Chueyee Yang

Ep. 226 – Emmet Ó Briain on the Right way to use Consumer Insights and how Many Researchers get it Wrong

My guest today is Emmet Ó Briain, founder of Quiddity. Established in 2010, Quiddity offers methodologically innovative research, particularly in the use and analysis of naturally-occurring data (and especially language) in consumer and social research. Prior to founding Quiddity, Emmet spent 6 years at Ipsos as Research Development Director. Additionally, he has recently made a name for himself as the naming guru on LinkedIn. 

Find Emmet Online:

LinkedIn

Email: emmet@quiddity.ie

Website: www.quiddity.ie

Find Us Online: 

Social Media: @happymrxp

LinkedIn

Website: happymr.com

This Episode’s Sponsor: 

This episode is brought to you by HubUx. HubUx reduces project management costs by 90%. Think of HubUx as your personal AI project manager, taking care of all your recruitment and interview coordination needs in the background. The platform connects you with the right providers and sample based on your research and project needs. For more information, please visit HubUx.com.



[00:00]

On Episode 226, I’m interviewing Emmet Ó Briain, founder of Quiddity, but first a word from our sponsor.

[00:09]  

This episode is brought to you by HubUx.  HubUx is a productivity tool for qualitative research.  It creates a seamless workflow across your tools and team.  Originally, came up with the idea as I was listening to research professionals in both the quant and qual space complain about and articulate the pain, I guess more succinctly, around managing qualitative research.  The one big problem with qualitative is it’s synchronous in nature, and it requires 100% of the attention of the respondent. This creates a big barrier, and, I believe, a tremendous opportunity inside of the marketplace.  So what we do is we take the tools that you use; we integrate them into a work flow so that, ultimately, you enter in your project details, that is, who it is that you want to talk to, when you want to talk to them, whether it’s a focus group, in-person, or virtual or IDI’s or ethnos; and we connect you to those right people in the times that you want to have those conversations or connections – Push-Button Qualitative Insights, HubUx.  If you have any questions, reach out to me directly. I would appreciate it. Jamin@HubUx.com

[01:34]  

Hi, I’m Jamin, and you’re listening to the Happy Market Research Podcast.  My guest today is Emmet Ó Briain, founder of Quiddity.  Established in 2010, Quiddity offers methodologically innovative research, particularly in the use and analysis of naturally occurring data, and especially language, in consumer and social research.  Prior to founding Quiddity, Emmet spent six years at Ipsos as research development director. Additionally, he has recently made a name for himself on several of my threads where he has become the naming guru.  Emmet, thanks very much for being on the Happy Market Research Podcast today.  

[02:09]  

No, thanks very much for inviting me.  It’s a distinctly uncomfortable experience, but I’m hoping to…  

[02:20]  

So, the methodology that I use on this show is literally 75% human where we kind of tell our own story and 25% value.  Hopefully, the package is “The medicine can taste great and make us feel a little bit better as well.” 

[02:35]

Well, if it helps, part of my philosophy is that the 75% human is where most of the value comes from and research as well.  So there should be a nice sort of convergence there.

[02:48]

I love that.  Well, let’s start off.  Tell us a little bit about your parents and how did your upbringing inform your career? 

[02:54]   

It’s a difficult question because neither of them were involved in research at all.  Both of them left school very, very early. My dad went back to education in his late 30s, ended up going back to university.  So, as a young kid, this was mind-blowing to me: there was an adult man going to college. I didn’t realize it was an unusual thing to do.  But what it taught was that it’s never too late to learn. The philosophy of life-long learning, I got that from my dad. But also, it’s great value in doing things differently.  Striking out on your own doing things maybe that other people might not encourage you to do or might discourage you to do that if you sort of have the motivation and the incentive that following your own path is rewarding intellectually, spiritually – if not, always financially – but it’s a good way to go.  So I would think that was very much an inspiration for me.   

My mother was quite creative.  She worked. I wouldn’t say her profession defined her, but she was very much into gardening.  And the idea of creative is very important to me in research. And I think it’s important for any business that you’re able to think laterally; you’re able to make connections between things that aren’t obvious.  So, I think the environment that you’re raised in just has such a huge influence on the type of values and the type of things that you see as interesting or that interests you.   

[04:20]  

So, digging in with your dad a little bit, who went back late 30s:  Was the juice worth the squeeze, as they say? Did uh…? 

[04:29]

I have no idea what that means.

[04:30] 

Sorry, so it’s, I guess, an Americanism.  

[04:34]

I was thinking today at the line from George Bernard Shaw.  It’s about Americans and English people being divided by a common language. 

[04:44]

That’s so, so great!  And that would be a perfect segway if I was ready to move on, but I’m not yet.  But talking about your dad, he invested time and money in going back and getting his education or furthering his education.  Was there a positive outcome that warranted that investment?

[05:01]    

He became a teacher then.  

[05:03]

So a complete pivot on his career.  

[05:05]

Oh, no, so he was a fitter, (I don’t know what you call them in America, as again divided by a common language), so installing conveyers and things like that – very manual work.  He took up German and Spanish, went back to university, trained as a teacher, and became a teacher. So it was a vocation, which then, obviously, changed his life.

[05:28] 

Wow!  That is amazing.  That’s a huge transition.  

[05:32]

Yeah, before he was a teacher, he was one of these people, and you meet them in life, that are almost in the wrong jobs.  Maybe, they haven’t found a path into the job they should be in, and sometimes life is just like that. He was lucky enough to find a job that he was really, really motivated by.  

[05:53]

Man, what a significant move, especially later in your life, right?  Being willing to step out and take that kind of a career change. Did he wind up retiring in that profession?

[06:05]

He did.  As you say, it takes a lot, and it’s different worlds as well.  And I think that’s one of the things I’ve always found interesting.  And not to go over: I don’t want to spend 45 minutes talking about my dad and my family.  

[06:20] 

I don’t either.  

[06:21]

Hey, it’s good stuff!  One of the things was that he was born in Ireland, moved to England like a lot of people did at his time for work, then move back to Ireland.  And, when he moved back to Ireland, he was very young, but he had an English accent. So he was an outsider in Ireland. He was always a bit of an outsider.  And, for me, part of my research is very much more about sociology and psychology. So I’m interested in looking at the big picture and where things fit in within the culture.  And I think that sort of outsider-perspective was… (I’m sure he didn’t want to transfer it to me.) But it is something where you try to work out how a culture works and how do you fit in and what are the certain nuances that you have to learn to fit in or that you can even observe.  So, that sort of stuff… It’s the type of thing that a lot of people just pick up instinctively and don’t make their career. People who are successful in any walk of life have to have that sort of sensitivity to environment, but I was always interested from an analytic perspective, always sort of looking on rather than possibly actively participating.     

[07:28]

Right.  Talking about language, Quiddity, you started this business in 2010, I believe.  Tell me about the name.

[07:36]

So, the name is…  One of the things about that…  So, Quiddity means the essence of something, what makes a thing a thing, which is an awful, awful definition of something.  I know you’re interested in entrepreneurship and things like that. So you’ve got the Quiddity, you have an entrepreneur. “What makes an entrepreneur an entrepreneur?”  “How do you know?” “What is it about someone that would make you recognize him as an entrepreneur?” It could be psychological characteristics; it could be the way they hold themselves, the way they interact; the way, I suppose, they are very welcoming of relationships, managing relationships and things like that.  So Quiddity is really about the essence of something.  

[08:20]

Yeah, as soon as I booked this interview with you, I looked it up, and it was, “The essence of a thing,” like getting to the real name.  I got to be honest: I didn’t know what the word meant, being an American.    

[08:37]

No, no, to be honest, most people…  It’s actually a talking point for most people when I either introduce myself or they get in contact with me.  I mean for the first few years it was… I don’t know if you’re (and I won’t judge you if aren’t) but if you’re familiar with Harry Potter.   

[08:54]      

Of course.

[08:54]

In Harry Potter, Quidditch, the sport Quidditch…  Everyone thought that… People would just go, “Quidditch, is that the Harry Potter thing?”  So people thought I was some sort of… I founded a business to make a Harry Potter reference.  I like Harry Potter, but that wasn’t my motivation.   

[09:12]

That wasn’t the intent, no.  The reason I think it’s so relevant for our industry because our industry is bent on human understanding and discovery…  When you kind of pull back, language is the thing that… Embedded in language is culture and connectivity. It’s our lens by which we understand and process the world and really define it.  And, if you look across different languages… (I learned this very early in my career, my very first project that was in America, Europe, and Japan.) And the Japanese translation, I thought it was like the hardest thing for me to get done because every time I would have a translation company do (and these are local people in Japan) do the translation, the client would read the translation and say, “No, this is wrong.”  And so, the point being that there’s a lot of ambiguity around the way that you should ask that question in context of what the intent was and also the person that was going through it. So I started doing a lot of research actually on language at that particular point, not that I’m a linguist or expert on the subject. But it has, as I’ve gotten older, become really apparent to me that language is tool by which, obviously, we communicate, but then, ultimately, understand and process and fit into the world.  So, Quiddity – you came up with the name because you felt like it was specifically addressing what or communicating what to the market place? What was the signal with that name?      

[10:42]  

So, I liked the idea of detailed description.  One of the things that, I think, that market research often does is examines or studies things from the point of view of, say, the client or the corporation or organization that’s trying to understand, which gives a particular perspective, and it sort of emphasizes, I suppose, the corporate priorities or the client priorities, which is absolutely fine.  But there’s another perspective, which is the perspective of, I suppose, sort of the more human-centered but also trying to understand a phenomenon within the broader culture. So there’s two moves: The first move is putting the client’s perspective to the rear and putting the customer’s perspective ahead of it. But then there’s a second move, which is then putting that customer perspective within a broader context.   And I think that helps you get at the essence of something. So it’s just a, you know, like it’s taking research seriously, and it’s trying to promote a perspective that I think is perhaps not used as much within market research.   

[11:55] 

That’s really interesting. Can you give us a specific project?  Obviously, you probably can’t name the client or details of outcome, but like an example of how it’s played out.

[12:08] 

Yeah. So, it’s not incredibly, well, I’ll say it’s not incredibly unusual. It is unusual because it’s such a small perspective of say the overall market research industry, but it’s aligned with broader sort of cultural approaches like semiotics and ethnography. The approach I use is discourse analysis, which is based on language. So what I’m trying to do is I’m trying to study and investigate cultural phenomenon. So they’re the things that are shared by people rather than looking at the things that are like emotions or needs or motivations that are the properties of individuals.  I’m looking at phenomenon that exist within the broader culture. An example of that might be one of the things I do a lot for advertising companies is early stages of creative development or, when they’re working out a brand proposition, they might have a particular idea that they want to see, what sort of conversations are happening within the broader culture.

I said I have a preference for naturally occurring data, and that just means that data that isn’t necessarily provoked by research.  So one of the things that would be online conversations.  So an example would be a car manufacturer.  We’re interested in looking at proposition around electric vehicles.  And the proposition they’d hit on was the car of tomorrow.  Now, I might be misremembering this. It could’ve been the car of the future was the car of tomorrow.  And they wanted me to look at and see what sort of cultural groups or what sort of cultural discourses that resonated with.  And there were three sorts of areas where that idea resonated or didn’t resonate. One was among very environmentally friendly people, who didn’t drive a lot.  Basically, they saw electric vehicles as the car of tomorrow because they saw moving towards, you know, a society which was less reliant on the automobile. So it was more environmentally friendly. So the car of tomorrow actually meant a future that there were fewer cars. There was another group of people that were early adopters. They liked electric vehicles ‘cause they were a new thing and they were a new technology, but they weren’t particularly motivated by the idea that it was new motor technology. They just liked any new technology. So they were also early adopters with computers, earlier adopters with home heating systems, early adopters with anything. Again, the idea of the car of tomorrow, it wasn’t the car that the motivating factor; it was the fact that it resonated with some of the sorts of the culture of early adopters. People who are looking to buy a new car, the car of tomorrow actually had a negative connotation. So one of the discourses around electric vehicles is that the technology is still immature, still not quite mature, you know, for long drives. It’s not robust enough. So the car of tomorrow was something that was actually undermined amongst, you know, sort of the bulk of motorists because they saw the car of tomorrow as being something not for today. So I was looking at essentially the cultural discourses that were happening within society, both say within news reporting, online talk, you know, going to car groups, talking with them and things like that.  It’s this idea that we can get insights from outside the consumer, from outside the client that exists within the culture and they’re the types of things that are sometimes underexamined, I believe.   

[15:27]  

Yeah, that’s such a great illustration of how context is really important in language, informing kind of the, not kind of, exactly the messaging that needs to go out to the market to resonate with them. So there has been a lot of transition inside of market research, and I mean market research as a broad insights category.  So you have obviously consumer experience, which is just blowing up right now, growing at a like a 22% year over year for the next five years is projected.  You have user experience, which has been growing, which is predominantly centered more on qualitative assessments. What do you see as the role of insights in a modern brand and how do you think it’s going to evolve over the next five years?

[16:12]  

Not that I’m a cynic or critic or a contrarian.  (I am a bit partly of all three of those things.)  From my perspective, sometimes I think that insight is used too much as a crutch:  so as a way, as a basis for making decisions rather than a basis for informing decisions.  And I think in the past it’s a phenomenon that’s accelerated.  You know, in the past 10 years, I’d say. There’s very much a managerial orientation towards insights.  So they’re used essentially to make internal decisions a lot of the time.  And what I mean make decisions, I don’t mean someone gets these insights and goes, “Oh, let’s see what we can make of this. Let’s get another bit of evidence.”  I think sometimes the insights are used as a proxy for decision-making. And I don’t think that’s a good thing, and I don’t think that’s the value of insights.

So I think – I’m not entirely sure – but I think there is a recognition that this is happening and there is, you know, a push back against just talking about insights in terms of de-risk, you know, just talking about insights in terms of making things safer or talking about…  And going back to discourse analysis, If you think about always talking about insights as a way of making things safer, of making things secure, of de-risking, it’s very much a perspective that is at odds with an entrepreneurial mindset, which is about taking a gamble, taking a risk, being creative, being innovative.  So I think the language around insights…  Sometimes we have to be careful about how we talk about insights because the way in which we talk about them constructs what they are and determines to a certain degree, how they’re used. One of the things I’d worry about is that we don’t talk about insights as the basis for creativity, as basis for making connections between different ideas and talking about them positively. So, as you said, insights is a broad chart. So, obviously, there are forms of insight and forms of research which absolutely have to be used for security and have to be used for making sure that decisions made are secure and are based on robust evidence. But at the same time, I don’t think that can be all of insights. And I think that the language has to ensure that it covers, you know, the possibility for insights to be a source of creativity and a source of innovation as well.

[18:43]

That’s an interesting point of view. You know, and you’re right that it’s all about data-driven decisions because those are supposedly going to create the best possible outcomes. When you look at the companies that are widely successful, especially recently, you’ve got like Shopify and Zoom, who also did an IPO.  Shopify, of course, I think is in Canada.  So you know, and that’s just two, right? There’s a lot of other ones.  With respect to those IPOs, you listen to the founder’s stories and there wasn’t, I mean, I would say it was more like a stumbling along as opposed to this like real clear, laser-focus point of view on this, Well, I will do this.  This is the input and this will be the output.”

[19:26]   

I think, you know, that’s essentially how I would see…   Certainly, while I do quantitative research and I have a background in quantitative research, most of the research I did, vast bulk, is qualitative.   And one of the things I like about qualitative research is that it’s often easier to have strategic conversations around qualitative research because there isn’t the absoluteness, there isn’t the certainty.  Sometimes quantitative results can be a bit restrictive in what you can do with them. So I like the idea of insights as being, you know, as you said, a sort of, you know.  A lot of entrepreneurs, not that they’re stumbling around, but you know, you don’t necessarily…  There’s a serendipity about, you know, some of the things that happen, big businesses, you know, where, which necessarily can’t be foreseen.  But I liked the idea that insights are used as a way of illumination rather than support.

That’s the old joke about how a drunkard uses a lamppost:  so he uses one for support. We should use them instead for illumination. I would like the idea of insights to be used, you know, more positively. Sometimes I think there is a discourse of risk and a discourse of danger and a threat of, you know, not having enough evidence when the very best evidence will always be, you know, partial.  I suppose is one thing to keep in mind.  People talk about MPS and criticized MPS.  Absolutely, every methodology has its flaws. What’s important is to recognize that no solution, no approach to research is partial.  And really the best thing to do is to be fairly liberal about the range of research approaches that you use. I think that’s sometimes hard when you have a very structured approach to research.

[21:12]  

Yeah, I like that.  I like the squishiness associated with it, but then also the discipline associated with that point of view, right? It’s the quant tells us what and the qual tells us why and really understanding and uncovering and then creating a narrative around the data.  You know, we don’t, nobody has 2.3 kids. It’s hard for us as humans to be able to view the world through a lens of, you know, absolute sort of pie charts and what have you.

[21:42]

You know, all those representations are necessary, but they’re always partial. And I think that’s something that’s sometimes forgotten is that whatever our 100, 200 slides, you know, deck of slides says, it’s always partial.  I think if there’s one thing that I would like the insight industry really to take on is that idea of more of a critical perspective about the limitations of the…  There’s nothing wrong with it, you know, with being partial, you know, being limited.  But I think there’s a…  Sometimes there’s a reluctance to talk about the limitations of particular approaches or methodologies because, you know, we’re operating commercial businesses and no one wants to go out and go, “Hey my, my approach is, is partially flawed.”  It’s not a, a really great proposition.  But that’s the truth.  And I think clients understand that, and I think it’s to have the confidence to say, “Hey, my approach is partial, but it gives you this perspective, and it emphasizes this particular insight.”

And there’s another perspective, which emphasizes this element of insight. They’re different. They could be complementary. Sometimes, they’re even competitive, but I don’t think it’s helped by someone coming along and saying, “Look, I can predict 100% of what all of your customers are going to do in the next week,” because you know it’s not real. It’s not realistic. And clients, you know, are experienced enough. They see enough venders but they know the limitations of different approaches. So I think, you know, there is a degree to which there’s a methodological maturity where we acknowledge that there are limitations to certain approaches.  And I think what that does is it opens up the room for lots of different approaches rather than undermining trust in a particular solution. I think that transparency and that accountability actually promotes, you know, confidence. If you’re acknowledging that there are limitations, then at least somebody you know is going to believe that you’re being transparent. 

[23:36]

So with that framework, are you seeing like your projection over the next five years, you know, your crystal ball, which all researchers like to think we have, right?  Do you think there’s going to be material growth in one specific methodology?

[23:48]

The one thing I think, you know, because I do look at things from a sort of a social, cultural lens, is that I don’t think market research is exceptional, you know, in that it’s not different from any other industry. And I think the current trends and the trend of the last 50 years since the beginning of the market research industry has been a trend towards more automation. I think that’s absolutely, you can’t get away from that. I don’t necessarily think that’s 100% a good thing. And I think one of the things that we’re seeing in other industries, particularly within the tech industry, is more consideration of the broader context for how technology is used. And I think that’s something that could be interesting from an insight perspective. So when you look at AI, and I’m just back from Florence actually, where the ACL conference was on.  I wasn’t there. My wife is a computational linguist, so I have an ear to all these things that are happening in AI. Is that explainable A4I is going to be a huge thing in terms of AI. So there’s a concern that a lot of artificial intelligence systems and the use of algorithms that they’re not necessarily transparent, that we don’t really understand how they work and that they might actually, I suppose, reproduce bias rather than be a very unbiased objective view.  So I think that’s interesting from an insight point of view. So while there will be this emphasis of automation, there’s nothing you can do.  As companies get larger, the incentive and the need to automate more parts of the organization, you know, increases. So I think that’s non-negotiable. HR is automated; finances automated; of course, insights are going to be to be automated.     

But I think parallel to that for the insights industry particularly, I think there will be this need for greater consideration of the transparency of certain approaches. And within AI, that means more sociologists, more ethnographers, more people from the humanities, who are being brought in to complement this very technical, very automated processes to try and add a layer of an interpretability to them and to try and add, I suppose, to try and explain them and help people understand that when they’re using these automated systems, you know what’s going on inside and the sort of assumptions that they’re built on, the sort of assumptions that they emphasize.  So that would be, I suppose I’m saying that because I’m a sociologist and I’m hoping that that will be a big trend, but I do believe that you have to look to other industries to see where the trends are.

[26:27]

I would imagine being married to a computational linguist, you don’t win many arguments.

[26:31]  

Depends what they’re about.

[26:33]

Probably enough said on that subject. So tell us what is your personal motto?  

[26:39]

Oh, God.  Honestly, I, sorry, I did see that question. I don’t, I don’t know. I don’t.  I have, I wouldn’t. I’m not a complicated person, but I am a complicating person. So I tend to ask more questions than give answers. So I couldn’t ever have a single, have a single, oh sorry. What’s my motto?  No, I don’t have a motto. I was going to say something about in… I was going to say something about in the presence of data. So, I even though we’ve had this great conversation, just you know, shooting the breeze, I do like having a piece of data to discuss things while we’re just discussing them. So that’s what I like about insights. One of the things that brought me to market research was when you’re having an argument and you have a big piece of data and you’re using that as, you know, as maybe a starting point for discussion or you know, “What do we make of this?  What does this say?” So, you know, like discuss. I do like discussing things in the presence of data, but that’s certainly not a motto that would be put on my tombstone.  

[27:46]

You know what?  Data creates this like super comfortable.  My worst moment is happy hour, like the social things that happen at events.  That is like, all I want to do is not go to that. But I love sitting down and talking about business problems or, like you said, data or something like that tangible, it creates a security.

[28:06] 

No, that is…  Look, that is my…  I mean I’m very frustrating as a colleague because I do, I only really feel comfortable talking.  I like talking about work. You know, like I’m interested in it so I, you know, I like it. You know, I like talking about work and as you say, you know, there’s a certain security. But actually, you know, you find that a lot of people who are really interested in what they’re doing like talking about work as well.

[28:34]

My guest today has been Emmet Ó Briain, founder of Quiddity.  Thank you, Emmet, very much for joining me on the Happy Market Research Podcast. 

[28:43]

Thank you very much, Jamin.  

[28:44]

Everyone else, if you found value in this episode, please take time: screen capture, share it.  I really appreciate that. It helps other industry professionals like yourself find it. Emmet, it has been a joy.  I will, of course, include your contact information in the show notes, but just in case people don’t click there, how would somebody get in contact with you?

[29:01]

LinkedIn or email Emmet@Quiddity.ie

[29:05]

And, of course, email is probably a really good way to do that, but that information will be on the website and in the show notes.  Have a great rest of your day, everybody. And Emmet, again, thanks.

[29:18]

This episode is brought to you by HubUx.  HubUx is a productivity tool for qualitative research.  It creates a seamless workflow across your tools and team.  Originally, came up with the idea as I was listening to research professionals in both the quant and qual space complain about and articulate the pain, I guess more succinctly, around managing qualitative research.  The one big problem with qualitative is it’s synchronous in nature, and it requires 100% of the attention of the respondent. This creates a big barrier, and, I believe, a tremendous opportunity inside of the marketplace.  So what we do is we take the tools that you use; we integrate them into a work flow so that, ultimately, you enter in your project details, that is, who it is that you want to talk to, when you want to talk to them, whether it’s a focus group, in-person, or virtual or IDI’s or ethnos; and we connect you to those right people in the times that you want to have those conversations or connections – Push-Button Qualitative Insights, HubUx.  If you have any questions, reach out to me directly. I would appreciate it. Jamin@HubUx.com

Ep. 225 – Jenny Karubian – Hacks for Building a Successful Market Research Agency

My guest today is Jenny Karubian, Founder and CEO of Ready to Launch. Founded in 2014, Ready to Launch is a consultancy based in LA that helps firms launch new ideas, products, and technologies that better serve their customers. Jenny has extensive experience as an ethnographic researcher and is a professor of anthropology, sociology and gender studies.

Find Jenny Online:

LinkedIn

Website: https://readytolaunchresearch.com

Find Us Online: 

Social Media: @happymrxp

LinkedIn

This Episode’s Sponsor: 

This episode is brought to you by HubUx. HubUx reduces project management costs by 90%. Think of HubUx as your personal AI project manager, taking care of all your recruitment and interview coordination needs in the background. The platform connects you with the right providers and sample based on your research and project needs. For more information, please visit HubUx.com.


[00:00]

On Episode 225, I’m interviewing Jenny Karubian, founder and CEO of Ready to Launch, but first a word from sponsor.

[00:09]  

This episode is brought to you by HubUx.  HubUx is a productivity tool for qualitative research.  It creates a seamless workflow across your tools and team.  Originally, came up with the idea as I was listening to research professionals in both the quant and qual space complain about and articulate the pain, I guess more succinctly, around managing qualitative research.  The one big problem with qualitative is it’s synchronous in nature, and it requires 100% of the attention of the respondent. This creates a big barrier, and, I believe, a tremendous opportunity inside of the marketplace.  So what we do is we take the tools that you use; we integrate them into a work flow so that, ultimately, you enter in your project details, that is, who it is that you want to talk to, when you want to talk to them, whether it’s a focus group, in-person, or virtual or IDI’s or ethnos; and we connect you to those right people in the times that you want to have those conversations or connections – Push-Button Qualitative Insights, HubUx.  If you have any questions, reach out to me directly. I would appreciate it. Jamin@HubUx.com

[01:36]  

My guest today is Jenny Karubian, founder and CEO of Ready to Launch.  Founded in 2014, Ready to Launch is a consultancy based in L.A. that helps firms launch new ideas, products, and technologies that better serve their customers.  Jenny has extensive experience as an ethnographer researcher and is a professor of anthropology, sociology, and gender studies. Jenny, thanks very much for joining me on the Happy Market Research Podcast today.   

[02:04]  

Thanks for having me, Jamin.

[02:05]

So, you started an agency 2014.  I’ve just got to start the conversation with, “Why in the world did you start an agency?”  Obviously, I’ve started a couple of companies. It’s really hard to start a company. An agency is like, for me, would be considered one of the really hard, hard things because you got to be hunter and you’ve also got to be a farmer and you’ve got to be invoicing and the whole rest of it as well.     

[02:35]

So I think some hybrid of “I’m a glutton for punishment” but “I’m also very dedicated” probably why I started the agency.  But, also, really, I’m very much in love with the research process, and I also enjoy working with clients. I enjoy so many aspects of working in the research industry that working for a much larger agency doesn’t give the opportunity to work on projects from end to end the way that it does in an ownership and leadership position.  So I think that that motivated a lot of the reasons why I wanted to start my own business.  

[03:15]   

So I get the “why” you’d want to start it maybe, but there’s risk when you start a business, right?  Not only is it a time thing there’s a lot of money there. What gave you that courage to be able to step out and do it yourself?

[03:28]

Initially, it was out of necessity.  So, when I finished school, I got out of school right in the middle of the financial crisis.  And, at that time, while recovery was just starting to happen, there really weren’t any jobs for recent graduates.  And what I found is that companies were willing to give me project work, which now everyone calls the “gig economy.”  Nobody called it the gig economy at that point; they just called it, “We’ll take you on as a contractor but we won’t give you a full-time job.”  And so, at the time, I needed work, and I started picking up contracts as much as I could and networking and trying to work in market research on just a contract basis.                    

And I didn’t realize that I had started my own company until I was actually chatting with one of my clients, and my client was complaining that he had to go to an all-day meeting.  And I said, “Oh, I don’t have to do those.” And he said, “Yeah, that’s why you own your own business.” And I thought, “Oh, I own my own business! Look at that.” I hadn’t really thought about it that way:  It never came across in my mind as “This is a risk” or weighing it against other options. I just was very focused on that I wanted to work in research because I loved doing research, and I was going to do it however I needed to.  And then, after doing that for five years just as a freelancer, then I started an agency but, at that point, I was so comfortable with it that that kind of fear and anxiety that you’re describing just wasn’t there at point.    

[05:06]  

Yeah, there is something about like when I started Decipher, almost I just didn’t care about it not working.  I mean I wanted it to work a lot. But it was worst case scenario is I’m going to get another job. Fear didn’t really enter into the equation.  Whereas, conversely, I’m starting a new business, which will be announced relatively soon… I have a lot more sort of like… You know life has a way of educating you on how terrifying it can be.  Oh, my gosh. All on a sudden, you hear a stat like, I don’t know, 8 out of 10 startups fail or whatever, and it starts informing that fear factor, which, to you point, I think, if it doesn’t exist, in a lot of ways, ignorance can be bliss.    

[05:54]

Right, right.  And I really like doing my own thing.  I don’t answer well to authority, just to be honest.  I do much better kind of running my own show. And so, just getting a little taste of freelancing at the very beginning set me off on this path to want to do my own thing so much so it’s scarier…  The idea of going and working for someone else from 9 to 5 is much more frightening than having a business that may or may not stay afloat. It’s scarier giving up my freedom.  

[06:28] 

Interesting.  Which is funny, right, because you think about how much time you spend inside of the business and it’s probably a lot more than you’d spend working for somebody else.

[06:36]

Oh, absolutely.  I wouldn’t work crazy hours for somebody else that I work for myself.  Absolutely not. But, when it’s for myself, it’s much more fulfilling, and it gives me a sense of purpose whereas I don’t know if I would have…  I feel that if I were working at two o’clock in the morning on some company initiative for someone else that I would feel pretty resentful versus working on it for myself.  Then, there’s that rush of adrenalin of “Hey, this could work” or the satisfaction of working on something that I think could be really successful.     

[07:12]

I heard a podcast recently, and they were talking about entrepreneurship and what really makes entrepreneurs versus people that excel inside of established businesses like iconic firms like Google or Facebook or whatever agencies.  And they actually boiled it down to really grades in high school and college. And they said that people that get straight A’s or excel in grades in academia are oftentimes really more bent on refining and improving current processes whereas people that tend maybe not do as well in academia will be a little bit more in the rebellious or like “I don’t want to spend my time here.  I want to spend it where I want to spend it” sort of framework. So, you’re kind of interesting in that, obviously, you’re a professor of anthropology, sociology, and gender studies. So I have to believe that you have a stellar track record in academia.  

[08:09]    

High school, not so much.  

[08:13]

Interesting. 

[08:18]

I’ve had some theories about this.  I believe that, once people go into the higher levels of academia…  So, I was ABD when I left graduate school, which translates to All But Dissertation.  So, I spend six years doing graduate training, essentially. At those levels, there’s very little that’s translatable to working on teams and doing things that are in the work place because at that level it’s all about training, individual work and individual thought.  At that point, there’s no group projects; there’s really no working with other people; the focus is only on the individual mind. And so, I think that coming out of that, the idea of going and working on teams and working in big companies really wasn’t part of my mindset because I’d been trained as an individual for such a long time.  Although it might be a little bit different in the lower levels of college, but once the Master degrees are completed and you get into those higher levels, it changes quite a bit.

[09:24]  

Interesting.  So, a successful founder, congratulations. 

[09:28]

Thanks.

[09:29]

What advice would you give somebody who is starting a business in this space today?  

[09:34]

I think that I know a lot of people like myself who started as moderators and worked, have started agencies as a result or people who are moderating and are thinking about growing.  And one of the key pieces of advice is that, if moderating is all that they want to be doing or reporting or analyzing (kind of the research process), if that’s the only piece they want to be doing, then I wouldn’t recommend starting an agency because really they should only be doing it if they’re OK with doing all of the other business processes ‘cause I think that’s something I didn’t really expect.  I came to this as a moderator/researcher and, all of a sudden, I’m spending very little of my time doing that work anymore. I do a portion of it, but so much of my time is spent running the business. And so, I think that would be the advice: is be ready to be doing not just the research skills but also a whole lot of operations and management and invoicing and things like that that are more on the business end they need to be prepared for.       

[10:46] 

Yeah, a lot of people, I think, have this misnomer where it’s “I’m going to spend my time doing the stuff I love and I’m really good at.”  Maybe if you’re technically inclined, that might be coding or, if you’re in sales, it might be sales or whatever project delivery, project delivery.  But the reality is you actually spend a lot less time in that space and wind up having to hire out that sort of expertise so that you can focus on the actual business function and making sure that the wheels don’t fall off the bus.    

[11:18]

Absolutely.  I feel as though I’m spending a lot of time making up for the MBA that I never got and reading a lot of books ‘cause I never went to business school.  I’ve never even taken a business class, but yet I’ve owned a business for five years. So I do read quite a bit to try and fill in that gap. So maybe that would be a secondary piece of advice for the entrepreneur that wants to start in this:  if you don’t have a business background, you should probably get to reading as many business books as you can to make up for it.  

[11:50]

So have you surrounded yourself with some people in your network that you use as kind of references that might have that business expertise?

[11:59]

Yes, and I’ve hired my team also.  I have some people on my team that are much more on the business side than on the research side, and that’s been very, very helpful.  So, a couple of my research team, they all have backgrounds similar to me, but then I have some people on marketing and project management that much more on the business side and the technology side, and that’s been very helpful.   

[12:27]   

So, I combed through your website yesterday in preparation for this.  I, actually, found something very interesting, at least to me, and unique inside of the market research space as it relates to a consultancy.  So, what you’ve done is you’ve got a recommended set of methodologies that are set by specific verticals or industries such as beauty, tobacco/cannabis, alcohol, food and beverage, health care and pharma.  The reason I think that is so unique… And just to describe it to the users, you would select what your industry is and then it’ll come up with a set of (I’ll call it four, but it is a different number) of standardized approaches, methodologies like ad testing or whatever, A&U or what have you, that would be utilized in that specific industry.  So I was curious what’s the strategy around that. Is it more of proof of expertise or is it you like guiding the user in terms of where your area of specialty is?   

[13:32]

So, that has a couple of objectives.  No. 1 – We attract a lot of startups to our agency.  I think part of it is because the name Ready to Launch is just a little bit sticky and appeals to the startup community.  So a lot of folks who call are startups. A lot of them have never done research before, and they’re calling us and they’re looking for a little bit of education.  So that piece was to give a little bit of client education so that, if they can find an industry that is relevant to them, then they can get an idea of what kind of research they might want to do.  Most of time when I have clients who call that have not done research before, typically they say they want either a focus group or a survey. You and I know that there are a variety of different methods that be used but, for folks who are new to this game, they are not aware.  So they usually use one of those two words. That was part of the motivation for having these recommended approaches by industry was to show what we can do. And those are based off of past projects that we had that have been very successful. So those methodologies, that wasn’t just something that we made up; that was based off of past projects that we’ve done and just to give them an idea of what they might want to be doing if they really haven’t thought through it yet.               

[14:57]      

That’s actually really powerful when you think about it because it immediately…  As a lead comes in, then it immediately frames for you how you need to talk to them and what specifically about.  I think, at minimum, it creates a shortcut in the relationship as to is it fit or not fit and also it creates a ton of social trust because you’re referencing a white paper or previous experiences that have had successful outcomes, I guess I should say.  That’s a really interesting and cool hack that you’ve applied. It almost felt like one of those self-serve kinds of options but, obviously, it’s not. It kind of like funnels down, but it’s a really cool hack.     

[15:44]

Thank you, and I feel that it’s been pretty effective ‘cause a lot of times when clients call, they’ve looked at that and they’ve gone through it before they’ve even gotten on the phone with me, which is wonderful.  That was kind of the point of it so that they can get a little bit of education before they get on the phone and they can have an idea of what kinds of ways we might approach their category and also let them know that we have expertise in that category.  Oftentimes, one of the questions people ask is, “Well, what do you research?” The truth is we research everything, but that’s a little bit nebulous for people who aren’t familiar with how market research works. So that’s why we have it broken down industry by industry so that people can see things that are relevant to them, projects that we might have done in the past that could be relevant to the kind of work they’re trying to do.     

[16:29]

Of all the people I’ve interviewed, you have the most extensive experience in ethnographic research.  How does ethnography fit into your approach?

[16:42]  

To begin with, ethnography is my training and my background.  That’s how I came into the industry initially from anthropology.  So really informs the kind of work. It informs all of our in-person approaches. Most of the researchers on my team were also trained academically.  So, a lot of it is a viewpoint, and that viewpoint is that in order to really understand, our consumers is we have to know them as people. And in order to get to know people as people, we need to sit with them and talk with them and see what their lives are like and understand their context and understand them as whole people:  people that have families and homes and messy lives and things like that. So, I think that the approach, even though every project isn’t ethnographic, the ethnographic viewpoint informs all of the research that we’re doing in terms of understanding consumers at that very human level.      

[17:46]  

You think about some of the big projects, big breakthrough projects that have happened inside of the industry.  One of my favorite examples is the refrigerator box that I think it was Coke came up with (maybe, it was Pepsi).  Anyway, basically they take, I think, it’s 18 cans and it fits nicely inside of your refrigerator, right? You, of course, I’m sure remember the story.  So, the ethnographers would live with the families. They identified that the families would go to Costco; they would buy these big things of Coke or Pepsi, so soda, and they would then, because they’re so big, they didn’t fit in the refrigerator, and then they would put it inside of their garage where it would sit ‘cause they’re never cold and they’re out of reach when it’s time to drink.  A by-product of that ethnography was to create this nifty, little (it’s even used for beer now, right?) box that you could basically just prop up. It’s fits really tightly in your refrigerator. So, it’s nice. Are you doing that level of ethnography for some of your clients?        

[18:48]  

Sure.  Yeah, we certainly do.  And then we’ve done a lot of things that are really foundational.  So, we did a study. It was very high-profile. So I can talk about it because it’s been published and presented many, many times (this isn’t confidential) that we did run for the boating industry a couple of years back.  There’s an agency that handles all the marketing for the boating industry, and they had a big research initiative to find out who are their consumers. They had an idea in their minds of who their consumers were, but they realized that that may not actually be true.  And so, we went all over the country, and we met people who were looking to buy boats. And we talked to people about all the things that they do for fun and how they spend their leisure time, how they spend time with their families, what did they do before they had families.  And what we came up with was this very interesting portrait of potential boat owners that was very different from what the assumption was in terms of their target consumers. So, their target consumers up until that point were essentially middle-aged men who were in the kind of affluent class.  And what we found, especially in more coastal states, is that a lot of young families wanted to have boats. And the reason why was because most of them had traveled quite extensively and had very adventurous lives before they settled down and had kids. And so, having a boat was something they aspired to because it was a way to have adventures on a much more local basis without having to put their whole family on a plane or do something like that, and it was something they could do to have a daytime adventure without having to run and backpack in Europe or something like that.  And we found a lot of different segments, a lot of different nuances. And, essentially, it’s completely transformed the way that the boating industry is marketing as well as who they’re marketing to and taking into account this much younger consumer and what that looks like.

[20:50]

What kind of timeframe is around a project like that?  To your point, it’s such a foundational piece of knowledge that would really inform the whole thing, right? 

[21:01]  

It took a year; that took a solid year.  

[21:03]  

OK, yeah.

[21:04]  

‘Cause we had to do some segmentation work at the beginning even to understand who they were.  Once that was done, then we could do the ethnography. And then we did a follow-up study to that at the other end, talking to people who had gotten rid of their boats and why because part of the goal is to get people to buy boats but then the other part was getting them to keep them.  And so, that was the other challenge. Once we added on that additional piece, that took two years. So these ethnographic studies are much lengthier, especially when we’re doing a lot of travel around the country and multi-market and multi-segment, things like that. They are a much bigger time commitment, and they tend to be a little bit higher profile.  

[21:46]  

So, kind of on the flip side, we’ve seen a rise in market research technology firms.  These are usually more like quick-hit-type insight framework – some of them, not all of them, but some of them.  Many of them are focused on qualitative solutions at quantitative base sizes. What are you seeing that you think is interesting from a marketing research technology perspective that’s kind of up and coming in our space?   

[22:15]

So, what you said about using a qualitative approach with quantitative sizes makes me think of Remesh specifically.  That’s a really interesting tool to use when we have… Sometimes, clients will come to us with… They want qualitative work but we can tell that they are very quant-minded.  And so, when we have quant-minded clients who… They want to do a focus group but they want… Sometimes, they’ll say, “Oh, well, maybe we can have 100 consumers, and we can just do 15 or 20 focus groups or something that,” which isn’t very efficient or cost-effective.  That’s when we really recommend using these kinds of tools that are hybrid with qual and quant because we can get that qual nuance but then we have the quant numbers to back it up. So, that’s, I think, really interesting, and I’ve found that clients really like that. Conversely, the other approach is, obviously, to do qualitative and then do a more traditional survey or something like that afterward to validate the results.  So it’s collapsing these two phases into one. So that’s interesting and exciting, and I’m finding clients like that quite a bit. And then, I’m also seeing a lot of tools for doing agile work, which is great. I’ve done a lot of work with Discuss.io in the past where we’re doing kind of agile insights. I presented with some people from their team about some methodologies we’d come up with where we can go from recruit to report in seven days.  That’s some really interesting and fast-moving, fast-paced kind of research that I find really exciting.  

[23:51]

So, Discuss.io, they’ve hit hard, right?  They came out of the Unilever space and then the incubator that Unilever has and then got quickly a global footprint, which I thought was very interesting from a respondent point of view and difficult to do.  It’s hard at a quant basis but it’s even harder at a qualitative basis to do that. Do you see those technologies, whether Remesh or others, as maybe channel partners? Again, this is just me talking: I still think there’s a huge space for agencies to help the brands with even the integration of these tools and utilization of the insights.     

[24:36]   

What do you mean by channel partner?  Tell me a little bit more about that.

[24:40]

Sorry, what I mean specifically (and I might have been using the wrong word) but is that… almost like a co-sell opportunity because they have, presumably, large customer bases and broad reach, but they’re not necessarily deep within those relationships.  So, they’re not doing a year-long ethnography; they’re doing these quick-hit insight engagements, which, of course, are SaaS models or whatever. But my point is that they’re hundreds or maybe a few thousand dollars as opposed to what a normal agency would charge for these types of projects.  So, my question is really in line with, “Do you see a partnership opportunity with technologies like this that”, like “Are you looking for a preferred partner to help expand or install the insights in a meaningful way inside of your potentially new customer base?”  

[25:44]  

Sure, Discuss.io has leveraged my agency for the last five years.  When Discuss.io was in its infancy, so was my agency. And so, we’ve been partnering on a lot of high-profile projects over time.  Most specifically, there’s a global Mondelez study that they’ve presented with IIeX and a few other conferences. There was a big global initiative, and we partnered with them on that.  And that was run, I think, in 15 different countries. And so, we partnered with them on a variety of different qualitative services, including all of the analysis and a lot of moderating and different things like that.  We’ve done quite a bit with them and also, we partnered with them in terms of… If they want this kind of recruit to report the really quick-turn stuff, our agency is trained and is knowledgeable on how to work that with them and the clients.  So, if clients come to them and they want one of these very quick-turn projects, they’ll leverage our agency on something like that.  

A really good example that we worked on a study for a client.  They had come up with a new food product, and they wanted to know how to position it.  And so, over the course of two days, we did ten interviews in two days, and in between each interview, their design team was working on different kind of design elements for it and product positioning.  And so, we would do an interview; the design team would be listening in. Based off of what came out in the interview in terms of what people were interested in for this food product, they would design a label for that and then we would test it on the next one.  And then, we kept refining it and refining and refining it. By the time we got to interview No. 10, they had assets that they could show for a company-wide meeting that they were going to be doing for presenting their new food product to the whole company. And so, we were able to get through that in just two days in terms of the interviewing and then one more day to turn around the report.  So, it was really efficient, and it was a very good tool for the kinds of things that they needed, but I don’t know how many other… That’s a really specialized kind of approach that we worked on with them.          

[28:04]

Totally.  Those partnerships take a ton of time to develop.  And it’s interesting that you’re experiencing that kind of connection with…   There’s a right place, right time, and then also fit is really important, but the reason I bring it up…  And I wrote a real brief blog post on LinkedIn recently to this point. But I interviewed an insights professional, market research professional at Georgia Pacific recently, and in that interview, she’s telling me about how the face of partnerships has really evolved from a kind of this wholesale outsource model to a “I might do the data collection, but I want somebody to walk alongside me on the analytics and implications to the business.”  So, the size of her engagements, interestingly enough, are about the same, but they’re just choosing to spend a lot less money on the actual operational consideration and a lot more money on the implications side of it.       

[29:03] 

Mm, hmm, which is what we’ve done in a lot of ways with Discuss.

[29:06]

Last question and then we’ll get you out of the hotseat.  So, what is the one project, even though we’ve talked about a couple already, that you are most proud of?

[29:14]

So, we’re in an ongoing client relationship with a non-profit organization that’s in the oncology space.  And so, we do a lot of research for them; in fact, I think we’re going to working on, I think, 14 projects this year in 2019.  And so, we do a lot of work in the oncology space, trying to help make patients’ lives better, essentially. And so, there’s a lot of different kinds of materials that we put together with them or assessing their needs.  And this runs across a variety of different oncological spaces. And I think I’m most proud of that because it really feels like we’re making a difference: we’re talking with patients; we’re talking with their caregivers; we’re talking to people who are really at their most vulnerable and looking for ways to make their lives better.  And so, I’d say that’s probably what I’m most proud of at this point just because it feels like it has a real impact on people’s lives.  

[30:09]    

Gosh, that’s actually…  That’s pretty meaningful point of view.  I love that, I love that dual purpose. So, my guest today has been Jenny Karubian.  Jenny, if somebody wants to get in contact with you, how would they do that?    

[30:22]

They can either go on the website, which is readytolaunchresearch.com, all spelled out.  Or they can email me at jenny@readytolaunchresearch.com or find me on LinkedIn – Jenny Karubian.

[30:35]

Jenny, thanks so much for being on the Happy Market Research Podcast today.

[30:38]  

Thank you.

[30:39]

Everyone else, really appreciate your time.  As always, I hope that you found a ton of value inside of this episode.  Please reach out to Jenny if you have questions about any of this kind of stuff.  I’m going to give you a little spoiler: She has some marketing expertise as well; so, if I were you and I was an agency, I would at least ping her and say, “Jamin mentioned this.  What do you think?” That’s all I got for you guys today. As always, like, share. It goes a long way in helping other people like yourself find these episodes. Have a great rest of your day.  

[31:15]

This episode is brought to you by HubUx.  HubUx is a productivity tool for qualitative research.  It creates a seamless workflow across your tools and team.  Originally, came up with the idea as I was listening to research professionals in both the quant and qual space complain about and articulate the pain, I guess more succinctly, around managing qualitative research.  The one big problem with qualitative is it’s synchronous in nature, and it requires 100% of the attention of the respondent. This creates a big barrier, and, I believe, a tremendous opportunity inside of the marketplace.  So what we do is we take the tools that you use; we integrate them into a work flow so that, ultimately, you enter in your project details, that is, who it is that you want to talk to, when you want to talk to them, whether it’s a focus group, in-person, or virtual or IDI’s or ethnos; and we connect you to those right people in the times that you want to have those conversations or connections – Push-Button Qualitative Insights, HubUx.  If you have any questions, reach out to me directly. I would appreciate it. Jamin@HubUx.com  Have a great rest of your day.  

Ep. 224 – Michael Yaksich – How one of the Fastest Startups in the Silicon Valley is Using Market Research

My guest today is Michael Yaksich, Director of Customer Strategy at Cruise. Headquartered in San Francisco, Cruise is a self driving technology company that will offer a ride hailing service initially in San Francisco. Prior to joining Cruise, Michael has worked in insights at Hyundai, BrandIQ, Cadillac, and Honda.

Find Michael Online:

LinkedIn

Website: https://getcruise.com

Find Us Online: 

Social Media: @happymrxp

LinkedIn

This Episode’s Sponsor: 

This episode is brought to you by Clearworks. Clearworks is an insights, innovation, and customer-experience company. They help clients understand their customers better, identify opportunities for innovation, and create products, services, and experiences that matter. Their clients are diverse in size and industry but share one important thing: a passion to drive more business by driving more meaningful human connection. For more information, please visit them at www.clearworks.net.


[00:01]

On Episode 224, I’m interviewing Michael Yaksich, the Director of Customer Strategy at Cruise, but first a word from our sponsor.

[00:07]  

This episode is brought to you by Clearworks.  So, we have a couple of sponsors on our show. I just want to underscore how much I appreciate those of you who have sponsored the Happy Market Research Podcast.  It makes a ton of value to the ecosystem that is actually transcending market research right now. I say “transcending”; that’s probably the wrong framework, but exceeding, moving beyond into user experience research as well as data analytics and insights.

Clearworks, thank you so much for your sponsorship.  For those of you who don’t know, they are insights and innovation and customer experience company.  They help their clients understand their customers better, identify opportunities for innovation, and create products, services, and experience that actually matter.  Their clients are diverse, both in size and industry, probably like all of ours, but they do share one important thing, which is a passion to drive more business by driving more meaningful human connections.  You can find them online at www.clearworks.net.  Again, it’s www.clearworks.net.  And again, thank you so much for your time.     

[01:20]  

Hi, I’m Jamin Brazil, and you’re listening to the Happy Market Research Podcast.  My guest today is Michael Yaksich, Director of Customer Strategy at Cruise. Headquartered in San Francisco, Cruise is a self-driving technology company that will offer a ride-hailing service initially in San Francisco.  Prior to joining Cruise, Michael has worked in insights at Hyundai, BrandIQ, Cadillac, and Honda. Michael, thanks for being on the Happy Market Research Podcast today.

[01:51]  

Yeah, thanks for having me.

[01:53]

So, tell us a little bit about how you wound up in research.

[01:56]

At least from all the people I’ve ever spoken to, it’s pretty common to say it’s not something you set out to do from the get-go.  I mean how many people have said “Yes” that you might have asked this question to in the past? 

[02:09]   

One.  So, I’ve done over 140 interviews, and I’ve had, I believe, one.  I might be mistaken, but I really think it’s one person that said intentionally they set out in college to be in market research or consumer insights.  

[02:23]

Yeah, I feel like that it’s more of a recent phenomenon.  I mean, growing up, my career aspirations went from wanting to be a marine biologist at Sea World and all those things that come with that and all the way to wanting to go into finance and then eventually discovering sociology in school, which kind of led me down this path.  I guess that’s a normal discipline that might lead people down this pathway. But, to me, ultimately, in the end, I think people are really drawn to what they’re passionate about because of something they’ve gone through or something they’ve experienced or even where they come from.  And I think that where-you-come-from piece was really strong from me. I grew up outside of Youngstown, Ohio, if you might know where that’s at. It’s a pretty blue-collar part of the country where people… I guess the best way to describe it is that people really have been left behind economically there for quite a number of years.  And so, really what was important was family and community and even your neighborhood because that’s where your support and your encouragement and everything came from. And so, because of that, you really took on a type of responsibility even as a kid to look out for everybody. And so, I think, thinking about this question and thinking through this question a little bit more, that to me, at least, on a deeper level I think led me towards this career pathway to be more inclined to think about other people’s points of views or needs or, you know, what’s really motivating them or what’s going to help make their lives better.     

[03:58]  

Yeah, that’s interesting.  You have the majority of the population in the U.S. that lives on the coasts…  And it’s interesting I come from Fresno or central California, which is a similar demographic profile. Especially in context of being in California, you wouldn’t think of Fresno necessarily being some of the poorest… one of the poorest zip codes in America.  But it definitely creates a level of empathy that, if harnessed correctly, can help you want to understand consumers and just people in general, maybe even beyond just a consumption pattern and help identify where you can add value in a true way to that to people’s lives.      

[04:47]

Mm-hmm, exactly.

[04:48] 

That other thing that’s interesting is this intellectual curiosity, I would say, is probably a theme I’ve seen among people that have entered into this space as it relates with human behavior.  I think, if you look now, you’ve got University of Massachusetts, they have a Master’s program focused on market research; Georgia does as well. Both of those colleges, incidentally, have a 100% job placement rate prior to graduation, which is unbelievable.  So, just goes to show you that while the C-level or the executive level inside of the insights space now… while marketing research and UX, they didn’t really have… That wasn’t a known career path 15, even 10 years ago, whereas now it seems like it’s becoming, it’s scaling up and becoming a much bigger part of the corporate ecosystem.  So, people are being a little bit more intentional as it relates with their area of focus and desire in a career.      

[05:55]

At least from my perspective…  My team is called customer strategy; we’re not market research.  And to me, that really represents the evolution of the traditional market research function.  It is the next step. And, when you think about it that way, you take a different perspective, right?  You’re touching many functions like you would in research, but you’re not only doing and executing research:  You have Big Data involved, analytics, some machine learning, even design research techniques. But it goes beyond just the insights, just beyond that piece of the execution to really try to drive customer centricity into the heart and soul of the business, all the way from the business activities to, I would say, the culture, the ethos that people have that work there, right?  And that’s a big, big pivot.      

[06:55]

A huge pivot, it’s a huge pivot.  At a corporate level… So, there are two things that are interesting for me on this front.  One is you’re talking about a shift in corporate behavior. This week I dropped the episode with Estrella Lopez-Brea of a cereal partnership between Nestlé and General Mills.  In that, she actually said this is the most exciting time to be in an insights function because for the first time, we’re getting the red carpet rolled out to us from the boardroom.  She also referenced this… I think I can share… Well, anyway I might be able to share a slide with you of it. The Watermark Report, it’s a longitudinal study on the Fortune 500, and it identifies changes in the laggards and the leaders inside of that ecosystem.  Unambiguously, the communality across the companies that are successful are customer-centric, whereas the companies that are not are the laggards, the underperformers, the anchors on the S&P. It’s just so factually based as to, if you don’t have the customer in the center of your decisions, then you are going to not succeed.  But then the other side of your point, which I think is really interesting, is this evolution of market research into strategy. I’ve never heard in these episodes so far somebody articulate it exactly like that, but I think it’s an important point because (and I have been talking a lot about this point) that it is about 5 to 1 in market researchers to UX researchers or professionals; user experience is what I mean by that.  So in the corporate ecosystem, you’re seeing a lot of focus centered around… and there’s a difference in the type of work that they do. Basically, market researchers are more broadly capable – I’d put it like that – whereas the UX seems to be very centric to product and so they go much deeper and then they also go up and down the value chain farther with respect to the insights and the decisions that are made. But, your point about this strategy is, I think, really on point, which is to say it is the evolution or the next phase for where research is moving inside of the decision tree.        

[09:30]    

Mm-hmm, it’s become less functional.  At least, that’s my opinion on it, my perspective.

[09:37]

That’s interesting.  All right, well, so tell me a little bit about Cruise if you don’t mind.  You guys are a startup. I’m sure everything is confidential, and that’s totally fine.  I know you’re headquartered in San Francisco. Of course, I reviewed the website. What drew you to this particular startup?

[09:54]

Well, it’s extremely exciting.  A lot of my background was in automotive, and so, I was pretty familiar with the space.  When I was at Cadillac, we had super-cruise technology there, which is semi-autonomous, hands-free driving, that the team I was part of did some work around after the vehicle was launched to understand how people were using it and their level of satisfaction and the like to help improve the feature for the car itself.  But, to me, Cruise really represents a little bit of what we were talking about in the beginning of podcast about, you know, why did I get into research? Why did I get into this pathway to begin with? Because, as I mentioned, it was just a general point of view to better people and better people’s lives and understand their perspective…  And so, there’s that curiosity element, as you said, the research component, searching for the answer or the insight, right? But then there is also what I like to call the softer component, which is really that the brand and the company really has people at the center of it, and everything that we’re doing, even as you read in the introduction, is around making people’s lives better.  And it’s a huge challenge, and it’s a huge opportunity as well. It’s something that will completely at scale or maybe even not at scale change how we live, the nature of how we live. Deaths could be completely eliminated, right, as for example. I see it as both a win-win and both sides of the coin for me as to why I joined it. Also, because I really wanted to take on another opportunity to build a team.  This is the second time I’ve gone about doing it. The first time at Cadillac was just with a smaller insights group within a larger, much larger, organization. This time around it’s just completely from the ground up in every way possible, I mean, every way possible.       

[12:04]  

How exciting.  That’s a perfect place to be able to sit.  I mean you’re solving… As a life-long commuter, I’m in the Bay Area or L.A. weekly.  Actually, after this recording, I’m jumping into the car and going to San Francisco, ironically.  But we’re always in the state of spending, it feels like, spending time traveling. Man, if that problem can get solved that would be…  Talk about an improvement to overall life! And then the other part, Gosh, being able to build something from the ground up in context of a team and a product, for that matter, that’s an exciting opportunity.  How do you go about uh…? I know there’s a war on talent. I think that’s how people are casting it now in the Bay Area. How do you go about attracting people?

[12:53]

Well, that’s actually a really good question.  Well, right now we have to hire a lot of people.  So I believe by the end of the year we’re going to be doubling our organization, going from, I think, around 1,400 right now.  (We were 1,200 when I joined in January.) So it’s going to be over, maybe over 2,000. The vast majority of the people will be in engineering and data science ‘cause that’s the bulk of the work.  But I can say we do have one assistant manager position open on my team. I’ll reach out there. If anybody listening is interested, they definitely can reach out to me.    

[13:34]

Yeah, for sure, totally.  And if you shoot me the job description, I’ll post it on LinkedIn.  

[13:40]

Yeah, definitely growing tremendously.  Word of mouth is big. In the Bay Area, it is a war for talent, especially on the engineering side.  But, to me, I’ve hired two people on my team recently; we’ve been together for about three months now – the three of us.  I think it really comes down to the challenge that’s presented and the opportunity to be building something that’s this big from the ground up.  Working at Cruise and in this space is, basically, a once-in-a-lifetime kind of opportunity because not many people get to literally lay, at least even from a customer strategy or research perspective…  Not many people get to say that they were at the birth of something and built the foundational knowledge before there was anything that could be built. And what I mean by that is not that research hasn’t been conducted in the self-driving space at all.  Of course, there’s been research there. But there’s a lot of kind of first-evers that we’re doing here, right, because the level of specificity and the commercial intent and drive behind everything we’re doing as a team is much more intense because it’s actually a business, right?  It’s not necessarily just exploration or learning or extremely broad-based in its applications. So I think that that adds a new level to everything.        

[15:15] 

So, I got a…  When Tesla released the Model 3, the low priced one, I went ahead and traded in my gas- or diesel-guzzling truck and then purchased one.  And it has completely changed my… And it’s not like fully autonomous, right, but it has completely changed my life with respect to how the cruise control operates.  It took me, I want to say, the better part of almost two months just to get acclimated to… It’s almost like a trust factor is how I put it, like a dating relationship in a lot of ways with the technology.  I mean that in all sincerity. It was a completely different. Like, “I don’t trust you, Tesla.” Yeah, right. So, anyway.          

[16:03]

I mean that right there is one of the biggest challenges and even things that I’m really intent on understanding overall.  Trust is one way of thinking about it, but understanding what will drive adoption for Cruise or even for the technology without it becoming something of a long-term novelty to people, right?  That’s a very, very essential question right now. And so, for context, right, for context purposes for this, today people are mostly exposed to the idea of self-driving technology from kind of sensationalized media, right:  so, crashes of Teslas and crashes of their cars. So there’s some awareness out there about mobility. But there’s not really common understanding; there’s definitely some skepticism. And even the ways that benefits of the technology or what a service like Cruise would provide or even the forms that the technology could take haven’t really been made tangible, I think, for people to understand or even relate to.  As I’ve kind of put it before some other colleagues, it’s like we’re not only building a brand here, we’re also at the forefront of building an entirely new category that just doesn’t exist, right? And it takes a lot of work to do that.        

[17:35]

I actually think it’s a lot like the horse and buggy versus the original automobile.  When I say it’s disruptive from a driving perspective, it’s that different. It’s like a different sort of a thing.   

[17:47]

Mm-hmm.  Yeah, that’s exactly it.  So what I’m thinking about on a deeper consumer level is how can we unlock what we call tensions, right?  That example that you gave is kind of a tension in transportation: a tension between something that’s familiar like riding in a car or even on a horse or whatever and the technology or solution that comes along that disrupts it and makes you have to give up part of what makes that experience familiar to you in the first place, right?   

[18:20]   

Right.

[18:21]

That right there is sort of the challenge, and what we’re trying to do is really hone in on it and identify and begin working like today on how we can even think about accelerating adoption when the cars are on the road…  I mean the cars are on the road in San Francisco but when people, consumers, are in the cars ‘cause marketing, pricing, product experience – all that stuff – is going to have an impact. But I feel like, as you’re pointing out, with a change that’s of this magnitude overall, it still begs the question of, “Is there something else going on?”  “Is there something that we’re not taking into consideration?” because the horse to automobile… That’s a good example of a similar thing happening, but that was so long ago, right? There’s not much data on that. We can’t really go look back and come up with a KPI, you know.     

[19:17]      

That’s true.  It would be funny to do that though, I think.  Anyway, yeah. So, you’re building a team. How do you go about finding new vendors and research partners?

[19:29]

So, honestly, I’ve traditionally relied on word of mouth.  That’s been my biggest go-to for the most part or if something piques my interest.  It’s really the combination of those two. It’s like marketing: it’s like right time, right place, right person.  It’s all those things. But, most the time, I’ve been thinking about this a lot quite recently because I’m really focused on building the team’s capabilities out.  So, right now, tools, solutions, anything that goes beyond survey platforms ‘because we do have a survey platform. We have two now that we can leverage in-house because agility is key for us…are really the things I’m most interested in.  Anything that adds the value and can still push the envelope is also of interest too. But then, as I mentioned, customer strategy (It’s just not research)… We’re also leveraging analytics and doing a lot with third-party data. Something that I’ve been looking for a lot has been behavioral data related to how people just move about cities, San Francisco, all cities, major cities.  So, it’s modes of transportation. Who are the people themselves who do this? You name it ‘cause there’s so much of wealth of data out there to be tapped into and it’s an area that the opportunity that’s unlocked by the technology is so great that you do have to cast the net broad as well. So trying to get at that behavioral piece, I think, is really, really essential. And that’s, again, something that we would look at for a partner to help provide.              

[21:17]

OK, that’s actually a bunch of gold right there for our listeners.  I’d imagine that LinkedIn is a pretty good way for people to be able to contact you if they feel they have some value they could add?  

[21:26]  

Yeah, yeah, that’s absolutely fine.  Again, we’re really taking on a big challenge with a lot of unknowns.  The research is “in concept” because no consumer is experiencing the technology right now.  So, capabilities and solutions that we can leverage quickly as a team are…that would help speak to these challenges would be of very high interest to me.   

[21:57]  

So, in context of all the unknowns, what sort of tips, tricks, or methodologies, techniques, whatever are you leveraging to understand the heart of the consumer?   

[22:06]  

So, I’ll back it up a bit.  My team only has been together for about three months now.  I’m just giving you the context. But we’re small, but we’re mighty.  So, we’ve actually conducted about six projects.  

[22:22]

Nice.

[22:22]  

Yeah, so, it’s quite a bit.  We’re averaging about two a month, which is actually, I think, pretty impressive.  So, most of the work has been really focused around unpacking what drives the decisions people are making as it relates to ordering a ride, the space we’re going to go into and offer service to customers; and also unpacking benefits and barriers as it relates to building our brand and understanding how people think about the technology overall.  On the research front, I guess there’s a lot of opportunities as you can imagine, but there aren’t really, I’d say, tips or tricks around technique and method. I think anything we can do to get people as close as we can to the experience in what we’re developing, it’s really going to help us. And that’s something that we’re working on and trying to do ‘cause it’s a lot of confidentiality involved, but we definitely want to do that.  But I’d say just being only here for a few months and less than a year now, I’m just building up the capabilities in the team quickly. I probably could say that there’s a couple of tips or perspectives that have kind of emerged for me or kind of come up for me ‘cause, again, this is my first foray into tech. And it’s not just traditional tech; it’s super-emerging tech.   

[23:42]  

Deep.

[23:44]  

It’s forefront, leading edge.  So, I think for anyone who’s going into this space or applying for the job to join the team, a couple things have stood out for me.  One was I’ve learned that when you go into a new category, you have to really be humble about what you do because you’re just not going to know the answer or be able to find the data or you’re going to fail quite frankly because it’s unknown territory.  And you have to be open and willing to doing that and so I think it takes a level of humility, especially as a researcher. A lot of people really want to, “Can I get the right answer?” “Can I really deliver the insight?” “Can I get there to really help move the needle?”  And, in this space, you want to be prepared to not know. And sometimes you want to be prepared to go beyond the data because you won’t be able to find the exact data that you need. It’s not just the insight but also the inference; it’s that extra piece; it’s the consultative moment. I think focus is important, so being focused especially when you have to execute.  And what I mean by that is really like I think what’s successful for people in this space is that you have to be really purposeful on how you spend your time and your resources. The way that I’ve described it to people is you have to Marie Kondo everything and anything when possible because you can’t take on every request there is. And I think this is a common thing for people in research and insights.  You can’t necessarily take on every request. You have to know when not to because taking on everything that’s coming from everywhere will get you absolutely nowhere.  

[25:42]

Right.

[25:43]

And the environment is extremely fast paced.  So, you have to Marie Kondo: You have to remove the things or be willing to remove the things that are not going to help get you to the goal.

[25:55]   

Yeah, and that’s part of one of the biggest impediments that I’ve seen over the last 20 years, is we continue to see what I call research bloat because all these disparate stakeholders continue to weigh in and research just tries to accommodate these disparate objectives and then, ultimately, you wind up watering down the research to the point where it’s really not particularly useful, at least in the specificity of the original reason it was spawned.  I think that’s a really important point of exercising the discipline around the research that it maintains the focus and so that you’re able to (one) get it to field quick and get the answer quick and then iterate it as you need to.  

[26:40]

Mm-hmm.  We’ve been very rigorous on my team with this in that we line-by-line align company objective, departmental objective, research objective, only one line.  It’s all got to be seamless all the way through before it gets greenlighted because it’s pretty much a race to the market. It’s a big technology, and a lot of people are racing to get there.  And you have to be focused with it; you have to be focused. I think being open is important and that goes with this. And what I mean by being open is don’t get wedded to a project; don’t fall in love with the research.  And you definitely have to have the capability to either execute it quickly or change things on the fly. And, if either one cannot be done, you just have to be open; you have to be ready for things to change on a dime ‘cause it’s just part of the nature of the pace and of the development of the technology and the work that we’re doing.  

And then the other thing that I think is important and this to me kind of comes from, I think it kind of comes from experience of being in this territory and in tech and in San Francisco is that I encourage my team to also be hungry, so always be thinking ahead to help move everybody you work with forward.  And so, what I mean by that is (and this is sort of part of the customer strategy piece that we were talking about earlier) is how do YOU help maintain the momentum; how do YOU think ahead and anticipate what the team might need. So it’s like anything you can do as a researcher that you can ensure that the customer’s voice is brought into the decision-making process sooner than people ever would have expected it to be, I think, is just a surprise and a delight across the board and everybody benefits from it.  And that’s not just planning a calendar, but I think it comes from this idea of having the passion and being hungry in what you want to do and what you want to achieve.    

[28:49]

Totally.  So, with respect to you sitting on the bleeding edge of innovation, what’s your perspective over the next two to three years on AR, VR, voice, etc.?  How’s that going to be mixed into the insights function?

[29:07] 

Oh, there’s probably a ton of ways.  I mean like if it lives up to the promise with the speed and the bandwidth, it could be completely game-changing for everything that’s done on an executional level.  You know the accuracy of the data, the volume of data you could collect is much, much more. There could be totally new solutions out there, I think. And even from the perspective of this is there’s also tighter integration with the experience the customer actually has.  And that’s where this gets very exciting for people who are or brands that are actually going to leverage AR, VR, voice, and anything that’s enabled through their technology or their offering with 5G because you’ll have much more of a seamless connection to the customer and you can create a new value proposition with them that will help you improve their lives and improve their experience while also providing you with the information and the insight that you need.  So, I think that’s one area as well: that’s there’s going to be that tighter integration, something like I don’t think we’ve seen yet. We’re getting there, but we haven’t seen it. I feel like it’s much been more like a technology as a tool applied versus more of a seamless integration between the technology and the experience and the data and the insights portion as well. Some other areas: I think we’re going to get next generation creative and concept testing.  If you think we’re going to be able to interact with questions, place people in situations… Even right now you put people with a big headset on and all of that, I think there’s just going to be much more capability as a result of the technology, especially when you’re trying to understand or simulate different messages or different interfaces or things that you want to provide during an in-car experience or even like testing in CPG space like shelf testing, package testing.  Right now, it’s all just straight choice tasks or interviewing people. That’s kind of what we’re doing. We haven’t really found (at least, I haven’t seen it) a very optimal kind of AR, VR experience that has been created.   

I’d say another spot was…  I think qualitative’s going to be…  Qualitative research just keeps getting better and better or at least they’ve done it through technology, keeps landing on that side in terms of how I see things.  Live streaming: you’ll have the bandwidth; you’ll have the capability to do that. Interview people at tasks. And I think even the AI and the machine learning aspect’s going to be even more exciting because right now we have chat bots but they’re pretty…  I think they’re going to be basic compared to what we’re going to see and even voice and leveraging the data that’s collected qualitatively through voice, even conversations, will be much more insightful so that kind of lends and blends the idea of qualitative being part of the Big Data solution as well.    

[32:18]

That’s really interesting.  Sorry, really quick, but you’re piggybacking on a theme that I’ve been seeing, which is technology (AI, etc.) NLP, facial recognition is making qualitative accessible and now being able to do it at scale.  Because before the analytics, the data collection was tough but the analytics was impossible once you got past 10 or 20 people. And now, all of a sudden, you can actually have the tools by which you can analyze and get to what all this disparate data actually is trying to bubble up as truth.  Yeah, it’s an interesting time for qualitative; I’m very bullish on it and its market share over the next 5 to 10 years.     

[32:58]    

Yeah, and I think even imagery is a space that’s interesting, you know, not just the voice.  But you can think of it derived what people put out on social. How do we think about that more and the opportunity that’s provided just through maybe more derived forms of insights versus researched forms?  Also, I think that our understanding of the customer journey’s also going to get a real boost from this technology. So, there should be much more of a wealth of location-based data. Behavioral data, in particular, I think is the most exciting.  I feel like every time we talk about the customer journey, it’s changing or it’s being reframed or sometimes even overly complex or overwhelming sometimes. And I think the promise of the technology will help us be able to parse out, simplify, and derive new meaning and insight from it and from what’s actually going on.  I think that that’s another piece that can really be unlocked.    

[34:07]

So, when you kind of pull back and look over your career across automotive and innovation, what is the project that you’re the most proud of?

[34:17]

Yeah, that’s a good question.  I’d have to say it’s actually quite recent.  I finished it up before I left Cadillac several months ago to join Cruise.  So, during my time there, I led a pretty long project where we dove into the current state of loyalty for the brand and even built a pretty robust model of loyalty drivers in the luxury automotive space, just trying to identify what specific levers from a product perspective, a communication perspective, an incentive perspective – all these different levers we could rely on to move the needle for us, right?  And what made the project so rewarding for me was (of course, that piece of it was very rewarding) was that I had the opportunity in my role there because I wasn’t just a senior manager of insights but also led strategic initiatives. And so, within that project itself, I was able to spearhead the development, execution of an all new marketing program basically to drive loyalty. And so, the result of the program actually led to incremental sales of the brand and generated some pretty good profit for the company overall.  And so, I think that to me was that tangible result and being part of it is not the norm for everybody who’s in research or customer-strategy-related field at all. You don’t often get to travel the whole pathway. And I tend to think we, as professionals, usually led through influence and not execution, for the most part. So, that I think was a point of pride because it was all the way from insights generation and understanding and learning and working throughout the organization with various partners to actually get something that really struck a chord with consumers so much and really helped the bottom line of the company overall.              

[36:22]  

Yeah, that’s funny in that the biggest complaint that I hear among researchers is they feel a little bit coggish relative to the product life cycle as opposed to conception all the way through to execution into the marketplace.  A lot of times you don’t have the satisfaction – the success or failure – of the assertions that you made in the research phases. In some ways, you feel like an outsider relative to the larger engine that’s driving the business forward.  That’s probably a little bit biased towards the agency side in our world as opposed to the internal researchers, but I still hear that from the internal researchers as well. It’s not surprising to me that would be the satisfaction that we’d get, you’d get specifically, would be connected to a project that had that sort of full market implication and execution point of view.    

[37:26]

Yeah, exactly.  But also, I think that’s where…  I’m going back to like the question is “What is customer strategy?”  I think that’s where the promise of the idea of customer strategy is the evolution of market research.  Within organizations, it’s kind of key. It’s about you going beyond; it’s alleviating or even going beyond the idea of plugging into a type of process and being actually integrated and having the seat at the table in that partnership way.  It should not be standardized to really be there.  

[38:06]

So, the not be standardized, you mean by that like cookie-cutter? 

[38:10]

I think, well, it’s different for every category; it’s different for every vertical.  But to not have that feeling: as professionals you don’t ever want to feel like you’re expendable or replaceable or anybody can come in and just be the cog that helps turn.  You don’t want that; you want that value. And so, even thinking about a functionality, the dangers of AI and machine learning and data processing is that you could see a future where some of the things that we do today are completely replaced.  And so, what is the value-add? Well, that strategic portion of it, that integration into everything that shapes completely with the organization is around the customer. You’re dressing the organization as such I think plays into that.  

[39:04] 

OK, cool.  I like that a lot.  Do you have a personal motto?

[39:09]

Actually, it was given to me by my partner:  going places. I don’t know why he gave me that.

[39:18]

I think I know.  

[39:22]

It sounds like…  Actually, it sounds like…  I have to look it up. Is it Toyota?  It’s not Toyota’s, Toyota’s motto.  

[05:29]   

I don’t know.  I’ve heard it.

[39:32]

Oh, “Let’s go places”.  That’s Toyota. I had to look that up there for a second.  It’s kind of funny and ironic, right, being in automotive transportation.

[39:44]      

Automotive. Yeah, for sure, I like that a lot.  That’s actually really good. That is very much a nice life motto, and it seems fitting for you.  My guest today has been Michael Yaksich, Director of Consumer Strategy at Cruise. Thank you, Michael, for joining me on the Happy Market Research Podcast today.

[40:04]

Thanks so much.  Take care.

[40:05]

Everyone else, if you would please take the time to “Like” this show, share it with friends, colleagues, family members.  As always, my mom’s really proud when you leave a 5-star review; so, if you’d take time to do that, it’d make my Christmas time a little better too.  Thanks so much. Have a wonderful rest of your day.

[40:26]  

This episode is brought to you by Clearworks.  They are an insights, innovation and customer experience company.  They help their clients understand their customers better, identify opportunities for innovation, and create products, services, and experience that actually matter.  Their clients are diverse, both in size and industry. They do share one important thing, which is a passion to drive more business by driving more meaningful human connections.  You can find them online at www.clearworks.net.  Again, it’s www.clearworks.net.  And again, thank you so much for your time.     

PAW 2019 Conference Series – Tony Ayaz – Gemini Data Inc.

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Tony Ayaz, CEO and Co-founder of Gemini Data Inc.

Find Tony Online:

Email: tony.ayaz@geminidata.com

LinkedIn

Gemini Data Inc.


[00:02]

Tony, when did you start Gemini?

[00:03]  

We started the company in 2015.

[00:06]  

2015.  We’re in the Happy Market Research Podcast right now.  We’re at Predictive Analytics World and Marketing Analytics World and there’s lots of worlds in this particular conference. I think there’s like twelve.  Have you guys been to this conference before? 

[00:19]  

This is actually our first time at this conference.  And for us, I think it’s a win because what we’re looking for is real users with real pain a little bit beyond the typical IT folks that we’re looking for.

[00:32]  

Got it.  So, you’re based out of San Francisco.  You started the business in 2015; so, you’ve had some success obviously.  Tell me a little bit about what you guys do.

[00:41]

Sure.  At Gemini Data, we help our customers with digital transformation initiatives.  What I mean by that is we help customers achieve data availability. And data availability is a necessary requirement today if you’re really looking to do something significant or on digital transformation, AI, or ML initiatives.  And what we mean by that is that you have to access to data and you have to make that data available. And there’s a lot of talk about out-of-the-box machine learning solutions and things that are out there in the market. But the reality is that if you’re doing complex things and trying to run your business, you need data diversity, and you only get that through data availability.  And so, what we do is we leverage the customer’s existing investments in various, different data platforms: it could be in a CSV; it could be in a data lake. It doesn’t really matter to us. We have a method that we apply called Zero Copy Data Virtualization that actually takes your data that’s sourced without you to move or copy that data or do the complex ETL processes that we’ve all been used to for the past two to three decades, which just simply doesn’t scale with AI.    

[01:47]

Data diversity is a term I’ve never heard before, but it is my favorite one in this conference.  Diversity is something that we’ve… we’re becoming more and more aware, especially in the Bay Area, like Silicon Valley…  I’d say globally you’re seeing… The math is that if you have more diversity in your senior leadership team, then you have a better world view, which gives you an improved advantage in the marketplace, right?       

[02:12]   

Exactly.

[02:13]

And what’s interesting is how you’re connecting that in with data.  It isn’t about a single… right? It’s about different types of data.  You mentioned CSV versus data lake, which are vastly different, like profoundly different.  Your system is able to ingest both of those? 

[02:32]  

I wouldn’t say ingest, access those systems.

[02:35]

Got it

[02:36]

We don’t want you to move or copy the data, but we allow you to access it in a unified way.

[02:40]

OK, cool.  So that bypasses some PIII?

[02:42]

Yes, it bypasses it in the sense that you’re giving access to people that should have access to it.  So we follow the same protocols of data access they have as their role or authority would provide them.  But we take it a step further of looking into five years from now, Zero Trust Networks are going to be deployed, which is a new, let’s call it, security protocol or methodology, which basically changes things versus where we’re at today:  It’s the perimeter of defense, which I’m going to put firewalls around things; I’m going to give you access to things you should have; and then when you’re not an employee, for example, I take you off. Think of this as more of a real-time basis of how you should have access, when you should have access, right by you as a user using the system.  Nobody has to manually set things up for you. The machine kind of knows what you should have access to, what you shouldn’t. It protects you. And this is something that’s far more deeper and can evolve, but you can only do that by applying modern architectures that have been around less than five years, I would say, to go to this next level for security.      

[03:43]

That’s really interesting.  You’ve been in the industry a long time.  What do you see as some big trends both from things that have evolved relatively recently in the last two to three years and then where we’re going in the next two to three years?

[03:56]

If I may go even a little past two to three years…  So, in 2005 is where I like to start is when the evolution of Big Data started, right?  It was the dotcom crash, but then things were coming up. Big Data, grab all the data that’s going to solve world hunger.  It’s going be awesome.

[04:12]  

I actually think I saw that tweet.

[04:15]

Yeah, probably.  Right. At the time, there was nothing wrong with that.  That’s what you had to do. There’s a whole bunch of data coming in.  Nobody knew how to collect it. So the idea was centralize all this data.  Just grab it. And then there was a lot of successful companies that came through. which one of I had the pleasure of being at.  It was called Splunk in the early days. We grabbed the data, brought it in, and centralized it, made it easy for people. Well, that was 2005, and at the same time, data lakes came out and the whole Dupe and Open Source.  Fast forward to 2013 or into current time, you’re dealing with data chaos. And what’s happening is now that everybody has actually collected everything you could imagine. I call it a messy filing cabinet. Imagine if you went to your filing cabinet and didn’t have proper files and you just shoved papers in there, every time you need to go look through the papers, you have to sift through one by one.  Now, think about the petabytes of data that’s out there.      

[05:04]

Mind-blowing.

[05:05]

That strategy does not work.  If you’re just collecting it, you’re making it very had to access.  And so where we’re going tomorrow, meaning the industry, from an AI perspective is back to that point about AI needs data diversity, right?  You need to make sure that you’re looking at all different data. So, if somebody tells you to move your data somewhere else and port it here or put it in the cloud, they’re doing a dissatisfaction because we’re playing the same game again.  You’re moving that data again, waiting to get access to it, and what I think customers would need today, and if they’re thinking about AI, is I’ve made my investments but I need to make it easier to access. And the way we access that is we make it easy for you to apply standard [unclear] that’s been around for three decades, and you can use it across all these complex systems and bring the data together.  Whether it’s CSV, whether it’s in a database or a data lake, it doesn’t really matter; we’re giving a uniform way to access it.     

[05:58]

And then, it’s accessed and then is there also display and interact with on the other side of it?  

[06:03]

Absolutely.  So, we have our interface that you can look at the data; we integrate it with a graph database:  much like you can use LinkedIn to see your first- or second-degree contacts. Image if you could do that with your data.  So we bring the data together; we allow you to see the relationships, which that by itself provides a significant value to customers because 51% of data scientists dilemma is getting access to the right data set to apply machine learning.  And, if you’re an analytics person, you’re relying on IT way too much to get that access. So we provide that as an option. We have other analytics capabilities on top of that. But the other thing that we do that’s interesting is, if you’ve invested in a Tableau or a Looker or a Business Intelligence of your choice, we don’t want to disrupt the business user.  So, they want data diversity. So we actually can send that data into their BI tool of choice as well. 

[06:51]

So you’re really fitting like an API basically or this middle ware (I don’t know what the right framework is), but that allows a Rosetta Stone of sorts, right, where it’s able to then interpret that messy data structured and then…   

[07:03]   

Yeah, think of it as a…  If you want to classify data management and data integration technologies that have been around for two, three decades, we’re now at a point that they’re trying to apply that towards AI, which basically means there’s a lot of consulting and ETL and time and preparation and people needed to do that.  With the amount of data that is being spit out and what you need to do with AI, that doesn’t scale. So, we’re bringing a modern approach from a cloud prospective how you can access data to source, not move it, and accelerate the analytics process. 

[07:33]

Oh, that’s huge, that’s huge.  That speed-to-insight is what’s king right now.  

[07:37]      

Exactly.  And to your question about the industry, there’s been recent acquisitions with Tableau and everything that’s happened.  In our opinion, that’s kind of validated the need for the market. Now look, if I’m Sales Force and I have the large, diverse data sets and I need to integrate them together and bring Tableau together into that, that’s a fantastic purchase.  But what if you’re not ready to make that migration to the cloud? What if your data is on premise? What if you don’t want to move it around? And customers need to leverage those systems and bring that power to them. But, in reality, what also people have to think about is how am I going to make it easier for my business users, who are not technical, to get access to that data.  And that’s why we really rely on sequel or we make it a graph-interaction with the data so everybody can understand it. We all know the challenges of hiring technical talent.     

[08:23]

War on talent for like the last three years and getting bloodier.  It’s just mind-blowing what’s happening right now on that front.  

[08:32]

Exactly.

[08:33] 

So, who’s your ideal customer?

[08:36]

Starting from the top, I would probably say a Chief Data Officer or anybody in that function or reporting within that group.  Below that, I would say Head of Analytics or business intelligence leaders. And then I would say a layer below that, it would be data engineers that are sometimes tasked with getting this and we can provide tremendous automation for those folks as well.

[08:56]         

Got it.  Favorite customer story?

[08:58]

Favorite customer story I would say is in the health care industry.  I’m not at liberty to mention the customer but… 

[09:02]  

We never are if it’s pharma or health care.  It’s always top secret.  

[09:06]  

It’s a health care but the best quotes I heard from the Chief Technology Officer was that “Hey, you guys bring the best of both worlds to me.”  He goes, “I have my data governance people here that are always telling me how to protect the data and make sure that we don’t violate any compliance issues or things like that.  Then I have my Chief Research side that’s always looking at cutting-edge, innovative things. And they’re supposed to look at AI how to improve things. And you guys are bringing the best of both worlds.”  And what he meant by that was we’re moving in those data-sharing economy that let’s say you’re a researcher for cancer and you have a cure for some ailment of cancer; and I’m a numbers-cruncher, but I have all this data based on medical device data that when we applied this medication to that we could solve that problem; and a third party may be a health care provider trying to see how many patients are accessing that or could improve the lives of people or reduce insurance rates or whatever may be.  This is all stored in different areas. If we could actually share our data in the context that we had together, those are tremendous things that we can solve together. And that’s why I really like the health care side where we’re giving them access to so many different data sources that can have profound effects on the better good and health and other beings’ aspects of life that we can hopefully provide for our customers.      

[10:21]  

That is super powerful.  I love that story. That just crystallizes the importance of the work that you guys are doing.  

[10:26]  

Exactly.  Thank you.

[10:27]

If someone wants to get in contact with you or Gemini data, how would they do that?

[10:32]

You go to our website standardofgeminidata.com or contact geminidata.com.  I’m always accessible, so Tony@geminidata.com.  Yeah, it’s very easy to talk to us.

[10:45]   

Yeah, perfect.  Tony, thanks so much for being on the Happy Market Research Podcast.

[10:48]

Thank you for having us.

[10:50]  

Everybody else that’s listening to this show, please take the time to screenshot it.  This is my – sorry to the other guests – favorite episode so far. The data-sharing part I thought was really interesting that Tony brought up.  The data-sharing economy: that is such a powerful framework for us to start understanding how we’re going to make better decisions as we incorporate more data diversity in those.  So definitely take the time, screenshot this. Hope you tag us on Twitter, LinkedIn, whatever your social media platform of choice is. Thanks so much for all the support. Have a great rest of your day.

PAW 2019 Conference Series – Satish Pala – Indium Software

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Satish Pala, Senior Vice President, Digital at Indium Software.

Find Satish Online:

Email: satish.pala@indiumsoft.com

LinkedIn

Indium Software


[00:02]

You’re listening to the Happy Market Research Podcast.  I’ve already screwed up this intro a couple of times. I apologize for that to my wonderful guests.  Right now, I’ve got Satish Pala, Indium Software. He is a market veteran, been in the industry for 20 years.  Welcome to the podcast.

[00:22]  

Thanks.  Thank you so much.

[00:23]  

We are live today at Predictive Analytics World and Marketing Insights World and all kinds of worlds.  I think there’s 12 shows. It’s a lot of shows. That’s right, yeah. From health care… Everybody’s using insights.  What do you see as one of the big trends in the industry?

[00:42]  

So, one of the things that I see is people having a lot of data, people thinking a data analytics solution will help them but have no direction or a strategy towards these concerns of theirs.  So they are trying to do what I call a prototype-based analytics. So, they would want to spend some money, figure out what insight could come up with that amount of data they have in a shorter cycle, maybe a month or two.  And then they figure out how it is going to impact their business. And based on the business impact, they’re trying to spend more, invest more in the scheme of data analytics where they can get impact, a positive impact on their business.   So, for example, if a company who’s having products related to customer, consumer, for example, electronics and they see the trend in electronics buying has reduced, they would want to analyze this challenge. They would want to analyze why the trend is so in terms of how the consumer behavior is.  So they would want to take the data that they have and analyze the patterns of consumer behavior and identify how they could fix it or, I would say, how they could work around these challenges that they’re facing. Having analysis on data insights on data can give them a picture of what they need to do in the near future.     

[02:04]

So, Indium Software, you guys have been around awhile?

[02:08]

Yeah, we’ve been around for almost 20 years.  And, as you said, we are veterans in the IT services.  We deal with quality solutions, services. Primary, I would say the portfolio that we solve, is at once analytics; then we have Big Data analytics, where in we help customers do data engineering, where we bring in all the data into a warehouse or data lake.  We give them business intelligence solutions; we give them visualization solutions; we also offer descriptive analytics solutions; we also offer advanced analytics, predictive-modeling-type solutions. We also help them integrate these solutions into their web portals.  So, one of the key challenges we have noticed is that, while they have done their analytics model on a dashboard or a visualization layer, they are trying to figure out how do I input this into my day-to-day life or day-to-day operations. So that is something we really focus on and help the customers with.       

[03:07]   

You know it used to be the case that data was far away from the person that needed it, and now it’s moving really close, even like integrating insights into the decisions that are the workflows of the practitioners that are inside of the brands.  

[03:25]

I don’t want to interfere in your questioning, but the thought you have is right.  People have identified analytical models; people have insights. But how do they operationalize these insights?  How do they put in the business…?

[03:37]  

I love that term “operationalize.”

[03:39]

So, we normally have an analytical model life cycle that starts with data preparation, modeling, model management, and operationalization of analytics.  So, this is something that we’re very good at. We take the insights and put it into the business process so that the business can see the value out of this model.  So that is one of the challenges where each business is trying to see how my model helps me. OK, I invested so much in analytics, but how does it help me in regular day-to-day operations.  Is there an automatic, seamless way of identifying the solutions for these challenges I have? Can these models be running a business like a regular day-to-day work instead of some data scientist coming to me with a report?  So that is the key. And the seamless integration of modeling into business processes is called operationalization, which we are very good at.

[04:30] 

That’s fantastic.  Do you have a favorite customer story?    

[04:35]

Oh, yeah.  So, I have a couple of stories, but let me focus on the one. So, let me focus on the one where we have helped them predict, I would say, anomalies on their production line or on production output.  We also helped them identify all the parts or the assembly line that they have, which can fail in a few weeks. For example, let me elaborate. This is a manufacturing company, and it manufactures a very niche product, which has advanced machines that are fabricating this product.  They have a challenge where they want to identify the anomalies in this product because the product is so niche, the quality needs to be optimum quality, right? So they wanted to analyze the data that is available with them to identify all the products or all the output of this assembly line that can be outside the specifications that they have.  So, we had helped them build an analytic model to detect the outliers, I would say. These are the anomalies. That is one, which helped them make the productivity higher, maybe make it, I would say, the assembly-production process was much more efficient than before.    

[05:56]

So they used that data in order to identify where the anomalies were taking place.  They could focus on improving it, whether it was like a lean approach or maybe a machine or…

[06:07]    

There were various things that they could do.  They could identify problematic machines that were fabricating these devices.  Or they could identify the process, by which they are fabricating. They could identify maybe a change to the product itself.  Let’s say you’re getting ten out of a million products. Is it the right number? Should I make it zero defect, zero outliers. So, that is something that is very important for them to have zero outliers because it’s a very costly product and niche product.    

[06:38]

Yeah, for sure.  

[06:39]

The second thing that we have helped them with is these assembly lines or these machines that fabricate this product, they have various sensors on top of them.  So, these sensors generate events. And these events, for example… I’ll just give an example, right? So, one of the sensors is a temperature sensor, the other one probably related to luminosity.  So, the outcome is that they want to identify how safe is my assembly line, how safe is my assembly station. And they have data available from these sensors. So, there are thresholds set for this sensor data that   if the temperature goes above so much Fahrenheit or the luminosity is above so much levels, which is safe zone, they would want to flag it, saying that this particular assembly station could fail in two weeks or three weeks.  Would you like to do preventative maintenance so that it is safe and you don’t have an accident later? So it’s pretty advanced because we had to bring in the IoT data. So this sensor data generates IoT data.

[07:44]  

Are you partnering with somebody to do the IoT?

[07:49]

No, we do it ourselves.  

[07:51]

It’s all yours, yep, right.  You have some kind of like sensor that you’ve installed on the…?  

[07:57]

OK, in this particular scenario, the sensors were already installed by them. 

[07:59] 

OK, got it.

[08:01]

We helped them ingest the data from these sensors into a data platform, which we built.  And then, we developed an analytical model to identify outliers, the anomalies. We also built a predictive model to identify when a part can fail or when a station can fail.

[08:15]

So it’s predictive maintenance is what’s happening.

[08:20]

Predictive maintenance is the key.  Two things: one is identify the outliers; second is predictive maintenance, which is very important because it is better you perform preventative maintenance rather than reactive where you have to spend a lot of money.    

[08:33] 

Yep, makes sense.

[08:35]

And this particular company built it for themselves.  Now that it’s working for them, they are productizing it, monetizing it, positioning it for their vendors.  So, that’s the nice story and we’re very proud of this because it really helped them improve their efficiency. 

[08:50]      

Indium Software, if somebody wants to get in contact with you, how would they do that?

[08:53]

So, you could go right into the web portal www.IndiumSoftware.com and then you will see key-members profiles available there.  Or you could go to LinkedIn or you could go to social media, Twitter.  We’re available everywhere, and we will contact you right away.

[09:08]

Satish, thanks for being on the Happy Market Research Podcast.

[09:11]  

No problem.  Thanks, it’s my pleasure.

[09:12]  

If you enjoyed this episode, please take the time to screen capture, share it on social media.  Special thanks to Predictive Analytics World. Really appreciate you guys hosting us. Have a wonderful rest of your day.

PAW 2019 Conference Series – Ryohei Fujimaki – dotData

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Ryohei Fujimaki, Founder and CEO of dotData.

Find Ryohei Online:

Email: ryohei.fujimaki@dotdata.com

LinkedIn

dotData


[00:02]

My guest today is Ryohei with dotData.  Actually, you guys have the premier booth location on site at this year’s Predictive Analytics Conference.

[00:15]  

That’s true.

[00:16] 

Yeah, what do you guys think about the show so far?

[00:19]  

Yeah, this show…  This is the first time to sponsor this show.  And the show is great: like a lot of relevant audiences.  And we have the keynote presentation for 20 minutes. It’s very well received.  A lot of data scientists are looking for new trend in this industry. So we really like it. 

[00:37]  

Yeah, it’s pretty well attended.  I really like the layout of it. I feel like there’s a nice cross-pollination between the speaking events that are happening and then the exhibit floor.  It’s really well laid out. And the attendee list is pretty good.  

[00:52]

Yeah, that’s true, that’s true.  It’s a very good mixture of the technical audience and the business audience.  So, we really have the good conversation with a lot about 10 days in our booth.  

[01:01]

Yeah, that’s great.  And your booth, by the way, I think is spectacular.  It’s the perfect booth kind of as the entry point ‘cause it’s very welcoming and you feel like you just go sit down and have a nice conversation.      

[01:12]   

And also, we have the Lunch and Learn Session to talk more about data science automation, particularly for Python users.  There are a lot of people standing, taking a lot of memos, and they are taking maybe thousands of pictures. So that’s a very, very good session we had.   

[01:28]

Oh, that’s great.  I’m sorry… I’ve been doing podcasts straight through like every 20 minutes or so.  So I haven’t been able to attend any of the content, unfortunately, which is very disappointing ‘cause it seems like it’d be interesting.  So, dotData, what do you guys do?

[01:46]  

Yeah, so, dotData we are offering end-to-end data science automation.  And, basically, we are the first and only company who can automate end-to-end data science process from raw data through data on the feature engineering and machine learning in production.  In particular, dotData we have the very strong artificial intelligence technology that automates the feature engineering process. The feature engineering process was told it’s not possible to automate because that’s a black art of domain expert.  But we invented the really strong technology, our world is going to explore a lot of business hypotheses with automated expertise.     

[02:28]

So congratulations.  That is a very hard problem to solve.  Do you have a favorite customer story?

[02:36] 

Yeah.  So, the most favorite story I have is our first customer, of course.  That was a very, very exciting moment. Actually, that is the project we kind of decided to launch the company.  And that was one of the top 15 banks in the world, a very, very huge bank. And they have the data science team. And their problem is, first of all, they have no sufficient data scientists.  It takes a very long time to complete a data science project: each project takes three to four months by a couple of data scientists. What we have achieved in that project was literally we just took out tons of huge table, huge data in the bank, and they applied dotData technology.  Just within a day, we delivered the outcomes to the customer. And the outcomes are even better than the result of the data science team. The customer was so impressed and so excited because that is really accelerating their process. It used to take months, but now they can complete a project in a couple of days.  It’s a huge acceleration 

[03:51]

Even with a better outcome.

[03:53]

Yeah, even a better outcome.

[03:54]    

When you’re interacting with a customer, are you interacting with the data science team pretty heavily?  Do they see you as a partner? Or do they see you a little bit as a competitor?  

[04:08]

Uh, no, we are actually quite good partner because we are always telling them automation is not something to replace a data scientist, but it helps data science team in a couple ways.  First thing is acceleration. Just imagine they can run a data science project within a couple of days. Eventually, data science project is turnaround. They have to try a lot of different ideas and learning what works and what doesn’t work.  That turnaround agility is a key for succeeding in data science project. So that is one way we are going to help. Another way is what we call democratize data science. Experienced data scientists should very focus on high-impact project or technically challenging project.  But there are a lot of templated projects, standard, common projects that even non-data scientists can execute with our data science automation. So data scientists are not our competitors at all; rather, we are helping them be more efficient, more effective.   

[05:19]

So that’s your user.  I like your framework of this democratization.  It feels to me like more and more people are leveraging AI-based technology in order to make informed business decisions.  Your tool, obviously, is a great example of this. What other trends have you seen, having been in the industry for over a decade, what have you seen that’s evolved and where do you think the industry is going in the next three to five years?   

[05:47]  

So, actually, for these couple of years to maybe two to three years, first of all:  automation of machine learning and data science is going to be a very, very big momentum because there’s a lack of data scientists, a lot of data science projects that eventually fail maybe because of data scientists, because domain expertise, because communication between business and data science.  There’s a lot of reasons. One the other hand, automation can address a lot of these issues. It’s not replacing data scientists, again, while it is going to address a lot of industry challenges. This is the first step. The second step we are seeing just imagine automation enables us to build a lot of machine-learning models very, very easily.  Today, we are talking about 10 models, 50 models but two to three years later, we are talking about 100 or even 1000 machine-learning models. What happens is operationalize this model, maintenance of this model: those are going to be a very big problem in the next three to five years. So this is another area we are working very hard.   

[06:55]

So the issue there is it’s hard to maintain all these disparate, niche models.

[07:01]

Yeah, because the value to build a model is getting lower and lower.

[07:06]

That’s very interesting.  Haven’t actually heard anybody articulate that point before but that’s fascinating.

[07:13] 

Yeah.

[07:13]

Yeah, for sure.  If somebody wants to get in contact with you or sales at dotData, how would they do that?

[07:20]

Yeah, please visit us at dotData.com or LinkedIn and please download our White Paper or look at the webinar and let us discuss in detail.

[07:31]

It’s been an honor having you on the Happy Market Research Podcast today.

[05:34]   

Yeah, thank you very much.

[07:35]

And for all of you who are listening, please take the time to screen shot and share this content.  There’s a ton of value wrapped up into these types of conversations as you get just a microview of a major player inside of the analytics space.  I hope you enjoyed it, found value. Have a wonderful rest of your day.

PAW 2019 Conference Series – Mike Galvin – Metis

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Mike Galvin, Executive Director of Data Science Corporate Training at Metis.

Find Mike Online:

Email: michael@thisismetis.com

LinkedIn

Metis


[00:02]

Hi, I’m Jamin, and you’re listening to the Happy Market Research Podcast.  We are live today at Predictive Analytics World. My last guest at the show is Mike at Metis.  Tell me a little bit about the company.   

[00:17] 

Sure, so, as you know, that in analytics, data science talent…  There’s a huge gap and there’s a large demand for it. So, that’s where we come into play.  We’re a data science and analytics training company. We’re part of Kaplan. So, if you’ve heard of Kaplan, the global education company…  We’re about six years old, launched organically, and we work with companies to upskill their staff, both technically and non-technically in kind of all things data science and analytics (data literacy, tools, machine learning).    

[00:45]  

That’s awesome.  I actually think data science is the No. 1 job right now, nationally; I’m not sure if it’s global but certainly in the U.S.  And there’s a big gap in terms of the need, the desire to hire from big companies and small companies, for that matter, and the available workforce.  Sounds like you guys are playing a big part in, after people are hired, that subsequent improvements and ongoing skills training.  

[01:15]  

That’s part of what we do.  There’s a little bit more. So, we have an accredited boot- camp that’s twelve weeks long.  We have it in New York, San Francisco, Seattle, and Chicago. That is a retail consumer product for people who want to shift into or pivot their careers into data science roles.

[01:30]  

How is that helping?

[01:32]

So, that helps with the data acquisition, intel acquisition pipeline at the entry level.  Then, there’s the corporate training business, which is where I work. Within the corporate training business, we work with companies who not only upskill their existing tech talent in data sciences and in new areas and new tools and things like that but also their broader workforce; sometimes, even not technical in C-suite all the way down to individual contributors to build that literacy and fluency so that they can interact and collaborate with the data science teams more.    

[02:01]

Oh, that’s very cool, very cool.  Do you uh… On the engagement side of things, do you guys also have placement, help companies with placement or job candidate as you’re doing…?  It seems like there’s that middle piece between people want to pivot their careers, right; so, you’re training at the data camps, etc. And then, all of a sudden, there’s like the need, which you’re training people internally, right; so, the space in the middle is, “I want to hire.”     

[02:32]  

So, not directly but indirectly.  So on the bootcamp side, part of that is getting people jobs.  So we have an entire career support team. 

[02:40]

Oh, you do then.

[02:41]  

Yeah.

[02:41]

OK, got it.

[02:41]

To get people into actual data science jobs.  And over the past five-and-a-half, six years, we developed a huge network of hiring partners that we work with, and this ranges from Apple and Facebook to IBM and Ooze and all the way down to smaller companies as well, depending on who it is.  We started with the bootcamp, but that hiring network is really how we kind of started getting to the data science corporate training space ‘cause we started talking to them and realized, “Hey, there’s not only this entry-level hiring partner…”  

[03:12]

See, I think that’s really important because you’re offering really the whole product for the corporation, solving three distinct problems for them in that framework.  And that’s a really powerful, awesome place to be able to sit, which again because of the sheer value and the network effect that you have because, obviously, you have the pivot people or whatever (the trainees, if you will) and then ongoing training inside of their corporate experience.  So that sounds like a very compelling product.   

[03:52]

It’s an entire end-to-end journey really.

[03:55]     

Exactly, end-to-end.  Right, totally, or end-to open end…

[03:58]

End-to open end.  That’s right. I like that.

[03:59]

So, tell me a little bit.  Do you have a favorite customer story? 

[04:03]  

Oh, wow, there’s so many.

[04:05]

Every customer story is a favorite, but you have to pick one.

[04:09]

Sure, so, I’ll just give you a recent example.  So, we were working with a consulting company that works mostly in government and telecom.  And one of their key issues was they’re primarily Excel users and some of the problems they were encountering required a little more advanced analytics but also, they had really large data sets that Excel couldn’t handle anymore.  And so, that’s where we came in. And we put together a curriculum for them to train their consultants and principals and everyone kind of within the organization in Python to help add to their Excel workflow; and delivered the training a few months ago; and got some results out recently.  And they have a 22.5-times increase in speed of developing… doing their analyses.  

[05:03]

That’s unbelievable.

[5:03] 

Yeah, that’s pretty cool because I always love to hear the impact that the training actually has because sometimes it’s hard to connect the dots to ROI.  And in a case like that, it’s really apparent.

[05:12]

So, now, are you actually creating customized curriculum per customer or is it more black box?

[05:18]

It’s a little bit of both.  So, we do have more what I would call off-the-shelf courses that we deliver, and it’s constantly evolving based on demands we’re seeing in the market.  But we also work with companies to develop more custom, bespoke products depending on their needs.  

[05:33]

Got it.

[05:33]   

A lot of times we re-purpose what we have but contextualize it to their particular use cases.  So to give you one example: Working with a client, who is a large Fortune 500 and financial institution, and they have a talent-acquisition pipeline problem; they want to create data scientists.  So, what they’re doing is hiring STEM graduates right out of college and putting them through a twelve-week-long, on-boarding program. Now, we can re-purpose kind of our off-the-shelf, bootcamp curriculum for that, but what’s really important to a company like the one we’re working with is can we integrate in their use cases, their data sets.  Some of their tools have some of their data scientists and machine learning engineers come in and contextualize it so they get a better flavor of the type of work that actually may be doing once they roll into their full-time positions.    

[06:22]

That’s awesome.  That is a great story.  What do you think about the show?

[06:26]      

It’s great.  First time at Predictive Analytics World.  So, so far, so good. Talking with a lot of great people, had a lot of great conversations.  Pretty diverse crowd, which I love. I just gave a Lunch-and-Learn talk: (It went well) “Building Organizational Competencies for Data Science.”

[06:44]

I’m sorry I missed that.  It sounds very interesting.  They got me tethered to the booth here. So…

[06:52]

I’ll let it slide this time.   

[06:53]  

Thank you, thank you.  

[06:54]  

Had a great turnout.  Lots of engagement. So it was an overall good session, and I’ve had a great time at the conference so far.  

[07:01]  

Good.  Hopefully, it’s a lot of good leads.

[07:03]  

Well, fingers crossed.

[07:05]

Yeah, yeah.

[07:05]

Only time will tell.

[07:07]   

That’s the truth of it.  That’s the truth of it. If somebody wants to get in contact with you, how would they do that?  

[07:13]

Sure, so, one is our website:  Thisismetis M – E – T- I – S.com.  You can kind of explore the website and go from there.  If they want to reach out to me about corporate training, you can reach me at corporatetraining@thisismetis.com, and then my email is Michael@thisismetis.com  

[07:31]  

Michael, thank you for being on the Happy Market Research Podcast.

[07:33]

Great, thank you.

[07:34]

Everybody else, I hope you found a ton of value.  I certainly did in this episode. If you please take time, screen share, distribute it on social media.  I would really appreciate it. Thank you, all. Have a wonderful rest of your day. 

PAW 2019 Conference Series – Matt Cowell – QuantHub

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Matt Cowell, CEO of QuantHub.

Find Matt Online:

Email: mcowell@quanthub.com

LinkedIn

QuantHub


[00:02]

Hi, I’m Jamin.  You’re listening to the Happy Market Research Podcast.  My guest today is Matt Cowell, CEO of QuantHub. Matt, I’m really excited about this conversation we’re going to have.

[00:13]  

Yeah, yeah.  Thanks for having me on.  I appreciate it.

[00:14]  

So, you guys have a super interesting product concept or business that I am thrilled to dive into.  It looks like (now, keep in mind I haven’t spoken to anybody but just kind of walking to the booth and looking at your video) it looks like you are a… almost fit inside of the HR layer for data scientists.  Is that kind of…? 

[00:42]  

Yeah, that’s correct.  Really data scientists, data engineers, data analysts, all advanced analytics professionals. 

[00:48]  

Got it, got it.  Well, I should be quiet.  Why don’t you tell us a little bit about your business.

[00:52]

We actually help companies that are hiring in that area, whether it’s data science or data engineering.  These roles are really popular right now; they’re really hot. Everyone wants to be in this field. And so, what’s happening in the industry is when people post a position, they’re getting a ton of candidates.  And so, we found that people are spending just an inordinate amount of time doing the technical screening for those candidates. So they’re taking their most valuable resources and they’re having them do interviews all day long.  

[01:18]

Which is insane.

[01:19]   

Yeah, which is crazy, right?  So, there’s a shortage of these people.  And the ones that they do have are actually spending all day interviewing.  And so, we have this problem ourselves. We’re actually a spin-out of a data science consulting company.  And so we built a platform to do…  

[01:32]

I don’t know what’s happening behind you but…  It’s like Lord of the Rings

[01:36]  

It’s like I’m dropping the mic right now.  I’ve got sound effects on the podcast. It’s incredible.  And so…  

[01:44]

Wait, wait for it, wait for it.  My God, what is happening in there?

[01:50] 

Incredible timing, incredible timing.  

[01:52]

Literally, the whole time this has never happened.

[01:54]

This is amazing.

[01:55]    

OK.

[01:56]

OK.  And so, we actually built a skill testing and data challenge platform for these roles.  In the interview process, what would happen is you would actually get candidates that would probably come in by the hundreds because the job’s very hot.  And then, you would send them some sort of skill test in machine learning or data wrangling, data exploration or R, Python, you know, all the kind of relevant skills, depending on the job; and use that to sort of screen out people.  And then you might also send data challenges that are more hands-on.     

[02:26]

So, those exercises, are they done remote or are they done on-site? 

[02:31]  

Yeah, they’re web-based.  They’re all web-based exercises.  They’re all graded automatically. And so, it takes about two minutes for a HR person or a tech lead to send out these challenges.  And then, they just start getting scores in from candidates.

[02:45]

I think it’s fascinating.  And it looked like there’s a score card of your overall quality of talent or talent quality, sorry.  That I thought was really interesting because it identifies potential weak spots that you need to create a bridge for from a training perspective.

[03:04]

Right.  So, you’ll see companies out there that do assessments like we do.  And so, I think what’s different about us is we don’t just do that for hiring.  We want to apply that to the entire really talent process. So, bringing on people, of course, that’s a normal use case.  People use assessments, but also to your existing team. We want to help benchmark the skills of your team, help people figure out sort of where they want to go in their careers and actually that sort of skill gap that they have to get there, and then align them with training.  So, if you’re a leader of a team, you could see that sort of skill graph and see where you’re deficient. You can work with your individual team members and figure out where they want to go and then what skills they need to get there and then align them with training. The problem we’re trying to solve there is there’s so much training; there’s so many companies now focused on training in this field that it’s a little overwhelming.  People don’t know where to start. And so, what we’re trying to do is help them actually make it much more targeted. And so, I don’t want to go to an intro to data science course. Maybe I just need to learn NLP. And so, let’s actually determine that through assessments and benchmarking.    

[04:04]

What’s interesting is at scale, all of a sudden, you can be like…  Part of the meta here is you could become the greater of the data science camps that are proliferating everywhere, right?  You could say these guys are generating… You know this is their score. 

[4:18] 

It’s a normal evolution of content.  So, if you think about a company like Trip Advisor, why do they exist?  They exist because there’s so much content out there that someone needed to come in and aggregate and point people in the right direction.  And I feel like that’s exactly where we’re going to fit. There’s so much training content someone needs to come in as an aggregator and point people in the right direction.  And that’s kind of where we’ll fit.    

[04:41]

That’s a fun thing too from an SEO perspective.  You really have an opportunity to differentiate yourself and then jump materially in the rankings, which isn’t bad either.  

[04:53]

That’s the hope.

[04:54]

That’s right.  That’s interesting.  So, you’ve been part of the industry for a while.

[04:58]   

Yeah, I’ve been in product and tech for a long time and a little bit in analytics.  So, in the last couple of years now, I’ve been exclusively focused on the analytics space, which has been fun ‘cause it’s pretty hot.    

[05:09]

Yeah, I love it.  When you look forward in the next three to five years, what do you see as a trend?

[05:15]      

Yeah, I think people get worried about jobs being automated and gotten rid of and displaced, I should say, by automation.  What I think is interesting about this field is the role of data scientist is not something that is easily automated. You know there’s all these companies, even here at this conference, that are doing automated-type model-building and whatnot, but the actual data science part of it is pretty tough.  That’s pretty tough to automate. So I think that will continue to evolve and become more and more something that companies just have to invest in. So I think we’re at a great point here where all companies are doing something in this area. And so, for us, I feel like we’re at a good place in the industry as it continues to evolve and people invest more and more in this.  So I feel like that’s an interesting… It’s an interesting time.

[06:09]

Observationally, it feels a lot like kind of the web as the web started to scale up:  kind of mid-Yahoo. I’m so old I have that point of reference, right?

[06:20]

Me too.

[06:21]  

Which is exciting for me because you can really see how this thing is going to J-curve.

[06:26]  

Yeah, yeah, definitely.

[06:27]  

Everybody knows they need to do this:  employ ML or AI, etc. into their overall business processes and decision-making.  But they’re not exactly sure how or where, but at the same time they’re willing to sequester big pieces of corporate budgets in order to solve that problem.  So it’s a nice land-grab opportunity. To your earlier point, the ones that are adding value like the data camps or whatever and creating great outcomes for the businesses, I think there’s mega opportunities over the next five years.   

[07:04]

Yeah, another thing, I think, is happening or will happen is my background is more on the product and tech side within software development.  I gave a talk yesterday that there are things that we do in that field: embracing product management, agile and lean concepts that, for some reason, we’re not yet really embracing as much in the analytics space.  So I think that will continue to evolve. We’ll learn from our sister fields and I think that’s going to help us deliver better outcomes. I think right now we’re all excited about the cool tech but not always delivering on the outcome.  So I think we need to align better with the business strategy, which is not straightforward to do, but there are roles out there that – product management being one of them – that, if we embrace that, I think we’ll have a better shot.     

[7:48] 

Yeah, totally.  One of the CEO’s that I’ve interviewed had this term “data diversity.”  And I thought that was creating better outcomes for businesses. That’s not like single source; it’s looking across the organization in the market in order to find out whether it’s consumer data or employee data or what have you.  So the more you that you can create like benchmarks, third-party validations and then internal… You know start triangulating this point of truth. 

[08:14]  

Yeah, yeah, that makes sense.

[08:16]  

Uh, what do you think about the show?

[08:19]  

I think it’s good.  This is our first time out here at this show in Vegas, and it’s been good.  What’s interesting, as an exhibitor, is that there’s no traffic when sessions are going on, which actually must mean that it’s really good content.  Normally, people don’t go to all the sessions like this. So it’s like a ghost town here during sessions.   

[08:39]

Yeah, it really is.

[08:40]

Because of all the special effects behind me that you just heard.

[08:43]   

I think they just did that for you.  That’s hilarious.  

[08:48]

But it’s been good.

[08:49]  

It’s been good.  I like how they’ve laid it out.  I think there’s lots of good cross-pollination in between the speaking sessions.  And the food’s been pretty good. 

[08:57]

The food is exceptional.  I go to a lot of these shows, and the food here is exceptional.   

[09:02] 

Me too.

[09:04]

Yeah, I’m going to need to work out when I get home.

[09:05]

I know.  

[09:07]    

So, if somebody wants to get in contact with you or sales at QuantHub, how would they do that?

[09:12]

Well, you can go to QuantHub.com and we’re offering a free trial.  You can email sales@QuantHub.com, and we’ll be right back in touch.  And so, easy to get in touch with us.  And it’s super easy to get started. We work with companies that are testing and assessing 5,000 people a month and startup companies that are doing 5 a month.  

[09:32]

So you got a nice spectrum there.

[09:34]  

It’s been a little interesting how it’s played out that way, but it’s really easy to get started.  If you’re not sure how many candidates coming in a month, then you could still get started really inexpensively.  

[09:44]

Do you guys also, you know…  Thinking about it… I imagine you’re having some visibility where the prospects, candidates are getting sourced from.  Do you see that as something that as a service or auxiliary partnership?   

[10:01]

Yeah, so for us, actually that is an area that we’re just now starting to get into.  We’re doing public challenges, “public” meaning in the data science and the data engineering community.  And so, what we want to build up is actually a community of people that go through these challenges and actually vet their skills and help them benchmark where they are but then also align them with people, companies that are looking to hire people like them.  And so, I think that’ll be an interesting opportunity. Obviously, we’re not doing that with the database of people coming from a company but, if we’re doing that with public challenges and whatnot, then we can help on the sourcing side, which is a pain point that companies have as well.  They’re not able to find all the best people. And so, we want to help with sourcing and vetting. Right now, we’re more on the vetting end and development; we also want to help on the sourcing side.  

[10:49]

Yeah, makes a lot of sense actually.  I think more and more I’m seeing single source being a direction that businesses are going to right now.  So the more you can offer the complete package, the simpler their lives are.

[11:01] 

Absolutely, yeah, without a doubt.  We’re very niche-focused; we exclusively focused in advanced analytics.  We think this field has neat characteristics and there’s plenty of room to run here.  And so, we’re not planning on getting out into other spaces. We want to get in this space and know it really well and be excellent at this.  We came out of a data science consulting company; so, this is sort of in our DNA.

[11:24]

Totally.

[11:25]

And we think this role is different:  This is not a programming role. A lot of companies we’ve seen treat it like a programming role and really miss the statistics side and the modeling side.  And so, we really come at it from that perspective more than anything else.  

[11:38]

It’s got to be refreshing from the customer point of view.

[11:40]   

Yeah, yeah, we’ve had customer actually displace others that were more programmer-based assessments.  We test on programming, of course, but displace those companies because we were more statistically oriented.

[11:52]

Right, exactly.  I think historically that’s been overlooked, but I do think that’s getting rectified right now from a priority perspective.  

[12:00]      

Yeah, yeah, definitely, definitely.

[12:02]

So, sales@QuantHub.com.  My guest today has been Matt, the CEO.  Matt, thanks for joining me on the Happy Market Research Podcast.  

[12:09]

Yeah, it’s been a pleasure.  I appreciate it.

[12:11]  

Everybody else, if you found value in this episode, please take the time to screen shot it, share it on social media.  I really appreciate it, and I hope you have a great rest of your day.

PAW 2019 Conference Series – Mark Do Couto – Altair

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Mark Do Couto, VP of Worldwide Sales, Data Intelligence at Altair.

Find Mark Online:

Email: mdocouto@altair.com

LinkedIn

Altair


[00:02]

My guest today is Mark Do Couto, Altair.  Mark, what do you think about the show so far?  

[00:08]  

It’s a great show.  It seems like there’s a good turnout.  Lot of great, interesting customers here.  It’s always good to see a lot of traffic and fellow vendors that are out here supporting.

[00:20]  

I really like how they laid everything out.  This is my first time at this particular event, and I feel like they’ve really nailed the cross-pollination between the attendees and the exhibit hall.

[00:29]  

Yeah, it’s really cool that some of the speaking engagements and stages are kind of opened into the exhibit hall; so, it makes a more natural flow.  

[00:37]  

Yeah, totally.  So, Altair – got it, right – is a recent acquisition or acquirer.  Tell me a little bit about the core business. 

[00:46]

Yeah, so, Altair, as a company, is traditionally a manufacturing and engineering company.  Focus on design, lot of time spent in the automotive, aerospace. But they really want to look into data and really get into the data space.  So Altair acquired Datawatch in December of last year. And Datawatch has a full suite of products, which goes straight from data preparation to predictive analytics to our own visualization software.  And most of our customers are based in financial services, which is completely outside of what Altair focuses on, which is a reason they were really excited. They were excited for us to stay in the market that we’ve been playing in, but also see how we can bring this into kind of the traditional manufacturing and engineering space.         

[01:28]

Oh, that’s interesting.  So it sounds it’s 0like a value-add to their existing, their core business but then maybe moving into another market?  

[01:35]   

Exactly.  So they, basically, have now acquired a company that already has customer and share in another market, but we’re having really interesting conversations with their traditional customer base:  a lot of helping predict machine failure, doing predictive maintenance on the manufacturing floor. So some really interesting use cases are coming out of their traditional customer base.   

[01:57]

That is actually really interesting.  Yeah, the downtime on, you know, if a cog goes out on a machine is catastrophic from an ROI perspective, right?

[02:06]  

Absolutely.

[02:07]

So being able to predict when machines are going to need maintenance and that kind of thing is actually a big industry that a lot of people don’t know about.

[02:15] 

Even some of the major automotive manufacturers that Altair traditionally works with, they have like trucking fleets.  So, if a truck is out on the road and it goes down, that delay can cost a lot too; so, we’re looking at ways that we can help predict when maintenance and support’s going to be needed on those trucks and get it done before it goes out on a cross-country haul.  

[02:36]

So, tell me about who’s your ideal customer?  Not, not..I mean Altair, obviously, is kind of this big but more in line with your core. 

[02:51]

Yeah, absolutely.  

[02:47]    

Datawatch or before.

[02:50]

So, the core of the business is really around how you’re leveraging the data that you have, but how you’re doing it in a way that you don’t need to be traditional data scientist.  So, really, it’s what Gartner would call your “citizen data scientist.” So, it’s a visual space to leverage data, build predictive models, and then automatically export that into a BI tool or even automatically generate code from that so that you can plug it into a deployment engine.  So our customer base is really people that are looking to leverage data science without having to get a Ph.D. in it. So it’s more of the “citizen data scientist,” but, in the same breath, if people who do have experience win R and Python, you can leverage that within our tool as well. So you can do some of that coding stuff if there are people on-team that want to do it.  But it’s all about keeping everything transparent. We could automate a lot of things if we wanted to, but we like to show all the different steps because that allows for anyone in auditing and governance to be able to see everything that was done, full transparency and then take it from there.   

[03:55]

That’s actually really interesting about the need to not build automation, right?  The not building automation actually adds a layer of value because you have an audit function that can pay attention to those steps. 

[04:10]  

Yeah, our traditional core business was always around financial services; so, we work with all of the major banks, the user software to some extent.  And for them, that was key. That whole black box solution or magic button just didn’t work for them. They really need to see the steps that are taken right from where the data source was taken in from to where it was deployed and gone live.  So we’ve tried to keep everything transparent as possible so all those steps can be seen. There are parts that we optimize. So we do a lot in terms of optimization to make the performance a lot quicker, but our customers seem to really like that.  

[04:46]

Oh, that’s really interesting.  Since you’re in financial services, BII got to be a big part of the whole value prop.

[04:52]

Yep, absolutely.  It’s one of the reasons why we ensure that our software can be run on premise within their environment.  We do have a cloud-based solution that we can provide but, at the end of the day, we let our customers choose how they want they want to deploy the software, and we meet all the security protocols to ensure that their data is safe.  

[05:10]

How long have you been with the company?

[5:11] 

Been with the company just over seven years now.  

[05:14]

So quite a while.

[05:14]

Yeah, absolutely.

[05:15]

Yeah, no kidding.  So you’ve seen a lot of…  I mean this whole democratization of access and data science is, from my vantage point…  It used to be really hard to run statistical models. I did it back in the 90s when I first started my career.  And it was a pain in the ass. I mean there was a lot to it. 

[05:34]   

Well, I think the advent of data growth, like the exponential growth of data, is leading us down a path of…  There’s this concept of sampling data and taking a small portion of it and building models off that and then applying that on the larger portion.  The problem with that is, if you have so much data, so many customers, how do you know that you’re properly sampling. So it’s one of the reasons why we’ve developed a piece of our software that runs native in Spark.  So, if a customer has a Hadoop setup, you can run this within Spark, leveraging all of Spark’s technology and actually build predictive models against billions and billions of records and not have to wait one or two days to process all that.    

[06:14]

That is crazy, right?  The volume of data that we’re dealing with now versus even five years ago is just so…  And you kind of look forward… I was talking to another guest before and he was talking about how intelligence is starting to get built into the product spec at the very beginning of the inception of the new product, right?  So, opposed to being this like more, “Oh, Gosh, now we need to pay attention,” it’s like built into… So now you think about the explosion that that’s going to have because it’s going to open up a whole bunch of streams of data that we otherwise, haven’t been able to access.     

[06:49]      

And to that point, it’s how are we looking at that and figuring out the best way to leverage that?  If we continue to leverage data the way we’ve been doing it for the past 10, 15 years, we’re going to left behind.  If the advent of collecting data, streaming data, bringing that data together, we need to jump on that train and get on that path and make sure that we can now analyze our data in the same breath as where the data’s being collected.  And I think that’s what we’re trying to do with this Spark kind of innovation that we’ve put together.

[07:16]

Are you more multi-sourced data inputs, meaning not.. maybe it’s market; maybe it’s internal customer.  I guess in a broad wrapper it’s BI, but customer voice, that kind of stuff. Are you seeing that?  

[07:33]

Yeah, everyone wants to create the ideal data lake or kind of the one point for data, but I can’t think of anyone of our customers that has data just residing in one data source.  Multiple data sources is the common thread. The question becomes how do you manage those multiple data sources. One of the components of our software is a browser-based data preparation tool that allows you to access all those data sources and be able to create your own data sets within this browser and actually share that with your team.  It gives you that collaborative format where you can actually see a team member that’s put a data set together and be able to leverage that data set throughout, maybe, a predictive model, for example. 

[08:19]  

Got it.  So, being a veteran in the industry, looking forward what do you think is going to be different in the next three years?

[08:25]  

That’s a great question.  The advent of machine learning and AI, depending on who you talk to, those terminologies could be marketing spin or whatever the case may be.  I think the biggest change that we’ll see is, as data continues to exponentially grow, there’s going to be new technologies that are going to be put in place to help leverage that data as much as possible.  And it’s how we’re leveraging access to that data and getting those responses as quickly as possible. At the end of the day, everyone wants to do something real time but, if data is becoming exponentially bigger, that real-time response is going to get slower.  So, there’s going to be a technology, I believe, that’s going to come out to be able to address this, and it’ll be kind of the next wave of what Hadoop was a couple years ago.   

[09:10]  

That’s super interesting.  That’ll be fun to watch.

[09:12]

Yeah, this is a great space; it has been a great space for years.  

[09:16]  

So, if somebody wants to get in contact with you ‘cause they feel like they have a project or question about your business, how would they do that?

[09:21]  

Yeah, they can just go to www.altair.com.  I mean they could email me directly if they’d like.  My email is mdocouto@altair.com and I’m happy to answer any questions that come up or get any of my team members to support.

[09:37]  

Mark, thanks so much for being on the Happy Market Research Podcast.

[09:39]  

Not a problem.  Thank you very much.

[09:40]

Hey, everybody else, if you please take the time to screen capture this, share in on your social media.  If you found any value at all, I’d really appreciate you taking that effort. I hope you have a fantastic rest of your day.  And special thanks to Predictive Analytics World, Marketing Analytics World, all the worlds wrapped up into one. You guys are awesome.  Thanks for hosting.  

PAW 2019 Conference Series – Lawrence Cowan – Cicero Group

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Lawrence Cowan, Senior Partner and COO of Cicero Group.

Find Lawrence Online:

Email: lcowan@cicerogroup.com

LinkedIn

Cicero Group


[00:02]

Hi, I’m Jamin.  You’re listening to the Happy Market Research Podcast.  My guest today is Lawrence. He is a partner at Cicero Group.  We are live at Predictive Analytics World, Marketing Analytics World.  There’s eight worlds. I forget all the worlds, but we’re covering a gamut of health care, etc. etc.  Welcome to the show, Lawrence.

[00:21]  

Thanks, appreciate it.  Thanks for having me.

[00:22]  

Yeah, of course.  You guys are exhibiting here.

[00:25]  

We are.

[00:25]  

What do you think about the show?

[00:27]

It’s been a great experience so far.  I’ve been to three or four of these, and they get larger every year.  I think the organizers do a great job putting on the event. And the topics continue to expand as well.  So far, our interactions with the audience have been very engaging: great questions, hard questions. But we’ve enjoyed it so far.  

[00:50]

That’s great.  So, tell me a little bit about Cicero group.  What do you guys do?

[00:53]   

You bet.  So, Cicero Group is a full-service management consulting company.  We emphasize in data analytics and data strategy. Our roots were actually in market research.  And so, we identified the value of data early on in the strategic projects we were working on. And so, we leveraged primary market research in a lot of our work and have continued to expand that into more strategic transformation strategy, advanced analytics work as well.

[01:24]

That’s great.  One of the terms I’ve been hearing recently at this conference (I’ve never heard it before) is data diversity.  And I think that you’re hitting on that exact point, right? It’s about the primary data that’s collected and then really trying to triangulate truth even though sometimes it’s a lot more than three points.

[01:42]  

It really is.  Yeah, so, bringing new data to the table is critical in all the projects we work on.  Primary research happens to be a big one but, again, a lot of the topics people are talking about at the conference today are other sources of data:  secondary data, partner data. There’s so many opportunities with leveraging data these days.    

[02:04]

Got it.  Talk to me about who an ideal customer is.

[02:07] 

That’s a great question.  So, we serve a broad spectrum of industries and business functions.  I think our sweet spot, I would say, is more in the sales and marketing and insights and analytics functions within organizations.  And if you look at the services we’re offering, I think there’s a lot of value being provided to organizations that have RN-subscription-type businesses whether that’s SAS or even in B-to-C subscription-type businesses because our expertise is really in understanding the customer journey and finding those opportunities across the customer journey to improve that experience.

[02:46]

Do you have a favorite customer story?

[02:48]

That’s a great question.  There’s a lot, but one that comes to mind…  This one is actually from a few years back. One of our large clients a few years back was Groupon.  This was pre-IPO days.

[03:00]    

I actually worked with them pre-IPO days.   

[03:03]

Oh, you did.  Maybe we crossed paths.  You never know, yeah. So, at that time, Groupon didn’t have an insights function.  And so, we were brought in through a partnership to help them set up their customer and merchant feedback system globally.  I’m going to forget the number of countries and languages, but it was in the 20s and 30s of countries and languages that we designed their customer and merchant feedback system that has been critical to their growth and evolution as a company over time.  So, that was a really exciting one. And towards the tail end of our engagement, we helped to facilitate them bringing in an insights manager that took over the program that we helped to build and ran with it from there.  

[03:44]

You don’t remember the person’s name, do you?

[03:45]  

Uhm, Eric Rasmussen.

[03:47]

I totally know Eric.

[03:49]

You do.

[03:50]

I know him well.  It’s so funny.  

[03:52] 

That’s great.

[03:54]

Yeah, that is…  Small world…

[03:55]

So, I was on site in Chicago for six months prior to Eric being there.  And then, I… 

[04:02]

Yeah, ‘cause he was in the Bay Area.

[04:03]   

Yeah, he was at uh…  Shoot, I forget where he was at in the Bay Area.

[04:08]

I think he was working out of Palo Alto.

[04:11]      

Yeah, yeah, anyway so we crossed paths for about two months in the transition.

[04:17]

Gosh, yeah, I remember when he moved over to Chicago.

[04:19]

And I think he’s still there, right?

[04:21]  

Yeah, to my knowledge he is.  I haven’t connected with him in over a year.  That’s great. That is hilarious. So, it sounds like you’re operating in this really important sweet spot where you think about companies setting up trip wires if they’re in a premium model, opportunities to be able to upsell or add value to their user base.  As enterprise sales are looking a lot more like B2C historically has, which I’m seeing, it feels like you guys are operating in this great kind of growth space. Is that what your…? 

[04:59]  

Yeah, I think so.  I agree with your point.  The way companies and customers, whether it’s B2B or B2C, are buying is evolving.  They’re much more empowered; their voices are louder; they have more control, more just overall experience.  And that’s critical because it changes how a company goes to market.  

[05:25]  

Totally.

[05:25]

It changes how they price; it changes the whole customer journey from the experience to how you support them, how you service them, how you try to upsell them.  It’s evolving just because of the power that the end-customer has now. And so, for us, that’s a huge opportunity because it’s an opportunity to remind companies that, “You need to listen to your customer – whether it’s a B2B or it’s a partner, whomever it may be – you need to listen because they have a lot of power and you need to evolve around that to ensure that you can retain them as a customer.”  

[06:00]  

Yeah, exactly.  It used to be the case companies were who they said they were, and now it’s they are who their customer says they are. 

[06:04]  

That’s right.

[06:05]  

And the customer has a freakin’ mega microphone that they can project that voice across their constituents.

[06:11]  

And they have options.  It’s not just the voice.  There are getting fewer and fewer companies that can truly say they are uniquely different.  Not that that doesn’t exist today but there are so many opportunities for clients to go somewhere else. 

[06:27]

And, you know customer journey mapping is still really hard.  It’s one of those things where it’s easy to kind of conceptualize how it’s going to go on a map, but it’s getting more and more complex.  Like we’re not just email marketing anymore. In fact, email is probably a diminished sort of like focus.

[06:46]

Absolutely.  One area of expertise that we have is in that customer journey mapping.  And the critical piece of that is, again, thinking about it as a journey and not the touch points, right, and understanding the origination and the ultimate synthesis of where that journey is going to take customers and ensuring that you can measure the important parts of that journey because not every single touch point is critical in terms of measuring and making changes to the business.  You need to ensure that you’re monitoring the right touch points.     

[07:22]   

Yeah, got it.  That’s fascinating.  If somebody wants to get in contact Lawrence either with yourself or someone else at Cicero Group, how would they do that? 

[07:30]

You bet.  You can find us on the web at CiceroGroup.com.  They’re welcome to reach out to me directly, which is LCowan, C-O-W-A-N, @CiceroGroup.com as well. 

[07:40]  

It is an honor having you on the podcast.  Thanks so much for joining me.

[07:43]

You bet.  Happy to be here.

[07:44] 

Everybody else, if you found value in this episode, please take the time (15 seconds) screen capture, share it on social media (Twitter, LinkedIn, don’t care).  Love if you take the time and effort to do that. Hope you found it valuable; I certainly did. Have a great rest of your day.