Posts tagged Software
Artificial Intelligence in Consumer Insights

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

 

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

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

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

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

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

AI isn’t necessarily something everyone needs to know a whole lot about. I blogged recently, how I felt it was almost comical how many were mentioning AI and machine learning at MR conferences I was speaking at without seemingly any idea what it means. http://odintext.com/blog/machine-learning-and-artificial-intelligence-in-marketing-research/

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

I published the chart on the NGMR blog for those who are interested [ http://nextgenmr.com/grit-2017 ], and the differences couldn’t have been starker. Suppliers kept on using buzzwords like “technology”, “mobile” etc. whereas clients used real end result terms like “know how”, "speed" etc.

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

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

After that it’s all marketing and brand positioning.

 

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

Text Analytics Picks the 10 Strongest Super Bowls Ads

New Text Analytics PollTM Shows Which Super Bowl Ads Really Performed Best Well, it’s been five days since the Super Bowl, and pretty much everyone has cranked out a “definitive” best-and-worst ad list or some sort of top 10 ranking. And frankly, I think a lot of them are based on the wrong metrics.

Without a doubt, what makes a Super Bowl ad great differs from what makes a “normal” ad great. So what exactly qualifies a Super Bowl ad as a success or failure?

We could look at purchase consideration or intent, likelihood to recommend, or any of a dozen or more other popular advertising metrics, but that’s not what Super Bowl advertising effectiveness is really about.

Word of mouth has always been a big one and nowadays that means social media buzz. But does buzz equate to success? Ask the folks at Budweiser or Lumber 84.

Bottom line: This is a very expensive reach buy, first and foremost, and it’s a branding exercise.  I’ve shelled out $5 million (plus production costs) for 30 seconds to make a lasting and largely unconscious impression on the world’s biggest television audience.

As far as I’m concerned there need only be three objectives then:

  1. I want you to remember the ad;
  2. I want you to remember it’s my ad;
  3. I want you to feel positive about it.

Whether or not my ad met all of these criteria can be answered with one single unstructured question in a Text Analytics PollTM and quickly be analyzed by NLP software like OdinText with more valid results than any multiple-choice instrument.

Why a Text Analytics PollTM ?

Using a Likert scale to assess recall or awareness will only provide an aided response; I can’t ask you about an ad or brand without mentioning it. So I don’t really know if the ad was actually that memorable. And while a quantitative instrument can tell me whether or not you liked or disliked an ad, it also won’t tell me why.

Conversely, I can get the “why” from traditional qualitative tools like focus groups or IDIs, but not only would those insights be time-consuming, labor-intensive and expensive to gather, they wouldn’t be quantified.

But if I ask you to just tell me what you remember in your own words using a comment box, I can find out which ad was truly memorable, ascertain whether or not you truly recall the brand, determine whether the ad left a positive or negative impression on you and get a much deeper understanding of why. I can achieve all of this using one open-ended question. And with text analytics software like OdinText, I can quantify these results.

Which Super Bowl Ads Did “Best”?

We asked a random, gen pop sample of n=4,535 people (statistics with a confidence interval of +/- 1.46) one simple question:

“What Super Bowl ad stood out the most to you and why?”

Author’s note: We ran this survey Sunday night and closed it Monday night. We were originally planning to post the results on Tuesday, but decided to postpone it in favor of sharing what we felt were more pressing results from a Text Analytics PollTM we had conducted around President Trump’s immigration ban.

As you can see in the table below, this one simple question told us everything we needed to know…

Top 10 Super Bowl Ads: Memorability of Ad & Brand, and Degree of Positive Sentiment

The following ads are ranked according to memorability—respondents’ unaided recall of both the ad and the brand—accompanied by positive/negative sentiment breakout (blue for positive, orange for negative) in reverse order. Author’s note: The verbatim examples included here are [sic]

#10 Pepsi

 

 

As the sponsor of the Lady Gaga halftime show, one might expect Pepsi to do very well, but Lady Gaga may have literally stolen the show from Pepsi! In fact, the halftime show was actually mentioned more often in the comment data than Pepsi, and the two were infrequently mentioned together. Meanwhile, Pepsi’s ads were relatively unmemorable and much of the awareness we saw was in the form of negative sentiment.

Author’s note: Interestingly, social media monitoring services like Sprinkler had reported Pepsi “owned” the Super Bowl ad chatter on social media. I’ll say it not for the first time: social media (aka Twitter) can be full of spam often generated by agents of the brand.

 

#9 Buick

This is a case where the star of the ad, Cam Newton, didn’t eclipse the sponsor. People liked the pro footballer playing with the little kids and the tie-in to football seemed to work well. We saw this with Tom Brady in a different ad, too.

Buick with Cam Newton, cute and funny

I like the Buick ad because it let a bunch of kids play football with Cam Newton.

So what’s not to like, you say? How did it garner even a 13% unfavorable rating?

cam newton pushed little kids

The buick commercial, the concept was boring

Buick, it was not even funny

 

#8 Skittles

 

Skittles, made my kids laugh

The Skittles ad because it was funny and sort of relate-able. It shows how far one is willing to do something for someone.

Humor generally always does well, so what’s not to like?

The skittles commercial it made no sense

skittles, stupid with the burglar

Skittles, it was creepy. And what was with the gopher at the end?

 

#7 T-Mobile

Popular and a little risqué… [Note Also, Sprint Ads were often mis-remembered as T-Mobile, perhaps Halo effect and a reason Sprint didn't make the Top 10...]

The T-Mobile ‘fake your own death to escape Verizon bill’ it was very funny, and got its point across very well

T-mobile. very funny parodying 50 shades of gray to Verizon ‘screwing its customers!’

T-Mobile with Justin. Maybe because I'm a T-Mobile subscriber? Or Justin Bieber was dressed so well in a suit, and then he starts dancing and jumping like a maniac. The contrast makes it funny.

T mobile add where guy faking death. Most memorable. Light hearted. Got point across.

BUT not everyone is a Belieber

The t mobile justin biber. It was kinda lame

T-Mobile w/Justin Bieber - inane, juvenile, bordering on insulting

T-Mobile Unlimited Moves. It wasnt funny and Justin Bieber looked like the six flags guy.

T-Mobile, awkward dancing as they attempted to appeal to teenagers

 

#6  Audi

Audi took on gender equality with an appeal to fathers of daughters. The resulting ad was memorable in 6th place:

The audi one because it was meaningful

Audi - moving story and loved the message of what to tell daughters!

Audi. I have a daughter

Audi - moving story and loved the message of what to tell daughters!

However, not everyone liked mixing politics or social issues with their football (as we will see again for some of the other top ads):

AUDI and 84 Lumber. Keep your political message out of my entertainment

Least liked Audi because it was a liberal ad

 

#5 Coca-Cola

Ironically, even without sponsoring the halftime show, Coca-Cola beat Pepsi.

The coke commercial was really meaningful and symbolic

Coca Cola because of the embracing of diversity

Coca Cola True portrayal of America's diversity

The coke ad. I liked the pro-refugee stance.

coke america is beautiful commercial, very admirable

Coca Cola Commercial because it's all about being connected

Coke , showing we are still interconnected regardless of ethnicity

I liked the coca cola ad at the very beginning. I've seen it before but I think the message is so powerful and the commercial is beautifully executed.

But the ad was not received well by many, likely in part due to the politically-charged climate. Several advertisers ran messages that struck people as being politically biased or advancing a political agenda—something not everyone cared for…

Didn’t appreciate Coca Cola trying to make a political statement

I didn't like the Coke commercial. They showed it two years ago and the year before.

Google and Coke because they shoved their political views into my face.

 

#4 Mr. Clean

Who would have predicted MR. Clean for fourth place? The brand made good use of humor, and it stood out from the other ads by targeting women (but appealed to members of both genders).

Mr clean, it was funny - Female

Mr. Clean because I'm bald -  Male

Mr. Clean, relatable, memorable, hilarious. -Female

The Mr. Clean commercial, it was funny, tasty, and got the point across. Incredibly well done ad. – Male

Mr clean because my wife pointed it out – Male

mr clean because it relates to family, and parents that stay at home and clean. it was family friendly - Female

mr clean everything else sucked – Gender Not Specified

Some men though didn’t see the humor and or get the point, calling it “weird”. It wasn’t really that they disliked it intensely; they just felt it wasn’t for them.

 

#3 Lumber 84

Not many had heard of this company before the Super Bowl, but I’ll bet you know who they are now. The third most memorable ad, yes, but more than half of those who remembered it had nothing nice to say!

First, among those who liked the ad:

It was so touching

Audi, 84 lumber, both showed compassionate ads

84 lumber - it's the only one I can remember

84 Lumber - Showed what America is actually supposed to be.

they were obviously trying to get across a non- traditional message that didn't seem to be advertising. Also it was beautifully and compellingly produced.

Lumber 84 showed that not everyone wants a wall and that we understand there is power in diversity.

But the execution confused people and whatever the intention, the sponsor stepped into a controversy. Here the emotional sentiment (particularly anger) ran high and was prevalent in comments like “romanticized crime” and “forced politics”:

The Journey 84 ad, it just left me confused

The 84 lumber commercial. It didn't make sense

it was about illegals sneaking into America, i won't be shopping their anymore

Lumber 84 because it was politically offensive

84 lumber, clearly a political statement and uncalled for

84 lumber, Made no sense, Not going to look something up

#2 Kia

Ironically, with other brands going serious and political, Kia poked some fun with help from Melissa McCarthy. Kia’s investment in humor and McCarthy paid off in a big way, scoring the highest combination of memorability and positive sentiment, although to an extent the comedian eclipsed the brand.

Loved melissa McCarthy because she is hilarious and i love her.

Kia it was funny and not somber like most the others

The Buick one, the world of tanks ones and the eco friendly Melissa one because they were the funniest

The one with Melissa McCarthy because it made me laugh

KIA becuase it didn't feel like it was trying to sell me anything, just entertain with brand placement

 

#1 Budweiser

Yes, Budweiser took first place in terms of recall, but the perception of a political bent cost the king of beers. The ad, which featured one of the founders struggling as an immigrant, was apparently in the works before the Trump Immigration Order controversy. But even if that was the case, by choosing to air it Budweiser took a risk.

Likes:

I liked the Budweiser commercial reminded us all that not all white Europeans were always welcome in the US.

Budweiser. I love the reminder that we are all immigrants

Budweiser immigration. Shows Trump is an idiot, but we all know that

The Budweiser ad about how they were founded by an immigrant, because it was actually relevant to their company history

Budweiser, it was a beautiful immigrant's tale. Not overtly political

The Budweiser commercial because it shows what a true immigrant had to go through and even though many people thought it was to take a shot at Trump's travel ban it had nothing to do with it.

Dislikes:

Budweiser. Too liberal.

budweiser, too pro immigration

bud, adolfus was not ILLEGAL !

The Budweiser ad about immigration. Too political.

Budweiser, they shot themselves in the foot being that the man who immigrated into the U.S. did so legally.

Budweiser. Football/all sports should not involve politics. We need to relax sometimes.

So…who won?

Isn’t it obvious? I’d say Kia. Sure, Budweiser scored higher unaided awareness, but a significant portion of that was negative.

But it's all in the data, what do you think?

A Final Note on Text Analytics PollsTM 

It occurred to me in writing this post that about 11 years ago almost to the day I predicted that the survey of the future would be a one-question open-end, because that’s all people really want to tell you, and that’s all you’ll need.

Turns out I may have been right.

This week, we’ve shared results from three such surveys, a technique we've dubbed “Text Analytics PollTM .

These incredibly short, one-question polls allow us to field quickly to large samples with minimal burden on the respondent. And text analysis software such as OdinText enables us to quantify these huge quantities of comments.

But the real advantage to using text analytics polls is that the responses tell us so much more than whether someone agrees/disagrees or likes/dislikes. Using text analytics we can uncover why from respondents in their own words.

Thanks again for reading!

@TomHCAnderson @OdinText

Could a text analytics poll answer your burning marketing questions?  Contact us to see if a single-question open-ended survey makes sense for you!

 

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.

 

 

 

Why Machine Learning is Meaningless

Beware These Buzzwords! The Truth About "Machine Learning" and "Artificial Intelligence" Machine learning, artificial intelligence, deep learning… Unless you’ve been living under a rock, chances are you’ve heard these terms before. Indeed, they seem to have become a must for market researchers.

Unfortunately, so many precise terms have never meant so little!

For computer scientists these terms entail highly technical algorithms and mathematical frameworks; to the layman they are synonyms; but as far as most of us should be concerned, increasingly, they are meaningless.

My engineers would severely chastise me if I used these words incorrectly—an easy mistake to make since there is technically no correct or incorrect way to use these terms, only strict and less strict definitions.

Nor, evidently, is there any regulation about how they’re used for marketing purposes.

(To simplify the rest of this blog post, let’s stick with the term “machine learning” as a catch-all.)

Add to this ambiguity the fact that no sane company would ever divulge the specifics underpinning their machine learning solution for fear of intellectual property theft. Still others may just as easily hide behind an IP claim.

Bottom line: It is simply impossible for clients to know what they are actually getting from companies that claim to offer machine learning unless the company is able and chooses to patent said algorithm.

It’s an environment that is ripe for unprincipled or outright deceitful marketing claims.

A Tale of Two Retailers

Not all machine learning capabilities are created equal. To illustrate, let’s consider two fictitious competing online retailers who use machine learning to increase their add-on sales:

  • The first retailer suggests other items that may be of interest to the shopper by randomly picking a few items from the same category as the item in the shopper’s cart.

 

  • The second retailer builds a complex model of the customer, incorporating spending habits, demographic information and historical visits, then correlates that information with millions of other shoppers who have a similar profile, and finally suggests a few items of potential interest by analyzing all of that data.

In this simplistic example, both retailers can claim they use machine learning to improve shoppers’ experiences, but clearly the second retailer employs a much more sophisticated approach. It’s simply a matter of the standard to which they adhere.

This is precisely what I’m seeing in the insights marketplace today.

At the last market research conference I attended, I was stunned by how many vendors—no matter what they were selling—claimed their product leveraged advanced machine learning and artificial intelligence.

Many of the products being sold would not even benefit from what I would classify as machine learning because the problems they are solving are so simple.

Why run these data through a supercomputer and subject them to very complicated algorithms only to arrive at the same conclusions you could come to with basic math?

Even if all these companies actually did what they claimed, in many cases it would be silly or wasteful.

Ignore Buzzwords, Focus on Results

In this unregulated, buzzword-heavy environment, I urge you to worry less about what it’s called and focus instead on how the technology solves problems and meets your needs.

At OdinText, we use advanced algorithms that would be classified as machine learning/AI, yet we refrain from using these buzzwords because they don’t really say anything.

Look instead for efficacy, real-world results and testimonials from clients who have actually used the tool.

And ALWAYS ask for a real-time demo with your ACTUAL data!

Yours truly,

@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.