Posts tagged market research
2018 Predictions for Market Research and Analytics

What Kind of Researcher are You?

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

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

 

2018 The Best of Times & The Worst of Times

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

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

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

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

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

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

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

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

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

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

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

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

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

@TomHCAnderson

Artificial Intelligence in Consumer Insights

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

 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

After that it’s all marketing and brand positioning.

 

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

Marketing Research Blooper Reveals Lots of Surprises and Two Important Lessons

April Foolishness: What Happens When You Survey People in the Wrong Language?

I’m going to break with convention today and, in lieu of an April Fool’s gag, I’m going to tell you about an actual goof we recently made that yielded some unexpected but interesting results for researchers.

As you know, last week on the blog we highlighted findings from an international, multilingual Text Analytics Poll™ we conducted around culture. This particular poll spanned 10 countries and eight languages, and when we went to field it we accidentally sent the question to our U.S. sample in Portuguese!

Shockingly, in many cases, people STILL took the time to answer our question! How?

First, bear in mind that these Text Analytics Polls™ consist of only one question and it’s open-ended, not multiple choice. The methodology we use intercepts respondents online and requires them to type an answer to our question before they can proceed to content they’re interested in.

Under the circumstances, you might expect someone to simply type “n/a” or “don’t understand” or even some gibberish in order to move on quickly, and indeed we saw plenty of that. But in many cases, people took the time to thoughtfully point out the error, and even with wit.

Verbatim examples [sic]:

“Are you kidding me, an old american who can say ¡adios!”

“Tuesday they serve grilled cheese sandwiches.” “What the heck is that language?”

“No habla espanol”

“i have no idea what that means”

“2 years of Spanish class and I still don't understand”

Others expressed themselves more…colorfully…

“No, I don't speak illegal immigrant.”

“Speak English! I'm switching to News 13 Orlando. They have better coverage than FT.”

Author’s note:I suspect that last quote was from someone who was intercepted while trying to access a Financial Times article. ;-)

While a lot of people clearly assumed our question was written in Spanish, still others took the time to figure out what the language was and even to translate the question!

“I had to use google translate to understand the question.”

“what the heck does this mean i don't speak Portuguese”

But what surprised me most was that a lot of Americans actually answered our question—i.e., they complied with what we had asked—even though it was written in Portuguese. And many of those replies were in Spanish!!!

We caught our mistake quickly enough when we went to machine-translate the responses and we were told that replies to a question in Portuguese were now being translated from English to English, but two important lessons were learned here:

Takeaway One: Had we made this mistake with a multiple-choice instrument, we either might not have caught it until after the analysis or perhaps not at all. Not only would respondents not have been able to tell us that we had made a mistake, but they would’ve had the easy option of just clicking a response at random. And unless those random clicks amounted to a conspicuous pattern in the data, we could’ve potentially taken the data as valid!

Takeaway Two: The notion that people will not take the time to thoughtfully respond to an open-ended question is total bunk. People not only took the time to answer our question in detail when it was correctly served to them in their own language, but they even spared a thought for us when they didn’t understand the language!

I want to emphasize here that if you’re one of those researchers (and I used to be among this group, by the way) who thinks you can’t include an open-ended question in a quantitative instrument, compel the respondent to answer it, and get a meaningful answer to your question, you are not only mistaken but you’re doing yourself and your client a huge disservice.

Take it from this April fool, open-ended questions not only tell you what you didn’t know; they tell you what you didn’t know you didn’t know.

Thanks for reading. I’d love to hear what you think!

@TomHCAnderson

P.S. Find out how much more value an open-ended question can add to your survey using OdinText. Contact us to talk about it.

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 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.

When Oprah is President We Can Celebrate Family Day While Skiing!

Text Analytics Poll™ Shows What We’d Like Instead of Presidents Day It’s been less than a week since our Valentine’s Day poll unearthed what people dislike most about their sweethearts, and already another holiday is upon us! Though apparently for most of us it’s not much of a holiday at all; well over half of Americans say they do nothing to commemorate ‘Presidents Day.’

You’ll note I put the holiday in single quotes. That’s because there’s some confusion around the name. Federally, it’s recognized as Washington’s Birthday. At the state level, it’s known by a variety of names—President’s Day, Presidents’ Day, Presidents Day and others, again, depending on the state.

But the name is not the only inconsistency about Presidents Day. If you’re a federal employee OR you happen to be a state employee in a state where the holiday is observed OR you work for an employer who honors it, you get the day off work with pay. Schools may or may not be closed, but that again depends on where you live.

As for what we’re observing exactly, well, that also depends on the state, but people generally regard the holiday as an occasion to honor either George Washington, alone, or Washington and Abraham Lincoln, or U.S. presidents, in general.

Perhaps the one consistent aspect of this holiday is the sales? It’s particularly popular among purveyors of automobiles, mattresses, and furniture.

Yes, it’s a patriotic sort of holiday, but on the whole, we suspected that ‘Presidents Day’ fell on the weaker end of the American holiday spectrum, so we investigated a little bit…

About this Text Analytics Poll™

In this example for our ongoing series demonstrating the efficiency, flexibility, and practicality of the Text Analytics Poll™ for insights generation, we opted for a light-hearted poll using a smaller sample* than usual. While text analytics have obvious value when applied to larger-scale data where human reading or coding is impossible or too expensive, you’ll see here that OdinText also works very effectively with smaller samples!

I’ll also emphasize that the goal of these little Text Analytics Polls™ is not to conduct a perfect study, but to very quickly design and field a survey with only one open-ended question, analyze the results with OdinText, and report the findings in here on this blog. (The only thing that takes a little time—usually 2-3 days—is the data collection.)

So while the research is representative of the general online population, and the text analytics coding applied with 100% consistency throughout the entire sample, this very speedy exercise is meant to inspire users of OdinText to use the software in new ways to answer new questions. It is not meant to be an exhaustive exploration of the topic. We welcome our users to comment and suggest improvements in the questions we ask and make suggestions for future topics!

Enough said, on to the results…

A Holiday In Search of a Celebrant in Search of a Holiday…

Poll I: Americans Celebrate on the Slopes, Not in Stores

When we asked Americans how they typically celebrate Presidents Day, the vast majority told us they don’t. And those few of us lucky enough to have the day off from work tend to not do much outside of sleeping.

But the surprise came from those few who actually said they do something on Presidents Day!

We expected people to say they go shopping on Presidents Day, but the most popular activity mentioned (after nothing and sleeping) was skiing! And skiing was followed by 2) barbecuing and 3) spending time with friends—not shopping.

Poll II: Change it to Family Day?

So, maybe as far as holidays go, Presidents Day is a tad lackluster? Could we do better?

We asked Americans:

Q. If we could create a new holiday instead of Presidents Day, what new holiday would you suggest we celebrate?

While some people indicated Presidents Day is fine as is, among those who suggested a new holiday there was no shortage of creativity!

The three most frequently mentioned ideas by large margins for replacement of Presidents Day were 1) Leaders/Heroes Day, 2) Native American Day (this holiday already exists, so maybe it could benefit from some publicity?) and 3) Family Day (which is celebrated in parts of Canada and other countries).

People also seemed to like the idea of shifting the date and making a holiday out of other important annually occurring events that lent themselves to a day off in practical terms like Election Day, Super Bowl Monday and, my personal favorite, Taxpayer Day on April 15!

Poll III: From Celebrity Apprentice to Celebrity POTUS

Donald Trump isn’t the first person in history to have not held elected office before becoming president, but he is definitely the first POTUS to have had his own reality TV show! Being Presidents Day, we thought it might be fun to see who else from outside of politics might interest Americans…

 Q: If you could pick any celebrity outside of politics to be President, who would it be?

 

Looks like we could have our first female president if Oprah ever decides to run. The media mogul’s name just rolled off people’s tongues, followed very closely by George Clooney, with Morgan Freeman in a respectable third.

Let Them Tell You in Their Own Words

In closing, I’ll remind you that none of these data were generated by a multiple-choice instrument, but via unaided text comments from people in their own words.

What never ceases to amaze me about these exercises is how even when we give people license to say whatever crazy thing they can think up—without any prompts or restrictions—people often have the same thoughts. And so open-ends lend themselves nicely to quantification using a platform like OdinText.

If you’re among the lucky folks who have the holiday off, enjoy the slopes!

Until next time, Happy Presidents Day!

@TomHCAnderson

PS.  Do you have an idea for our next Text Analytics Poll™? We’d love to hear from you. Or, why not use OdinText to analyze your own data!

[*Today’s OdinText Text Analytics PollTM sample of n=500 U.S. online representative respondents has been sourced through of Google Surveys. The sample has a confidence interval of +/- 4.38 at the 95% Confidence Level. Larger samples have a smaller confidence level. Subgroup analyses within the sample have a larger confidence interval.]

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

65 CEOs Share Thoughts on Insights

Insight Association’s Inaugural CEO Summit: Future Tied to Collaboration and Technology Writing this at the Miami Airport as I’ve just finished up a great 3 day meeting of the minds at the new Insights Association’s first official event, the Marketing Research CEO Summit.

Though this event was formerly part of the Marketing Research Association (MRA), after the merger between The MRA and the Council for American Survey Research Organizations (CASRO), it is now is part of the greater and brand new Insights Association. This is also the reason I chose to attend the event for the first time this year. I like many others are eager for positive change in our industry and optimistically welcome new initiatives (as I mentioned in a post on their founding earlier this month).

Steve Schlesinger, CEO of Schlesinger Associates and Merrill Dubrow of M/A/R/C Research did a great job putting together and hosting the event.

While the obvious benefit of any event like this is the attendees and not the speakers, we had some other interesting and well respected client guests including Walmart’s Urvi Bhandari, Merck’s Lisa Courtade, Electrolux’s Brett Townsend and Dhan Kashyap from Humana. Their very candid evaluations of how well the industry is delivering *Hint* it’s not even close to as well as we think, was worth the cost of attendance.

Getting back to the attendees though, Market researchers as a breed are a cautious bunch and CEO’s in any industry are likely going to be “Alpha’s”. Quickly gaining trust and enabling sharing among this audience of would be competitors is not an easy task. Partly this was made possible via a fun case study competition sponsored by La Quinta CEO Keith Cline who also spoke at the event.

Another interesting aspect of the event was the Hot Seat interviews wherein a handful of the CEO’s in attendance were asked a series of tough and sometimes semi personal questions. I was one of those selected for this impromptu exercise and was asked what I thought about various aspects of the future of marketing research including digital/social (which I like to separate from other text analytics), and of course the topic of machine learning/AI which seems to be on everyone’s mind. For that reason I’ve decided to do a short blog post on AI and Machine learning later this week.

What I’d like to end this post with though is in re-answering one of the questions which I think Merrill indirectly asked me, and which I was asked by a couple of other attendees. I think the question is also related to the future of research. Do you think of yourself as a Marketing Research co. CEO or a software CEO? [Prior to founding OdinText Inc. in 2015 I ran boutique research firm Anderson Analytics for 10 years]

I admit it’s a tricky question, and obviously if I didn’t consider myself at least in part a marketing research CEO I wouldn’t have attended. Yet many of our software users definitely aren’t market researchers.

So here goes, I think we as an industry have an important skill set and understanding of our clients that no outsider has. I’m proud of this background and like other speakers including ZappiStore’s CRO Ryan Barry and Dan Foreman of Hatted pointed out, the future is not in resisting technology, nor is it necessarily in building your own technology, which can be time consuming and wasteful, but it’s about embracing technology and often learning how to rent or partner with technology experts and adding what you are best at (often data and as importantly consultative insights and strategy).

Several of the CEO’s I spoke with separately admitted having tried various internal technology builds which either weren’t right, or in some cases may have been right when the effort began, but didn’t evolve quickly enough and so was outdated when they did come to market.

Yet it was also quite clear to most of these CEO’s that while it’s critical to watch out for new technology oriented entrants into the market research space, more often than not these simply do not have the knowledge necessary to deliver truly actionable insights. Companies like IBM Watson for instance, certainly have a strong brand name in computers, but their offering as a plug in for marketing research API’s is sorely lacking to say the least.

The point is, knowledge and trust is what we have in good supply at both the event and in our industry in overall. The key to evolving is to remember the knowledge and best practices our industry was based on while being open to understanding outside technologies and ideas, yet resisting the urge to just try to copy them. Importantly as Merrill Dubrow pointed out, there are tremendous benefits in overcoming your fear of collaborating with other research and technology companies and partnering.

This is the idea I’m most optimistic about coming away from the conference. I made several new friends at the event, and I welcome anyone who attended to please reach out if they have are any questions in regard to text analytics and data mining software and discussing potential mutually beneficial relationships.

Until Next Year!

@TomHCAnderson

 

ABOUT ODINTEXT

OdinText is a patented SaaS (software-as-a-service) platform for advanced analytics. Fortune 500 companies such as Disney and Shell Oil use OdinText to mine insights from complex, unstructured text data. The technology is available through the venture-backed Stamford, CT firm of the same name founded by Tom H. C. Anderson, a recognized authority and pioneer in the field of text analytics with more than two decades of experience in market research. Anderson and OdinText have received numerous awards for innovation from industry associations such as ESOMAR, CASRO, the ARF and the American Marketing Association. He tweets under the handle @tomhcanderson. Request OdinText Info or a free demo here.