD ABOUT PEPSI

https://www.youtube.com/watch?v=2z3EUY1aXdY

https://www.youtube.com/watch?v=0gHYd67OumQ

Pepsi, what can I say.  You may be surprised that yet again, Pepsi performed poorly compared to the other brands I mentoned considering that their name was all over the Super Bowl during the Half Time Show. And yet, it may be that the real winner of Halftime is the brand of the performer, which this year was Justin Timberlake. We saw a similar pattern last year as well.

 

@TomHCAnderson

PS. See Your Data +OdinText

 

Market Research CEO’s Summarized and Text Analyzed

A Summary of the 2018 Insights Association CEO Summit Last year I summarized the CEO Summit theme as ‘Technology Partnering’. This year the two words I’d choose would be ‘Change’ and ‘Partnering’.

Change

It was widely agreed that successful companies can’t stand still in a changing industry. Changing doesn’t necessarily mean adding more Technology. In my opinion it means doing something completely new. If profits aren’t increasing, and your team isn’t happy, stop and think.

Personally, I believe change can even be backwards looking. Sometimes we’ve done something successful in the past, that more recently we have forgotten to do. A conference like the Insight Association’s CEO Summit can remind you of these things when you hear stories about what is working for others, and you think, hey I did that a while back, and had forgotten about it, it’s time to try it again, perhaps in a slightly new way that matches your current conditions.

The theme of the day, which I believe was expressed by different CEO’s in different ways had to do with incremental change. Changing a bit at a time. “Changing 1% Per Day”, or my personal long time favorite answer to the question “How do you eat an elephant?”, answer “one bite at a time”.

I like the new “1% per day” though because of the focus on the present and need for continuous improvement and change. [Zain Raj, CEO of Shapiro + Raj, really drove this home]

Partnering

Partnering was a theme I wrote about last year as well. I do think if you come to a conference like this, and don’t have it in mind, you’re missing a big opportunity.

As usual at conferences there are many little side meetings.  A good partnership in my experience doesn’t have to be some grand M&A, it must be more than words, there must be execution.

The CEO’s of Nielsen, Kantar, TNS, and IPSOS don’t attend the Insights Association Summit. This is a chance for start-ups, smaller and mid-sized firms to learn from each other, to begin partnerships, and offer better innovative products and services to our clients than the larger and somewhat slower moving firms can.

Jamin Brazil, formerly CEO of two successful research firms, Decipher and FocusVision, spoke on a different type of partnership than those between companies. He drew on his experience with long-term business partner Jayme Plunkett. His humble yet undeniably successful story is an interesting one.

As part of his talk he had surveyed the attendees at the CEO summit. As with most surveys, the data was “Mixed” (structured and unstructured), and so he had used OdinText to analyze the results. I’ll include 2 of his slides below.

First, comparing the market research industry data to other industries, he had found that we as an industry seem more likely to partner, and tend to do so longer/more successfully than CEO’s of other industries.

While sample size here was very small, OdinText’s AI was still able to detect some directional patterns in the data. For instance, when considering the Pro’s and Cons of Partnering, Marketing Research CEO’s who have partnered longer were much less likely to be concerned with ‘Decision Making’ issues and agreeing on specific ‘Goals and Roles’, and instead more likely to focus on ‘Sharing’, and ‘Finance’, while those in shorter relationships tended to be more focused on the former, and less on the latter.

Also, perhaps not surprisingly, those who were more favorable and successful in partnering had a very different, more positive and productive outlook related to the idea of partnering. This manifested in several ways including the tone and word choice. In fact, those who had more difficulty with the idea of partnering tended to be more likely to use more formal terminology like the word “Partner” instead of more familiar and affectionate terminology such as “best friend” and describing partnering “as a marriage”. As one of the many CEO’s who had responded to the survey said it, “You Fight and are Challenged to Make Decision – Best Decision Ever”, that certainly sounds like a marriage to me!

I know I for one can see the benefits of partnering, and have seen it work great in many other research companies. Another such company is Critical Mix where attending Co-CEO Keith Price and his Co-founder Hugh Davis, have also had a very long and successful relationship. Keith did a great job on the now infamous ‘CEO-Summit Hot Seat’, and echoed some of these findings.

Ultimately Partnerships and Partnering are to some degree about timing. But if we aren’t on the lookout for good partners, whether inside our business or outside with another business, we’re likely to miss these chances. Clearly based on what I saw partnering offer the opportunity of not just more profit, less risk and stress, but also as a way to make our journeys more fun.

How do you plan to change or partner in 2018? Looking forward to hearing your thoughts, at OdinText we’re always looking to partner with researchers who have good data and want to improve their insights.

@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

2018 Predictions for Market Research and Analytics

What Kind of Researcher are You?

It’s that time of year again where RFL Communications and Greenbook request predictions from market researchers on what trends they expect to see in the new year. Of course no one knows for sure, but some are interesting fun to read and I always like searching for the overall patterns, if any.

That said, here’s the one I submitted this year. I’m curios to to hear yours as well.

 

2018 The Best of Times & The Worst of Times

 The gap between what I’ll call ‘Just Traditional Research’ and more flexible, fluid ‘Advanced Analytics Generalists’ will continue to grow.

 There are three groups of marketing researchers along this dimension. Some ‘Just Traditional’ researchers and companies will not be able to adapt and will want to continue doing just the focus groups or panel surveys they have been doing and will become increasingly out of touch.

 A second group will feign expertise in these not so new areas of data and text mining (Advanced Analytics), they will prefer to call it “AI and Machine Learning” of course, but without any meaningful change to their products, services or analysis. It will be a sales and marketing treatment only.

 Both these groups are rather process oriented. The former doesn’t want to change their process, the latter just want a shiny new process. In either case, the end goal suffers. For both of these two groups the future is dim indeed.

 A third group of researchers, the group OdinText is invested in, don’t try to improve and change because they think they must in order to survive, they were already doing it because they are genuinely curious and ambitious. They don’t just want to run that survey a little faster and a little cheaper, they want much more than that. They want to add significant value for their company via their analysis.

 They will invest in learning new tools and techniques, and yet will not expect these tools to magically do the work for them after they push a button. These are not lazy employees/managers, they are A type employees, and they are the future of what ‘Marketing Research/Analytics’ is to become.

 They realize their own ingenuity and sweat need to be coupled with the new technology to achieve a competitive advantage and surpass management expectations and their competition. They are excited by those prospects, not scared.

 I too am very excited about meeting and working with more of these true ‘Advanced Analytics Generalists’ and the Marketing Research Supplier firms who serve them and realize Co-Opetition with other firms with key strengths that they don’t have make more sense than buzz words and feigning expertise in all categories.

 For these ‘New Data Scientists’, no these ‘Next Gen Market Researchers’ 2018 will be the best of times!

It’s a BIT lengthy and general for a prediction. But I believe it’s a real trend that will continue to accelerate. Do you agree or disagree?  What are your predictions?

If you subscribe to RFL Communications Business Report you’ll be receiving the annual writeup on this topic there, you can check out the Greenbook version from 36 CEO’s online here.

While you can tell all those participating takes this with various degrees of seriousness, and answer with different Point of Views, I believe that reading all of them, and deciding what patterns if any are detectable across them is well worth the 30 minutes or so it takes to do this.

Again, very much appreciate YOUR thoughts and predictions as well, so please feel free to comment below.

@TomHCAnderson

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]

Congratulations 2017 NGMR Award Winners!

In case you weren’t at The Market Research Event (TMRE) last week and missed the news, here are The NGMR Award Winners for 2017. Winners across the three categories (Most Innovative Research Method, Industry Change Agent, and Outstanding Disruptive Start-Up were: Merck – Lisa Courtade, InsightsNow – David Lundahl, and IncognitoResearch – Greg Weston.

OdinText was proud to co-sponsor this year’s award ceremony with VoxPopMe.

Please join us in congratulating this year’s winners!

@OdinText

Why Your HR Survey is a Lie and How to Get the Truth

OdinText Discovers Job Satisfaction Drivers in Anonymous Employee Data

Employee satisfaction surveys go by different names – “stakeholder satisfaction,” “360-degree surveys,” “employee engagement.” I blog a lot about the shortcomings of survey data and problems like respondent and data quality due to bad sample, long surveys, and poorly structured questions (which assumes the researcher already knows the best questions to ask), but I haven’t talked much about human resources/employee surveys.

HR surveys have a different and bigger problem than consumer surveys. It’s not the sample; we know exactly who our employees are. We know their email, phone numbers and where they sit. Heck, we can even mandate 100% survey participation (and some companies do). In fact, I’ve spoken to Human Resources directors who actually survey their employees once per week. The reasoning goes something like, “our millennial employees are in high demand and we want to keep them happy.” But that’s not a problem, per se; in fact, I’m a believer in frequent data collection.

The Problem with Employee Satisfaction Surveys

Hr employee satisfaction survey research

NO ONE taking an employee survey trusts that their data will be kept anonymous and confidential. This is the case even when third parties promising to keep all data at an aggregate level are hired to conduct the fieldwork.

It really doesn’t matter that this mistrust may be unfounded or invalid, only that it exists. And as it happens, it isn’t entirely unfounded. In fact, I know a former senior HR manager at a Fortune 500 who admitted to successfully pressuring vendors into providing de-anonymized, individual-level data.

Even if you as an employee believed your data would remain anonymous, you might nonetheless be wary of being completely forthcoming. For instance, if you were one of three or four direct reports asked to rate your satisfaction with either your company or your manager on a five-point Likert scale, it might feel foolhardy to answer with anything less than a top-3-box score. There would be a lot of interest in who the ‘disgruntled’ employee was, after all.

This is a data problem, and there are two solutions:

  1. Text Analysis of Employee Satisfaction Comment Data

Unstructured, free-form comment data can be a window into the soul! I might never risk giving my company or supervisor anything below a top-2-box satisfaction rating on a Likert scale, but there are other ways to unearth the truth. For example, consider these open-ended questions:

Q: “What do you think about the prospects for your company’s success in the next two years?”

Or maybe a specific question about a boss I didn’t like? Such as:

Q: “Tell us about your relationship with your boss. Does he/she provide you with adequate X?”

While the respondent would obviously still be very careful about how he/she answers – probably even more so – it would be nearly impossible not to divulge important clues about how he/she really feels.

Why? Because we really aren’t very good at lying. We can’t help leaving emotional clues in spoken or written text that reveal our hidden emotions based on word choice.

Even in the absence of any negative terms or emotions, just the appearance of significantly lower levels of basic positive sentiment within a department or within a specific manager’s group might signal a serious problem.

  1. Anonymizing Employee Satisfaction Data

The other solution is to collect data that truly is more anonymous. This is a second unmet opportunity to improve employee satisfaction and engagement research. The trick is not just providing an option for anonymous feedback such as a suggestion box, but making it top-of-mind and encouraging more frequent anonymous feedback.

Obviously, many companies know their customers are discussing them and giving them ratings both anonymously and with their real profiles on various sites—Amazon.com, BazaarVoice.com, Airbnb, TripAdvisor and Yelp, to name just a few.

But what about employee data? Back during the dotcom boom, working for Snowball.com/Ign.com I recall everyone at the company and other dotcom’s regularly visiting F*ckedCompany.com (the asterisk was added by me) where anonymous feedback about management, impending layoffs, etc., would be shared. This isn’t necessarily a good thing for anyone except investors wanting to short a certain company.

Today there are sites like GlassDoor.com where employees rate larger companies on both work satisfaction, in general, and even the interview process. The problem with this site is that it tends to be focused more on larger companies (think Home Depot and Lowes), though there are many ratings for middle-market and smaller companies, too.

I think in the future there will be even more opportunities to access public reviews of satisfaction at companies, but also hopefully more private ways to collect unbiased data on your company’s employee satisfaction.

What to Expect from Text Analysis of Good/Anonymous Employee Data?

While I’ll be writing more on this topic in the future, what prompted the idea for this blog post was one of our most recent clients, TruckersReport.com. As the name suggests, TruckersReport.com is a trucking industry professional community that includes an anonymous discussion board.

Recently, OdinText deployed to analyzed anonymous, unstructured comments as well as structured ratings data from the TruckersReport.com’s website. Some rather unexpected findings were covered in a joint press release. For example, OdinText discovered 11 features driving job satisfaction, and salary didn’t make the top three! You may request a more detailed report here.

I look forward to your thoughts and questions related to either the above study or human resources analytics in general.

 

@TomHCAnderson

Curious About Marketing Research?

We need to hear from you!

Are you curious to understand what makes market researchers tick and what innovation really means in our industry? The Marketing Research Event (TMRE) and OdinText have teamed up to explore these questions. But we need your feedback today!

This is a somewhat different fun and interesting survey for professionals in our Industry.

While the survey is relatively short (<10 min), the majority of the questions are open-ended allowing you to answer in free form.

We would very much appreciate your thoughtful answer to these questions.

Text analytics will be used to analyze the results. We will be sharing findings at both The Market Research Event, as well as on the NGMR and OdinText blogs.

All answers are anonymous. Thank you in advance for your feedback.