MRMW NA 2019 Podcast Series

MRMW NA 2019 Conference Series – Rob Pascale – MAi Research

Welcome to the MRMW NA 2019 Conference Series. Recorded live in Cincinnati, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Rob Pascale, President and Chief Analytics Officer at MAi Research.

Contact Rob Online:

LinkedIn

Robmp@MAIresearch.com

MAi Research


[00:00]

My guest is Robert Pascale, MAI Research.  You can find him online at MAI Research.com.  Love the .com by the way. They develop custom market research solutions to address important challenges.  Now, this was a really interesting episode for me ‘cause I have five kids, and he uses as an example diapers.  And one of their customers is a diaper company. And they were trying to figure out how to punch through from a messaging perspective.  Originally, the context was all about leak prevention, but after using their proprietary text analytics, which leverages a Bayesian model, they actually discovered that diaper rash was a bigger driver of consumer intent.  And communicating about the gel protection inside of the diaper is what would actually drive oversize returns. Now interestingly enough, after that company changed the messaging, Shzam! It actually took place. I hope you enjoy this episode.   

[01:02]  

My guest today – Rob Pascale, MAI Research.  Does it stand for something?

[0106:]  

Marketing Analysts Incorporated.  Initially, that’s what it was.

[01:10]

Got it.

[01:10]  

But recently, I guess maybe about ten years ago or so, we changed it to MAI Research.

[01:17]

I like it.  Yeah, IBM kind of did that.  M/A/R/C Research, who you might know, Merrill Dubrow, they did that as well.  And we are at MRMW. Ironically, I don’t have any idea what that stands for. Do you?  

[01:28]

I don’t.  [laughter]  

[01:30]   

Anyway, we’re live here in Cincinnati.  What do you think about the show so far?

[01:33]

It’s been good.  It’s been good to reach out and let people know about what we’ve been working on.  We also have a spin-off that we’ve been working on, which is Pathfinder Analytics, which is an analytics division that we’ve added into our…  We’ve kind of broken out from being just an analytics department and really focuses on four primary areas, which are text analytics, which is more focused on understanding the broader ideas that people are talking about rather than trying to bring it back to absolute definitions of words.  So we’re getting more of you can pick up some slang that way; you can pick up what are the ideas people really care about and how the words relate back to those ideas.

[02:16]  

I had a boss who used to say, actually I had a client that used to say, so yes, a boss:  “I want you to do what I mean, not what I say.” Is that kind of getting to the point of the sentiment and text analytics that you’re doing?    

[02:29]

Yeah, and actually it’s not sentiment analysis, which does try to assign different degrees of emotion or excitement, things like that, to definitions of words.  And so, what we’re doing is actually just letting the connections between the words people use in context define how the words are used. So we’re not going back to dictionary definitions; we’re not applying any kind of bias initially.  We’re letting people just however they speak defines the words.

[02:57]

That’s so interesting.  So, it’s kind of like Brama in that you define it by what it’s not.  Give me an example of a project and the outcome of that example.     

[03:06]

Sure.  So, let’s see.  There was one that we did where we scraped online reviews.  This was for a diaper product. And what we were able to find out was that when people talked about leak protection, they actually talked more about the softness of the diapers than it was about the actual leak protection itself.  So that actually changed the way the client thought about softness. We also found that… Another thing that we do is with Bayesian networking and by understanding the patterns in language and understanding how it connects to close-ended data, we can understand what’s important to driving some measure.  In this case, we took the star rating as well. So we were able to find out not just that people cared about leak protection but also there was an issue with absorbent gel. And the absorbent gel allowed us to understand more when we dove into the text analytics that it was an issue with the diaper itself.  And people were concerned that the absorbent gel was causing diaper rashes. And what we found was that it wasn’t the actual absorbent gel itself. It was what the absorbent gel was absorbing that was causing the rash. And so, they were able to change their landing page to counteract the negative expectations from people’s reviews about the absorbent gel.          

[04:27]

That’s so interesting ‘cause me as a consumer – and I’ve got five kids; so, I’m really familiar with that, the context of diaper and also the concern around diaper rash.  And in that context, I would have never in a million years thought about that. I would never have made that connection as a consumer, right? But now that you say it, it’s like perfectly clear to me.  And then you could communicate that to me and I’d be like: it’d be an Ah-hah moment for me; absolutely, that’s going to be the brand of choice. Super powerful. What’s your go-to-market? Who’s your ideal customer?       

[04:59]

We do a lot of CPG clients, but really, we can apply these techniques across financial institutions.  Really, there is no limit in terms of where it can be applied. We’ve applied it for B2B situations. There really is no…  

[05:19]

It seems there would be a natural fit in the app space for something like that ‘cause you get a lot of reviews.    

[05:25]

That’s a really good idea.

[05:29]

I mean, gosh, yeah, that just seems like a… I don’t know.  So, what do your terms of trade look like? In other words, how do people engage with you?

[05:35]

Typically, we are reaching out to other people ‘cause a lot of people haven’t heard of us.  

[05:38]

I never heard of you.

[05:40]   

Yeah, that’s part of the reason for being here today.  But we wind up reaching out to people, setting up meetings, and just kind of going through our capabilities.

[05:49]

You’re outbound.  So your target, your ideal customer, is it a market researcher…

[05:52]

Yes.

[05:53]

in a brand, or is it…?  OK.

[05:54]      

And marketing.  So we’re trying to reach out more to be in the marketing space, rather than just the researcher space.       

[06:00]

You see yourself really as a martech player?

[06:02]

Yes.

[06:03]

Do you guys have services wrapped around your stuff?  Your technology or is it not really a technology play?

[06:11]

Yeah, so everything is not a technology in itself.  We don’t every say that it’s a tool. It’s not a canned approach.  Everything we do is very custom. Some of the other things that we really specialize in are in segmentation and applying segmentation in ways that go beyond just the statistics side of it and really focuses on the business.

[06:33]

That makes sense.  What is the time frame on a project usually look like?  

[06:38]

For the text analytics side, it could be..

[06:41]  

Well for the diaper study.

[06:43]  

That’s a good one because we don’t have any field time in that since it’s just scraping online reviews.  So, a matter of three weeks or so.

[06:50]

OK. So relatively quickly.

[06:51]  

Yeah.

[06:51]

Oh, that’s super interesting.  Rob, thanks very much for being on the Happy Market Research Podcast.  Oh, and if somebody wants to get in contact with you?

[06:57]

Robmp@MAIresearch.com or you can go to just MAI Research.com or Pathfinder-analytics.com.  

[07:05]   

So that’s R-O-B. You said “Mary Parker”, MP, is that right?  m-p@MAIResearch.com. Got it.  Great. Thanks so much for being on the show.

[07:15]

Thank you.