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.