Posts in Conferences + Events
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

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!

 

Market Research CEO’s Summarized and Text Analyzed

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

Change

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

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

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

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

Partnering

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

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

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

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

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

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

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

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

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

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

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

@TomHCAnderson

Congratulations 2017 NGMR Award Winners!

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

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

Please join us in congratulating this year’s winners!

@OdinText

TMRE Wednesday 11:45 Track #5

At The TMRE (The Marketing Research Event) This Week? First, Thank you everyone who already took took our Next Gen Market Research Industry survey this week.  I know even a 10 minute survey is a bit too long, and there were definitely more Open-Ended questions that usual.

I know people are afraid of OE’s because they make you think, and we did push the envelope a little in having so many, but the insights they provide are really massive in comparison to structured questions.

Just want to remind everyone who will be at the TMRE in Orlando that the NGMR Awards Session proudly sponsored by OdinText and VoxPopMe will be held just before Malcolm Gladwell's talk at 8:50am TUESDAY morning.

The NGMR Survey will also be open to TMRE attendees and we have a special link going out via TMRE and on Twitter.

On WEDNESDAY 11:45 TRACK #5 we will be looking at top-line text analytics visualizations of some of the cool findings from the marketing research industry survey.

The survey top-line visualizations will serve as our discussion guide for an interesting and highly relevant discussion on our industry.

Join me with the NGMR Winners as our special guests as we explore how these unique insights uncovered pertain to innovation and the future for our industry.

Please stop by both sessions if at the TMRE. We’ll be sharing results on the blog later.

@TomHCAnderson

PS. You can still take the survey here 

Curious About Marketing Research?

We need to hear from you!

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

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

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

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

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

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

1 Analyst + 15K Comments in 8 languages + 2 hours = Awesome Insights!

1 Analyst + 15K Comments in 8 languages + 2 hours = Awesome Insights! Please join us on September 14th for this free live webinar co-hosted by TMRE.

Spaces limited/first come first serve, please register here.

We’ll be covering our extremely well received multi country, multi lingual analysis case study. I think you’ll be surprised at the implications and amazed that this kind of global research can now be done quickly and inexpensively by anyone.

Look forward to seeing you there!

@TomHCAnderson

Nominate The Best Market Researchers of 2017!

Next Gen Market Research Award Nominations Open

OdinText is a proud sponsor of the 2017 NGMR Awards at The Market Research Event (TMRE). Once again Women in Research (WIRe) has joined NGMR in celebrating those who are doing most to shake up marketing research.

Nominations are due in just two weeks, September 5th, Nomination form and instructions are available on the NGMR blog here.

Good Luck!

Your Friends @OdinText

 

[Interested in attending The Market Research Event? We'd love to see you there! Feel free to use our speaker code 'ODINTEXT' for a 20% Conference Discount]

Seven Text Analytics Myths Exposed at IIEX

What I Learned from Attendees in IIEX Text Analytics Sessions This week I had the opportunity to attend and to present at the Insights Innovation Exchange (IIEX) in Atlanta. This conference always provides a wonderful chance to connect with a lot of smart, forward-looking researchers.

For those who missed IIEX or weren’t able to attend my presentation, I provided a case study outlining how we conducted a massive international study in 10 countries and eight languages for almost no cost with results analyzed in just two hours. If you’d like to know more, feel free contact us for a free e-book detailing the project.

My presentation aside, what I’d like to cover here today actually came out of the Text Analytics Information Sessions we were asked to host on Monday, and which I’m pleased to report were well attended—notably by representatives from more than a few major supplier and client brands.

Text Analytics IIEX

I had originally anticipated that there would be more group conversation and peer-to-peer sharing, but it turned out that most of the attendees were less interested in talking than they were in learning, and so the sessions involved quite a bit of Q&A, with my colleague Tim Lynch and I fielding more questions about text analytics, generally, than expected.

What I took from these sessions was a sense that a lot of confusion and misperception around text analytics persists among researchers today and that the industry is urgently in need of more educational resources on the topic (more on this at the end of the blog).

I’ve cherry-picked for you here today the most common misconceptions revealed in these sessions. Hopefully, this will help dispel some persistent myths that do anyone interested in text analytics a huge disservice…

MYTH 1: Text analytics is synonymous with social media monitoring

As I feared, a common misconception about text analytics is that its primary application—and pretty much the extent of its practical utility—is for analyzing social media data. Nothing could be further from the truth!

While social media monitoring firms have done a great job marketing themselves, this is just ONE SMALL SUBSET of data that text analytics can be used to solve for. Moreover, while everyone seems fixated on social media analysis, in my honest opinion, social media monitoring is NOT where the greatest opportunity lies for using text analytics in market research.

And a word of caution: yes, text analytics platforms can easily handle social media data, but the same cannot be said about social media monitoring tools, so be careful not to limit yourself.

MYTH 2: Text analytics are perfect for analyzing qualitative transcripts

I cannot tell you how often I’ve been approach by researchers who want to use text analytics software to analyze focus group transcripts. My first response is always why would you want to do that?

Just because focus group data contains a lot of text doesn’t mean you should run it through a text analytics platform, unless you have very large qualitative communities or run the same exact group 10 times within a category.

Bear in mind, text analytics can be applied quite effectively to small samples (I actually didn’t think so until I learned otherwise from a client), but using small sample IDIs or focus groups doesn’t typically make a lot of sense because text analytics is all about pattern identification.

If you talk to just 15 physicians, for example, you’ll still need to read each of their comments. Text analysis may add additional value, but usually it isn’t worthwhile UNLESS you either have a large enough sample to mine for patterns AND/OR the data is extremely important/valuable (e.g., these are the top 15 MD PhDs in their field working on a life-saving cure).

MYTH 3: Sentiment is REALLY important and useful

Sentiment has been COMPLETELY hyped. In the majority of our text analytics projects sentiment isn’t even a factor. In fact, some firms purporting to offer “text analytics” only offer sentiment analysis. This is unbelievable to me. Having worked with text analytics for the past 15 years I don’t understand why someone would approach data that simplistically. There are so many other, potentially more useful and valuable ways to look at data.

When thinking about text analytics, relevant feature/topic extraction is most important. As important is how this can be turned into actionable advice or a recommended course of action. If you analyze data and come back to management with something as simplistic as “this is what makes people angry,” or happy, chances are you’ll soon be replaced by someone who can tell management how to increase return behavior and revenue.

MYTH 4: Look for AI and Machine Learning

I’ve blogged about this before, and it still drives me nuts!

Everyone seems hung up on this year’s buzzwords—“artificial Intelligence” (AI) and/or “machine learning”—and just about every possible vendor is touting them, whatever the solution they’re selling. For your purposes, I’m telling you they are meaningless.

This is not to say that AI and machine learning are not important—in fact, they’re integral components to the OdinText platform—but they’re terms that are misused, abused, and thrown about cavalierly without any explanation as to how or why they matter. If someone tells you their tool uses AI or machine learning, ask them what they mean by “AI” specifically and to explain precisely how that enables their tool to deliver differentiated results. I’ll wager you’ll walk away from that conversation without any better understanding of why AI is a feature they’re touting than you did before the conversation began. (For more information on this topic, again, read this post.)

Beware also other technical-sounding terms (including sentiment, mentioned above) that frequently crop up around text analytics like NLP (natural language processing), ontologies, taxonomies, support vector machines… I could go on.

If a sales person is throwing jargon like this at you, chances are they are using it to conceal their own lack of knowledge about text analytics.

Conversations should instead focus on: How do I quickly identify the most important topics/ideas mentioned by my customers? How do I know they are important? How do they affect my KPIs? Show me with my data how I can quickly do these things.

MYTH 5: All text analytics are basically the same

Text analytics are not a commoditizable, standardized sort of item. Unlike the deliverables from panel companies or survey vendors, the variety of potential forms text analytics can take is diverse and complex, ranging from more linguistically-based approaches to more mathematical/statistical solutions.

Beyond this, though, practical experience in the given field of application also comes into play. What experience do the developers have in answering problems in your specific field? This will impact underlying thinking as well as user interface considerations.

DO NOT assume that just because a feature is listed in one company’s sell sheet (see buzzwords above, for example), it is a must-have or even a good-to-have, and that you should look for it across vendors.

Again, always fall back to your own data. How does this software tell me how customer group A is different than Group B? How will I know the impact of topics X, Y and Z on sales? These are the questions to ask.

MYTH 6: Text analytics is as easy as just pressing a button and may be totally automated

I’m sorry, but again, no.

On the one hand there are extremely involved and expensive mechanical turk solutions you can purchase. Typically, using one of these solutions will require a few months to build a static dictionary for, say, your customer satisfaction data set, which is then dashboarded. You can easily expect to pay mid-six figures for something like this, and it won’t allow you to do any ad hoc analysis.

The other option is a pure AI/Machine learning solution like IBM’s Watson. It’s fast and cheap because it’s not valuable. (If it were, then IBM could charge a lot more for it.) Look for their case studies and actual customers who have been happy with their solutions. You won’t find many, if any.

Included in the same category as IBM Watson are Microsoft Azure, Amazon AWS and Google NLP tools, as well as vendors that do other things (surveys etc.); plug into one of these and they’ll claim they have “text analytics.” But these tools will not get management what it needs to make intelligent decisions.

The optimal solution is somewhere in between, where machine and human meet in the most effective and intuitive manner. This will mean high-value analysis. What you get back in terms of value of insights depends on the quality of data and the analytical thinking brought to bear by the analyst—just like on any quantitative data project!

MYTH 7: There are lots of great resources for learning about text analytics

Sadly, the net of these IIEX groups on Monday was that it became clear to me that we still don’t have ANY solid educational or training resources devoted to text analytics in this industry. NONE!!!

MR trade orgs don’t offer any; the top masters and MBA programs in research don’t offer much; Burke Institute (whose training I love, by the way) doesn’t offer any...

There aren’t any good books on the subject, either; they’re either way too academic and 10+ years behind, or they’re sales tools in disguise, or it’s just a chapter in a book written by a research generalist who does not specialize in text analytics.

We need educational and training resources rather desperately, it seems.

I plan on continuing to do my part by lecturing on the subject at a few MBA classes each year. I’ve also offered to work with the Burke Institute and the University of Georgia’s Terry School’s Master of Marketing Research program on developing resources.

BUT in the meantime, if you have any questions about text analytics, generally, and totally apart from OdinText, please consider me a resource. Feel free to ping me on LinkedIn or via the info request button here.

I hope this was helpful. Thanks for reading and I welcome your comments!

@TomHCAnderson

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.

OdinText Voted #1 Most Innovative Market Research Company in North America and #4 Worldwide!

GRIT Industry Survey Ranks OdinText #1 Most Innovative Market Research Services Provider in North America and #4 Worldwide, Up 32 Places to Become Fastest Rising Company! I am thrilled to announce that the GreenBook Research Industry Trends (GRIT) Report just came out today and the industry has ranked OdinText the #1 most innovative research services provider in North America and the #4 most innovative research company in the world!

GRIT Top 5 Marketing Research Firms 2017

It’s only been one year since we first debuted on the list—and only two years since OdinText launched—and we’ve already jumped 32 spots, making us the fastest rising company on the list.

As a start-up, it’s a huge honor to appear alongside venerable research giants like Nielsen and Ipsos. We’ve come a long way in an incredibly short time, but to be ranked as the most innovative research provider in North America by members of the industry really raises the bar for us.

I’m so very grateful to our users and fans for voting for us, but honestly our research industry clients are the real innovators. We simply provide the tool; you make the magic happen. I’m frequently blown away by the creativity many of you bring to bear using OdinText to unearth insights in ways even I hadn’t thought of.

Thanks also to GreenBook Blog’s Editor-in-Chief and Publisher of GRIT Lenny Murphy for all of his hard work and for calling OdinText “a stand-out example of a technology-enabled solution based on established, applied research principles” and “definitely one to watch!” (Check out our press release for more details.)

Lastly, my congratulations to the other fantastic and innovative research providers named to the GRIT Top 50. All of the companies on this list are worth a close look, and some of the new up-and-comers may be top the list in coming years (check all 50 firms and the full report here).

Thanks again for your support and congratulations to the GRIT Top 50!

@TomHCAnderson

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.