Posts in Predictive Analytics
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

 

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

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]

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

OdinText Predicts What Television Shows You Will Like

How Text Analytics Rescued Me from a #ShowHole!  My wife and I recently found ourselves in the uncomfortable condition commonly known as a “show hole.” Are you familiar with this?

A show hole refers to the state of loss and aimlessness one experiences after completing—often via binge-watching—the last episode in a beloved TV series without having a successor program queued up. The term was popularized by an Amazon Fire campaign a couple years ago, and you’ll find it hashtagged all over social media these days by people desperately in need of relief.

The show hole is an interesting phenomenon that speaks to how dramatically audience consumption habits have changed with the advent of the DVR, streaming and on-demand services like Netflix, Hulu, Amazon, etc. But what’s curious to me is how such a clearly great need continues to go relatively unmet.

Of course, subscribers to on-demand services have help in the way of recommendations algorithms. Netflix, in particular, has famously invested extensively in developing predictive analytics to suggest other shows to watch.

Still, the preponderance of cries for help on social media would seem to indicate that for many people these solutions have fallen short.

Indeed, it appears that people tend to prefer recommendations from other people, which introduces a different set of problems.

The Problem with Recommendations, Ratings and Reviews from People

In my own disappointing search for what to watch next, I found #showhole aplenty on Twitter, but the platform doesn’t lend itself well to discussion, so most of those who tweet about it get left hanging. Usually it’s just a “Help! I’m in a #showhole!” message from someone after finishing a series, but hardly anyone tweets a reply with suggestions.

Note: Because Twitter isn’t well-suited to this kind of interaction, your standard social media monitoring tool—most of them rely on Twitter data—wouldn’t be effective for the type of analysis we’ll cover today.

I did, however, find a ton of recommendation activity occurring on Facebook, Reddit and a variety of other discussion boards and community-based sites, including surprising places like babycenter.com—a support community for pregnant women and new moms—replete with threads where members actually recommend series’ for other members to try next.

This Yelp-like model for getting out of a show hole has its own limitations, though. How do I know I’ll like what you like? Is it enough to assume that since we’re both new moms that we’ll enjoy the same shows? Or if we both enjoyed one program, that I’ll enjoy whatever else you’ve watched? Similarly, if I ask you on a scale of 1-10 to rate a show, how would that information be useful to me if we don’t have the same tastes? Remember also that we’re looking across genres. Our tastes in dramas might be similar, while our tastes in comedies could be worlds apart.

In short, we have all of these people providing recommendations online, but the recommendations really aren’t any more helpful to the prospective viewer than star-based ratings and reviews. I.e., the show hole sufferer is forced to audition each of these programs until he/she finds one that fits—a time-consuming and potentially frustrating process!

How Text Analytics Can Make These Recommendations Useful

As I pondered the recommendations I saw online, it occurred to me that if I could apply text analytics to identify preference patterns based on recommendations from a broad enough swath of people, I might arrive at a recommendation suited to the unique tastes of my wife and I that we could then invest time in with a high confidence level.

Happily, I discovered that when suggesting new shows to watch via social media, people tend to provide more than one recommendation, and these recommendations usually are not limited to a single genre. This means we have sufficient preference data at the individual level, which, if aggregated from enough people, could form the basis for a predictive model.

In a very short time period, I was able to scrape (collect) several thousand recommendations across a variety of sources online. It’s worth noting that just about every single network that the average American has access to was represented in this data. This is important because someone who uses HBO GO, for example, is obviously more likely to watch and recommend programming from that network than someone does not subscribe to it.

We then layered predictive analytics atop the data using OdinText to see whether text analytics could solve my show hole dilemma. Specifically, I wanted to see what other shows are most frequently co-occurring with shows that my wife and I like in these online recommendations. (OdinText has a few ways it can help in cases like this, including the co-occurrence visualization covered in a recent post on this blog by my colleague, Gosia Skorek, here.)

It’s also important to emphasize here that we accomplished this analysis without asking a single question of anyone, although this type of data could be very nicely augmented with survey data.

OdinText Rescues Tom and His Wife from Their Show Hole!

This data was more challenging than I expected, but OdinText enabled us to find a model that delivered!

Below you’ll find examples of preference clouds based on the co-occurrence of mentions harvested from several thousand recommendations across discussion boards and other social media (excluding Twitter).

Essentially, you’re seeing OdinText’s recommendation for what you should watch next based on the series you’ve just completed.

In our case, my wife and I had completed the most recent episode of “The Walking Dead” on AMC—now on hiatus through February—and, as you can see, OdinText recommended we watch “Goliath” on Amazon.

Not only had I never heard of this series, but when I looked it up I was skeptical that we’d enjoy it because my wife and I are not particularly fond of legal dramas.

It turned out that OdinText’s prediction was spot on; we’re both hooked on “Goliath”!

I'll probably check out "Drunk History" on Comedy Central next...

Attention Show Hole Sufferers: Let OdinText Get You Out!

I think this exercise demonstrates the versatility of the OdinText platform. With a little creativity, OdinText can not only provide breakthrough consumer insights, but solve problems of all stripes.

Here are a few more examples. You’ll note that quite often the suggestions cut across networks. Even though obviously someone recommending something on HBO will be more likely to have seen and to recommend other shows on that network, the model often makes suggestions that are quite surprising, cutting across networks and time. Here are just a few:

Above we have OdinText’s recommendations for anyone who likes “Luke Cage.” (I haven’t seen it and typically am not a fan of super hero stuff, but I ran it as I saw in the data that the show was very popular) “Luke Cage” fans might also like “Daredevil,” “Stranger Things” (which I did love), and “The Flash.” The first three here are all on Netflix, the last one is on CW.

You don’t have to be a premium channel streaming snob to benefit here. If you like the popular sitcom “Big Bang Theory” on CBS, you may well also like their new “2 Broke Girls”, and “Last Man Standing” or “Modern Family” on ABC.

Some of the best shows, in my opinion, are often also ironically less popular and less frequently mentioned. Two such shows are HBO’s “Deadwood,” for which OdinText recommended one good fit—Poldark,” a BBC series--and Netflix’s “Peaky Blinders,” for which OdinText suggests trying “Downton Abbey.

I was honestly so impressed with OdinText’s recommendations that I’m entertaining building a suggestion app based on this model. (And unlike Netflix, I didn’t need dozens of developers and millions of dollars to get the right answer.)

I may also refine the underlying model a bit, as well as update the underlying data in a few months when there are enough new series being mentioned to make doing so worthwhile.

In the meantime, I feel obliged to offer immediate assistance to those poor souls in the throes of a show hole today!

If you’re stuck in a show hole, post the title of your recent favorite series in the comment section of today’s blog. OdinText will tell the first 10 people who respond what to watch next. Then come back and tell us how OdinText did.

I look forward to your comments!

@TomHCanderson

Ps. See firsthand how OdinText can help you learn what really matters to your customers and predict real behavior. Contact us for a demo using your own data here!

About Tom H. C. Anderson

Tom H. C. Anderson is the founder and managing partner of OdinText, a venture-backed firm based in Stamford, CT whose eponymous, patented SAS platform is used by Fortune 500 companies like Disney, Coca-Cola and Shell Oil to mine insights from complex, unstructured and mixed data. A recognized authority and pioneer in the field of text analytics with more than two decades of experience in market research, Anderson is the recipient of numerous awards for innovation from industry associations such as CASRO, ESOMAR and the ARF. He was named one of the "Four under 40" market research leaders by the American Marketing Association in 2010. He tweets under the handle @tomhcanderson.