Do You Know the Top 10 Slang Words for 2019?

Drip and Tea, two words you may yet know?

Here at OdinText we’re all about understanding sentiment, emotions and opinions and linking these very human feelings to business performance. In so doing, the deeper meaning of the language customers use is an important and fascinating topic to us. Of course no part of language is more dynamic than slang.

In our annual post on trending terms we decided again this year to skip political oriented buzz words such as “fake news” and “snow flake” (the term impeachment has been on the rise), and refocus this post just on slang.

 “Lit” remained in the #1 spot once again this year, followed by “Yeet” which had moved up from #10 last year.

Of greatest interest this year are two brand new words we just began tracking this year, and yet they’ve already made it onto our top 10 list! At #8 we have the term “Tea”, and in #3 we have “Drip/Dripping”, a fast moving term which may end up being one of the more popular for 2019. Do you know what they mean? [You check your answers in our definitions below]

Top 10 Slang Words for This Year

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*Bold terms are new this year

Lit

“That party was Lit yo!”

#1 Lit Holding at #1 third year in a row, Lit (like on fire) literally remains “cool” for now. It has climbed from 4th place back at beginning of 2016, it may be that its position gets challenged by something fast moving like Drip, but it would probably need to be a term with as general a meaning as cool.

 

Yeet.jpg

YEET!

I’m so excited,yeet!

#2 Yeet Surprisingly, a term that was tied for 10th last year has jumped to #2, no small feat. It’s original popularity came from a new dance move and subsequent internet video meme. But as is common with slang it can transform and take on multiple meanings. By morphing into an expletive meaning excitement, it has increased in popularity.

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Drip

Wow, you drippin!

#3 Drip Drip and Dripping came out of nowhere. Drip is closely connected to Swag, Swagger, Bling and Ice, and it is quickly replacing these. It’s popularity can be traced to various rap artists who began using the term [see genius hip hop lyrics chart below showing use of Drip VS Swag etc. in lyrics].

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If you’ve got enough money, gold and jewels, then you have Drip or are Dripping.


bet.jpg

Bet

“Clean your room”

“bet!”

#4 Bet Bet moved from #18 to #8 last year, and is now at a solid #4. That kind of movement almost always has to do with new usage and/or inclusion into some popular lyrics or meme. Moving from a simple term indicating agreement, e.g. “want to go to the movies?” “Sure, Bet!”, bet has been changing to just mean “yes”, and then, more importantly and ironically the total opposite of agreement, meaning doubt and sarcasm or simply the opposite of what someone wants or No. “Yo can you help me clean my room” “Bet (leaves walks out of door)”. It has even come to be used as a sort of replacement to Yolo., but the newest and most popular meaning currently is as the opposite of the older meanings, a negative sign of disbelief. Basically a sarcastic "No".

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Fetch

“That’s so fetch!”

#5 Fetch Fetch climbed from 6th place back at end of 2015 and is basically maintaining its place at 5th this year. This one is for white girls it seems, and is among the most female scewed slang term we track. This term was popularized by the movie Mean Girls and means cool/chic.

Dope

“Dope dude”

#6 Dope Dope can be used a number of ways (see 2016 definition), including as a synonym for Lit, basically high in quality or mind blowing

Bro

“What up bro?”

#7 Bro It seems “Bruh” which reached a high of #10 back in 2015/2016  has been flat and/or morphed back to the more common “Bro”. While there have even been female variants like Bra (in part promoted by advertising related to breast cancer - someone who supports you when you have breast cancer). The meaning of Bruh has been changing for sometime from a term of endearment (brother), to Bruh?! Meaning  “Oh no… why did you do that?!”, and now it seems, back to the more simple term of endearment, Bro.

Tea

“Spill the tea yo!”

#8 Tea Here’s another brand new up and comer we just began tracking quite recently. If someone asks you for the Tea, they’re nt talking about a hot beverage, they want the juicy gossip!



Dab

“That deserves a dab!”

#9 Dab The meme-able dance move known as ‘dabbing’ spawned and gave way to the term with similar origin “Yeet”, which has taken on more meaning and popularity than dab. While this term continues to slowly wane in popularity, its use together with marijuana may help it morph and remain in use. More something you do than say, after a win or achieving something like a touch down in football, the player might dab to celebrate the awesomeness of it.

Shook

“Wow, I’m shook”

#10 Shook Last year we mentioned Shook as a bonus term you may want to keep your eyes on. It was a popular meme, and the term’s meaning varies slightly depending on the context. Generally, “shook” refers to a state of fear or of being shocked or stunned. It can also refer to a state of being deeply affected by an experience (implicitly traumatic) or even the way one might be momentarily struck by the beauty of a romantic prospect ala Elvis Presley’s “I’m All Shook Up.” Of course it can also be used sarcastically, when one is definitely not surprised.

In any given year there are usually about 50 or so slang terms that appear often enough for us to consider active and track for our Top-10. Other than geographic differences, there are usually differences related to gender, age and other demographics. For instance while “Fetch” and “Bae” are far more likely to be used by women, Yeet and Woke are more popular among males. Terms like “Lit”and “Yeet” skew younger, the term “Dang” is rather Southern, whereas “lit” and “Bae” are more popular in the North East.

That’s our top 10 list starting us off for 2019. If there is a term you’re curious about and wondering whether it’s just popular locally, or is getting a broader foot hold let us know and we’ll look into it.

@TomHCAnderson

Top 5 Text Analytics Tips of The Year

Happy 2019 & Top Posts of the Year


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

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

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

#1 A New Trend in Qualitative Research

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

 

#2 Trend Watching +OdinText

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

 

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

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

 

#4 Advertising Effectiveness +OdinText

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


#5 The State of Marketing Research Innovation

What You Missed at IIEX 2018 – 3 Takeaways

 

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

Wishing you an exciting and prosperous 2019!

Your friends @OdinText

Listening to Employees Where it Really Matters

World-Renowned Hospitals Use OdinText to Listen to Valuable Employee Feedback and Prioritize Resources

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This year again, OdinText was honored to be voted among the Most Innovative Marketing Research Suppliers in the World (2018 Greenbook Research Industry Trends GRIT Report).

As is custom, each of the Top Marketing Research Suppliers are invited to submit a case study for inclusion in the annual e-book showcasing the best of the best in consumer insights.

Last year our case study highlighted how OdinText can use customer comment data to better understand drivers of customer satisfaction and NPS (Net Promoter Score), as well as  predict  return behavior and revenue! This year we chose a case which highlights how OdinText can be used for Workforce Analytics to leverage employee feedback for continuous improvement, increased employee engagement and satisfaction.

As the Director of Human Resources, Greene Memorial Hospital and Soin Medical Center comments on the experience with OdinText in the Health Care & Human Resources:

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“The magnitude and detail of OdinText is amazing! [OdinText] pinpoints exactly areas that we can really work on. Other vendors just give us material and we have to hunt and peck. For not knowing anything about our industry, this software is amazing! You know atmosphere, what’s changing and what’s not… This blows me away.”

You can view an abbreviated case study in the e-book or on the Greenbook site tomorrow. However we are also happy to share a slightly more detailed case with you. To find out how world class hospitals improve through stakeholder feedback follow the link below:

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Turning staff feedback into action: OdinText artificial intelligence platformreveals what drives employeesatisfaction at Kettering HealthNetwork’s Greene Memorial Hospitaland Soin Medical Center!

Again Thank You to Greenbook and everyone who selected us as most innovative, and congratulations to all the other great companies this year!

If you are curious how YOUR Data +OdinText will provide more powerful Insights Guaranteed you can request more information here.

Tom H. C. Anderson Chief Research Officer @OdinText

2018 Next Gen Market Research Award Winners

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

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

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

Without further ado, here are this years’ deserving winners

Outstanding Disruptive Start-Up

Opinion Economy (CEO Ted Waz accepting)

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

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

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

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

 

Industry Change Agent of the Year

ThinkNow Co-Founder & Principal, Mario X. Carrasco

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

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

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

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

 

Most Innovative Research Method

Align – Susan Ferrari

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

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

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

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

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

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

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

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

Please join me in congratulating this years’ winners!

Big thank you also to the Judging Committee:

Mike Gadd, CEO Gadd Research

Kristin Luck, Luck Collective

Tom De Ruyke, Insites Consulting

Steve August, Poinyent/August & Wonder

Scott Upham, Valient Market Research

 

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

@TomHCAnderson

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

The State of Marketing Research Innovation

What You Missed at IIEX 2018 – 3 Takeaways Walking the floor at the Insights Innovation Exchange (IIEX) for a day and a half with our new CEO, Andy Greenawalt, we spoke to several friends, client and supplier side partners, and ducked into quite a few exciting startup sessions.

Three things struck me this year:

-Insights Technology is Finally Getting More Innovative. By that I mean there are no longer just the slight immaterial modifications to existing ways of doing things, but actual innovation that has disruptive implications (passive monitoring, blockchain, image recognition, more intelligent automation…).

As expected most of this innovation is coming from startups, many of which, while they have interesting ideas, have little to no experience in marketing research - and have yet to prove their use cases.

-A Few Marketing Research Suppliers are picking up their consulting game. Surprisingly perhaps, in this area it seems that change is coming from the Qualitative side. For a while qualitative looked like a race to the bottom in terms of price, even more so than what was happening in Quantitative Research. But there are now a handful of Image/Brand/Ideation ‘Agencies’ whose primary methodologies are qualitative who are leading the way to a higher value proposition. There are a couple, but I will mention two I’ve been most impressed with specifically, Brandtrust and Shapiro+Raj, Bravo!

-The Opportunity. I think the larger opportunity if there is one, lies in the ability of the traditional players to partner with and help prove the use cases of some of these newer startup technologies. Incorporating them into consulting processes with higher end value propositions, similar to what the qualitative agencies I noted above have done.

This seems to be both an opportunity and a real challenge. Can Old help New, and New help Old? It may be more likely that the end clients, especially those that are more open to DIY processes will be the ones that select and prove the use cases of these new technologies offered by the next generation of startups, and therefore benefit the most.

While this too is good, I fear that by leaving some of the traditional companies behind we will lose some institutional thinking and sound methodology along the way.

Either way, I’m more optimistic on new Marketing Research Tech than I’ve ever been.

Keep in mind though, Innovation in Marketing Research should be about more than just speed and lower cost (automation). It should be even more about doing things better, giving the companies and clients we work for an information advantage!

@TomHCAnderson

OdinText Names Andy Greenawalt CEO
Andy Greenawalt to lead OdinText accelerated growth phase

We are happy to announce serial Inc. 500 entrepreneur Andy Greenawalt as CEO effective June 1. OdinText founder and current CEO Tom H.C. Anderson will transition to the roles of Chief Research Officer and Chairman.

Andy Greenawalt

An accomplished tech entrepreneur and leader, Greenawalt has successfully built two Inc. 500 SaaS (software as a service) businesses. Most recently, he was CEO of Continuity, a pioneer in the Regulatory Technology industry, and he remains chairman of its board. Prior to Continuity, Greenawalt founded Perimeter eSecurity, now part of BAE Systems, serving as CEO and CTO and on its board. He is a graduate of the University of Massachusetts, Amherst with a degree in Philosophy and Cognitive Linguistics.

“With more Fortune 500 companies choosing OdinText, Andy Greenawalt’s credentials in innovation, his successful record of building SaaS businesses, and his singular focus on creating customer value make him a perfect fit to lead OdinText through its next phase of growth,” said Anderson.

“OdinText is a truly rare startup with Fortune 500 enterprise customers —  the most sophisticated buyers in the world,” said Greenawalt. “This is a testament to the vision and team that Tom Anderson has assembled and it’s a great position to be starting from as a pioneer in the text analytics market. The company is very well positioned to bring a new platform to bear and serve as a cornerstone to the smart enterprise of the future.”

Alison Malloy, the lead investor in OdinText from Connecticut Innovations, stated, “Connecticut Innovations has worked with Andy Greenawalt for 20 years. We have absolute confidence that he’s the right person to realize the market potential of OdinText — which has pioneered the next generation of text analytics — allowing Tom Anderson to focus on the research needed to continue to develop and lead the market with industry-leading products.”

“OdinText has developed patented IP, raised pre-seed funding and created an MVP product,” Greenawalt said. “OdinText is a transformative solution that is now poised to redefine how businesses improve satisfaction, retention and revenue. We expect to grow dramatically.”

What you Need to Know Before Buying AI/Machine Learning

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

In case you missed our session on Artificial Intelligence and Machine Learning (AI/ML) at the Insights Associations’ NEXT conference last week, I thought I would share a bit on the blog about what you missed. We had a full room, with some great questions both during and after the session. However, 30 minutes wasn’t enough time to cover everything thoroughly. In the end we agreed on four takeaways:

  • AI is part of how research & insights pros will address the ever-increasing demand for fast research results
  • AI Helps focus on the most important data
  • AI can’t compensate for bad data
  • AI isn’t perfect

So today I thought I would share seven additional points about AI/ML that I often get questions on, and then at the end of this post I’m going to share the ‘Cliff Notes’, i.e. I’m going to share just the 2 most important things you really need to know.  So, unless you want to geek out with me a bit, feel free to scroll to the bottom.

OK, first, before we can talk about anything, we need to define what Artificial Intelligence (AI) is and isn’t.

1. AI/ML definition is somewhat fuzzy

AI, and more specifically machine learning (ML) is a term that is abused almost as often as it is used. On the one hand this is because a lot of folks are inaccurately claiming to be using it, but also because not unlike big data, its definitions can be a bit unclear, and don’t always make perfect sense.

Let’s take this common 3-part regression analysis process:

  1. Data Prep (pre-processing including cleaning, feature identification, and dimension reduction)
  2. Regression
  3. Analysis of process & reporting

This process, even if automated would not be considered machine learning. However, switch out regression with a machine learning technique like Neural Nets, SVM, Decision Trees or Random Forests and bang, it’s machine learning. Why?

Regression models are also created to predict something, and they also require training data. If the data is linear, then there is no way any of these other models will beat regression in terms of ROI. So why would regression not be considered machine learning?

Who knows. Probably just because the first writers of the first few academic papers on ML refenced these techniques and not regression as ML. It really doesn’t make much sense.

2. There are basically 2 types of ML

Some ML approaches are binary like SVM (Support Vector Machines), for predicting something like male or female, and others like Decision Trees are multi class classification.

If you are using decision trees to predict an NPS rating on an 11 point scale then that’s a multi class problem. However, you can ‘trick’ binary techniques like SVM to solve the multi class problem by setting it up to run multiple times.

Either way, you are predicting something.

3. ML can be slow

Depending on the approach used, like Neural Nets for instance, training a model can take several days on a normal computer. There are other issues with Neural Nets as well, like the difficulty for humans to understand and control what they are doing.

But let’s focus on speed for now. Of course, if you can apply a previously trained model on very similar data, then results will be very fast indeed. This isn’t always possible though

If your goal is to insert ML into a process to solve a problem which a user is waiting for, then training an algorithm might not be a very good solution. If another technique, ‘machine learning’ or not, can solve the problem much faster with similar accuracy, then that should be the approach to use.

4. Neural Nets are not like the brain

I’ll pick on Neural Nets a bit more, because they are almost a buzz word unto themselves. That’s because a lot of people have claimed they work like the human brain. This isn’t true. If we’re going to be honest, we’re not sure how the human brain works. In fact, what we do know about the human brain makes me think the human brain is quite different.

The human brain contains nearly 90 billion neurons, each with thousands of synapses. Some of these fire and send information for a given task, some will not fire, and yet others fire and do not send any information. The fact is we don’t know exactly why. This is something we are still working on with hopes that new more powerful quantum computers may give us some insight.

We can however map some functions of the brain to robotics to do things like lift arms, without knowing exactly what happens in between.

There is one problematic similarity between the brain and Neural Nets though. That is, we’re not quite sure how Neural Nets work either. When running a Neural Net, we cannot easily control or explain what happens in the intermediary nodes. So, this (along with speed I mentioned earlier) is more of a reason to be cautious about using Neural Nets.

5. Not All Problems are best solved with Machine Learning

Are all problems best solved with ML? No, probably not.

Take pricing as an example. People have solved for this problem for years, and there are many different solutions depending on your unique situation. These solutions can factor in everything from supply and demand, to cost.

Introducing machine learning, or even just a simpler non-ML based automated technique can sometimes cause unexpected problems. As an example, consider the automated real-time pricing model which Uber used to model supply and demand as inputs. When fares skyrocketed to over $1,000 as drunk people were looking for a ride on New Years eve, the model created a lot of angry customers and bad press.

More on dangers of AI/ML in a bit…

6. It’s harder to beat humans than you think

One of the reasons ML is often touted as a solution is because of how much better than humans computers allegedly are. While theoretically there is truth to this, when applied to real world situations we often see a less ideal picture.

Take self driving cars as an example. Until recently they were touted as “safer than humans”. That was until they began crashing and blowing up.

Take the recent Tesla crash as an example. The AI/ML accidentally latched onto an older faded lane line rather than the newly painted correct lane line and proceeded without breaking, at full speed, into a head on collision with a divider. A specific fatal mistake no human would have been likely to make.

The truth is if we remove driving under the influence and falling asleep from the statistics (two things that are illegal anyway), then human accident statistics are incredibly low.

7. ML is Context Specific!

This is an important one. IBM Watson might be able to Google Lady Gaga’s age quickly, but Watson will be completely useless in identifying her in a picture. Machine learning solutions are extremely context specific.

This context specificity also comes into play when training any type of model. The model will only be as good as the training data used to create it, and the similarity to future data it is uses for predictions.

Model validation methods only test the accuracy of the model on the same exact type of data (typically a random portion of the same data), it does not test the quality of the data itself, nor the application of this model on future data other than the training data.

Be wary of anyone who claims their AI does all sorts of things well, or does it with extremely 100% accuracy.

My final point about Machine Learning & two Cliff Notes…

If some of the above points make it sound as if I’m not bullish on machine learning, I want to clarify that in fact I am. At OdinText we are continuously testing and implementing ML when it makes sense. I’m confident that we as an industry will get better and better at machine learning.

In the case of Tesla above, there are numerous ways to make the computers more efficient, including using special paint that would be easier for computer cameras to see, and traffic lights that send signals telling the computer stating “I am red”, “I am Green” etc., rather than having to guess it via color/light sensing. Things will certainly change, and AI/ML will play an important part.

However, immediately after my talk at the Insights Association I had two very interesting conversations on how to “identify the right AI solution”? In both instances, the buyer was evaluating vendors that made a lot of claims. Way too many in my opinion.

If you forget everything else from today’s post, please remember these two simple Cliff Notes on AI:

  1. You Don’t Buy AI, you buy a solution that does a good job solving your need (which may or may not involve AI)
  2. Remember AI is context specific, and not perfect. Stay away from anyone who says anything else. Select vendors you know you can trust.

There’s no way to know whether something is AI or not without looking at the code.

Unlike academics who share everything under peer review, companies protect their IP, Trade Secrets and code, so there will technically be no way for you to evaluate whether something actually is “AI” or not.

However, the good news is, this makes your job easier. Rather than reviewing someone’s code your job is simply still to decide whether the products solves your needs well or not.

In fact, in my opinion it is far more important to choose a vendor who is honest with you about what they can do to solve your problems. If a vendor claims they have AI everywhere that solves all kinds of various needs, and does so with 100% accuracy, run!

@TomHCAnderson

AI and Machine Learning NEXT at The Insights Association
Insight practitioners from Aon, Conagra and Verizon speak out on what they think about AI and Machine Learning

Artificial Intelligence and Machine Learning are hot topics today in many fields, and marketing research is no  exception. At the Insights Association’s NEXT conference on May 1 in NYC I've been asked to take part in a practitioner panel on AI to share a bit about how we are using AI in natural language processing and analytics at OdinText.

While AI is an important part of what data mining and text analytics software providers like OdinText do, before the conference I thought I’d reach out to a couple of the client-side colleagues to see what they think about the subject.

With me today I have David Lo, Associate Partner at the Scorpio Partnership (a collaboration between McLagan and the Aon Hewitt Corporation) Thatcher Schulte, Sr. Director, Strategic Insights at Conagra Brands, and Jonathan Schwedel, Consumer & Marketplace Insights at Verizon, all who will also be speaking at NEXT.

THCA: Artificial Intelligence means different things to different people and companies. What does it mean to you, and how if at all you are planning to use it in your departments?

Thatcher Schulte – Conagra:

Artificial intelligence is like many concepts we discuss in business, it’s a catch all that loses its meaning as more and more people use it.  I’ve even heard people refer to “Macros” as AI.  To me it means trying to make machines make decisions like people would, but that would beg the question on whether it would be “intelligent.”  I make stupid decisions all the time.

We’re working with Voice to make inferences on what help consumers might need as they make decisions around food.

Jonathan Schwedel – Verizon:

I'm not a consumer insight professional - I'm a data analyst who works in the insights department, so my perspective is different. There are teams in other parts of Verizon who are doing a lot with more standard artificial intelligence and machine learning approaches, so I want to be careful not to conflate the term with broader advanced analytics. I have this image of cognitive scientists sitting in a lab, and am tempted to reduce "AI" to that.

For our specific insights efforts, we work on initiatives that are AI-adjacent - with automation, predictive modeling, machine learning, and natural language processing, but with a few exceptions those efforts are not scaled up, and are ad hoc on a project by project basis. We dabble with a lot of the techniques that are highlighted at NEXT, but I'm not knowledgeable enough about our day to day custom research efforts to speak well to them. One of the selling points of the knowledge management system we are launching is that it's supposed to leverage machine learning to push the most relevant content to our researchers and partners around our company.

David Lo – Scorpio Partnership/McLagan:

Working in the financial services space and specifically within wealth management, AI is a hot topic as it relates to how it will change advice delivery

[we are looking at using it for] Customer journey mapping through the various touchpoints they have with an organization.

 

THCA: There’s a lot of hype these days around AI. What is your impression on what you’ve been hearing, and about the companies you’ve been hearing it from, is it believable?

Thatcher Schulte - Conagra:

I don’t get pitched on AI a lot except through email, which frankly hurts the purpose of those people pitching me solutions.  I don’t read emails from vendors.

Jonathan Schwedel – Verizon:

It's easy to tell if someone does not have a minimum level of domain expertise. The idea that any tool or platform can provide instant shortcuts is fiction. Most of the value in these techniques are very matter of fact and practical. Fantastic claims demand a higher level of scrutiny. If instead the conversation is about how much faster, cheaper, or easier they are, those are at least claims that can be quickly evaluated.

David Lo – Scorpio Partnership/McLagan:

Definitely a lot of hype.  I think as it relates to efficiency, the hype is real.  We will continue to see complex tasks such as trade execution optimized through AI.

 

THCA: For the Insights function specifically, how ready do you think the idea of completely unsupervised vs. supervised/guided AI is? In other words, do you think that the one size fits all AI provided by likes of Microsoft, Amazon, Google and IBM are very useful for research, or does AI need to be more customized and fine tuned/guided before it can be very useful to you?

And related to this, what areas of Market Research do you thing AI currently is better suited to AI?

 Thatcher Schulte - Conagra:

Data sets are more important to me than the solutions that are in the market.  Food decision making is specialized and complex and it varies greatly by what life stage you are in and where you live. Valid data around those factors are frankly more important than the company we push the data through.

David Lo – Scorpio Partnership/McLagan:

Guard rails are always important, particularly as it relates to unique customer needs.

[In terms of usefulness to market research], Data mining

Jonathan Schwedel – Verizon:

Most custom quantitative research studies use small sample sizes, making it often not feasible to do bespoke advanced analytics. When you are working with much larger data sets (the kind you'd see in analytics as a function as opposed to insights), AWS and Azure let you scale, especially with limited resources. It's a good general approach to use algorithmic type approaches with brand new data sets, and then start customizing when you hit the point of diminishing returns, in a way that your work can later be automated at scale.

[In regard to marketing research] It depends how you're defining research - are we broadening that to customer experience? Then text analytics is a most prominent area, because there are many prominent use cases for large companies at the enterprise level. If "market research" covers broader buckets of customer data, then there's potentially a lot you can do.

 

THCA: OK, so which areas are currently less well suited to AI?

David Lo – Scorpio Partnership/McLagan:

Hard to say, but probably less suited toward qualitative research.  In my line of business we do a lot of work among UHNW investors where sample sizes are very small and there isn’t a lot of activity in the online space.

Jonathan Schwedel – Verizon:

I think sample size is often an issue when talking about research studies. Then it comes down to the research design. Is the machine learning component going to be baked in from the start, or is it just bolted on? A lot of these efforts are difficult to quantify. Verizon's insights group learns things all the time from talking to and observing consumers that we would not have otherwise thought to ask.

 

THCA: Does anyone have thoughts on usefulness of chat bots and/or other social media/twitter bots currently?

Jonathan Schwedel – Verizon:

They could potentially allow you to collect a lot more data, and reach under-represented consumers groups in the channels that they want to be in. A lot of our team's focus at Verizon is on the user experience and building a great digital experience for our customers. I think they will be important tools to understand and improve in that area.

 

THCA: Realistically where do you see AI in market research being 3-4 years from now?

David Lo – Scorpio Partnership/McLagan:

Integrated more fully with traditional quantitative research techniques, with researchers re-focusing their efforts on the more creative and thoughtful interpretations of the output.

Jonathan Schwedel – Verizon:

They will provide some new techniques that will be important for specific use cases, but I think the bulk of the fruitful efforts will come from automation and improved scalability. The desire to do more with less is pretty universal, and there's a good roadmap there. The prospect of genuinely groundbreaking insights offers a lot more uncertainty, but it would be great if we do see that level of innovation.

 

Big thanks to Jonathan, David and Thatcher for sharing their insights and opinions on AI.

If you’re interested in further discussion on AI and Machine Learning please feel free too post a comment here, or join me for the 'What’s New & What’s Ahead for AI & Machine Learning?' Panel on May 1st . I will be joined by John Colias of Decision Analyst, Andrew Konya of remesh, and moderator Kathryn Korostoff of Research Rockstar.

-Tom H. C. Anderson @OdinText

 

PS. If you would like to learn more about how OdinText can help you better understand your customers and employees feel free to request more info here. If you’re planning on attending the confernece feel free use my speaker code for a $150 discount [ODINTEXT]. I look forward to seeing some of you at the event!

 

GRIT Survey 2018

Celebrating Innovative Companies in Marketing Research

It's that time of year again when Greenbook fields their biannual GRIT market research industry survey.

Thankfully it looks like the Greenbook team has made the survey a bit shorter than last year. I do encourage fellow researchers to take the survey, as it does give everyone some direction in terms of where things seem to be heading.

You can take the survey here.

Thanks in advance for your participation.

@TomHCAnderson

PS. This is the GRIT survey which looks for the most innovative insights companies, both supplier side and client side. We encourage you to give some thought to this section as well. Its nice to recognize up and coming companies, as well as your go-to favorites.

I also want to take this time thank everyone who voted for OdinText in the most innovative supplier category last year. We were very encouraged by the support and have been working harder than ever to release a brand new version of the software next month!