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Welcome to the 2019 CRC Series. Recorded live in Orlando, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Naira Musallam, Co-Founder of SightX.

Find Naira Online:

LinkedIn: www.linkedin.com/in/naira-musallam-36385592

Website: sightx.io

Find Jamin Online:

Email: jamin@happymr.com 

LinkedIn: www.linkedin.com/in/jaminbrazil

Twitter: www.twitter.com/jaminbrazil 

Find Us Online: 

Twitter: www.twitter.com/happymrxp 

LinkedIn: www.linkedin.com/company/happymarketresearch 

Facebook: www.facebook.com/happymrxp 

Website: www.happymr.com 


[00:00]

Hi, this is Jamin. You’re listening to the Happy Market Research Podcast. The next set of episodes are conversations I had at this year’s Corporate Researchers Conference or CRC. This is put on by the Insights Association in Orlando, Florida. I had quite a few interesting conversations highlighting specific companies that exhibited this year as well as a couple of speakers, Wells Fargo, IBM, etc. I hope you have a really good rest of your day and enjoy these short episodes.

[00:31]

Hi, this is Jamin. You are listening to Happy Market Research Podcast. My guest today is Naira Musallam PhD and co-founder of SightX, one of my absolute favorite companies in the industry right now. Welcome to the Happy Market Research Podcast.

[00:47]

Thank you for having me and thanks for the shout-out.

[00:50]

Yeah, of course. Well, thanks for creating technology that is adding a ton of value to the market research space. Tell us a little bit about SightX.

[00:58]

Yes, So, SightX came about to solve for the problem that many in this space experience, and that is one for the fragmentation. The idea that you have to rely on multiple tools to be able to go from collection to cleaning, to formatting, to restructuring, to uploading into another platform. We thought we can do a little bit better by streamlining the process one. So, it is a technology that fits for the needs of 2019 and to also solve for the issue of expertise. The world that we live in really expect everybody nowadays in the space to be experts in what they’re doing, to get it right, to get it deep, to get it fast with the massive amount of data. And so, how do you do that? Like how do you deliver better, faster, deeper with outdated technology? So, we said we’re going to just develop a technology that enables people to deliver on these expectations.

[02:28]

I think that there’s a couple of points that I’ve separated out, right? One is the disparate number of tools that are required in order to do a research project. On a qualitative basis, there’s approximately six to eight tools that are used just to recruit people. It’s really funny… Between Excel. Anyway, that’s a different topic. But my point is that… Well, Stacey Walker, Head of Insights for Adobe told me, ”I’m sick of all the tools. I don’t want to hear about another new tool,” which is really interesting, right?

So, the ability to be able to consolidate your work into a single platform means that you’re getting consistent data structures, consistent ways of interacting with that data, analyzing that data. It should create a tremendous amount of velocity improvement.

[03:19]

Yeah, I fully agree and sympathize with the sentiment of not wanting to see one more tool. Look, I always tell people nowadays we have so many options, right? It’s more about how are you experiencing it and how are you getting there. So, to even take it out of market research, I say you want to go from New York to California. Do you want to ride on a bus or do you want to take a helicopter that is waiting outside of your home, right? Like for me, these are like the analogies, like these are the tradeoffs and these are like the solutions, quite frankly, that we have to continuously and think about. How do we do better? How do we make it a better experience? How do we get people there faster without compromising? And that’s really, really important without compromising on the depth.

[04:32]

So, you have a PhD. What’s a PhD in?

[04:34]

Statistics and psychology.

[04:36]

Okay. That’s like the perfect intersection for consumer insights, isn’t it?

[04:41]

You can argue so, you can argue so. I think about what we’re doing. I actually think in our lifetime, we’re going to see a revolution in education. I don’t think it’s going to… like PhDs don’t scale. It’s a great degree I’ve got, and if anything, I would say like the value out of it was setting me up to be open to learn and learn fast and figure it out. Now, I don’t think we would have the model of doing that for five to seven years. I think is going to change. I think is going to change, and I don’t think it scales. So, the more we can scale expertise and PhDs, I think like this is where we succeed as a society.

[05:35]

I love that. I think that’s so powerful. And you’re right that like while the PhD or the educational component doesn’t necessarily scale, the application of the knowledge and then into technology that does scale, which is, I think, one of the things I really like about SightX. So, on a practical basis, the type of projects that you’re doing are quantitative, right? So, it’s survey-based. Can you give us an example of what a soup-to-nuts project looks like? Like what would a user use you for?

[06:09]

Yes, our users tend to be from different industries, CPG, media and entertainment and everything tech, everything in between and the type of projects they run could, for example, they range obviously and it could be around trying to understand their consumer segment them, right? And this is what typically SightX enables them to do. So, you collect the data and let’s say you have 30 questions, and now you’re going to start segmenting. So, the users can go the traditional way of saying, “I want to segment this demographic group. I would like to add this age group and then see what the results look like.” And they user is able to do that with a few clicks. And so, you’re taking process that could take hours or days and you’re bringing it down to 30 seconds. And then, the user can start experimenting with this area of unsupervised learning. So, without having to have a PhD in statistics, what they could do is then click on this button that says, “Run segmentation for me that is not based on any assumptions of how I should segment the audience.” And what happens is actually pretty cool because now you’ll have accessibility. You have the time and accessibility to do it. Then the user clicks on this one button says, “Segment without any assumptions.” And what happens is one of two things. Sometimes the unsupervised segmentation aligns with the segments that the business leader intuitively thought of. And sometimes you end up with completely different personas. And that opens up like awesome conversation among consumer insights team to say, “Okay, how come we ended up with a little bit of different segmentation we didn’t think about?”

[08:22]

And so, I’ve done a little bit of segmentation work, and I call it one part – art, two parts – science. So, you’re always moving things around a little bit because what this histogram or however it is that you’re getting there, but it’s really helping you do is frame the discussion for who the persona is, right? And so, by your platform identifying set of optimal statistical tools to segment or come up with your segments, that’s really powerful. But then how are your users leveraging that if they don’t have the statistical knowledge? Is the information displayed in a way that a non-statistician would be able to understand and process and interact with?

[09:17]

Exactly. We built the tool in such a way that somebody who doesn’t have any background in statistics or analytics can actually understand it. And this was something really important for us to do and invest in: making sure that whatever we do on the platform is, doesn’t matter how complex the analytics part of it, is accessible to whoever is using it.

[09:49]

Are you in that process saying, “Okay, I want a five-factor solution”? Or you know what I mean? Is that six, three, that kind of thing?

[09:57]

Yes. We even adjusted the language. And so, factor, typically statisticians start associating it: are we doing latent analysis? Factor analysis? What are we doing here? And we even wanted to adjust the language in the sense to say, “Just speak personas.” And so, that user who’s there actually says, “I would like to create five personas. I would like to create seven personas. I would like to create three personas.” And then, it runs the unsupervised learning and creates them.

[10:36]

All right. So, last thing on a personal note: You are a mountain climber.

[10:41]

I am.

[10:42]

And you climb mountains. What is your favorite mountain climbing story?

[10:47]

My favorite mountain story is probably with my co-founder, Tim Lawton. And so, before starting our company, we went to climb Denali together. The first time, we were a part of a group, and we didn’t make it to the summit. We came down and then decided to go back up two weeks later and climb it again. And on the mountain, a lot of stories took place, but probably one of my favorite during that climb was when we were deciding on a summit day, whether we’re going to push to the summit or not because of weather conditions. And so, Tim and I have all of these conversations around whether we should play it by ear. Do we collect the data live? Or do we make a decision prior to that and then decide to push? So, we have like this debate, and we need to decide what is going to be the turning point. And I was more on the side of let’s just like watch and see what happens. But funny enough, we decided there was going to be a window. We go on the mountain and then there is a blizzard. And so, we decide it’s bad. So, we need to turn… Denali is not happening for us, I guess. And so, we started going down the mountain for three hours, and suddenly there was sun. And so, we stop and Tim goes—the guy who’s all about making decision prior—and says, “I think we should wait and watch what happens, and then decide if we were going back up.”

Long story short, we go back up, decide to turn back, and an eight-hour climb ended up being for us 21-hour climb because on the way we also had an accident. And so, it was a tough day, but we made it to the summit. I share that as one of my favorite story because it was failing and failing and failing and failing and debating and arguing. Different personalities bring in different points of view. But at the end of the day, it made us… The reason we made the summit because we were so different in our approaches to it.

[13:42]

I love that. And you’re right, yin and yang. And then if that exists in a spirit of cooperation and respect, then you’re able to actually find a more complete point of view or truth on what you should do. So, congratulations on finding a very good co-founder.

[13:58]

He’s the great, fantastic co-founder.

[14:01]

Great story. Oh, so, if someone wants to get in contact with you?

[14:04]

If somebody wants to contact me, they can either email me at Naira@SightX.io, or they can just reach us on our website: go to SightX.io, and there’s contact button there and that way they can get in touch.

[14:25]

Perfect. And, of course, we’ll include that information in the show notes. Thank you so much for your time today. Enjoy the rest of the show.

[14:29]

Thank you for having me, Jamin.