Podcast Series

The Happy Market Research podcast publishes interviews with insight leaders on the last Tuesday of the month.

PAW 2019 Conference Series – Krishna Kallakuri – diwo

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 Krishna Kallakuri, Founder and CEO of diwo.

Find Krishna Online:

Email: kkallakuri@getdiwo.com

LinkedIn

diwo


[00:02]

You’re listening to the Happy Market Research Podcast.  I’m Jamin Brazil, your host. We are live at Predictive Analytics World, Marketing Analytics World.  There’s lots of worlds. I think there’s nine. Anyway, there’s health care.  

[00:14]  

Quite a few.

[00:15]  

Yeah, quite a few.  My guest today is Krishna.  He is the CEO of diwo. Tell me a little bit about what diwo does.

[00:24]  

Diwo is a cognitive decision-making platform.  So, the word “cognitive” means the whole idea of how can you take the knowledge aspect of your data, teach it to a machine, and can the machine help the humans with their decision-making process, can it augment.  When the human is confused, can the machine help him or “This is a better choice for you today,” “This is how you should react in your business,” and “This is the possible outcome.” So, that is the whole motion around diwo.  That’s exactly why we call it “data in, wisdom out.” We want to help the business community with the decision-making process where we are able to reduce their cognitive load of their human brain, reduce cognitive confusion and, most importantly, augment the decision-making process.    

[01:10]  

That is really interesting.  I like the way that you’re framing that out:  basically, making decisions a little bit easier while still using insights as the premise.  So, when did you guys start the business?  

[01:24]

So, it’s about four-and-a-half years ago.

[01:26]

Fairly recent.  You have a favorite customer story?

[01:29]   

Yes, I do.  We actually started our first engagement with a fast fashion retailer.  When you look at the process that they had on how they were managing quite a few of their stores across the country, one of their biggest challenges was which product needs to be on the shelf at what location and at what time.  When you look at these simple questions, you may think the retailers have figured it out, they have so much experience in business. But the real problem that this customer was facing is they can’t put the right product at the right time at the right location because the customers are not reacting to it or either the product is not on the shelf at the right time, right?  So, when you look at the whole business model, it was directly impacting on the revenues because, if a product leaves a warehouse, it’s never coming back. Either you sell it or you promote it or you throw it. So the challenge that we were asked to solve for them is how do we allocate the right product at right location at the right time for the upcoming season. So, diwo, as a platform, it was able to really consolidate the customer behavior, the product behavior, the location behavior and, most importantly, it was able to embed the complete business process around it, which means now all the merchandisers, they know, “OK, 20 weeks ahead, this is where I have demand”  “This is how I need to create a buy plan, which means now OK, I need 1,000 units for these 10 locations.” Now, they have enough time to procure the material; they have enough time to manufacture the product; and then, when they manufacture the product, diwo, as a platform, is able to tell them the merchandisers that “OK, this is how you need to deploy,” which means, “Now, OK, you put 100 units in store A or put 100 units in store B or what are the cases.” So, we are really closing that whole loop. This what we mean by taking insights to a whole new level, right? How can you help that business community start consuming analytics instead of producing analytics? It’s not about creating data science models or producing insights.  That’s where most people stop, but we want to address the last mile in business. Is diwo able to contribute towards one of your milestones in your business process. So, that’s where we are really focused. So, that story that I just described is now creating a significant impact on their business, which is really saving them millions of dollars.     

[04:05]

That’s fantastic.  I love the framework of just-in-time retail inventory management and then building predictive models that illustrate consumer behavior, which just empowers the retailer in a tremendous way.  It’s a massive consumer advantage. Give me another example of where you guys are rolling this out. Is it predominantly in retail management or are there other sectors?

[04:31]  

Diwo is very agnostic of any retail.  So, our second use case started with an insurance company in Australia.  

[04:38]

Oh, wow!  Totally different.

[04:39]

So, one of their biggest struggles was they wanted to identify what could be your pricing philosophy for the upcoming renewal season.  If they react to a certain customer segment where the policy is increased by 5%, they don’t understand the impact of that, which means, “Is the customer going to stay with me or am I going to churn?”  It’s very challenging situation for them in Australia. The reason is because of all the data privacy policies they have in place. If you as a driver, if you have five accidents in a given year, and if you want to just go switch to another insurance risk provider, you could just say, “I have no accidents.”  So you, basically, have no access to his driver’s history or nothing. So, in such difficult situations, now you’re always about retaining your existing customer-base rather than going after new customer-base. So, diwo, as a platform, was able to tell them, “OK, for the upcoming season, these are the segments that I see that are highly risky, but, if you react to a policy change, there is a 90%-likely chance that you will lose these segments and these are the reasons why.”  So helping them really stay ahead of the pricing optimization for the upcoming renewal season was very critical because even 1%, 2% churn in their business is a significant impact for them in billions of dollars.        

[06:08]

Wow!

[06:08] 

So this is a great use case that we solved for them.  And, as we speak, we’re also working with another major financial firm in New York, helping them with how they balance the aspect of delinquent customer segments.  So, if you look at most financial organizations, they pretty much know that if a customer segment is going to be delinquent. It’s inevitable; they can’t control that.  They, at least, have to figure out, “OK, if my delinquency rate is increasing, how do I add new revenue streams into the business?” Diwo, as a platform, was able to address that key problem on how do you manage your risk around these portfolios and these segments are likely to be delinquent.  So you’ll have to approve more accounts in this segment. Even though they are highly risky in nature, there is a good chance that you will make more revenue for a shorter time for this high-risk customer too. So very interesting use case that we are solving for them too.  

[07:08]

Yeah, that is very interesting.  You’ve been in the industry a long time.  What do you see as a major trend and what do you think is going to change in the next three to five years?  How are we going to different?

[07:20]

I have a positive impression about this and a negative impression about the changes that we are looking at in the industry.  At least from what we see, most organizations are adopting technology just for the sake of it, not knowing what the outcome of that technology should look like, which means technology is never aligned to the business application.  That’s one major challenge that we see in the space. And the second aspect, especially when you talk about AI in general, again, most communities are so passionate about the experience part of the customer: “Did I detect your face?”  “Did I read your emotion?” That’s all great, you know. It’s very technologically savvy kind of a process that you can bring in your business. But people are forgetting the fundamental aspect around the operational aspect: “Did I first increase the efficiency in business before I put experience part?” and “When I combine experience and efficiency, can I make that more effective?”  That’s what people are missing out. So I think probably we’ll see some adoption from the business community, but, if you are just stuck with experience, I think that is a problem that we all have to solve or put our heads together to help people educate. You have to climb this ladder one step at a time. You don’t come from top-down; you go bottom-up. That’s where we think the industry is headed.    

[08:58]    

I really like that framing.  I think you’re right that it is a 1+1=3 as you incorporate what you’re talking about, which is the basic business structural insight function and then also layering in, whether it’s the emotional connection or what have you, experience-based part of it as, again, one of the legs of the stool that help the business make the right decisions. 

[09:22]

Absolutely.

[09:23]

I think we’ve seen a democratization of access to insights over the last five years.  It’s been kind of unprecedented. So, it used to be the case it was really hard to get insights, you know, data, and then ultimately…  You know, we went from a… We just been in such a data gathering stage inside of the corporations. And yet, at the same time, we feel like – or I feel like – we’re not necessarily smarter just because we have all the data.  Is part of the thesis then figuring out… like taking the customer’s data and then structuring it in a way that can be analyzed and useful for insights?

[10:04]  

At least our perspective about insights are experience that we have gone through working with several large enterprises.  Is this whole notion of, “All right, let’s build a data lake; let’s hire a data scientist; let’s build insights.” That’s almost you’re trying to dig for nuggets in a desert.  You don’t start your journey with data. You start your journey with value, right? What is the business problem we’re trying to solve? What is the value that you want to demonstrate coming out of that exercise?  Is there a business reason to do that? Is there an optimization aspect that we’re trying to address or is there efficiency or whatever the case is, right? Unless you start your journey and reverse engineer the process back to the data, this whole aspect of building insights will just remain another deep forest with no direction for anybody.  That’s at least what we think is happening today as for insights because building insights is a very highly technical data scientist-kind of a role. Now, when people don’t understand the true business problem, what insights are you building and what value are they creating? That’s the biggest question.  

[11:22]

Yeah, it’s not enough just to have insights for insights sake.  You really got to have your objective in mind. What your KPI’s are and how that’s going to be a lever and move the business in the right direction.  

[11:36]

Absolutely.  It’s all about business levers.  Unless you cannot move some of those levers with these insights, they’re no good to you.  

[11:42]

Right.  Yeah, that’s fantastic.  If somebody wants to get in contact with you, how would they do that, or the business diwo? 

[11:48] 

We have a sales team; we have a business development team.  They could visit us at getdiwo.com. They could simply reach out by just filling a simple form.  They can always shoot an email to hello@getdiwo.com.  And one of us will reach back out to the audience to help them understand what we deal with here.  

[12:11]

Krishna, thank you so much for being on the Happy Market Research Podcast.  

[12:14]

Thank you, thank you, Jamin.  It was a pleasure speaking to you this afternoon.  

[12:18]

And all of you who found value in this episode, if you please take the time to screen capture this episode, share it on LinkedIn, share it on Twitter, social media platform of your choice.  Love to get the feedback from you. Have a great rest of your day.

PAW 2019 Conference Series – Jeff Todd – Wolfram Research

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 Jeff Todd, Senior Account Executive at Wolfram Research.  

Find Jeff Online:

Email: jtodd@wolfram.com

LinkedIn

Wolfram


[00:02]

This is Jamin, and you’re listening to the Happy Market Research Podcast live today at Predictive Analytics World.  I have Jeff Todd, the Senior Technology Expert at Wolfram Research, Inc. Jeff, thank very much for being on the podcast today.

[00:14]  

Thanks for having me, Jamin.

[00:18]  

What do you think about the show?

[00:20]  

I think it’s great.  There’s a collaborative spirit that I feel like a lot of the people here…  As an industry, I think people are all trying to solve a lot of hard problems now.  For a while, I think they were trying to get the data all into one place. Now that they’ve got it there, they kind of the real problem ahead of them.  So I think everyone is up against the same wall. And so, rather than trying to everyone get their own competitive edge and find a way to outsmart the other people, they’re all kind of just here to learn and figure out the same problems, which is really exciting.      

[00:50]  

It definitely feels like the rising-tide principle’s applying here, where it’s a lot less cut-throat and a lot more collaborative, recognizing the fact that we’re at the very early stages of massive growth inside of major and I call it minor (not in a negative way) but smaller organizations.  At the end of the day, whether it’s optimizing your production line or creating outstanding customer experiences, data plays a key part in the entire ecosystem for success. And the proportion of the decisions that are happening in the organization are still uninformed. In that way I’m saying we’ve got an outsized opportunity in front of us for growth as it relates with insights inside of companies. 

[01:40]

Yeah, I think that’s exactly right.  I think too we see many organizations that are trying to achieve automation for a variety of tasks that have traditionally been manual tasks.  And that’ll divert a lot of human resources. And you certainly hear about machine learning, neural nets, AI as a fear of interruption to folks like cab drivers and truck drivers and replacing them, and the question of what do these people do.  Obviously, there’s going to be a place for those people to go. It always evolves and expands, but I think it’s very exciting that you have both the type of AI and machine learning outcomes that you have where we’ll be able to get more insights out of the data to be able to make new decisions, make new discoveries, new innovations and kind of push things past and have that growth that you’re describing.  At the same time, I think we’ll see a lot of our world start to change a little bit as things that we’ve traditionally been used to interacting with humans become less and less so, which we already see at the shopping markets, grocery stores interact less and less everyday people on the way out.

[02:39]   

Yeah, yeah.  Let’s back up a little bit.  You’ve been in the industry for a while.  What do you see as one of the megatrends? How are we going to be different in three to five years

[02:53]

Well, I think autonomous driving is going to be probably one of the first major things that most of us see a huge change in our regular day-to-day world.  I think that it’s already happening. Cars are already on the road as we can tell. I was just riding along with a friend of mine, who had a Tesla, marveling at the ingenuity of speeding down the highway

[03:09]  

The autopilot is crazy!

[03:12]

It’s amazing.  I was actually just transfixed on the screen watching it interpret all the cars around, and, in fact, asking my friend how his experience was.  Mentioned that it actually reacts faster than he reacts when someone is about to do something dangerous.     

[03:27]

So, I drove from Fresno to Las Vegas as opposed to fly.  It’s a six-hour drive, one-hour flight. But, by the time you’re done messing around and I had to take some stuff (this equipment and things like that); so, “I’m just going to drive, enjoy it.”  I used autopilot about 30% of the way. I wound up getting a flat because I didn’t use autopilot. And I thought I would speed and I hit a bad spot and yadi yadi yada. Anyway, dang it, why did I take over?  I should have let the machine do it? 

[03:59]

Right, I think you’ll find that’s going to be…  Even people who have fears of the future and issues potentially with it…  I know my friend was ready to embrace that technology; his wife was not. She was very scared to get into the car.  

[04:12]

My wife’s the same.

[04:13]

Yeah, and use that.  But she took a long trip (several hours), and that just completely changed her mind:  the fact that she could kind of just sit back and relax to some degree while the car did the work.  I think people are going to come around to the use of automation to not only vehicles but other parts of their lives.  I will be surprised if we don’t see a little bit of a backlash at first from that as well as maybe even a return or a renaissance of human interaction.  As we get more and more separated from that experience, I think people actually in certain areas want to come back to that.

[04:47]

I think that’s a really good example.  You’re seeing that right now with a little bit more investment for Amazon into brick and mortar bookstores, which is hilarious.  I think about the grocery disruption, as you already pointed out, this happening in the grocer space where now you have fully automated (you just load up your cart and leave) frameworks.  But there’s some interactions in the grocery store that I really like. So I can see that, after the pendulum sort of swings all the way over, I can see like you having a human butcher or that kind person you can talk to and ask questions and still get that connection.  To your earlier point, it’s not going away; it’s going to create higher value opportunities for us as opposed to worrying about the ones and zeros side of life. It’s an exciting time.    

[05:42]  

I agree.  

[05:43]

I checked out Wolfram a little while ago, and I’ve been on Wolfram Alpha for about the last 20 minutes because WolframAlpha.com, it’s like a Google-like search engine, but it uses an LP (now this is a lay person telling you what you do).

[06:04]

You’re doing great.  Keep going.

[08:04]

It’s all humility at this point.  Uses an LP to and a bunch of fancy math to…  You can ask it a question and it will search many, many different data sources after it interprets your question and then it answers it in a way that looks like a human being put a report together for you.  And then it also incidentally has all the reference points so you can see what data’s been used in the context it’s been gathered, etc., etc. So WolframAlpha is a really neat resource for anybody that wants to get more information about a certain subject, which is crazy that I just now found out about it. I feel like I’ve been behind, but we can bring that to light now with this podcast.  Maybe you can tell us a little bit about Wolfram and what it is you guys do.

[06:54]

Sure.  So, Wolfram Research is a 30-year-old technology company.  We’ve been around a long time.

[06:59]

That’s 30 years, folks.

[07:00]

30 years, 31 probably here in June.  Actually, so as the brain child of Stephen Wolfram…  Dr. Stephen Wolfram was kind of the run-of-the-mill physics genius.

[07:10]   

20-year-old Ph.D.

[07:12]

15.

[07:13]      

15, I’m sorry.  My bad.

[07:15]

No problem.

[07:15]

That’s a big difference.

[07:16

It is a big difference.  It is a big difference. So, in his research, he found that he was growing frustrated.  That if I wanted to pursue statistics and probability, I had to learn one language. If I wanted to do machine learning, I had to learn a whole new language.  If I wanted to do image processing, yet another language. And I had to make them all work together. I believe the belief was that that was not an ideal situation.  There should be one language, a uniform approach to any kind of computation; it should be high level; it should be intelligent; it should be automated; it should be integrated.  There should be a lot of intelligence put into it so that I, the end-user, have the quickest route from my question to my answer with the least amount of coding.  

And so, that’s what we’ve been doing over the past 30 years.  And that’s one of the reasons why Wolfram Alpha can do what it does as well as why you haven’t seen another Wolfram Alpha from any other competitor come out in that time because there just isn’t a way to replicate that.  You’d have to replicate the 20 years of that work that led up to the ability to create something like that. So, we work with all kinds of industries. We don’t have any one particular segment or market that we’re heavy in.  We work with anyone who needs to do computation, which is pretty much everybody these days. 

[08:18] 

Yeah, to say the least.  The programming language that you developed, that was in the 80s, right?

[08:25]

That’s right.

[08:25]         

Yeah, so, I mean a long time.  There’s been a lot of heritage, I guess.  I’m really surprised I haven’t heard more about Wolfram.  I mean I was doing stats through the 90s, using SPSS predominantly.  Intuitively, from what I’ve gathered so far, it looks like I could get similar outcomes easier had I been using Wolfram.   

[08:51]

Yeah, there definitely has been a progression of features that have been added into the language over time.  I think, in the beginning, a lot of the symbolic math was predominantly what people knew it for and used it for.  So, it got a heavy presence in academia, and I think then, as it matures, most people maybe had the idea that it was this symbolic package.  That’s what we started with. It certainly could do numerics, but in its day, that wasn’t the focus. Later down the road, we began implementing just continuous and huge amounts of functionality, entire sets of functionality that would equate to a third-party program, several third-party programs each release.  And so, we began to become this extremely comprehensive system that, I think, as you say not as many people knew about. And so, they would come and revisit. And I think I have yet to have someone visit the booth or come and talk to in person where their jaw doesn’t hit the floor as least at some point in time where we show them in one function what they remember taking them half a week and 100 lines of code to try to figure out.    

[09:48]  

So, Wolfram Alpha’s free.  How do you make money?

[09:52]  

So, Wolfram Alpha is free.  There are some Pro Features that you can subscribe to.  So, a lot of students are customers of ours, and they will go in and they’ll put in all their kind of difficult symbolic integrals and differential equations, even calculus and algebra, as they’re trying to learn.  And one of the nice things about the Pro Subscription, it’ll actually allow you to show steps on every one of the problems. I certainly wish I had that when I was in college because I had my teacher for about one hour a day and then I had the back of the book with all the answers, but I didn’t have how to solve everything for the rest of the time.  And so, Wolfram Alpha to some degree has actually been a substitute teacher for many people in helping them work through that. We also have a custom version of Wolfram Alpha…

[10:31]  

Really quick before you talk about the custom version.  So, have you seen Incredibles 2?  

[10:34]

Yes.

[10:35]

So, like that scene where he’s like, “New math!”  That’s like right there thinking, “That’s awesome.  I can’t wait to get home and show my kids how smart I am.”

[10:45]   

That’s right.  Well, you know and I’ve had so many people say, “Thank God.  It saved my life in college. You guys were the reason I graduated.”  Every time I have a… meet somebody, I say, “Do you have kids?” And they say, “Yeah, I’ve got some people who are just about heading into middle school or high school.”  I say, “Go find Wolfram Alpha. It’s going to save your life when they come home and show you the math they’ve never seen before. It’s going to help you out.”      

[11:07]

All right.  You’re saying Pro.

[11:09]  

Yeah, so we actually, as a means of making money, so we actually have the ability to take the technology of Wolfram Alpha, the free site, and everything that we’ve put into that and layer that on top of organizational data.  So, imagine being in your corporation and you – the CEO, CFO, manager, employees – being able to ask natural English questions of your own data and being able to get that back, as you said, as a report like a human gave to you ad hoc with no pre-scripting, with no business intelligence, no IT guy that’s gone off and made this one-off thing for you.  It has the intelligence kind of baked into it. So there’s an AI sits between the person asking the question, all of the data, and all the algorithms.  You ask a question. It goes out, finds the data, finds the appropriate algorithms, applies it, supplies you back with the reports, the answers in real time.  So you can continue to ask questions, rather than wait two weeks for Report No. 1, wait two weeks for Report No. 3, and then forget, “What did I even ask for?”      

[11:59]

What kind of data is it able to query?  I’m thinking about at an organizational level.  Like are you guys pulling in stuff from Excel files or…? 

[12:09]

Yeah, it could be any.  It supports 180 different file formats.  So that could be Excel files, CSV, text files; it could be JPEGs, GIFs; it could be a wide range (HDF5); it could be a wide range of file types.  It could be any kind of database; it could be streaming. For example, we have financial data in the free version. You can ask it, “What was Microsoft’s last 30 days closing price?”  That will change: if you ask it throughout the day, that will change as it updates ‘cause it’s a streaming service that we’ll pull down from. So there’s a variety of different data sources that you can feed into that.  It could be images. You could perform image-processing techniques in natural language on images you have somewhere in the company. So, it’s a variety of things that they would want. And it could be any department: sales could be looking at sales figures; HR could see what team member is under which manager; they could see what projects people are working on; who has a birthday today.  There’s all kinds of variety of things that they could use as well as engineers could have entire specs of their models of their machines that they might want to ask, “Does this bolt fit this specification?” And we have connectivity, and we’ve worked with Amazon Alexa; we’ve worked with Apple Siri. So there’s actually ways that you could ask a question live. And I’ve actually worked with folks like Dow Chemical where they’ve got their hands in the gloves; they’re doing chemical experiments and they would like to ask a question.  Maybe, they need to know the melting point of methanol right now.          

[13:34]

It seems important to me right now.  I’ve never thought of it until just now.

[13:38]

That’s right.

[13:38]

The most important thing in the world.

[13:39]

The most important thing in the world.  So they want to ask that question. Instead of having to stop the experiment, pull their hands out of the gloves, walk 15 minutes back to their office, ask the question, come back, they could just ask, “Alexa, ask Wolfram for the melting point of methanol.”  Get that and then continue the experiment.

[13:52]  

That’s really cool.  Is part of the challenge in the market, it’s so broad?

[13:58]

Absolutely.  It’s a really hard thing to have a little bit to help every single person.  People often ask, “Which market do you guys market to? Which vertical are you guys in?  What solution space do you guys fill?” And I often have to just show them like a list of literally every other technical program that exists and say, “This is our list that we compete with.  This is our list that we have to know a little bit about” because in a given week I might talk to Morgan Stanley; I might talk to Pfizer; I might talk to Disney; I might talk to Nationwide Insurance and all about different things.  They’re all doing computation, working with data, but they’re all after different industries and different markets. And we kind of have to be able to know a little about everything, which can be difficult.    

[14:42]

Yeah, for sure.  Well, the good news math is math regardless of the sector.  So the applications… The context is important though, to your point, and you do need to be like a subject matter expert, so to speak, in every freakin’ sector. 

[14:54]

It can be tough, it can be tough.

[14:57]

As you…  You think about the success that you’ve had over the last few years, do you have a specific customer story that resonates with you?  Like, “Gosh, this was such a great outcome” or example of them applying Wolfram to their business, then getting some benefit.   

[15:14]

I think any time you can be part of a technology or integrate a technology that you use every day or that you can point toward.  I think a lot of times our technology gets used in a way that get obfuscated. It was in the research, in the R&D step, or it was by some engineer.  You never get to see the eventual outcome. People don’t necessarily know that you were part of it. Obviously, it’s nice for me to know that when people are using iPhones or when I’m at home talking to my Amazon Alexa, there’s a part of Wolfram in the back-end maybe helping with that.  On a personal note though, I was talking with someone here from NIH about a potential project with them. We worked with a company called Christy Health. Christy was actually a person that had a really hard life. They were, I think, the first heart and liver transplant in the western U.S.  They also had a kidney transplant last year. And so, Christy was the wife, partner of the person that ran Christy Health.  

In doing so, he found this huge issue with reporting medical data.  So, as he would do dialysis and as he would work with her on all these things, he found that there was just… things were getting scrawled on notes; things were not getting reported back correctly; and it made it really difficult for him and for Christy to be able to advance and to be able to get better and be treated correctly and for doctors to understand what they have gone through.  And so, he actually used our technology to stand up services that other people like him and like Christy could actually download themselves to better manage their own health because it’s so important. And so, when I get a chance to work with organizations or work with people who are really making a difference in our day-to-day lives for things that really matter like people’s lives and their health and their love for each other, that’s actually a really great thing to be a part of.    

[16:53]

Oh, for sure.  That’s awesome.  I love that story.  You’ve seen the industry evolution.  Looking forward three to five years, how are we going to different? 

[17:03]

That’s a tough question.  

[17:04]

It’s actually really tough.  

[17:07]

Yeah, and usually I think we rely on Stephen Wolfram to look five years in the future for us.  

[17:12]

Right, yeah, there you go.

[17:13]

That’ll be out.  So, maybe, I’ll just look and see what’s going to be in Mathmatica 13 and I can tell you what the next three to five years is going to be.  

[17:19]

The Nostradamus of our day.  

[17:21]

Interesting story, right?  Today, I think you’ll see in the news Python and Jupiter are just discovering and touting the idea of notebooks as an interface for programming.  And many people are saying, “Oh, this is great. Now I can marry my text in line with my code in line with my results. And it’s an awesome way to show people all these results.”  And we always get a little bit of chuckle because we actually had the notebook 30 years ago, and that’s been our main way of working in Mathmatica for all that time. So, talk about living in the future, I mean that was started 30 years ago, and it’s just now kind of becoming “the thing.” 

[17:53]

Yeah, it’s funny, huh?  Like all these old school practices, like even the cloud, we were doing cloud computing back in 2000, but it was not called that. Everybody launched their cloud solutions and it took me like two years to figure out I needed to start calling what we’re doing the cloud.  It’s funny. 

[18:11]

Terminology changes, I think we’re going to see a lot more…  It’s interesting because every, I think, four to five years there seems to be trend.  Before all the machine learning, neural networks, and AI, which is like the huge trend right now, it was all Big Data;  it was “How do I process all this data and where do I put it?” and “I need to set up a data lake.” and “I need to figure out where we can have a strategy for that.”  And before that, there was probably GPU computing. And before that, it was grids and clusters and “How do I get more out of stuff?” These days, I don’t hear about grids and clusters anymore.  I don’t even hear that much about GPU even though it’s still a great part of the competitional landscape. So I don’t what the next four- or five-year thing is going to be where everyone goes crazy and goes after it, and it’s going to be the next big, huge topic that we have a conference around.          

[18:55]

Yeah, that’s right.  That’s awesome, that’s awesome.  So, conference has been interesting:  great speakers, great attendees, lead generations.  All that going pretty well for you guys?    

[19:05]

Yeah.  You know I’ve been at shows that have had 1,000 people; I’ve had shows that have been 50,000 people.  And it’s really about whether you’re at a show where your message and your technology is relevant to the people that are there.  And I’ve been to shows where we get 1,000 leads, and not a single one of them is really worth anything because it was just not anyone that relevant to what we do.  And we’ve been at a show like today where almost every single person I talk to has an extremely relevant problem that we can help to solve. So certainly, I’d much rather have a discussion with those people every day all day than just be at a show and just chug numbers all day.    

[19:38]

They are my favorite type of people.  Jeff, if somebody wants to get in contact with you or Wolfram, how would they do that?  

[19:45]

You can email me at JTodd@Wolfram.com.  Certainly, you can just call in Wolfram direct line:  1-800-WOLFRAM and ask for me personally. I’d be happy to talk with you.  Or you can come to our booth here at Predictive Analytics World if anyone’s listening and come on down and we’ll be happy to walk you through a demo.  

[20:04]

Of course, we’ll include that information in the show notes for those of you who are listening.  Jeff, thanks very much for being on the Happy Market Research Podcast.  

[20:10]

Thank you so much for having me.

[20:12]

Those of you who have found value in this episode, if you please take the time, screenshot, share on social media (LinkedIn, Twitter), I’d greatly appreciate it.  Have a wonderful rest of your day.    

PAW 2019 Conference Series – James Taylor – Decision Management Solutions

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 James Taylor, CEO of Decision Management Solutions.

Find James Online:

Email: james@decisionmanagementsolutions.com

LinkedIn

Decision Management Solutions


[00:02]

My guest today is James Taylor.  We are live at Predictive Analytics World, Marketing Analytics World.  His business is Decision…

[00:11]  

Management Solutions.

[00:14]  

Got it.  You are chairing a track today.  

[00:18] 

Yeah, I’m monitoring the business track.  I’m kicking it off right after the keynotes, and then I’m monitoring it for the next couple of days.

[00:24]  

That’s great.  Obviously, you know who’s on the panel.

[00:28]

Yeah, there’s a panel; there’s a whole bunch of presenters.  It tends to be the ones who are not so much talking about how to build a predictive analytic model so much as how to use a predictive analytics model:  how to put it into production, how to get people to adopt it, what the challenges are, getting people to understand what a model does, and those non-modeling kinds of things. 

[00:49]

Which actually the better that goes, the bigger the lever of the model, right?  I mean… 

[00:55]   

Yeah, I’d go further.  I would say that if you…  There’s not difference between business value and analytic values.  If your analytic, however good it is, isn’t actually being used, then it has no value. 

[01:05]

That’s right, that’s right.  The ROI on that’s really easy to calculate.  Now, this is not your first show.

[01:11]  

No, I’ve been, I think, to every Predictive Analytics World.  I’ve been coming here a long time. I know Eric and the Rising Media folks well.  And it’s been fun to watch it grow and watch it change from really a very niche kind of show where’s there’s people from financial services and credit cards to one where all sorts of people are here.  It’s great.

[01:31]

Now, your business, tell us a little bit about what it is that you guys do.

[01:34] 

So, Decision Management Solutions focuses on companies that are really trying to automate decision-making.  So, there’s some high-volume transaction where it’s not obvious what to do. So, you have to decide what to do for each transaction.  And, obviously, that’s driving a lot of analytics. A lot of people what they want to do is they want to use machine learning, analytics, AI to make a better decision about these transactions, but they’re high enough volume that you can’t just show stuff to people and hope the people can handle the transaction, you got to automate it.  So we help build those automated systems, into which you can embed these kinds of analytic models.   

[02:08]

Do you have like a favorite project or ideal customer-type?

[02:12]

Uh, my favorite project probably is around like “next best offer, next best action” kinds of things.  If you’re a multi-line company, you’ve got lots of different products or your products have complex eligibility like insurance products, then it’s easy to say, “Oh, you should make the next best offer to this customer this moment in the customer journey.”  But actually, figuring out what that offer is – given what they already own, what they’re allowed to buy, which products go with which other products, what the rules are – is a non-trivial problem for most companies. And those systems tend to be more fun ‘cause they’re not as heavily regulated as some other decisions.  So you don’t have to worry about the law quite as much as you do; you have to worry a little but not a lot. The key concern is privacy but the kinds of systems we build don’t need to who you are, they just need to know things about you.  

[02:57]    

So, you’re not dealing with PII, that’s how you’re able to bypass.

[02:59]

No PII, exactly.  It’s one of our sort of rules of thumb.  We don’t care who you are; we just need to know what kind of person you are, what kind of products you own.  We need that data, but we don’t need to know which particular customer we’re talking to. And that makes it…  It’s great: you can use the cloud; you can use advanced analytics.

[03:15]  

It opens up a lot of stuff as soon as you can divorce the PII from the actual….. It’s such a big issue.

[03:25]

It’s a big deal, yes, and particularly as companies are trying to move to the cloud, and particularly with analytics, and you need that horsepower.  And they get very nervous when you start making decisions about customers ‘cause they all want to know about PII. So we have lots of meetings with infosec people, who come in, and we describe how it works and they go, “Oh, OK.  We’re done.”  

[03:43]

Oh, that’s a very nice shortcut.

[03:45]

It is.

[03:46] 

Because you’ve been part of the ecosystem for a while, what has been like one of the big trends that you’re seeing emerge in the space?

[03:53]

So, I think there’s a couple of things that have really changed.  One is, when we started, getting from what the analytics team built to something you could execute was often a huge production:  a lot of coding, a lot of recoding. And so, we would talk about deploying the model as a big barrier. Nowadays, you look at the modern tools that are out there, and they got one-button deployment.  And one of my colleagues has this great phrase: He says, “You know it’s not about deployment; it’s about employment.” However easy it is to deploy the model, are you employing it? Are you doing something with the model?  And so, that’s always been our focus, but now we can’t talk about deploying the model as a sort of phrase ‘cause people go, “But I’ve got a button for that.” I go, “Yeah, I know you have a button for that. Now you have an API that calls your model.”  Still, “Is anybody using it? How’s it going to be used? Can you explain how it’s being used? Can see if it’s being used appropriately? Can you see what the key factors were in the model when you used the model ‘cause you don’t care what the factors were when you didn’t use the model?  You only cared when you used the model.” All those kinds of questions come up. That’s really shifted the conversation away from that.      

The other thing, I think I would say, is I see more IT departments that care more about analytics.  When we started, the IT departments were like, “Don’t talk to me about the predictive analytics guys.  They run their own server; they do they own thing. I just try not to fret about it.” And now, I wouldn’t say that they’re totally in it. But we meet analytics teams that are part of IT; there’s a lot more integration.  A lot of BI teams are trying to get into data science, particularly analytics. So there’s a lot more overlap. I think that’s a really good thing because I think IT in most big companies is the key barrier for a lot of analytics projects.  If you don’t get IT to buy in, then you’re not getting there.    

[05:37]

Yeah, absolutely.  It’s a big budget and also a gatekeeper.

[05:40]   

‘Cause they’re like, “You’re going to destabilize my system with this probability nonsense?  No!’” So you have to find a way to get through that problem.    

[05:48]

Yeah, yeah, for sure.  You’ve got a lot of management consulting around the actual, not just the execution, but, as you said, the employment of those insights inside of the organization.  What are you seeing as other trends? How’s the space going to be different in the next two years?  

[06:08]      

I think one of my pet projects is get business analysts, more generally, to include in their requirements the need for machine learning, the need for analytics ‘cause one of the things I see at the moment is the only people who are really thinking about how you might use machine learning are people who know how to build machine learning models.  But in most big companies, they don’t write the requirements documents; they don’t write the specs. And so, by the time, they get involved the requirements document’s written; the IT budget is set; the project’s up and running. And then, you’re constantly trying to bolt things in and add things to dashboards. And it’s never integrated. And so, one of my pet projects is to get business analysts to be much more specific about saying, “This system is making this decision.  I wonder if we could use data to make it better. What would that look like?” And get them to drive that into their requirements so that right at the beginning of a system’s life cycle, people are talking about, “What decision does this system make? How do we use analytics to get better at that decision?” We’re going to get scale in companies. 

[07:08]

So you really have to move upstream.

[07:09]

Oh, yeah, right.  My title of my talk today is “Doing It Backwards” ‘cause I feel like people say, “I’ve got the data.  See what interesting things I can find out about the data. And then I see if I can use that.” And I’m like, “Well, that’s backwards.”  You actually need to start by saying, “What problem am I trying to solve?” and then see what analytics you need to solve it, and then see what data you need to build the analytics.  They’re like, “Well, but where’s the research? Where’s the hypothesis testing? And I’m like, “Yeah, if you’re the kind of company that’s good at research, you should do a little bit of research.”  But most companies are terrible at that kind of stuff. And you’re going to be the same kind of company in ten years time that you are now. So focus on the things you have to do to be that kind of company and use analytics to get better at them.  Rather than trying, “Maybe, there’s a new business in my data.” Well, maybe, but probably not. If you’re an insurance company today, in ten years time, you’re going to be an insurance company. The only question is whether you can use data and analytics to be a more profitable, more effective, more successful insurance company or not.  So that means you got to start by thinking about, “What does it mean to be an insurance company? What decisions do I make?”   

[08:18]  

Isn’t that interesting how it’s like, it’s really helping frame the business as a whole beyond just…  That was a very good answer.   

[08:27]  

Apparently.

[08:31] 

What I find just fascinating is that we’ve seen this like democratization of insights or analytics across the organization.  So, it used to be relegated to a few, and now it feels like it’s seeping through many, many different, even IT, which is amazing.  So, as we continue to evolve… I loved the way you depicted it: moving upstream and being involved in the early stages of the product and then having that actually built into the key requirements.  I mean it’s going to be an exciting world because really brands are at a spot where they get to decide if they’re going to win or lose on Day 0, based on how they’re incorporating analytics into their platforms.   

[09:18]

Yeah, absolutely.  One of my favorite phrases I often talk to my typical customer is I like to say a “big, boring company.”  And people keep telling them to use machine learning and AI. They got to be agile and nimble and think like a startup.  And I’m like, “No, being big and boring is actually part of their value proposition. Buying life insurance, you’re buying it from big, boring company.  You’re banking with a big, boring company. That’s kind of the point, right? If you want to buy a tractor, you want to buy it from a big, boring company, right, ‘cause that’s the point, right?  And so, it’s not enough if we say only small nimble startups, digital natives can use analytics pervasively. Big, boring companies have to be able to use it pervasively. And that means we’ve got to fit the way they build systems, the way they think about their business, which tends to be lots of documentation, lots of thinking, long lead times, requirements. If we don’t get data thinking into that process, they’re never going to be pervasive users of data.  And they need to be.   

[10:19]  

My guest today has been James Taylor.  If somebody wants to get in contact with you or your firm, how would they do that?

[10:25]  

So, the easiest way to find it is DecisionManagementSolutions.com.  It’s a really long URL but all the words are spelled exactly the way you say it. 

[10:32]  

It’s great for SEO, by the way.

[10:33]  

That’s always good.  And then, James@decisionmanagementsolutions.com gets me.  And that’s an easy way to find us.

[10:39]

James, thanks so much for being on the show.

[10:40]

Thanks very much.

[10:41]   

Everybody else, if you please take the time to screen shot and share this episode.  Special thanks to Predictive Analytics World and Marketing Analytics World for hosting this particular episode.  I hope you have a fantastic rest of your day.

PAW 2019 Conference Series – Gerhard Pilcher – Elder Research

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 Gerhard Pilcher, CEO of Elder Research.

Find Gerhard Online:

Email: gerhard.pilcher@datamininglab.com

LinkedIn

Elder Research


[00:02]

I’ve been working with data in the technology field, but in a weird…  I have a really weird… I have a really crazy career path. 

[00:11]  

All right.  Let’s hear it.

[00:12]  

Before I went to technology, I was CTO for a telecom firm, inventing the new DSL subscriber line (high-speed over copper).

[00:20]  

What year is this?

[00:21]  

This was in the years from about ’90 to about ’99.

[00:26]

So early 90s, all the 90s.

[00:28]

All the 90s.

[00:28]   

That was a crazy time, right?

[00:29]

It was a crazy time, and things were booming.  It was insane. Well, then had a tragedy sort of happen in my family and realized I wasn’t spending time with my young sons and wife; so, I bought a road construction company.     

[00:42]  

Was that better?  I don’t know.

[00:45]

I don’t know.  It was crazy. It was like being a farmer.  You know you’re dependent upon weather and uh…  But the interesting thing is technology was just starting to come into the road construction business.  So it was fun. I got to do some things like do oil sampling in predictive maintenance on our large equipment, which allowed us to avoid downtime and more costly repairs.  I got to bring in GPS grading. And, instead of having people out there pulling strings with wooden stakes and doing traditional surveying, the operators had computers in their equipment, and they could see where the road grade needed to be.  And people don’t realize how much goes into actually building a road and compacting it and getting it ready for pavement. And so, that was a lot of fun. And I ended up we were cutting roads at a higher quality than the state was able to measure that quality; and ended up getting invited to Washington, D.C., to talk about grading and that kind of thing and how they could bring their quality processes along because they were actually taking quality out of the roadways.  So a little bit crazy career there.  

And then I decided at a late age that I’m going to go back to grad school and study more about this statistics and analytics stuff.  And so, my son was just going to be a freshman at NC State and was looking for a place to live. And I made this decision with my wife, but he wasn’t quite aware of what was going on.  And so, I called him one day; I said, “You know you’re having trouble finding a place to live. I’ve got the perfect place for you. It’s an older roommate, probably can cook and clean a little bit for you, but it seems like a decent guy you know.  What do you think?” He said, “Well, give me his number. I’ll call him and check him out.” So I started rattling off my phone number, and he goes, “Wait a minute. That’s your phone number.” “Oh, yeah, that’s my phone number.”  

[02:27]

That’s a crazy story.

[02:29]

“What do you think about spending a freshman year of school with your dad?”      

[02:34]

I don’t know if I’d like that very much, but it’s funny, as a dad, I’d love that.  So, fast forward where we are now, there’s been so much evolution inside of our space.  What do you see as one of the megatrends that’s going to like the next kind of wave that takes us the next three to four years?  

[02:51]

Gosh, I think the next wave really is about calming down from some of the hype of data science and realizing how valuable it is but realizing that I have to have a foundation to really accelerate data science through my organization.  So I think people are starting to think more about their data strategy and how do I treat data more like I treat cash flow into my business. Cash comes into the business; I want to increase the value of that cash as it moves through the business so that I’m worth more at end of line.  We call that internal rate of return. Well, I think people are realizing that data is in that same space. We can actually use data as a currency to improve our business. And some signs of that and some signals of that, if you think about it… If you go back to the mid-80s and you look at what the difference was between our book value of companies, on average across all the publicly traded companies and what the market capitalization or what the market was willing to pay for that company, there was about a 15%-gap on average (some more, some less, of course).

Now today fast forward, that gap is about 87%.  What is the difference in that gap between the market valuation and our book value?  And many people think we’re not accounting for the value of data. Interesting we’re in the information age and accountants haven’t figured out how to put data as an asset on our balance sheet so we can account for that.  So, there’s a lot of trading of data for other services and things that are all off-balance-sheet and non-taxable and been used as a currency but not being recognized officially as a currency or as value in the way that we account for things publicly, right?  And so, many companies are starting to value data internally, and it helps them understand how to make better investments of data and how much to invest in data     

[04:44]

I have never heard – like I’ve talked to a lot of people – the analogy that you just did of connecting the dollar that goes into a business to the dollar that goes out of a business, right?  What’s the x-return on that dollar? Data has not ever been framed like that. Maybe, I’ve been a bad listener but in that exact way. But I think that’s exactly the way that we need to be framing it, and it’s about how do we maximize the return or the overall value, which ROI on insights is something I’ve heard a lot, but not framed like from an accounting lens.  And I think that’s exactly how we have to be thinking about it. That makes perfect sense to me.   

[05:24]

Wow, great because I’m trying to take that message to CFOs and everything and get them to think that way ‘cause they’re saying, “Why are we doing this data analytics?  Why are we spending this money?” I said, “Look at it the other way. Why do you spend money in your business?”  

[05:36]  

See it as an asset on your balance sheet.

[05:38]

Right.  How do you manage that?  How do you manage that asset?  You’ve got ways to understand that.

[05:42]

And it’s funny.  And it’s more transparent than something like goodwill.  

[05:47]

Right, it’s much more transparent.  You can measure it. Back in ’86, a guy named Appleton talked about data, the data we create like in normal operations or the data we might buy.  Once we start combining that, putting context around it, asking questions, he said it becomes information. Once we start building models around that and making decisions around that, then it becomes knowledge or intelligence for our business.  And we work in the intelligence space as well at Elder Research, and we know the value of that data and increasing value of that data. So it’s one way to think about it. It was actually a Gartner person wrote a book last year called Infonomics.  And I’m sorry I can’t pull the author’s name out of my head.  But it’s a great book, and it gives you some ways to measure data as an asset.  Measures that are used by the M&A community and things like that. And so, I think they are good ways, and I’m encouraging the companies that we’re working with to begin thinking about that and measuring data in that way.   

[06:46] 

Info what?

[06:46]

Infonomics.

[06:48]

Infonomics.  So, tell me about your business; tell me about Elder Research.

[06:52]

Elder Research, we’ve been around a little more than 25 years.  So, we were doing this thing we called data mining that’s now got this new cool term called data science for a long, long time across a lot of different verticals and have gotten a lot of experience.  We actually started out building hedge fund trading models. That’s a hard place to make money and to survive, right?  

[07:16]   

You go to be right.

[07:17]

Got to be right.  And you got to know that you’re right.  And it’s one of the things that we evangelize.  So much of data science today you see done in not a disciplined manner.  So, coffee’s good for you; coffee’s bad for you. Wine’s good for you; wine’s bad for you.  So you just drink both, and you hope they balance out.  

[07:32]      

I do.  If I’m right once and wrong once, maybe to your point, but average I’m perfect. 

[07:39]

Average, I’m good.  I’m going to the mean.

[07:42]

That’s right.   

[07:44] 

So, anyway, so I think that that part of it is really important.  So then, what happened in 2001 after 9/11, our founder, Dr. John Elder, was really well known in the space, and the President asked him to come sit on a committee to look at how our intelligence community could share data better.  So you now have the Office of the Director of National Intelligence, who is supposed to bring data together from our intelligence agencies to make sure we don’t miss another 9/11-style attack. Well, that got us into the intelligence world and a large piece of our business now is in that space, trying to help with counter-intelligence and insider threat and things like that.  Then, we expanded into the federal civil government, working for places like the IRS, FCC, Health and Human Services…

[08:35]  

Highly regulated spaces.

[08:37]  

Highly regulated, Department of Labor, trying to help them with some of their problems and some of the things they need to tackle so that we can use our tax dollars in a very efficient way.  So, that’s a little history.

[08:48]  

Give me your favorite customer story.

[08:51]

Wow!  We have this thing called recency bias.  So I’ll give you a recent one. I can’t really reveal the client, but what was fun about it and ties into what, I think. the questions you’re getting at and digging around here is they asked me to come in and help them with a long-term analytic strategy plan.  Now this is a company that had sort of put together an analytics team, hired 40 people, and then said, “What do we do with them?” Which is exactly opposite the way I would approach the problem or encourage them.   

[09:25]

Ironically, big companies that do it this way.  

[09:28]   

Yeah, yeah, they try to figure it out as they go.  

[09:30]

“We need AI.  Let’s hire a bunch of people, and then we’ll figure out why.”

[09:33]  

Yeah, and then we don’t even have the business questions, and the people are doing this, and they start having attrition and everything, you know?  And so, I said, “OK, well.” They shared with me what their two-year plan was. And they said, “We got the two-year plan. we think, down pretty well.  We want you to help us with three years. Tell us what technology we should use and all this. And, by the way, you have three days to figure it out.”

[09:54]

No problem.

[09:55]  

I said, “OK.”  Well, I’s spent some time with the company and spent some time in their operations to understand it a little bit better.  And so, I really thought about this, and I really thought about it from the technology point of view. And here’s what happened.  If you think about technology, and we’re in it all the time in data science: GPUs, MS processing, natural language processing, and those are some of things that we’re interested in.  What we thought was state-of-the-art four months ago has been surpassed four times in that four-month period. So, what I actually went back to them and said was, “I think you’re focused on the wrong thing.  Don’t focus on technology. Focus on what analytics can do for the business in the three- to five-year timeframes.” So, as an example, one of the things in their two-year plan was self-serve: how do people come in and get some of our analytics and data in a self-serve model?  So, I said, “That’s good. That’s a good two-year plan. Three-year plan: instead of deciding what technology ‘cause it’s going to be different by the time you get there, let’s look at how do you turn that into what I would call instant awareness. So, instead of self-serve, you’re pushing now information that, based on your analytics, you know they should be thinking about.  Think about how that would work in a health care space. It could be amazing, right? And so, now they’re self-serving on things that you’ve already given them information about or a direction you’ve already pointed them in. So, rather than thinking of it through a technology lens, look at it through a business lens and say, ‘What should we be doing in the business in three years?’  And then, you’ll be better able to select a technology as it comes along during that period, right?” So. That’s it.

[11:37]  

That’s super interesting.  Thinking about the space in general, it feels to me like we are, it feels to me like we are entering into like a big uplift in businesses generally becoming more analytics in their decision-making process.  Is that what you’re feeling? Where do you think we are on that J-curve? Are we like at the beginnings of it? Middle of it? Cresting? 

[12:01]  

I think we’re just at that knee of the curve where it’s really, really taking off, and they’re figuring out.  And part of that is because there’s a cultural change that has to happen, all right? And people have to think that way.  And a lot of it is too, we’re overhyped. And I love technology. Don’t get me wrong. And I’m not trying to slam technology or product providers.  But what I’m seeing is, look, they’re out there saying, “Hey, I’m going to give you analytic platforms and things are going to be wonderful.” But, if we hadn’t defined that business problem well, if we’ve just bought a platform and hired a bunch of people and we don’t even know what business problems we’re going to solve or even alignment with our mission and strategy, then we’re going to fail fast, you know, and hard.  And it’s not going to be pretty.  

And then we start getting the data organized and together and visible to everybody, transparent.  Then the data science. We really have great tools in data science to be able to build models once we have data and a problem to solve.  And then, the really hard part that we need to think about is how do we put it into somebody’s hands so it fundamentally impacts their decision process on a day-to-day basis, right?  And we don’t think about that enough. We think, “Oh, we just put it in a BI tool or whatever.” But now we have dashboards of dashboards of dashboards, you know. And nobody looks at them.  So, we hadn’t thought about that delivery process that says. And if you think about analytics, the whole purpose is to impact, to give better information into a decision process. If we haven’t done that, then we’ve failed.  But part of that is delivering it to you so that it makes absolute sense to you; it’s intuitive to you; and I’ve anticipated the next three questions you’re going to ask, right? So then, you can ask those questions and get some confidence in the data that you’re seeing.  But we have to do those things well, and we don’t think about them enough. And then, beyond that is how do I begin to get people to adopt and change over to that.  

[13:45]  

This is an exciting time.  

[13:47]

Yeah, it’s very exciting.

[13:48]

We are probably going to get run over.

[13:50]   

Yes, we are.  There are people all around us, making lots of noise.  Oh, my gosh.

[13:54]

It’s terrifying.  If somebody wants to get in contact with you or someone else at Elder Research, how would they do that? 

[14:00]  

They can go to the web and look for DataMiningLab.com or ElderResearch.com.  And either way, they’ll find us and jump on our website. I’m listed on that website.  Everybody else is. We love to get contacts in, and happy to talk to people. 

[14:15]

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

[14:18]

Super, thanks for having me. 

[14:19]

Everybody else, I hope that you will take the time to screen capture this, share it on social media.  Really appreciate your time and attention. As always, have a wonderful day.

PAW 2019 Conference Series – Brian Shindurling – Big Squid

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 Brian Shindurling, VP of Marketing at Big Squid.

Find Brain Online:

Email: bshindurling@bigsquid.com

LinkedIn

Big Squid


[00:02]

Hi, I’m Jamin, and you are listening to the Happy Market Research Podcast.  We are live at Predictive Analytics World, Marketing Analytics World, and many other worlds.  My guest right now is Brian, VP of Marketing at Big Squid. You guys have the coolest booth, I think, on the show floor by the way.   

[00:20]  

Oh, thank you.

[00:20]  

The hot pink’s great.  I have a kick-ass sticker that I’ve added to my bag.  Thanks very much for the tchotchke stuff.

[00:26]  

Absolutely.

[00:27]  

Tell me a little bit about the company.

[00:29]

Cool, so, we’re Big Squid.  We’re based in Salt Lake City, Utah.  We’ve been around for ten years actually.  The company’s kind of evolved out the marketing and analytics world.  We’ve done a lot of business intelligence consulting in our years past.  And, really, kind of the genesis of where we are today was we were consistently hearing a lot of the same challenges with our customers where leveraging business intelligence for an analytics environment, you’re building really interesting and cool dashboards all the time, but you’re always looking at data in a historical context, which led our customers to the next questions, which is “What’s going to happen?  “Why is it happening?” and “What can I do about it?”     

[01:07]

I mean that’s the Holy Grail.

[01:09]   

Absolutely.  That’s right.  So, a couple of years ago, we launched our product we call Kracken.  It’s an automated machine-learning platform.    

[01:16]

Bad-ass name.

[01:17]  

Thank you, thank you.  And the approach that we’ve taken is to integrate with the analytics infrastructure that BI analysts are using every day.  So, most of your enterprise data warehouses that are on the market today, most of the major business intelligence platforms…  We’re able to round-trip data in and out of those environments where we’re basically just enhancing it with predictive metrics to give analysts a little bit better idea of what’s going to happen.  

[01:39]

That’s really cool.  What kind of data are you dealing with?  

[01:42] 

It depends.  We deal with all kinds of different data.  We’re kind of a horizontal play. We work with companies across basically any vertical that you can think of.  The data that we play with is always structured, again coming out of kind of that business intelligence environment.  

[01:57]

So it’s been cleaned.

[01:58]

It’s been pretty well curated, pretty well cleaned.  This is data that has been used or is being used for reporting on a day-to-day basis.  So we’re lucky in that sense; there’s already been some thought behind the business questions that we’re trying to support with analytics.  Yeah, structured data and set up in a way that it’s being used in reporting environments.   

[02:18]    

Who is an ideal customer?

[02:20]

Good question.  So, our ideal customer is the BI-analyst or data engineer, those that are leveraging these platforms like a Snowflake and/or a Tableau, Looker, Click (Places where they’re leveraging data on a day-to-day basis to derive insights and then reporting on and telling stories to their executive stakeholders about what’s happening in the business and what do we think is going to happen, how should we be thinking about making things better.  But they haven’t really been classically trained on data science and machine learning in the past. So what we’ve done is we’ve created a platform that enables them to very easily navigate towards that concept that Gartner calls a “citizen data scientist.” So, leveraging an automated platform that really brings the R and the Python, the math, and the stats that most data science practitioners have in their back pocket to the analyst who is more closely aligned usually with the executive personnel, the stakeholders who are making decisions on a day-to-day basis.  

[03:21]

There’s been a lot of movement not just in this space but across the board like in primary data and others where it’s like this democratization of access to the actual insight, which prior was almost impossible because it required, at a minimum, some advanced math and stats, which is like Master’s level.  And now, it’s like you’ve got these solutions that are allowing the common folk, as you said Gartner cited correctly, the citizens… So, when you kind of frame things out a little bit more, is the buyer the analyst inside of the organization? Or is it happening at the CTO level? Who’s the one that’s sequestering the budget? 

[04:10]  

Another great question.  It’s an interesting space.  This is absolutely an emerging market; it’s red hot right now.  What’s fascinating is most often companies haven’t budgeted specifically for… 

[04:22]

It’s like a new dollar, right?

[04:23]

It’s a new dollar, absolutely.  That’s a good way to describe it.  So for us, we take an approach where we try to create champions, our actual users, people who are excited about being able to expand their skill set to have a bigger impact on their organization, to make a name for themselves.  But in almost every occasion, we have to navigate our way up to executive level and, most frequently, C-level sponsorship in order to reallocate budget into the environment where they can make an acquisition of a product like ours.   

[04:51]

Yeah, totally.  Is the initial sales strategy a little bit like a B2C.  I know you’re not connecting in it at that level. But does it feel a little bit more starting in the trenches of the organization?

[5:04] 

It can be, yeah.  I like to think it the way we approach the market as kind of top-down and bottom-up.

[05:10]

Yeah.  Both directions?

[05:11]

Both directions, yeah.  And, if we’re lucky, we meet in the middle.  We have an informed executive, and we have a champion who knows exactly how they want to leverage it, and they’re pushing for the insights that they can build. 

[05:21]

So, with all the success that you’ve had (this next question is going to be kind of hard to answer) but I’d like you to pick one example of a project where it was just your favorite.  You connected with it; it seemed like it went really, really well; clearer kind of view of how people are using you.

[05:40]   

Yeah, OK, cool.  I’d say for me my favorite customer story is probably that of Skullcandy.  For those who don’t know, Skullcandy is a very innovative player in the audio space, kind of a life style brand.  They build headphones of all shapes, colors, and sizes.   

[05:56]

I have some.

[05:56]      

OK, good. I hope you enjoy them.  The story with Skullcandy is they were trying to leverage some kind of predictive analytics to inform the way they’re developing their products.  So, what they were able to do, actually leveraging some NLP earlier on in their data pipeline, they were able to call out key words in customer reviews from places like Amazon and things like that.  So they were getting an understanding of, from a commentary perspective, where are things failing. If we have a one- or two-star review, what are the key words that are being used over and over again.  We took the outcome of the NLP model and started to feed it into a supervised machine-learning model with all of the attributes around specific products, new products going to market. And the cool part about their story is they’re able to now predict what new products that they’re launching are going to have failures and where.  So, before they go too far into market with a new product, they have the opportunity to retool, improve the quality of their product, increase the customer experience, make a better product, improve the user’s experience, make everything feel better, and lower their warranty claims costs overall. So they’re having to deal with less claims coming inbound; they’re having to deal with less returns and shipping costs and sending free product, and all those types of things.         

[07:13]

That’s really cool.  That is really cool. I love that.  So, you said… It sounded like what they were leveraging was existing reviews like Amazon-type, Yelp-type stuff, right?

[07:28]

Yep, exactly.

[07:30]  

So, you’re able to incorporate things like social media as well as primary data sources, any type of data. 

[07:35]  

Sure, yeah, yeah.  If the data is being captured as long as it can be piped into a data warehouse type environment, we can ETL things together.  It becomes a really rich data set that’s fit for machine learning.  

[07:45]  

That’s really cool.  So, what do you think about the show?

[07:47]

The show’s been outstanding.  It’s been really, really fun, actually.  We’ve had a lot of fun dealing with a lot of data science practitioners and kind of getting their insights on where and how they might leverage something that’s a little bit more automated, where their pain points are, and how these emerging technologies can speed up their workflows and, ultimately, enable them to do more.  We’re also had some really fascinating conversations with analysts who are reading and learning and trying to understand this data science space. I think that’s why a lot of people have come to this event; overall, it’s just to learn more about what is this predictive analytics world, so to speak. So it’s been really fun. I feel like our message has been resonating really well, and it’s been a very productive show for us, absolutely.     

[08:28]  

I think the attendees here are…  I mean you have this really nice cross-section between business professionals (these are like buyers) and then also executioners (the people that are actually doing the analyst side of things).  So, you’ve got a very nice representation across those two. And then, you got a lot of big brands, right? Big budgets are represented as well. And then, the show floor feels very friendly.  

[08:49]  

Yeah, I agree.

[08:51]  

Yeah, it’s kind of nice.

[08:52]  

Yeah, it’s been really fun.  We’ve had upwards of 15, 20 people hanging out in our booth at any given time.  Wish we had some Polaroid action going on. Come to the booth and get a Polaroid.  And people have just been kind of light-hearted and having a good time. It’s been fun.

[09:06]

That’s great.  Brian, if somebody wants to get in contact with you or sales at Big Squid, how would they do that?

[09:11]

Probably the best bet is to visit our website BigSquid.com.  Obviously, fill out a contact form there. Really easy to find.  You can also check out a free trial on our website as well. Sign up and get a 14-day trial and get kind of a feel for what automated machine learning looks like and feels like and what types of insights you can derive. 

[09:31]   

And, of course, we’ll include that information in the show notes.  Brian, thanks for being on the Happy Market Research Podcast.

[09:34]

Thank you very much for having me.

[09:36]  

Everybody else, appreciate your time.  This show is coming to an end. If you found value in this episode like I did, I hope you will screen capture, share it on social media, LinkedIn, Twitter.  I’d greatly appreciate it. Have a wonderful rest of your day.  

PAW 2019 Conference Series – Bob Selfridge – TMMData

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 Bob Selfridge, CEO and CTO of TMMData.

Find Bob Online:

Email: bob.selfridge@tmmdata.com

LinkedIn

TTMData


[00:02]

Hi, you’re listening to the Happy Market Research Podcast.  I’m Jamin, your host. Today I’ve got Bob with TMM Data. We are live at Predictive Analytics World, Marketing Analytics World, Health Care.

[00:16]  

There’s a giant list…

[00:17]  

There’s a giant list.

[0:18]  

of events.  Email is in there somewhere.  Deep Analytics, there’s lots of good stuff here. 

[00:23]  

The email one to me is kind of interesting.  I’m like, “Gosh, email’s dying.” 

[00:28]

You think?  You think so?

[00:30]

Yeah, open rates are decreasing.  There’s actually divisions of companies now that aren’t even responding to emails.  They only use Slack and other…anyway. So, Bob, thanks for being on the show.   

[00:42]   

Thank you for having me.  I appreciate it.

[00:44]

Let’s start out.  Talk a little bit about TMM Data.  What do you guys do

[00:48]  

Sure.  We’re about a 13-year-old company.  I started it back in 2008 on my front porch, closed in the building, brought people in:  one of those good, old-fashioned, kind of bootstrap things. We really started TMM was originally Track My Marketing.  So we were all about channel marketing and, ah… Well, at that point, it was multi-channel; now it’s omni-channel ‘cause we have to have cool new words.  But being able to set this up so that you could do reporting.  

[01:14]

That was like early pre-social media focus market too.

[01:18]

Right, right.  2008: I mean it was around, but nobody was really diving in.  It was more of a fun thing to have that us old folks were starting to play with more so than day-to-day operational thing that it is now.  But we started measuring that around 2012. My phrase is, as I like to say, “It’s about the data dummy, not about specifically marketing data or analytics.”  And we really refocused the company to becoming a data-integration company. We saw, while campaign management and marketing analytics is still very the core of what we do, we find that a lot of folks are still struggling with simple things.   They’ve got spreadsheets coming in email; they’ve got, of course, 7,000 marketing technologies out there, floating out there in the atmosphere that they need to pull that data in and merge it and meld it and marry it and all the cool phrases we use now.  So, our goal is to make it easier for analysts, whether they be predictive analysts, marketing analysts, financial analysts, just analysts that are fighting. We just did a survey with Digital Analytics Association here last year. People are spending 40% to 60% of their day just copying and pasting, cleaning data to start doing their work.  And our ultimate mission at the company, our official mission statement is “Meet data needs painlessly,” very short and sweet.   

[02:32]

Oh, I love that.

[02:33]

But we want to be able to allow analysts to come in at 8 in the morning and start doing their job instead of spending four to six hours cleaning up the data to then move to the next step to start doing their job.  So, that’s kind of ultimately our goal in life.

[02:47]

An ROI on that is really easy to get to.  That’s the nice part.

[02:50]

Well, it is.  Unfortunately, for us and unfortunately for some of the analysts, the really good analysts, end up doing the extra work and working into the wee hours.  And the senior management still get all their reports because they just put in extra hours. So the ROI to the individuals on the ground, certainly the feet on the ground really know that there’s a great ROI to it ‘cause it saves them a lot of time.  Sometimes, although it’s getting much easier in the last two years, selling it up because they’re going to want to get my reports now. And it’s like, yeah, but you could get double the amount of reports, and better reports, had you not had to spend your whole day copying and pasting.     

[03:25]  

And the reality is there’s such high turnover among the people that are generating those reports.  And if you can have a little bit better quality of life… To your earlier point, even if… ‘Cause cleaning and structuring data sucks; it’s not very fun, right?  It’s very grindy kind of work. And the less of that you can do, the more interesting stuff you can do.   

[03:47]

Absolutely.  Wow, it’s funny you say that because we actually like to say we like to be the plumbers, is what we want to do with our platform.  So, like real plumbers, it’s not real friendly work; nobody likes it, but everybody has to have it. So, that’s ultimately where we want to be in an organization.    

[04:07]

Oh, that’s great.  So, who’s your ideal customer?

[04:10]

Traditionally, it’s been marketing departments for large enterprises or medium-to-large enterprises.  Some of our larger companies we work with like T-Mobile and Sony and Comcast, they’re marketing organizations that have a great IT technology environment where they work but the marketing information that they need access to is not a real high priority.  You know when there’s frustration there I say, “Look, the engineers at Comcast are keeping the internet running for about 75% of the country. They have higher priorities than the analytics data from yesterday that they want to try a new regression test on.”  So, what we want to do is fill that gap. We want to be friendly to IT because if we make IT uncomfortable with us or we try to shadow IT (those kind of things), it becomes a real problem for the organization, and it causes hardship for the folks we’re working with.  So ultimately, we want to be IT-friendly, but we want to be able to fill those gaps that IT can’t fulfill for marketing or the business side of the shop. And we’re finding more and more as BI departments are coming of age, we’re seeing that a lot in the last couple years where the middle ground either an IT person’s been assigned to run an analytics/BI group (‘cause we got to make the business folks happy) and try to fulfill the needs of both of those worlds at once.  That’s kind of where we’re coming in, and it’s been a good model there, which is kind of our second group, so to speak: so marketing departments directly and now the new BI/analytics groups that are being created in their centers of excellence, etc., throughout the industry. We work with them to be (I hate to say “middle ware” ‘cause that’s a bad word, or it was traditionally) but uh…   

[05:53]

I’m sure there’ll be a new fancy way, word.  Don’t worry.

[05:59]

We’ll come up with something.

[06:01]

We will, absolutely.  We did clouds, one of my big pet peeves, terms.  We had remote servers before. But anyway… so, do you have a favorite customer story?

[06:13]   

Yeah, probably one of our oldest customers is Comcast.  Of course, for those in the country that aren’t aware of who they are, they’re Xfinity now that’s what they kind of rebranded.  But they’ve got phone services now; they have mobile services; they’ve got VoIP for telephone; they got internet security systems, and, shockingly, they have cable TV still.  That still exists too. And one of the challenges they’ve always had is trying to figure out what is actually happening with some of their customers. A really cool thing just from simple data-integration perspective that we’re able to do with them is one of their struggles has always been cost of customer services, right, on those.  And one of the neat little projects we were able to do was: We get notified when a new customer they call it connected, when I new customer gets plugged in. So, we have access to those records. So, we’re able to take that information, merge that with the service records to know when a truck physically went to a house and plugged somebody in.  And then the third piece is when did that customer log in and get their magic Xfinity account so they could start doing their cool stuff on Xfinity. Those three pieces of information started saying things like from order to logging in; that kind of told us the maturity level of the customer they found. That’s where we found. So, somebody the day they got their internet if they had their account within 6 hours, they’re probably pretty technically savvy.  And it just so happened that those people, all of their service requests were bottomed out; they were self-served; they didn’t want to deal with it. The folks that took six days became another level, and what they would start doing is immediately the very first time they opened their internet up, when they went in, they would put them to the Help page on how to set up their account so that they could move them along.

[07:57]

That’s huge.  

[07:58]      

So, you knew the segmentation.  And then the people that waited 30 days, they actually were starting to work through processes to do like a follow-up call because until they have that account there’s a lot of things they couldn’t do.  And, honestly, they couldn’t be tracked to know whether they were good customers or bad customers. More importantly, were they self-served or did they need extra help?      

[08:15]

I love that!

[08:16]

If it had been more than 30 days since they had their account (This is my personal favorite), we actually did a segment so that the very first page they came to was not the help-tech stuff.  They went to how-to-program-your-remote page ‘cause if 30 days to get an account, chances are they were struggling with how to even get the remote set up to watch TV. So that was the first thing they saw versus the knowledge base.  And being able to do that… And they were able… The mix of all of that reduced cost by almost two million dollars a month in services and calls to support centers.     

[08:45] 

And you know, by the way, the most important thing is the customer experience is better. 

[8:49]

Correct.  The customers weren’t frustrated because most of their calls from users was, “How do I program this stupid remote?” because they’d already tried and been frustrated.

[08:56]        

It’s very frustrating.  Yeah, exactly.  

[08:58]

It was just a really neat story.  And it was just a couple different data points from four different data systems, right?  None of them talked to each other because they didn’t need to. Normally, you order online; a ticket goes to a trucking system; the contractor goes to a house, plugs it in, and says, “Done.”  And none of that needed to be aligned, but once you start aligning it, you can do cool stuff.  

[09:16]  

When you think about like…  So, there’s different business owners there that you’re having to integrate as well, right?  It would be easy if it was just data, wouldn’t it?  

[09:22]  

Right.

[09:23]  

But the human part is…  So, you must have had like a champion internally at Comcast in order to…  that could navigate those channels successfully.  

[09:32]  

Well, yeah.  We actually were very lucky:  We had a person that we worked with for three or four years there, who left the company, went to chase something else, and then got bored, and turns out we had a position; so, we were able to hire her.  So, we didn’t hire her from a customer. We were trying to help… And she went back to serve the customer. 

[09:49]

So she knew…

[09:51]

So she knew the players to go chase down.  We had three or four champions: one from each of those channels of data.  It was very helpful to have that road map, so to speak, to go chase. So we got lucky in that case.  But there was a corporate sponsor that said, “Look, there’s only so much… With the competitive nature of the internet as it is, the margins there are not very high ‘cause they’re all competing very heavily between Fios and Comcast and now MyFi and others coming out.  Satellite’s gotten much better, much bigger recently. So they got to figure out where they’re going to make more money. Sometimes, it’s not about making more money; it’s about spending less money. We had a champion in the customer-success department, who was new, who said, “Look, there’s got to be a better way to do this ‘cause we’re just losing money hand-over-fist for things that seem unnecessary:  fighting with people over remotes. Why can’t we just get them what they need, you know?” Having forward thinkers like that, and that’s tough at a lot of organizations.  

A company the size of Comcast or Sony, they clearly have really good people they can recruit.  At the conference here, as we’ve talked to folks throughout the week, it’s been interesting ‘cause it really depends on the maturity level of who’s running your analytics department how deep they can go.  Someone, “I have my marketing data; I want to throw it somewhere and have pretty reports come up and I don’t know what to look for. So give it to me.” And others are coming to us with, “I work for a university, but I’m in the research lab for the medical department, and I’ve got all these specific needs.  Please don’t tell me you’re just giving me reports. I need to be able to play with the data, look at the data, work with it.” And we try to be more of a blank slate, which sometimes is not good for immature – not “immature” – new to the industry, right? Some industries are just catching up, and they’re just trying to figure out how their ad spends are.  That’s where they’re at. So we tend to work with the more mature, larger enterprise that can go deep.         

[11:43]   

That’s makes a lot of sense.  So, the conference’s been good?

[11:46]

So far, so good, yeah.  It’s been a neat mix with the different folks.  It is a little tough when you see somebody up, you got to kind of analyze, “OK, which conference are they attending?  Which portion of the conference are they attending?”  

[11:56]  

Eight conferences.

[11:57]

There’s eight different people.  The email folks have a different status they want to talk about.  Then the predictive versus the deep analytics want to go super technical, etc.  I’m very lucky: health care, they want to go security and privacy. Finance is like nothing can ever leave the building, you know.  So it’s been… That’s tough at a conference like this ‘cause there’s a great…, but it’s also a great mix. Sometimes people like to play “Stump the Chump.”  That’s the word I like to use. I welcome it. Yes, I really do ‘cause you get those deep analytics and predictive folks that are like hard-nosed coders and PhDs in data science, and they love to ask that kind of stuff.  And I’ve got a couple new acronyms this week that I had never heard of. So I got to look them up and learn something new; so, that was good. I hope they come back ‘cause I promised them I’d look it up and give them an answer.  So, yeah, it’s kind of fun. And then I get to play “Stump the Chump” ‘cause I had one super smart guy that… PMML, I think is the language: Predictive Modeling Meta Language or…            

[13:01]

I’ve never heard of it.  

[13:02]

I had not either, but it was a PhD from a university.  So, the next guy was another PhD, and I’m like, “So, have you ever heard of PMML?”  So I got to play it back and, when he said, “No,” I felt much better about myself. Turns out it’s just another format in XML.  So there’s really no magic to it. I’m talking geek now but…   

[13:22]

I love that.  I love that.  

[13:25]

“Stump the Chump”.

[13:25]

It’s so funny.

[13:26]

We’re still using SQL, right?  It’s amazing to me, like 30 years.  Is that what it’s been? It’s been a long time.  

[13:33]  

Well, yeah.  And then we created a whole industry of no SQL so that we could then build apps that let you do SQL and no SQL because that’s what we needed to do.  And my poor marketing staff are very confused when they said, “Well, how do you do no SQL or how do you do SQL with the no SQL?” She like, “I thought was no SQL.”  But we get there. it’s tough. We like to make up new terms. That’s always good.  

[13:54]

If somebody wants to get in contact with you at TMM Data, how would they do that?

[13:59

Simplest way – go to our website, www.TMMDATA or call our phone number, 855-55FORDATA.  See, easy, that’s kind of fun. 

[14:11]

I like that.

[14:10]

Feel free to visit our website.  We’re actually also do several other shows.  So you’ll probably catch us at other shows as well.  

[14:17]

And we will include that information in the show notes as well so people can have at their fingertips a click to the website.  Bob, thanks so much for being on the Happy Market Research Podcast today.

[14:27]

Thank you very much.  It’s been great talking to you.

[14:29]

Everyone else who’s listening, if you found value, please, please, please take the 30 seconds to screenshot this and another minute to post it on LinkedIn, Twitter, wherever your social personas exist.  Be greatly appreciated. Have a wonderful rest of your day. 

Ep. 223 – PAW 2019 Conference Series – Allison Swihart – Syndetic

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 Allison Swihart, CEO and Co-founder of Syndetic.

Find Allison Online:

Email: allison@syndetic.co

LinkedIn

Syndetic


[00:02]

Hi, everyone.  I’m Jamin Brazil, and you’re listening to the Happy Market Research Podcast.  We are live today at Predictive Analytics World. I am standing with Allison, the CEO of an up and coming company Syndetic.  You can find information on them at Syndetic.co. Allison, thanks for being on the Happy Market Research Podcast.

[00:24]  

Thanks for having me.

[00:26]  

Well, let’s start with what do you think?  I mean this is Day 2 or 3.

[00:31]  

Day 2.

[00:32]  

Day 2 of the event.  What do you think about the event so far?

[00:34]

I think it’s been wonderful.  It feels like there’s a really good energy here compared to some of the other conferences that I’ve been to recently.  The audience is slightly more technical, which is great for us. And I am getting a lot of good feedback on the talks. And I think that the decision to kind of combine the different PAWs together into one Mega PAW.   

[01:01]

Eight PAWs

[01:01]   

Were there eight?  There have to four PAWs.  What kind of animal is an eight-pawed animal?  I mean… There should be four PAWs. Anyway, yeah, I think it was a good decision because everyone who came for the marketing side versus the kind of deep machine learning side is mingling together, and you’re getting lots of great conversation.    

[01:28]

It is actually interesting seeing that convergence of…  One of the terms that a previous CEO I had on the podcast said is “diversified data” or “data diversification,” meaning…  You see this in executive teams. So executive teams that have diverse gender, ethnicity, whatever, they outperform non-diverse groups.  It was interesting having him creating that connection with respect to different data. And I hadn’t actually thought of it in the way that you just articulated it, but that’s actually pretty relevant also where you’ve got broad disciplines that are now being combined into a single event, which is kind of cool.

[02:15] 

Yeah, yeah.  And having people of different titles and at different levels in the organizations, I think, is really important for conferences.  I know there’s a lot of summits where people kind of focus on just the CIO level, but I think at this event, in particular, we’ve talked to a bunch of people who are analysts, who are doing the actual work, which is really important to bring their perspective of the individual contributor, not just the manager.  The manager might be making the decision to purchase one piece of software over another, but you need the perspective of the kind of “boots-on-the-ground” in order to, I think, make the best decision. So, I think they’ve done really well.     

[02:56]

So, you have a relatively new company.

[02:58] 

Brand-new.  We just launched this year.

[3:00]

Congratulations.  What month?

[03:02]

March.

[03:03]    

Awesome.  So literally brand new.

[03:05]

Literally brand new.

[03:06]

Tell us what your company does.

[03:07]  

So, we are a virtual data warehouse, which means that we allow you to leave all of the data where it lives:  in databases, spreadsheets, third-part services that you access via API, cloud-hosted services like say you use SalesForce.  You have an HR system; you have an accounting system plus you have a non-premise Oracle database. Leave all that data where it is; we virtualize it into a data warehouse as if it were in the same physical location.  So you can query against it, using plain SQL, and define the right slice of data for every project you want to do, and then you can syndicate out the results in different ways. So you can take a slice of data for say you’re building an e-commerce website, and you want to expose a slice of data to Shopify, but you have data in different underlying systems.  Instead of having to copy the data all into one place, you can through Syndetic virtualize it, syndicate it out, without having to do the copying.    

[04:21]

That’s pretty cool.  

[04:23]

Yeah.  It’s actually been around…  So, data virtualization is not new.  It’s actually been around for a long time.  It used to be called data federation, but what we bring that’s different is that we’re the first web-based, cloud-native virtualization system, which is really important because it allows the business side and the IT side to collaborate inside of the same system to define that slice of data,  So, if you have, for instance, metadata that is living in a spreadsheet somewhere and it’s important that it live in that spreadsheet. Like we take a very business-friendly approach. We love spreadsheets. We think that there’s a reason that Excel continues to exist and everyone uses it. If there’s metadata living in that spreadsheet that you need for a specific purpose, you shouldn’t have to copy that into a data warehouse.  You should be able to use the workflow that’s appropriate for your business and still get that project done. So we cut time to deployment in half or more. And your analytics are not dependent on the last batch job that you ran to copy the data. There’s also a whole host of other benefits: security, abstraction…   

[05:44]

So, you’re not actually copying that data and moving it into…

[05:46] 

Exactly.

[05:46]

You’re just querying it based on where it resides.

[05:49]

And the queries get pushed down to the underlying data sets.  You can, if you want… We give you the option to materialize the view, it’s called, or cache for performance reasons.  But what we say is that should be project specific.

[06:01]

That was my next question.  There’s a lot of overhead.

[06:06]   

Yes.  So, well, actually the performance is very good even in the virtual, non-cached version.  But, if you’re running a query on a 850-million-row database, you might want to cache that, and you can just with the checkbox.  So we make it very easy. 

[06:26]

That’s awesome.

[06:27]      

Yeah, thank you.

[06:28]

Did you found the company?

[06:30]

Yes, I did.  Myself and one co-founder, the two of us.

[06:33]  

Two of you.  It’s terrifying, right?  

[06:35]  

Yeah, it’s terrifying although it’s very exciting.  And the two of us ran a previous tech company together.

[06:43]  

So good relationship.

[06:45]

Yes.  COO and CTO.  So it doesn’t feel entirely foreign.   

[06:52]  

You’ve ridden the ride before.

[06:54]  

Yes, yes, the crazy ride.

[06:57]  

Who is an ideal customer?

[06:58]  

An ideal customer for us is a company between 500 and 1500 people, big enough to have data in lots of different places.  Oftentimes these companies are going through M&A. And, when you do that, you’re acquiring not just operational overhead, maybe a revenue stream, but you’re also acquiring data sets.  And IT integration is a nightmare. So we provide a really good option for those types of companies. We’re industry-agnostic. Based on our personal backgrounds, most of the companies that we’re working with now come from finance or we also have some customers in the insurance and higher education space.  But really any company can use Syndetic.

[07:50]

It’s funny you say that, M&A.  So, the last company I was CEO of, there was eight acquisitions.  It was like this big kind of rollup sort of typical… And the data management on our side – because there was literally eight distinct data – and ironically all of them were data companies. 

[08:15]

Oh, that’s interesting.

[08:16]   

But it made it even more exciting.  So, having a unified view of performance and invoicing and customer usage, all that kind of stuff, would have been…  Like we spent a year and hundreds of thousands of dollars, trying to do the integration (actually, it was over a million dollars), trying to do the integrations, creating that sort of like – BI is not the right term – but overarching view, and it was a pain in the ass.

[08:47]

Yeah, there’s a phrase that gets thrown around very casually, is the “single source of truth,” right?  You want a “single source of truth” for your data. Our hypothesis or our opinion on that is that there’s never going to be a single source of truth.  It should be project specific. And you may want to use, for instance, if you saw a physical good… The name of your product that you want on your e-commerce website may be different than the name of the product that you need to use for regulatory filing or that you need to submit to a trade partner. And that might be for business reasons like you want to market your product in a different way than you care about putting onto a report, but it might also be for technical reasons.  You might be sending data to a trade partner or inputting data into a system and there might be character limits, right? So you by virtue of the way that you’re interacting with your other partners in other systems, you can’t use the same single source of truth. And so, we say that’s OK; you shouldn’t have to. There might be a product name that’s living in one system that’s different than a product name in the other system. And differentially, you want to use different versions of the truth at different times, and no one is better than the other.  

[10:03]  

So, we’ve seen a lot of growth in data, I mean, volumes of data, and it’s only expanding.  What do you see as some trends that are materializing right now like the last maybe three or four years?  And then, where do you think the industry’s headed?     

[10:20]

So, I think a really big trend and also on the people side…  A big trend is that companies are starting to hire data engineers and data scientists, and they are throwing them into or giving them a mandate of “bring predictive analytics to our organization,” but they’re not really preparing them with the tools that they need.  And so, I think they’re putting the cart before the horse a little bit in that they are so excited about the kind of end-state objective: “We’re going to spin up a bunch of Hadoop clusters; we’re going to have Big Data; we’re going to use machine learning; we’re going to do AI.”  And you can’t really do that without having all your ducks in a row on the integration side. You should buy the systems that are appropriate for the amount of data that you have is another big, core tenet of ours. So don’t buy a system that is meant for pedabytes of data if you don’t have pedabytes of data.  It will take you much longer to deploy; it’s going to be much more expensive; and you can do that later. Buy a system if you have terabytes of data or gigabytes of data. If all of your data is structured, don’t worry about fancy AI on unstructured data.   

[11:37] 

Not helpful.

[11:38]

Do the simple thing first.  And so, we think Syndetic is really well-positioned for that kind of incremental growth.  

[11:47]

…and supporting it.  As you look forward, what do you think, where do you think the industry’s headed?

[11:52]    

So, I think, for this conference in particular, talking about predictive analytics, I think that we can separate out a little bit the predictions and the analytics.  I think right now we’re still on the analytics phase where people are figuring out what to do with the data they already have. And it’s a little bit backward-looking.  “Let’s take data. Let’s figure out how to apply some statistical models on it to make better decisions.” I think we’ll get to the predictive part over the next four to five years where we can actually do things like decide how to allocate resources based on who we think our future customers are going to be, not necessarily based on who our customers are now.

[12:39]

Oh, that’s very interesting.  If somebody wants to get in contact with you, Allison, or somebody else on your sales team, how would they do that?

[12:48]

So, please go to www.Syndetic S-Y-N-D-E-T-I-C.co, and you can schedule a demo there or you can write to us at inquiries@Syndetic.co.

[13:05]  

Perfect.  Thanks so much for being on the Happy Market Research Podcast.

[13:07]

Thank you so much.

[13:08]

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

Ep. 222 – Menaka Gopinath – How Ipsos is Helping Top Brands get to the Heart of Consumers Through Online Communities

My guest today is Menaka Gopinath, President of Ipsos Social Media Exchange North America. Founded in 1975, Ipsos is one of the largest global market research and a consulting firm with worldwide headquarters in Paris, France. Menaka has held senior positions at Fuel Cycle and  was a Creator and Producer at Wilcox Sessions which was an online video series where musicians played an intimate performance in their living room. 

Find Menaka Online:

LinkedIn

Website: https://www.ipsos.com/en-us

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.


[00:00]

On Episode 2022, I’m interviewing Menaka, the President of Ipsos Social Media Exchange – North America, but first a word from our sponsor.

[00:11]

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.  In fact, recently we’ve been picking up shows like “Predictive Analytics World” and “Marketing Insights World.” These are two different shows that are great examples of where the Happy Market Research has a presence and, subsequently, an audience that is well outside of the normal market research vein.  So, 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.   

[01:43]    

Hi, I’m Jamin Brazil, and you’re listening to the Happy Market Research Podcast.  My guest today is Menaka Gopinath, President at Ipsos Social Media Exchange – North America.  Founded in 1975, Ipsos is one of the largest market research firms globally and is also a consulting firm with worldwide headquarters in Paris, France.  Menaka has held senior positions at FuelCycle and was a creator and producer at Wilcox Sessions. This is an interesting, little side hustle she’s got going on.  I might wind up cutting that piece. I know you talked to me about it. I actually found it really interesting, but we’ll see. Menaka, thanks so much for joining me on the Happy Market Research Podcast today.

[02:26]

Thanks for having me.  I’m glad to be here.  

[02:29]

Let’s start out with a little bit of context.  Tell us about your early years and how you wound up in market research.

[02:33]

Sure.  Well, I never really thought I’d end up in market research; so, that was a surprise.  But I have always been in the space of connecting with consumers. So when I first entered the work force out of college, I was working in the original startup boom at the end of the 90s.  And I was working at a company that was creating online communities, and it was really the early stages of what social media became to be like when everyone was adding forums or discussions on their websites.  And that was really my starting point in terms of entering the digital realm. And I really grew up in that space in marketing, using digital techniques. From that company, I moved into another agency where we were doing the starting years of viral marketing.  Do you remember that term?

[03:34]   

I do actually.  Gosh, I haven’t heard that in a long time.

[03:36]

Exactly.  So how do we better connect with consumers using the ability to spread things through digital mediums.  And through that experience, ended up at another company where I was doing like same things (guerrilla and viral marketing techniques).  And we were doing some communities as a way to really amplify the messages. So, bring together a group of passionate people, arm them with the messages, and have them push them out and amplify from there.  And that’s what actually lead me to FuelCycle. At the time, it was called Passenger. The founder was a gentleman that I actually worked with at another agency.  

And that company was all about taking this idea of bringing together a passionate group of consumers and leveraging them as a platform for amplifying brand messages.  And through that, we started realizing, “Wow, we’re learning a lot about these people that are just coming in to talk about whatever with us because they just want to talk about it.”  And slowly that really transitioned into what everyone would call market research. I think us as marketers weren’t really thinking about it as us being market researchers, but we were learning so many things from an insights perspective.  And slowly over time, our stakeholders on the client side ended up being a mix of marketing and insights people and stumbled into market research from there.     

[05:11]      

So, Toluna…  I don’t know if you’ve seen their most recent website, but they’ve actually moved to this thing called the Influencer Marketplace.  I thought that was an interesting kind of direction that you think about market research heading, right? So, anyway, it’s funny seeing the evolution, and really the I’ll put the authority shift more and more towards the respondent, right?  So, and that’s where, I think, Toluna is positioning. And it’s not just unique to them: you’re seeing this in other providers as well. But like this whole influencer marketing has been in such a growth mode and really what denotes an influencer… And this is where, I think, like on the community side, it’s really interesting too because big brands are paying a lot of attention to niche communities now. 

[06:03]

Yeah, that’s one thing that’s been a little funny for me and validating, I think, at points because when I first entered into Ipsos, the idea of really putting onus on the respondent (I didn’t even call them “respondent”; I called them “people”) was a novel idea.  It wasn’t really the norm, you know. It was more about getting the data we needed for the research that we were trying to address. And, for me, that’s always kind of been my starting point is the people that we’re talking to and how we engage them. And I’m seeing that happen in the market now.  Like that’s kind of an accepted approach these days, and that wasn’t always the case.  

So it’s great; I think it’s good because the people that are truly passionate or have an affinity about things, topics, whatever, there’s so much you can learn from them because they’re truly dedicated and want to have a constructive conversation about it.  And we’re seeing that… My oversight is on communities and social intelligence (aka social listening). And you’re seeing that on that side too. Like there’s a lot of people that are in the social department at their brand, and they’re doing, constructing insights just as much as a researcher insights function because of all those conversations that are happening about brands, topics, products, whatever.  So, yeah, absolutely.  

[07:31]

This is the part I think that where marketing research has a BIG advantage in the marketplace right now.  By “marketplace,” I mean broadly speaking inside of the corporate budgets, is to really leverage up the voice of the consumer.  There’s a study actually; I should publish this. Estrella’s, she’s with Nestlé. In her interview, she talks about the Watermark Report, and this is a fascinating report I had not heard of before.  I thought I had in the interviews, but I misspoke. Anyway, after doing the diligence on it, actually illustrated this… like it’s never been more clearer that companies that actually put the consumer first, they are winning, especially when you do analysis on the Fortune 500, which is what they looked at.  And they looked at the difference between the laggards, which are the significant underperformers in the marketplace, versus the overperformers. And the overperformers: it’s like a 7 or 8 to 1. It’s just like the amount of distance between them… You know the companies that are actually driving forward with not just lip service because everybody says they care about the consumer, but the ones that are actually employing these techniques.  It just feels like this is a really big opportunity for us in market research, and we’re just at the beginning, I think, of seeing this escalate into corporate budgets. 

[08:57] 

Yeah, I think it’s been in budgets but just in a different place.  And I think that’s what’s been new is that it hasn’t necessarily crossed over into the insights function.  But the value of leveraging your best consumers and collaborating with them, I think that concept isn’t new.  But you’re right. I think there’s definitely the shift in that people are prioritizing it more.

[09:23]

Yeah, for sure.  You actually said something that’s real interesting to me right there, which is where it sits, that insight sits.  Are you seeing the role at Ipsos of insights move from the strict like market research stage on the hill to other departments?

[09:42]

Oh, yeah, absolutely.  I mean I think, if anything, there’s just more involvement of stakeholders outside of just insights, and there’s greater importance for broader exposure and collaboration, right, because there’s elements of the marketing function that has analytics oversight, that has social oversight that insights is a part of but might not be in the day-to-day conversations.  So more and more, we’re seeing different stakeholders join that conversation and even getting briefs from people that might not even be in an insights function as well.   

[10:23]

Yeah, see, that’s another big opportunity for marketing research:  identifying the educational component inside of the organizations becomes…  They’re starting from a totally different point of reference. What is a concept test, for example, versus somebody that’s been steeped in market research Best Practices.  So it’s almost like in a lot of ways we have the opportunity to help educate and elevate the research and the insights that are done across the organization regardless of the tools that are being used.      

[10:51]  

Absolutely.  Yeah, I think that’s a big opportunity.  And that’s actually one of the reasons, I think, what I am doing in my team is…  There’s a lot of boutiques and agencies that do social intelligence and communities, but the fact that we’re able to bring a lot of the rigor and the academic strength and foundation that Ipsos brings to research and link that to these more emerging areas of insights is something that a lot of our clients really value.

[11:23]  

Yeah, this is such as important point.  I mean just because you have a scalpel doesn’t mean you should perform surgery.  I think that anyone can do a survey, for example, right? That’s not hard any more.  My mom can do a survey; that’s kind of like my benchmark. It doesn’t necessarily mean that she should and, if she does, she shouldn’t be directing the organization or decisions at an organizational level because the nuances of questions, just at a question level, a question-formation level is actually really important, right?  And that’s just one little element of research, having the added value and working with a professional agency.

You know the other thing that interesting and talking with many, many brands over the last year, it doesn’t feel like they’re pulling their spend out of their key relationships like they have with the big players such as yourself, Neilsen, whatever.  From their vantage point, it feels like where they’re leveraging you guys has been shifting, though. The actual relationships are becoming stronger because they’re leveraging more on a strategic level as opposed to logistical level. Is that something you guys are seeing?

[12:35]  

Yeah, I think so.  I think advisory is a really critical part of any kind of research.  It’s not just about executing the research; it’s about ensuring that we’re looking at the actionability piece of it and what that linkage to business decisions is.  So we’re not just like sending over the data and hope that it goes somewhere good. That’s a really critical piece in terms of closing the feedback loop on the actual business impact as well.  

[13:04]

Alright, well, let’s shift gears a little bit.  Tell us about the biggest challenge that you have overcome either personally or professionally.

[13:11]

Ahh…I have a lot of challenges.  I think going back to just starting at Ipsos, that was a big challenge for me, namely because I did come from marketing.  I was a marketer, not a researcher; so, that was a big cultural shift. It also was a huge organization, and I, like I said, I came from startups, tech startups, agencies.  So just that cultural shift was big. And to be completely honest, I was recruited into Ipsos to build their communities’ business. And, back then (this was about eight years ago now) the idea of communities…  I think there was a little bit of fear around it, right? It was like, “Is that going to take away my business?” “Are people just going to do stuff in a community and not do stuff outside of a community anymore?”  So there was a lot of fear and just miseducation in terms of what a community is and what it can actually deliver and the value that it brings in an integrated fashion.  

And that was a big challenge to overcome in terms of building trust with my colleagues and ensuring that they didn’t see me and what we were building as a threat, but rather an opportunity and a way for Ipsos to look at different areas of growth and collaboration.  I think we’ve gotten to a really good place, I hope. But what I’m seeing now is that that integration piece is really one of most exciting pieces of what we can do with communities at Ipsos because we have clients that are doing things outside of the communities at Ipsos already and have the community and were able to bring all of these connection points together.  That’s really where the true power comes into play ‘cause communities aren’t good for everything. I’m not going to start looking at volumetric forecasting using a community. So really bringing those pieces together, that was a challenge because the easiest thing I could be is come in and be like, “Oh, we’re way cooler than you and… we’re the new phase.”        

[15:30]   

Everything’s a nail to a hammer.

[15:33]

Yeah, exactly, but it was really like how do we figure out how to work together and be better together.  

[15:39]  

Thinking about communities, is there a life cycle of the communities that are successful; in other words, is it a six-month focus or is it longitudinal in-perpetuity framework? 

[15:56]

So, if it was up to me, I think every brand would have a consumer community, of course.  We have communities that have been up for 3, 4, 5, years or even more. And I think what success looks like in those long-term community programs is that there’s never a point of just settling.  We’re always continuing to evolve. We define success metrics, and we track ourselves and are accountable against those and we continue to evolve those over time. We don’t just set those once and forget them.  Back to what I was saying before, really ensuring how we’re leveraging that community, that it’s laddering up to a larger purpose, that there’s key business objectives that we’re delivering against, and that we’re able to look at the actual impact of that learning on the business itself, like whether that’s the actual decisions it’s impacted, what that looks like in terms of making their broader prophecies easier.  

You know what success looks like can be a lot of different things for the organization, and it, obviously, changes over time.  But being really purposeful in terms of defining what that looks like and actually prioritizing tracking against that, I think, is really important to a successful community for any client.  I wouldn’t say there’s any client that couldn’t get value from a community. I think sometimes there’s missteps in terms of getting to narrow: You know like trying to focus, “I just want to talk to this one group of people because we don’t have market share with them.”  Sometimes that’s valuable, but maybe that’s a much shorter engagement. I think overall the broader success that I’ve seen over the years with community is when it’s truly integrated into the broader process within the organization and that adoption curve is really addressed as a strategic priority   

[18:00]

Are you seeing a lot of…?  Is it a lot of different methodologies that are applied against the communities or just a certain type, more narrow?  When I think about a community, I usually think about diary studies or narrow use cases.   

[18:15]

So, no.  When I talk about community, I’m talking about an online environment where members are recruited in. profiled, and there’s interactivity in there.  So there’s the ability for people to talk to one another, but there’s also opportunities to do more focused, qualitative interactions like diaries, for example.  And we’re also doing a lot of quantitative work. So it is a true combination of quant, qual, and collective interactions. With that capability, you’re able to do quite a bit in terms of learning.  So, it can be around just foundational understanding of who people are; it could be much more diagnostic or optimization work against concept development or ideation or advertising. There’s a lot of different ways that you can use it for understanding paths of purchase or what does that journey look like, U&A.  I mean there’s really endless ways you can leverage a community because at its core, a community is a way to have a dialogue and understand the consumer at a deeper level, ideally. That’s what I was saying earlier: I don’t think that it replaces certain things, but it absolutely strengthens and elevates your ability to do things in a more agile and meaningful way.     

[19:41]

Obviously, you’ve built a set of Best Practices.  I actually sold a community into Intuit, developed the software, etc., back in 2004.  So, super early days. It seemed like such a great idea, but what we didn’t have was any best practices built around that community management and so, inevitably, what happened was it felt a little more grindy, like we just needed to start engaging them on behalf of just or for the purpose of engagement as opposed to learning.  Do you have playbook that you employ? And what does that look like? 

[20:20]

Yeah, there’s layers to it.  Overarching, there’s a really critical approach to how you launch a community.  So, that’s the starting point. And this might sound like a No-Dah, but is having a business strategy and aligning, like a said before, like key objectives and success measures.  And you really have to hold yourself to those things because they’re not just put them down to paper just for the sake of it. Like you actually have to be thinking about what does that look like.  And that’s a collaboration; that’s not like my team can figure that out. We have to have that dialogue with the client or with the brand to make sure that those things are meaningful and they’re things that we can executive against.  So, that’s first. 

The second is having a really defined engagement and content strategy.  And what that looks like is laddering up against those business objectives and to build a real meaningful content plan that actually is going to deliver against the things that we’re saying we want to address from a business perspective but also aligning that with a member value proposition.  Like why do these people even want to join this community? Why are they going to come back? Like you actually have to have a reason for that, that you can actually bring to the table, right? It can’t be like, “I want to be the next Facebook.” That’s probably not what most brands can deliver.  But there’s generally something, right? There’s an affinity around the topic; there’s knowledge or insider information; or there’s some kind of value proposition that you can align on. And once you define that, then we execute against our Ten Golden Rules of Engagement, is what we call them. But they’re really like core principles that are driven by human behavior, behavioral science, like the things that just drive humans but within the digital environment.  If we execute against those in a consistent fashion, we see that it drives really strong engagement. And then the third piece is that…   

[22:24]

Sorry.  Really quick before you get into the third piece, I just have to ask.  This concept of that it’s not about the money, right, necessarily… I actually believe this 100%.  In the research on research I have done, I will get oversized returns if there is an emotional connection or interest in a brand or category as opposed to a strict incentive relationship.  Is that part of how you’re creating the communication strategy?

[22:59]

Yes, absolutely.  We’re actually about to put out a paper on research on research we did around this, around the importance of intrinsic motivation versus extrinsic.  And that’s not to say that we don’t use rewards or financial incentives, but it’s not the starting point. To establish a relationship based on money, you’re setting yourself up for that expectation but, if you establish a relationship based on more intrinsic and emotional drivers, like you said, then you have the opportunity to have people that feel like they’re truly part of an experience and that they actually have a voice at the table, whatever that potential value proposition is that you’re putting out there.  And that’s really critical because you’re kind of cheapening the relationship if it’s just about gift cards. And back to your earlier point, that’s an easy thing to fall back on. And I’m not going to say that we’re not guilty of it at times. Just to be like, “Oh, just throw a bunch of rewards at them to that we can try to push up participation.” But over time that never delivers against the quality and even quantity of learnings that you’re going to get.      

[24:15]

And the other thing that I wanted to mention before you get to your third leg here is…  Well, actually, not mention; it’s more of a question, right? What does the interplay look like with the community members outside of a research experiment such as a collaborative diary or something along those lines?  Is there any opportunity for communication? 

[24:35]

Yeah, we’re communicating with members all the time.  Our community…

[24:40]

But I mean member to member.

[24:42]

Member to member, oh, yeah, yeah.  Generally, we’ll ensure that there’s some layer of what we call engagement activities at a baseline.  And it’s not like you just throw up a discussion and forget about it. Like they’re highly curated; they’re moderated; and where members are able to talk to one another…  And it’s really about creating that sense of community. That’s the thing that makes me laugh sometimes where people say, “This is a community,” but there’s like no sense of actual community. 

[25:11]

Take a survey.  

[25:13]

Yes, exactly.  The word “community” is the word “community” for a reason, right?  So we take that pretty seriously in terms of the experience that we’re creating. 

[25:24]

Alright, got it.  So, I didn’t mean to interrupt you.  I’ll be quiet at least for the next leg.  

[25:31]

No, yeah, so the third piece is really about adoption and evangelism on the client side because it’s easy to set up a community like in an asylo.  So like someone on the insights team might invest in a community, and there could be someone across the hall that doesn’t even know that there’s a community within the organization.  And so, really ensuring that we set up a strategy with the client to define how we’re going to really market the community within their organization.  

[26:10]

I have never heard that before.  I actually think that’s probably one of the biggest “Ah-ha” moments for me I’ve had on the show.  That is brilliant.

[26:20]

And it’s critical.

[26:21]

What does that look like?  I’m very familiar with putting together a content strategies and content calendars and all that kind of stuff.  Is there a whole separate…? The way that you’re describing it, it sounds like it’s more of a strategic session with real clear deliverables in time.

[26:39]

Yeah, it really depends on…  Again, it’s how you embed this program within the existing organizational culture.  At some clients within their organization, they have quarterly townhalls; and they have a newsletter or whatever.  It’s like really like trying to vet all of the different touch points that are within the organization. 

[27:03]

Got it.

[27:04]  

And then the other layer to that is also just “How do they make decisions?” 

[27:09]  

So, it’s a lot of communication-channels sort of analysis and strategy ensuring that you get some or have the opportunity at least to get some air time. 

[27:19]  

Exactly, and that ladders back to the success measures, right?  It’s all connected. If you don’t drive adoption, you’re not going to do enough to drive the success measures and so forth.   

[27:32]

Tell me about the project that you are most proud of over your storied career.

[27:37]

Oh, boy.  

[27:41]   

I know it’s a lot of them, right?

[27:43]

A lot, but I don’t know that I’m able to share all of the details on this public forum.  But I’ll stay high level. There was a program that I managed earlier in my career. What I loved about it was this client didn’t shy away from what it meant to have the consumer be part of the process.  Like the consumer was part of the process literally at every stage from the beginning of the product inception, which meant it was just in a test tube, to the actual naming of that product, to like figuring out the packaging for that product, to figuring out which stores it should be in, to figuring out what media channels.  They literally had the consumers be pitched by the different media agencies and help make the decisions in terms of where they were going to place media and what that media looked like. They fully embraced this idea of having the consumer part of that process.  

I think for me, “Did we change the world?”  “Maybe not,” but for me what I loved about that program is just how unabashedly the client embraced the consumer being part of the process.  I think a lot of clients can be scared of that. I know there’s that Ford quote about, “If I ask my consumers, they would want a faster horse, right?”  That’s our job. Our job is not to just take what the consumer says and do it. Our job is to critically think about what that actually means and read between the lines and drive what that means from a strategic perspective.  But to shy away from actually engaging with your consumers and understand what they want and what they need and what they’re motivated by, that’s just missing the whole point of what we’re trying to do as insights professionals as well as people in business in general.  

[29:48]  

I love that.  Yeah, I love that.  I had one guest and she said that they measure, at a brand, and she said they measure the success of a project based on a single KPI, which is number of mentions (of the insights being mentioned) inside of the final brief, that’s used to make a business decision.  I thought that’s exactly right.   

[30:13]

Yeah, one of the things that I’ve been talking to my team a lot about lately is because…  I’m sure you’ve heard there’s this whole thing around purpose like brand purpose and brands.  Like they need to actually push forward what this society looks like for good. Corporations have more power in some ways than government.  And, when you think about what we’re doing as insights professionals, we’re like a direct line to consumers; we’re a direct line to what they need and what’ s driving them day-in, day-out.  I don’t know anyone who’s not at some level worried about plastic or sustainability, climate change… There’s a lot of things that are scary right now, right? And we actually have the opportunity to help educate our clients from a consumer perspective on how they can find purpose and drive that change.  And that’s a really amazing position to be in, right?   

[31:23]

Yeah, back to Estrella’s interview, she actually said in the end of it, talking about how for the first time in her career, she’s seeing the red carpet being rolled out to market research professionals, insights professionals to the boardroom.  I completely concur with that based on the interviews that we’ve been doing here. It just feels this is THE day. And it’s not just at the end or the top, right? It feels like it’s the beginning, which is a really exciting point in time.          

[31:53]

Yeah, absolutely.  Super cool.  

[31:55]

So, market research, we’ve got lots of challenges.  What are you seeing as the biggest challenge, whether it’s inside of Ipsos or with the customers that you service?

[32:05]

I mean I think this is broadly in the industry.  I think it’s just the finding the right combination of technology and humans. There’s a lot of things that I think historically research professionals have executed, but with technology and automation, they just don’t have to.  So it’s like really like ensuring that we’re elevating in the value chain, and automating the things that we can but not forgetting the things where “human intelligence” is still really critical. That’s going to be a continual challenge to find the right sweet spot, and it’s evolving every day, right, with the new technologies and capabilities and opportunities from that perspective.  But I think, to what you were just saying, in terms of elevating the position of insights and being part of those early conversations, not an afterthought (“Oh, we should do some research to make sure that this isn’t a bad idea), but being like really at the origin stage. That’s really, I think, the biggest pieces. It’s a challenge, but it’s definitely a necessity in terms of ensuring that we maintain relevance.  It’s really that idea of pushing ourselves up that value chain. 

[33:27]  

I like how you’re casting automation.  Automation isn’t about job replacement; it’s about job furthering.  The more that we can automate the disparate work flows that we have inside of the research processes, then the more time that we can spend on adding value and helping the brands, our customers, and our employers to drive value right in that emotional connection that is so important.  It’s definitely a partnership at least probably for the next 50 years from my vantage point. 

[33:59]

Yeah, absolutely.  I look at it as how can we find time to focus on the fun stuff, you know.    

[34:05]

Exactly, exactly.  So, when you look forward five years from now…  You’ve had the fortune of seeing a lot of transition in this insights space.  How are we going to be different as market researchers or user-experience professionals in five years?

[34:25]

Well, I think that there’s a lot that we know that we don’t know.  Does that make sense? That’s really where I’m seeing a lot of focus right now, is like “Are we being redundant just asking people these questions over and over again?”  “Are there better ways to understand this information?” And I feel like some of it is Big Brother and scary, but other pieces of it, it’s just like, I think, consumers are at the point where like, “I feel like I’ve already told you all of this, and shouldn’t you have some understanding of all this information?”  I see like technology and automation, machine learning (all of the things that are coming into play in the insights world) as really helping us to, like I said before, just like really establish a stronger foundation so that we can focus on the things that are actually going to drive things forward beyond that. So I just see us getting better and better at that ‘cause we ask a lot of questions we don’t have to ask… a lot.      

[35:28]

And we do.  Let’s just start with the basic one: gender.  Every survey, anyways. I have a whole rant there, but I’m going to forego it in the interests of maintaining an audience.  So, it sounds like what you’re saying is there’s this opportunity for us to be able to have better transition between the disparate data systems of that data so that we have a more complete respondent record.  In other words, what I’m trying to say is, whether it’s previously self-reported data or social or whatever (transactional, behavioral, etc.), the more that that gets unified, then the better, the smarter we’ll be as brand and insights professionals in making decisions and also to the point of being really tactical with the questions that we ask at the respondent level as opposed to the same question over and over and over again.   

[36:31]

Yeah, absolutely.  I think there’s an opportunity to be more economical with how we engage with people.  

[36:37]   

Yeah, that’s a much better way of saying it. You’ve been a part of a couple of high performance teams.  Tell me what are three characteristics of an All-Star employee.

[36:50]

It’s not three things.  Actually, my team runs by the credo, “Own it.”  It actually is an acronym, but ownership, I think, is a big piece of that.  So having that sense of accountability and knowing that whoever you are, whatever level you are that you have an important role to play and that your ownership of what you are accountable for is critical to everyone’s success.  So, that’s a big piece. The “W” is willingness to learn. And that’s really important as well because we don’t know everything ever; we are born to die unfinished. And the ability for us to continue evolving is about us having that open mind to understand and learn what’s out there and never settling, which is the “N,” to know that we can really continue to push ourselves forward.  

So, that’s really all-around curiosity and continuing to push what it means to be in our world, in our department, whatever that might be, or in our team.  And then implementing solutions and taking responsibility. I mean those are the big pieces that I think are really critical. So, always, if you want to come to me or anyone on the team with a problem, have upstarting point of what some of the solutions might be.  I think, ultimately, all those characteristics are really critical to a good team, but the No. 1 thing, for me, is about remembering that we’re all human and this really goes back to also just my broader philosophy around our community practice is that these are people, we are engaging with people, and we’re people, and we’re engaging with each other, and we need to connect with one another and respect one another.  And, if you don’t start with your people, then it’s going to be hard to engage with other people. So that’s probably the biggest thing.     

[39:07]      

Menaka, how did you come up with “Own it”?  That is the best set of core values I’ve ever heard.  It totally encapsulates everything that I believe, but in a way that I can actually walk about tomorrow or a week from now; recycle, which is freaking amazing.     

[39:24]

Own it!  It just started with me being like, “Just own it!” and then thinking about what that actually means to me.  Yeah, it’s become really embedded into my team because we actually use it in terms of how we look at performance, how we look at interviewing new people on the team; so, it’s really a framework for what we stand for culturally.    

[39:48] 

So, my last question is what is your personal motto?  I think you might have answered it.  

[39:56]

Yeah, own it!  Actually, I will say it’s a little different.  It’s “Just try it!” has been my personal motto.  Someone asked me this question recently, and I had to kind of think about it.  But I realized that this has been my motto throughout my life. I live in Los Angeles; I’m from Seattle, lived in New York, and San Francisco.  So, if anyone lives in any of those cities, they know that L.A. is not necessarily looked upon in the most positive light. So, me ending up in L.A. was definitely not a plan, but it was definitely a component of me meeting someone and saying, “You know what?  Just try it.” But I would say that’s in my personal life, but in my professional life as well, that’s definitely a motto for me because you don’t know until you try. Maybe it will be a total failure; maybe everyone will look at you like you’re crazy; maybe it will be amazing.  Like I don’t know. And I think that’s something that we just need to be more open to. Just trying it and seeing what happens and going from there.      

[41:07]

My guest today has been Menaka Gopinath, President, Ipsos Social Media Exchange – North America.  Thank you, Menaka, so much for joining me on the Happy Market Research Podcast.

[41:18]

Thanks so much for having me.  It’s been fun.

[41:20]  

It’s an absolute honor.  As always, I appreciate your time and attention, listeners.  If you would please do me a kindness. If you found value in this show, please, please, please, take the time to share it, take a screen shot of it, distribute it on LinkedIn.  I would love to interact with you if you have any questions, recommendations, or also guests. And you can always find this show and others like it on our website. Happy MR.com.  Have a great rest of your day.

[41:46]  

This episode is brought to you by Clearworks.  They are an insight, 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.  

NEXT 2019 Conference Series – Zoë Dowling – FocusVision

Welcome to the 2019 NEXT Conference Series. Recorded live in Chicago, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Zoë Dowling, SVP of Research at FocusVision.

Find Zoë Online:

LinkedIn

Website: https://www.focusvision.com


[00:02]

I’m Jamin Brazil, and you’re listening to the Happy Market Research Podcast.  We are live today at the NEXT Conference in Chicago. I have the wonderful Zoё with Focus Vision.  Zoё, how are you?   

[00:16]    

I’m great, thank you.  How are you doing?  

[00:17]

I’m good.  When did you get in?

[00:19]

Late last night, later last night.    

[00:22]

Oh, OK.  Kind of late.

[00:23] 

Kind of late.

[00:24]

Kind of late.  So, have you been to the NEXT Conference before?

[00:27] 

Do you know I have not.  And I’m actually really excited to…  It feels like the agenda is a little bit different.  There’s a lot more focus on nuts and bolts. I’m speaking here with Ted Saunders and Roddy Knowles on Mobilize Me, which is research on research.  You don’t see that at conferences these days. Why not? This is important. And so, real excited because there’s other presentations like that as well.    

[00:52]  

Yeah, totally.  You know it’s funny there’s an adjacent industry which is analyzing credit card transactions.  It’s a big, big, big space predominantly sold actually, sold into venture and PE and Wall Street firms so they can analyze like when there’s an issue with Chipotle, what its actual credit card transactions trending towards exactly.  What they’re doing is they release a bunch of research on research. And they’re constantly being quoted in Wall Street Journal, etc. It just truly amazes me that we, as researchers who have billions of transactions (not monetary), we don’t do a better job talking about that kind of stuff.    

[01:39]

Do you know from my perspective I think there’s been a trend away from that?  If you think five years ago, certainly ten years ago (of course, that puts us in the aging category) but this was all about web surveys, web data collection, mobile data collection, but also on the qualitative side, how do we create good engagement with online research community participants, and you just don’t see that now.  And, tell you what: we’ve not cracked the nut. It’s not a done deal.   

[02:07]

Not by a long shot.

[02:07]

We’re not doing amazing research, that kind of research on research.  But you’re right: there’s also a lot that we could dig in on the Big Data side.  Like what time of day? Thinking online communities, what time of day are people coming?  What’s the average? How do we aggregate that across projects? How do we then increase engagements?  How could we do some incentives? I think there’s a lot of different things we could do there to just make us better informed.  

[02:29]

Yeah, even something as basic as email-open rates over time.  I think with such an important topic… Rogier Verhulst with LinkedIn, he had mentioned that they’ve actually seen almost a complete decline, approaching a zero, with certain segments in the population just not accessing email anymore.  They’re utilizing a bunch of different tools to communicate. Before you and I because of my age…

[03:02]

That was tactful.

[03:03]

…I mean pre-social media, there wasn’t an alternative to…   You have like AIM or whatever (AOL kind of online messenger), but it wasn’t at scale.  And now, all of a sudden, there’s probably… I mean I could connect with you at least five different ways, not using email. 

[03:18]

And there’s different ways to get our attention.  And I think that’s interesting because if I think of both SMS but also think of push notifications ‘cause again thinking of them is a way to contact people that we are speaking to their participants.  That you see in the general population has gone up and down. And, far from SMS disappearing, it’s got its place but so does all the other messengers – WhatsApp, Facebook Messenger, and anything else you use – but then also push notifications.

[03:44]

Totally.

[03:45]

Just know, am I just waiting to get something?  So I’m going to engage with you, but I just want that notification to come up on my phone at that particular time.  

[03:52]

Focus Vision:  what are you most excited about right now?

[03:56]

We have a really exciting innovation coming out that we’ve been doing a lot of work on for certainly the last year, the last 18 months.  We’re going to be talking a lot more about that in the coming months, certainly coming September. So I’m hyped ‘cause I think it’s moving the needle, and that’s what we all need to be doing.

[04:18]

Oh, I can’t wait to hear about that.  It’s in the qual space or quant space?

[04:21]    

It is in qual but it kind of bridges both.

[04:24]

Got it, so qual at scale kind of a framework. 

[04:26]

Yeah, yeah, and it’s all in the analytical phase ‘cause that’s what we need, again I think as well.  We talk about better, faster, cheaper. And with AI and all of these machine learning and different types of tools that can help us get closer to that data, I don’t for one minute believe it’s going to replace our jobs ever, maybe.  

[04:44]

No way.  

[04:44] 

Hundreds of years?

[04:45]

I mean not in the next maybe 50 years.  Some kind of like abstract construct.

[04:48]   

And everything will change so much in a way that we can’t imagine, but certainly not now.  I think these tools are there to make us better at our jobs, to be able to get closer to the data quickly, and get some good truths from it.  I mean that’s what we need to do.  

[05:02]

Yeah, I mean we continue to hear data–rich, insights–poor.  And we have seen a proliferation of new tools and methods by which we can gather consumer insights, especially in the qual space, which has been really exciting over the last two years.  What we still aren’t doing a great job of is how in the world do I analyze that at scale. You have like individual fiefdoms where you’re seeing that kind of, but there still hasn’t been, from my point of view, a broader, holistic kind of approach.  You know, mTabs, but there again they’re really focused on the quant space. Anyway, yeah, I don’t know; we’ll see. I’m excited to see what you guys release.   

[05:43]      

Yeah, and I think there’s a lot of scope from innovation.  And I’m sure there are a lot of companies that have this thing in the works just because analyzing at scale, making use of the data.  We’re always talking about this Big Data we have at our fingertips; it’s going to make us better and smarter. And so, yeah, it’s going to be good.  

[06:00]

I’m excited about your talk.  We’ll post a link to it if you write a blog post when this thing launches.  If somebody wants to get in contact with you, how would they do that?

[06:06]

Find me through a variety of methods, as you know.  Anything from LinkedIn but also email me at FocusVision, ZDowling@FocusVision.com.  Love to hear from people.  

[06:17] 

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

[06:19]

Thank you for having me.

[06:20]

Everybody else, we’re live at the Insights Association.  Special thanks to the Insights Association for hosting us here on site as well as some of their other shows.  If you would like to learn more, check the show notes. Have a great rest of your day.

NEXT 2019 Conference Series – Thomas Fandrich & Mike DeGagne – quantilope

Welcome to the 2019 NEXT Conference Series. Recorded live in Chicago, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Thomas Fandrich, managing director US and co-founder of quantilope; and Mike Degagne, head of sales at quantilope.

Find Thomas Online:

LinkedIn

Website: https://www.quantilope.com/en

Find Mike Online:

Linkedin

Website: https://www.quantilope.com/en


[00:02]

You’re listening to the Happy Market Research Podcast; I’m Jamin.  And we have today some special guests. We are live at the NEXT Conference here in what is turning out to be a spectacular day outside although we are quarantined, I guess, in Chicago.  I’ve got Thomas and Mike with Quantilope. Guys, welcome.  

[00:22] – Tom     

Hi, Jamin.  Thanks for having us here.  Great to meet you.

[00:25]

It’s great to have you.  For just voice recognition, that is Tom.  

[00:29]

Hey, this is Mike from Quantilope.  

[00:33]

Yeah, you’re going to have to let him in.  

[00:43] – Tom   

We also have a lot of laughing.  So, now we can go to the serious part.  

[00:45]

I’m glad I called you here today.  Tell me what do you guys think about the show so far.

[00:50] – Tom      

Oh, we really like it since there are a lot of specialists here that have very serious knowledge about the industry and about market research.  It was great to get their perspective on a solution like ours, which is pretty much positioned on speed and substance, substance in terms of quantitative methods that go beyond simple service stuff.

[01:14]

Favorite session?  Have you guys been able to attend any sessions?  Mike, we’ll go with you on this one.

[01:20] – Mike

I have not; so, you should not go with me. 

[01:24] 

Tom, have you attended any?

[01:26] – Tom

Me neither, so ah…

[01:26]

It’s hard when you’re exhibiting to go to the sessions ‘cause you feel like that’s the moment the big customer…  You know Coke is going to walk by your booth as soon as I go here. The head of insights for CES. It’s this whole like FOMO, fear of missing out thing that happens.  I’m suffering from it from it too, which is killing me, because I really wanted to hear the storytelling session, but anyway… That didn’t happen, and I also didn’t get Coke.  So, waah. Anyway.   

All right, so, tell me exactly what you guys do.

[01:59] – Tom 

All right, so Quantilope is an Agile Insights platform, right, that automizes all the single steps in a market research project, which is kind of finding the right tool to tackle your research question, program the questionnaire, and infuse it with high-end methodologies like System 1 Conjoint, MaxDiff, then field it to get the data in real time and get it visualized and analyzed in real time through our analyze module, and build beautiful dashboards of all that stuff to share it internally and externally.  So it covers the entire process very seamless and automized.  

[02:35]  

Mike, you gave me a demo, didn’t you?  

[02:37] – Mike

I did not, but one of my guys did.  

[02:40]  

One of your guys did.  Yeah, right, exactly. So, one of things that I thought was super interesting about the fundamentals of the platform is that I can execute in MaxDiff really, really easily.  So, I have my survey that is built out; that’s straightforward. Not really a big USB. But now, all of a sudden, I just add my attributes and it makes a recommendation on what types of statistical approaches I may want to use, incorporate into my survey design in order to answer my questions.  And then I also really like the reporting side of it (not that I’m trying to sell for you guys). But I personally was really impressed ‘cause it was all streamlined and easy to use. Tell me about some of the more popular applications.

[03:33] – Mike

So, ah, I actually started out in market research 15 years ago, and the way that I was doing studies back then (Conjoint, MaxDiff, Key Driver) was building the survey, taking the data out, cleaning it in SPSS, using Syntax.  And then I took it, put it into a stats package to get the Conjoint. And then I had to take that data out, cross-tab it. I had to use ETABS at the time to auto-populate the 140-slide tracker study for Microsoft, and that would take two months.  And then I would present it, and then they would want to change the segment. And I would cry…on the inside.

[04:07]

Yeah, yeah.  I bet you were happy cashing that check.

[04:10] – Mike   

Well, I was working for somebody else.

[04:12]

I know, I know.  Somebody else was happy they’re cashing the check.

[04:16] – Mike  

Yes, and I was just happy to have a job post-recession.  But with our tool, what’s really powerful is that it can do a MaxDiff for something like this, Conjoint, Van Westendorp in 1 to 3 days and it’s because it was thoughtfully put together.  It’s not a usage and attitude survey platform that we duct-taped or staple-gunned these advanced methods on. And so, the most common usage is going in, using our really advanced library of techniques and then things like MaxDiff, Conjoint, Kano, and then fielding it within minutes, and then having the data back in 1 to 3 days with beautiful visualization.  Did I answer your question?   

[04:52]

Yeah, perfectly.  So, it sounds like when you guys are looking at the customer utilization, what is the No. 1…  Are they using MaxDiff? Is that No. 1 or is it…?   

[05:04] – Tom

I mean this No. 1 thing…  It’s really the portfolio of methodologies that people appreciate, that they have the freedom to do MaxDiff today, System 1 emphasis approach tomorrow, and the Key Driver analysis yesterday.  They love the broadness and the flexibility our software platform gives them.  

I would add that this flexibility is when you have a business problem.  There’s a lot of people that get caught up in research where they only do MaxDiffs.  When you’re introducing a new product… We’re getting a lot questions about CBD oil in like food products.  So, what does the category mean? When people think of CBD oil, what does that mean for the category? And how does that actually relate to the product itself, the brand itself?  Then from there, you can go through a whole different litany of research approaches to actually get the product idea, the product price point, the product packaging, the product claim.  And so, that’s what we find, that our clients are working with us from ideation all the way to bringing the product to market.    

[06:04]

So, do you guys do services or are you just providing the platform?  

[06:09] – Mike

Yes, this is actually another unique identifier for us is that we have…  Tom runs a team. And I know Bea is listening right now; she has a PhD in neuroscience.  Vanessa has an advanced degree in analytics. So, we have this team that we call the genius bar where you can come to them and ask questions.  And so, if you’re a brand manager and you don’t have experience working with MaxDiff or Conjoint, you come with a problem and we show how you do it.     

[06:32]  

So you’re supporting in the way of training on the tools and make recommendations to methodologies but not necessarily executing a project from A to Z.     

[06:41] – Mike 

We can.  The example that I give is it’s like getting into a Tesla for the first time.  You’re too afraid to turn on the autopilot yourself; so, someone else will sit with you and do it.  After a couple times you feel comfortable… 

[06:52]

Have you done the autopilot with a Tesla?  

[06:53]

I have, yeah.

[06:54]

So, the first time I did it, I was by myself on the freeway.  I’m not exaggerating: I thought there might be a 30% chance I’m going to run off the freeway.  I mean it’s that terrifying.

[07:03]

So you were white-knuckling it the whole time.

[07:05]

Literally.  I’m not going to let you control my life, Tesla.  Double tap and now I can’t stop. I’m completely attached.

[07:12] – Mike

Yep, exactly, but overcoming that fear, especially if it’s an advanced method that you don’t know a lot about, like Conjoint…  If you don’t understand the different product features, the price per pound or ounce, you really can create a Conjoint that doesn’t mean anything that someone with an advanced degree in analytics would be able to shoot holes in.  And so, we find that for the first 90 days, it’s a lot of training and hand-holding. And then soon we see these people that have upscaled their career by being able to learn how to do these things on their own.

[07:38]

So, thinking about that, you wouldn’t normally do a ever full factorial, right?  I’m thinking about like Conjoint. You know what I’m talking about? So you do a partial factorial.  So, are you able to then control how many cards respondents are ultimately answering for, which is kind of the trade-off of “Do I want partial data and then stitch it together?” or “Do I want a full…?  Everybody to see every iteration of the product feature set.”

[08:08] – Tom   

So, we are a ROCBC approach.  We are showing all attributes on the card.  But you don’t have to care about the efficiency of the design, the meaningfulness of the design.  This is just a click on a button. So we are optimizing the designs for you due to the efficiency criteria.

[08:26]

Got it, OK.  So you’re taking that into account already in an automated fashion.  

[08:30] – Tom

Yep, absolutely.

[08:30]

Got it.  Yeah, perfect.  Good. Who’s your ideal customer?  

[08:33] – Tom     

Our ideal customer has a lot of research needs and is very innovation-driven and tries to create new things, new products, new services, new advertisements; wants to explore new categories and all that stuff; so, is very active in terms of research; and is open to work with a platform like ours, right, which is super innovative as well and gives you the freedom to do a lot of the stuff maybe you have outsourced before to keep it under your own roof, to have full control and full transparency of all the steps in the process and all the data you are getting there.    

[09:17]

Mike, what is your favorite project?  

[09:20] – Mike

We did a Conjoint study.  There was a big CPG brand that got a call from a big box retailer, the big box retailer, that they were losing a losing a series of SKUs, and they were going to a generic in-house product and they had four days before they had to meet with the buyer at Walmart.  And they knew that they needed a more substantial approach to be able to make their point that this will hurt their consumers. And we were able to do it in that short turnaround time. That is an example of Agile Insights. So, the word “agile insights” is thrown around way too much today.  Agile insight is speed; there’s tons of free or nearly free speedy tools. But it’s speed and substance, and the substance is what creates agility in our opinion.  

[10:08]

So, talk to me about price point.  What does it look like? What’s the terms of trade when you do work with Quantilope?

[10:15] – Mike    

You know we are a SaaS company.  We’re a flat monthly-fee business, and it ranges from a few thousand dollars a month to tens of thousands of dollars a month, depending on are you running thousands of studies, are they all Conjoint. 

[10:28]

So, it’s like a price per study as opposed to per respondent or…?

[10:32] – Mike      

No, it’s actually access to the platform:  so, logins, trainings, genius bar, the types of studies.

[10:38]

Like basic something else pro or whatever.

[10:42] – Mike 

Yeah, and we’re flexible, right?  So, we understand what I think of as the SaaSification of market research.  The industry has worked on an ad hoc basis forever.

[10:51] 

It’s hard to turn that corner, but it is actually…  It’s funny ‘cause it’s becoming more and more normalized.  You’re seeing an increase in acceptance of SaaS model.

[10:59]

Yeah, absolutely.  To start, we also understand that there are so many vendors out there that have burned partners, promising the world.

[11:06]

Totally.  That happens a lot.

[11:07] – Mike 

So we think crawl-walk-run is a good strategy.  So if you want to start out in a different way, we’re very, very flexible.  We’ll explain to you until I’m blue in the face why the SaaS model is the right model, but again we understand that there’s procurement teams; there’s way of doing business.  

[11:23]  

If somebody wants to get in contact with you guys, how would they do that?

[11:27] – Tom   

Either shoot us a mail to sales.us@quantilope.com or visit us on our website; there is a contact formula there.

[11:35]

Perfect.  And, of course, we’ll include that information in the show notes.  Tom, Mike, thank you both, oh, yep…

[11:40] – Mike   

We’ve got one more thing.  

[11:41]

One more thing.  Well, bring in on.

[11:42] – Mike

One more thing…

[11:44]   

Everybody likes value; so, if you have two more things, that’s OK too.

[11:47]

You only get one.  We’re going to leave you one more, right? 

[11:50] 

They got to call.  They can get the second one.

[11:53]

Yeah.  So, I’ll give you half a sentence.  No, I’m just kidding. So, for the people listening to the Happy Market Research Podcast, if you reach out to us and you reference this, we’ll give you a free trial of the platform, and for the best customers out there, we would actually do a free pilot project for them.  

[12:08]

What!?

[12:09]

Yep, but you have to mention Happy Market Research.

[12:11]

Oh, I love this.  High five right now.

[12:13]

There you go.

[12:15]  

That is bad ass.  Thank you so much for bringing that value.  I tell you what: you know, Insights Nation, this is one of the big misses (and I don’t mean any disrespect from any other guest that I’ve ever had) but you actually have an audience that listens to this.  I’ve had FedEx reach out to me; I’ve had two dozen-ish brands that have, unsolicited, said, “Hey, thanks so much. I actually use this to help me with procurement, which is so funny because I never had that as a framework for why someone would tune into the show and actually don’t think that’s one of the core reasons why but I do think it’s an interesting by-product.  And so, it’s a great opportunity to be able to leverage like the audience and, if you have something valuable that you can pass on to them, to be able to do that. So thank you guys very much. I think that’s very generous of you. I actually do have one other question: Quantilope – I want to eat it ‘cause it sounds like cantaloupe and I like cantaloupe. How did you guys come up with the name?      

[13:14] – Mike

You’re going to have to reach out to us to get the ….

[13:19]

Oh, no!  I’ll not letting you go.

[13:21] – Tom

It’s a long story, but a nice story.

[13:22]

Is it, really?  Maybe some other time.

[13:23]

There’s a really nice story behind this.

[13:25]

All right, you guys.  Thank you so much for being on the Happy Market Research Podcast

[13:27] – Tom

Thank you for your time, Jamin.  I really enjoyed it.

[13:28] – Mike

Thank you!

[13:30]

Everybody else, if you found value in this show, please take the time to either rate it on the platform of your choice.  This particular episode, I would really appreciate it if you just took three seconds right now, screenshot it, share it on social media.  It’ll take maybe 120 seconds. This probably represents about an hour to two hours of production time. I would greatly appreciate it. Have a wonderful rest of your day.