Posts tagged Remesh
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!

 

IIEX 2016 Competition Showcases Innovation in Market Research

Artificial Intelligence, Mixed Data Analytics and Passive Listening Capture Minds - 2016 Insight Innovation Exchange

I’m just back from the IIEX conference in Atlanta, where OdinText competed in the Insight Innovation Competition. Although I was disappointed that we didn’t win, I’m pleased to report that the judges told me we placed a very close second.

IIeX 2016

IIeX 2016

Attending conferences like this affords me the opportunity to get a pulse on the industry, and I was struck by the fact that text analytics are no longer viewed as a shiny new toy in market research. In fact, as someone who has been working in the natural language processing field for so long, it’s actually somewhat remarkable to see how perceptions of text analytics have matured over just the last year.  Text analytics have become a must-have, and the market has a new wave of healthy competition as a result, which I think is further evidence of a healthy market.

Since OdinText goes beyond just text data and incorporates mixed data—text and quantitative—in our competition pitch we highlighted OdinText’s ability to essentially enable market researchers to do data science.

I strongly believe making data science more accessible is a huge opportunity that OdinText is uniquely positioned to solve, and it’s an area where market researchers can step up to meet a desperate need as we currently have a shortage of about 200,000 data scientists in the US alone.

(Check out this 5-minute video of my IIEX competition pitch and let me know what YOU think!)

Download PDF

Download PDF

You are also most welcome to download a PDF of the PPT presentation >>>

“Machine learning” appears to be the new buzz phrase in research circles, and at IIEX I was hard pressed to find a single vendor not claiming to use machine learning in some respect, no matter where on the service chain they fit. Honestly, though, I got the sense that many use the term without entirely understanding what it means.

We continue to leverage machine learning where it makes sense at OdinText, and there are a few other vendors out there who also clearly have an excellent grasp of the technique.

One such company—which took first place in the competition, in fact—was Remesh. They’re actually using machine learning in a very unique and novel way, by automating the role of an online moderator almost akin to a chat bot. They’ve positioned this as AI, and to replace humans completely with a computer is a holy grail for almost any industry.

I’m optimistic on AI in my field of data and text mining as well, but we’re still a ways off in terms of taking the human out of the mix, and so our goal at OdinText is to use the human as efficiently as possible.

While totally automating what a data scientist does is appealing, in the short term we’re happy with being able to allow a market researcher to do in a few hours what would take a typical data scientist with skills in advanced statistics, NLP, Python, R and C++ days or weeks to do.

Still I admit the prospect of AI replacing researchers completely is an interesting one—albeit not necessarily a popular one among the people who would be replaced—and it’s an area that I’m certainly thinking about.

Third place in the competition I understand was Beatgrid Media, which leverages smart phones (without using almost any battery life) to passively listen to audio streams from radio and TV and overlaying geo demographics with these panelists’ data to better predict advertising reach and efficacy. This is admittedly going to be a very hard field to break into by a start-up as there are many big players in the space who want to own their own measurement. And so this may have been one of the reasons Beatgrid had trouble taking more than third, even though they admittedly have some very interesting technology that could perhaps also be applied in other ways.

Let me know what you think!

(And if you’re interested in a demo of OdinText, please contact us here!)

Tom H.C. Anderson | @TomHCanderson | @OdinText

Tom H.C. Anderson

Tom H.C. Anderson

To learn more about how OdinText can help you understand what really matters to your customers and predict actual behavior,  please contact us or request a Free Demo here >

[NOTE: Tom H. C. Anderson is Founder of Next Generation Text Analytics software firm OdinText Inc. Click here for more Text Analytics Tips]