Posts in Software
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

Best 10 Text Analytics Tips Posts of The Year

Our Top 10 Most Read Data and Text Mining Posts of 2017

Thank you for reading our blog this year. The OdinText blog has quickly become even more popular than the Next Gen Market Research blog, and I really appreciate the thoughtful feedback we’ve gotten here on the blog, via Twitter, and email.

In case you’re curious, here are the most popular posts of the year:

#10 NFL Players Taking a Knee is More Complex and Polarizing Than We Think If a Topic is Worth Quantifying – It’s Also Worth Understanding The Why’s Behind It

#9 Text Analytics Picks The 10 Strongest Super Bowl Ads New Text Analytics Poll Shows Which Super Bowl Ads Really Performed Best

#8 Why Your HR Survey is a Lie and How to Get The Truth OdinText Discovers Job Satisfaction Drivers in Anonymous Employee Data

#7 Of Tears & Text Analytics (An OdinText User Story – Text Analytics Guest Post (AI Meets VOC))

#6 65 CEO’s Share Thoughts on Insights (Insights Associations Inaugural CEO Summit – A Future Tied to Collaboration and Technology)

#5 Why Machine Learning is Meaningless (Beware of Buzzwords! The Truth about ‘Machine Learning’ and ‘Artificial Intelligence’)

#4 Do You Speak Teen? OdinText Announces 2nd Annual List of Top 10 Slang Terms (How Text Analytics Can Help Marketers Move at the Speed of Slang)

#3 Text Analysis Reveals Potential French Election French Election Upset (Text Analytics Poll Showed How Close Le Pen Came to ‘Trumping’ Macron)

#2 Text Analytics Poll: Why We Unfriend on Facebook (You Can’t Handle The Truth (And Other Top Reasons Why We Unfriend on Facebook)

#1 What Americans Really Think About Trump’s Immigration Ban and Why (Text Analysis of What People Say in Their Own Words Reveals More Than Multi-Choice Surveys)

 

I thought I’d also check what our top 5 posts were from last year as well, here they are in case you missed them:

Top Posts From 2016

#1 Text Analysis Answers Is The Quran Really More Violent Than The Bible (3 Parts)

#2 Attensity Sold – What Does it Mean?

#3 Customer Satisfaction Surveys: What do Satisfied VS Dissatisfied Customers Talk About?

#4 What’s Really Wrong With Polling?

#5 What Your Customer Satisfaction Research Isn’t Telling You

Thanks again for reading and commenting. As always I welcome your thoughts and questions via LinkedIn, or feel free to request info on anything you’ve read above here.

Happy New Year!

@TomHCAnderson

Artificial Intelligence in Consumer Insights

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

 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

After that it’s all marketing and brand positioning.

 

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

The best data and text mining platform in marketing research can be yours free for the rest of 2017!

Commit to Text Analytics in 2018, and Start Free Today

We believe market researchers make the best data scientists. That is one of the reasons why I have decided to encourage as many market researchers as possible to take the plunge, and start using text analytics in 2018.

In order to encourage my colleagues to pull the trigger before the new year, we are offering free training and use of OdinText for the remainder of 2017, for anyone who begins their license in January of 2018.

Have you reached an insight plateau in your organization?   Growing research departments and organizations ensure that they are properly leveraging ALL of their data.

OdinText is different than other “text analytics software”. OdinText has 10 patent claims on mixed data analysis. In other words, you can upload your entire data files with ALL the variables in your data file into OdinText (comments, Likert scales, demographics, behaviors). Our easy interface, AI, visualizations and Predictive Analytics guide you to the important insights.

You can quickly begin to understand critical issues such as:

  • Which concept is performing best and why?
  • How can we decrease customer churn?
  • What does customer segment A really want?
  • What are the top-5 priorities driving satisfaction?
  • How can I increase return rate?

But don’t take my word for it. Let us show you with your own data!

Request More Information for a No Obligation Demo today, and find out why OdinText is the only text mining platform which has been recognized with awards from every market research trade organization, and why we were voted most innovative firm in North America in the 2017 GRIT Report!

If you like what you see and decide to license OdinText in 2018, we’ll get you trained and using OdinText for free in 2017! The faster you act, the more time you’ll have to give your organization value today.

We’ll have you trained and ready to kick off 2018 with a bang!

Click here for more information. The faster you act the more time you’ll have with OdinText.

-Your Friends @OdinText

 

PS. We haven’t forgotten about you our valued users. Look out for a special emails from us this quarter as we share our appreciation with you and more information on upcoming features.

Leif Erikson + Text Analytics: What’s the Connection?

What Data Explorers and Researchers Can Learn from Leif Erikson and Norse Mythology You may or may not be aware that Leif Erikson Day will be observed in the U.S. this October 9th. The holiday commemorates the discovery of North America by the Norwegian explorer for whom it’s named (long before Christopher Columbus took his voyage).

In Leif’s honor and the spirit of exploration, I thought this would be a good opportunity to indulge in two subjects near and dear to my heart: Norse mythology and text analytics.

Now you’re probably wondering what possible connection Norse mythology could have with text analytics. The answer lies in the name OdinText.

To my dismay, I’ve come to realize that very few people outside of Scandinavia and Germany share my enthusiasm for Norse mythology. And not infrequently I get asked what our name is all about.

So today I’m going to explain the connection for the edification and enlightenment of all!

odintexta

Odin: The One-Eyed God of Wisdom

OdinText derives its name from the Norse god, Odin, a figure similar in respects to the Greek god Zeus in that he’s something of a patriarch to a brood of other ancient deities.

Like his Greek counterpart, Odin is associated with specific human attributes and phenomena, chiefly battle and wisdom. It’s the latter quality that inspired us to adopt Odin’s name.

Legend has it that Odin—who is always depicted as missing an eye—sacrificed his eye in exchange for the “Wisdom of Ages.”

He is also said to have been attended by two ravens—Huginn (translation: “thought”) and Muninn (“memory” or “mind”)—who scoured the world each day for news and information and then reported it back to Odin each night.

Finally, Odin is credited with creating the runic alphabet, which until it was supplanted by Latin around 700 AD, was the source of text for many Germanic languages.

odintextravens3

Two Ravens and Breakthrough Insights

The notion of Odin’s two intelligence-gathering ravens appeals to me as a metaphor for what I believe is the ultimate application of text analytics: to answer questions we don’t know to ask.

Of course, text analytics are perfectly suited to answer any questions we can think to pose, but in today’s uncertain and rapidly changing environment, insight that leads to true competitive advantage often lies in the questions we don’t know to ask.

You may be familiar with the term “dark data,” for example, which was originally defined by Gartner as all of the data organizations collect in their daily operations that goes unexploited. That definition has now been expanded to include the ocean of data being generated by people every day, more than 80% of which is text-based.

Today, thanks to software like OdinText, we have the opportunity to scour and mine these oceans of text data for what we don’t know we don’t know. I’m talking about genuine, breakthrough insights, the sort that are discovered and not the product of a precision hunt.

So in celebration of Leif Erikson, the explorer, I urge you to join me in pursuit of discovering the unknown. You already have the data. Let’s put it to use.

You’ll be happy to know that unlike our friend, Odin, you won’t need to give an eye to acquire this knowledge. Just an hour of training.

Contact us for an OdinText demo today!

Yours faithfully,

Tom

TOM DEC 300X250 Tom H. C. Anderson OdinText Inc. www.odintext.com 888.891.3115 x 701

ABOUT ODINTEXT OdinText is a patented SaaS (software-as-a-service) platform for natural language processing and advanced text analysis. 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 CEO 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 is the recipient of numerous awards for innovation from industry associations such as ESOMAR, CASRO, the ARF and the American Marketing Association. He tweets under the handle @tomhcanderson.

How to Be a Text Analytics Rock Star in your Organization: 5 Steps

5 MUST DOs to SUCCESSFULLY Implement Text Analytics Software and Maximize its Potential in your Company! Introducing a new technology or approach for generating insights in any organization can be more challenging than one realizes. There is a succession of hurdles to overcome if you really want to achieve traction and make a lasting impact, and a misstep in any one of these can doom an otherwise promising new addition to your insights arsenal.

In fact, one of the top questions I get from managers these days is how to effectively implement something like text analytics software in the organization. The process begins well before a technology solution has been selected.

I’ve spoken with hundreds of users over the past few years at different types of companies of varying size in various industries. This post is based on those conversations, and to keep it simple—although we support a host of internal functions and disciplines—I’ll focus on one of the most popular and arguably best use cases: customer listening (marketing research).

1 Establishing Text Analytics Need

1 Establishing a Need for Text Analytics (Do you have mixed data?)

This one may sound obvious, but unfortunately it isn’t. I often talk to prospective text analytics users who want a software demo, but they don’t have a data set to use in the demo. In other words, they haven’t thought far enough along to determine specifically what data they would actually analyze using text analytics software.

Almost any company of middle-market size or above—especially if they are consumer-facing—will have data from various sources of VOC (Voice of Customer) that would be perfectly suited for text analytics, and which, it goes without saying, are not being exploited to their full insights potential. These data may be small or large, more or less frequently collected, and longitudinal or ad hoc in nature. Sources include survey data, customer feedback and email, online research community threads, and call center transcripts (to name just a few).

The point is, wherever there is at least one unstructured/text “comment” field in a dataset, there is an opportunity to tremendously enrich analysis by leveraging this data. Furthermore, most of the time, truly valuable data consists of some mix of structured and unstructured data (i.e., text and numeric data).

Inventory the data you already have and identify which data sets look ripe for exploring with text analytics. Then select one that fairly represents the data you expect to analyze with your future tool and use this data set for your demos.

What about social media?

Yes, social media is an increasingly popular source of data that text analytics users are eager to analyze. I’ll emphasize here that when people refer to social media data, social listening, etc., in the context of research, they are almost exclusively talking about Twitter data (sometimes without even realizing it).

When I sit down with clients, I prefer to distinguish and separate social/Twitter from the aforementioned other sources of data because traditional sources have already proven themselves valuable enough to collect and analyze on an ongoing basis. This is often not the case with social media/Twitter data.

Many people are under the impression that Twitter will yield a goldmine of insights. In actuality, the extent to which Twitter has any meaningful insights value is limited and depends highly on the category/industry. In fact, many CPG companies will find very little of interest on Twitter, while high-ticket service industry brands may find a bit more.

The point is that if your company has not yet collected or looked at social/Twitter data, it’s probably not critically important for you (and it shouldn’t be the primary reason you adopt text analytics).

Moreover, if you have already determined that social media is actually important, then you should be able to articulate more than one research objective around what you expect to be able to answer with that data. If you cannot, then social/Twitter data will probably not provide a good insights ROI for your organization, and I would strongly suggest that you focus on a traditional data set first.

Bottom line: Get your feet wet with text analytics using data that has already shown clear value!

2 Text Analytics Users

2 Identify the Text Analytics Software User/s (The “Analytics” in Text Analytics Requires an Analyst!)

Good text analytics is two parts science and one part art, therefore it will require a human analyst. It’s incumbent upon you to figure out who that person/s will be.

What you need to know about artificial intelligence and machine learning…

This may come as a shock to some folks, as many people have been led to believe that software leveraging artificial intelligence and machine learning effectively removes the need for a human analyst. This is utter nonsense.

IBM’s Watson comes to mind here. I’m not picking on Watson, but the notion that analyses that will produce meaningful insights can be completely conducted without a human analyst by a human-like computer that will automatically intuit everything and anything about any dataset is a complete fallacy and a PR gimmick.

I’ve blogged about AI & machine learning before here. Luckily for those of us in market research, a human analyst still needs to be involved for anything meaningful or useful to come from the analysis. (I say “luckily” because if this weren’t the case you and I would both be out of a job! But don’t worry; human analysts will not be replaced by machines any time soon.)

Back to identifying a human user…

Having hopefully dispelled any myths about not needing a human analyst, I want to emphasize that this does not and should not mean that you need to hire a data scientist. On the contrary, if the tool requires expertise in scripting, for example, chances are it’s not very intuitive and more of a programming tool better suited to academics.

Good text analytics tools for researchers should provide immediate applied value, and allow a common business practitioner to start analyzing any data set right away or with minimal training (I usually recommend about an hour or two). With a good tool, text analytics will be learned in the trenches using actual data for actual analysis that has real value for your company.

So who will this analyst be? Who will use the software? How much will they use it? Surprisingly a lot of companies get tripped up at this step, too, which overlaps with step #3.

Hint: Unicorns don’t exist!

They say a camel is a horse designed by a committee, but in my experience the enterprise designs a unicorn. The user should never be “everyone.”

Many companies—especially those in which procurement departments play a significant role in the decision—tend to oversimplify steps #1 and #2, and these buyers are more likely to fall for sweeping marketing promises by providers that claim to offer an one-in-all solution for everyone that can do anything.

Frequently in these cases, a long wish list of feature/attributes is compiled by a committee, often by adding wishes from various potential users in different functions across the company or by cobbling together features from very different types of text analytics software. This list ends up looking pretty unrealistic and usually calls for a solution that is suitable for all kinds of data, even calling for some sort of imaginary “merging” ability of completely non-complimentary data that do not have any common unique identifiers or even meta-level merge fields.

This theoretical software is also supposed to be equally useful for marketing, marketing research, customer service, sales, HR, PR, operations, and legal departments, and, of course, IT, too. Not only that, but it must be simple enough for everyone to understand the output without any training or prior analytical knowledge (i.e., static dashboards).

This is an insane expectation!

Applying the same logic, imagine if a hospital bought tools this way—if doctors across all departments from neurology to obgyn had to settle for, say, one scalpel. Oh, and by the way, it should also be useful for the maintenance department, because, after all, they need to cut things, too (like electrical wires or plumbing). This universal scalpel should also be useful for the administrative staff, because they have envelopes. A scalpel should be able to open an envelope, right?

Here’s the frank talk: if you put together a $150-$500K RFP, someone will answer it and claim to have the perfect one-size-fits-all universal scalpel. Good luck with that. (I feel especially sorry for the patients.)

There is no one-size-fits-all product. A text analytics decision should be handled at the department level according to that department’s unique data, objectives and staffing needs.

Will YOU be using it? Then you are the user. Congratulations!

3 Text Analytics Software

3 Identify a Text Analytics Software Solution

You’ve identified that you have data of value, and that you have at least one user to whom this new task will fall and for which they will be directly responsible. Now it’s time to find the right tool for this user with the best ROI.

Provided you’re not looking for the mythical unicorn I mentioned in step 2, this step should be an easy one.

ALWAYS request a demo with your own data. Text analytics software providers should be happy to sign a mutual NDA; in fact, most enterprise companies require it. This MNDA covers your data and any discussions regarding your business, as well as the IP of the software provider, so it’s a win-win.

Why is this so important? Anyone can put a mock demo together on a mock data set and make it look like it works. The ONLY way to evaluate a software provider is to do so with your own data—data that you are familiar with and that is relevant to you and your business objectives.

One more thing (touched on in step 2): You should approach vendors with an open mind each time. Do not use one vendor’s approach as the basis for assessing another vendor; judge them based on actual output. Does the software have all the features needed to discover/answer your business questions and meet your objectives AND is it easy to learn and use?

One more important tip…

Do NOT allow the vendor to have a lot of time beforehand with your data. If they do you will have no idea how much time they put into setting the demo up. For a $250K contract, a company might well invest two full-time analysts across the span of a couple of weeks to make your demo impressive. Sadly, they may even use “mechanical Turk” (human) coding.

I would advise allowing a vendor no more than a day or two with your data, so make sure to schedule the demo within a day or two of giving them the data. In some instances we’ve even been asked to do the demo the same day, or just an hour or two before receiving said data. Which data is that? The data we chose in step 1, of course!

4 Text Analytics Output

4 Expect Immediate and Ongoing Results

Congratulations! You’ve purchased your software, and hopefully you’ve received some basic training. Ideally you’ve begun using the software right away after the training.

You won’t be a text analytics master on day one, but if you have real data and real objectives and at least one person is responsible for using the tool (and that means that they will have at least a few hours per month for this purpose), then you are in very good shape.

By the way, if you are just getting started with text analytics and/or you have staffing issues, some text analytics vendors may be able to offer you some initial support and be available for special request ad hoc analysis and/or be able to suggest trusted third-party agencies who are trained in use of their tool to help you out in those cases.

Hopefully you didn’t buy the dashboard-only solution—the one everyone uses on all data with no analytical firepower. Instead, you were informed enough to select  the tool that does what you need it to do using your data whenever you need it. Now you’re able to answer business-critical questions in new ways and management will take notice!

5 Socializing Text Analytics

5 Socializing Text Analytics Findings (Recognition and Growth)

This last step is often neglected. It’s only fair that you get noticed for your smart software decision, and more importantly for the incredibly useful insights that you generate using text analytics. Often formerly stale data will come alive, and unstructured data usually has better predictive power than structured data.

Be prepared to evangelize your findings, and don’t be afraid to ask your software provider for suggestions about how to do so. In some cases, an initial small use case in one department ends up spreading to other departments. HR comes to marketing research asking, “Hey, I heard about that analysis you did. We think this data is kind of similar. Would you take a look at it?”

And then there are more formal opportunities, of course, if you are willing to share a case study in an article or conference presentation. The latter are not any more important than the former; in fact, the former is how you will ultimately be judged more immediately.

I hope the above was helpful. Please reach out if you have questions about any of the steps above. I would, of course, be honored if you included us in your process when you get to step 3, and happy to discuss steps 1 and 2 with you before that as well! Contact us to talk about it. 😊

Good luck!

@TomHCAnderson

Text Analytics Software Tom H C Anderson

 

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.

 

 

 

Poll: What Other Countries Think of Trump’s Immigration Order

Text Analytics PollTM Shows Australians, Brits, and Canadians  Angry About Executive Order Temporarily Barring Refugee (Part II of II)In my previous post, we compared text analysis of results from an open-ended survey instrument with a conventional Likert-scale rating poll to assess where 3,000 Americans really stand on President Trump’s controversial executive order temporarily barring refugees and people from seven predominately-Muslim countries from entering the U.S.

Today, we’re going to share results from an identical international study that asked approx. 9,000 people—3,000 people from each of three other countries—what they think about the U.S. immigration moratorium ordered by President Trump.

But first, a quick recap…

As I noted in the previous post, polling on this issue has been pretty consistent insomuch as Americans are closely divided in support/opposition, but the majority position flips depending on the poll. Consequently, the accuracy of polling has again been called into question by pundits on both sides of the issue.

By fielding the same question first in a multiple-choice response format and a second time providing only a text comment box for responses, and then comparing results, we were able to not only replicate the results of the former but gain a much deeper understanding of where Americans really stand on this issue.

Text analysis confirmed a much divided America with those opposing the ban just slightly outnumbering (<3%) those who support the order (42% vs 39%). Almost 20% of respondents had no opinion or were ambivalent on this issue.

Bear in mind that text analysis software such as OdinText enables us to process and quantify huge quantities of comments (in this case, more than 1500 replies from respondents using their own words) in order to arrive at the same percentages that one would get from a conventional multiple-choice survey.

But the real advantage to using an open-ended response format (versus a multiple-choice) to gauge opinion on an issue like this is that the responses also tell us so much more than whether someone agrees/disagrees or likes/dislikes. Using text analytics we uncovered people’s reasoning, the extent to which they are emotionally invested in the issue, and why.

Today we will be looking a little further into this topic with data from three additional countries: Australia, Canada and the UK.

A note about multi-lingual text analysis and the countries selected for this project…

Different software platforms handle different languages with various degrees of proficiency. OdinText analyzes most European languages quite well; however, analysis of Dutch, German, Spanish or Swedish text requires proficiency in said language by the analyst. (Of course, translated results, including and especially machine-translated results, work very well with text analytics.)

Not inconspicuously, each of the countries represented in our analysis here has an English-speaking population. But this was not the primary reason that we chose them; each of these countries has frequently been mentioned in news coverage related to the immigration ban: The UK because of Brexit, Australia because of a leaked telephone call between President Trump and its Prime Minister, and Canada due to its shared border and its Prime Minister’s comments on welcoming refugees affected by the immigration moratorium.

Like our previous U.S. population survey, we used a nationally-representative sample of n=3000 for each of these countries.

Opposition Highest in Canada, Lowest in the UK

It probably does not come as a surprise to anyone who’s been following this issue in the media that citizens outside of America are less likely to approve of President Trump’s immigration moratorium.

I had honestly expected Australians to be the most strongly opposed to the order in light of the highly-publicized and problematic telephone call transcript leaked last week between President Trump and the Australian Prime Minister (which, coincidentally, involved a refugee agreement). But interestingly, people from our close ally and neighbor to the north, Canada, were most strongly opposed to the executive order (67%). The UK had proportionately fewer opposing the ban than Australia (56% vs. 60%), but the numbers of people opposed to the policy in both countries significantly lagged the Canadians. Emotions Run High Abroad Deriving emotions from text is an interesting and effective measure for understanding people’s opinions and preferences (and more useful than the “sentiment” metrics often discussed in text analytics and, particularly, in social media monitoring circles).

The chart below features OdinText’s emotional analysis of comments for each of the four countries across what most psychologists agree constitute the eight major emotion categories:

We can see that while the single highest emotion in American comments is joy/happiness, the highest emotion in the other three countries is anger. Canadians are angriest. People in the UK and Australians exhibit somewhat greater sadness and disgust in their comments. Notably, disgust is an emotion that we typically only see rarely in food categories. Here it takes the form of vehement rejection with terms such as “sickened,” “revolting,” “vile,” and, very often, “disgusted.” It is also worth noting that in cases, people directed their displeasure at President Trump, personally.

Examples:

"Trump is a xenophobic, delusional, and narcissistic danger to the world." – Canadian (anger) “Most unhappy - this will worsen relationships between Muslims and Christians.” – Australian (sadness) "It's disgusting. You can't blame a whole race for the acts of some extremists! How many white people have shot up schools and such? Isn't that an act of terror? Ban guns instead. He's a vile little man.” –Australian (disgust)

UK comments contain the highest levels of fear/anxiety:

"I am outraged. A despicable act of racism and a real worry for what political moves may happen next." – UK (fear/anxiety)

That said, it is also important to point out that there is a sizeable group in each country who express soaring agreement to the level of joy:

“Great move! He should stop all people that promote beating of women” – Australian (joy) “Sounds bloody good would be ok for Australia too!” – Australian (joy) “EXCELLENT. Good to see a politician stick by his word” – UK (joy) “About time, I feel like it's a great idea, the United States needs to help their own people before others. If there is an ongoing war members of that country should not be allowed to migrate as the disease will spread.” – Canadian (joy)

Majority of Canadians Willing to Take Refugee Overflow Given Canada’s proximity to the U.S., and since people from Canada were the most strongly opposed to President Trump’s executive order, this raised the question of whether Canadians would then support a measure to absorb refugees that would be denied entrance to the U.S., as Prime Minister Justin Trudeau appears to support.

(Note: In a Jan. 31 late-night emergency debate, the Canadian Parliament did not increase its refugee cap of 25,000.)

 

A solid majority of Canadians would support such an action, although it’s worth noting that there is a significant difference between the numbers of Canadians who oppose the U.S. immigration moratorium (67%) and the number who indicated they would be willing to admit the refugees affected by the policy.

When asked a follow-up question on whether “Canada should accept all the refugees which are turned away by USA's Trump EO 13769,” only 45% of Canadians agreed with such a measure, 33% disagreed and 22% said they were not sure.

Final Thoughts: How This Differs from Other Polls Both the U.S. and the international versions of this study differ significantly from any other polls on this subject currently circulating in the media because they required respondents to answer the question in a text comment box in their own words, instead of just selecting from options on an “agree/disagree” Likert scale.

As a result, we were able to not only quantify support and opposition around this controversial subject, but also to gauge respondents’ emotional stake in the matter and to better understand the “why” underlying their positions.

While text analysis allows us to treat qualitative/unstructured data quantitatively, it’s important to remember that including a few quotes in any analysis can help profile and tell a richer story about your data and analysis.

We also used a substantially larger population sample for each of the countries surveyed than any of the conventional polls I’ve seen cited in the media. Because of our triangulated approach and the size of the sample, these findings are in my opinion the most accurate numbers currently available on this subject.

I welcome your thoughts!

@TomHCAnderson - @OdinText

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.

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.