SaaS Interviews with CEOs, Startups, Founders
1522 Can You Analyze Customer Feedback Emotions and Drive Real Value From Them?
24 Sep 2019
Chapter 1: What is the main topic discussed in this episode?
paired up with some research at a university, taking it and trying to understand and do sentiment analysis and really figure out how to drive attribution, whether it's decreasing your R&D cycle or something like that to drive real value into companies. Today, their team of 10 people based out there in California launched the company in 2018.
They're doing about five grand per month right now in revenue from a couple early customers. They raised about 250 grand, currently raising another 500 grand on a convertible note. Hello, everyone. My guest today is Dimitri Pavlov. He is a product leader on a mission to use technology to understand each other better.
Over a decade of nose-to-tail experience in Silicon Valley, that's enabled Dimitri to hone a distinct data-driven vision and cultivate an aptitude for creating dramatic category growth. Before, he had prior leadership roles at ADP due to Conduit, WellSphere, and Tyra Wireless. Dimitri, are you ready to take us to the top?
Let's go to Olympus Mons.
All right, man. I love that. Stitched Insights. What's the company do and what is the revenue model? How do you plan to make money?
Totally. Stitched Insights is actually transforming the ways that companies are using customer data currently. So we're a machine learning engine and we're capturing the underlying thoughts and emotions from customer data. And we're actually helping large companies right now gain billions of dollars worth of insights.
to help them influence how customers actually feel about their brand rather than just the express content. And we're actually monetizing this through a couple of ways. We found some early traction with some telecoms and some CPG companies. And we actually have a pretty simple, straightforward model where we charge 25K a quarter
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Chapter 2: How did Stitched Insights begin and what is its mission?
or for one data stream. So we can basically with with additional five K per additional data stream, a data stream would be something like internal customer support tickets like a data lake or something like customer reviews. So we're actually an evolution kind of of what NLP and sentiment analysis is.
And our technology comes from actually a lab called the World Wellbeing Project at a university of Pennsylvania. And we can jump into that in a minute here.
Yeah, and I'd love to. You mentioned earlier, though, I want to understand how early are you set on before the call? You are really early. What are you doing today in terms of revenue per month?
Totally. So we have a number of SOWs out right now with Fortune 100 and 500 folks. We've gone through some really, really solid early stage POCs with some of these guys, and they're now expanding into pretty large pilots. We have some recurring MRR, but it's way south of where we need to be at this point yet. We're aiming for 50K MRR within the next about 12 months or so.
When will you hit like 10K, you think?
10K probably is coming right around the corner within the next quarter. I think we'll be able to do that. Okay.
So what, you're at like four or five-ish right now, something like that? Yeah. By the way, every hundred million dollar company today started at nothing per month. So this just means when I have you on in a year, I'll be look, I'll say, look, I had him when he had nothing. This is great. Yeah, totally. So good. That's great.
And the five thousand right now you're doing per month, that's across how many customers?
Uh, so we have, uh, we're mostly on the SOWs right now. We have a couple of the early customers that we had when we launched in beta about a year ago. Uh, and they're still ongoing on the, on the previous platform. So this newer platform that we just launched is, is actually, you know, way more substantial and it costs a lot more.
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Chapter 3: What unique methods does Stitched Insights use for customer data analysis?
Yeah. Yeah. No, that's great. I mean, by the way, that's typical, but, but how many of the early folks do you have on? You're talking like three or four or five, something like that?
Uh, yeah. Yeah.
A couple. Okay, good. And then, um, are you going to force them into a higher price plan is a big thing. Everyone always hits when they're early is do you move them up?
Oh, right. So actually, we started with aiming at startups initially. So we partnered with GSV Labs. Here, actually, we're a portfolio company of GSV Labs. Oh, okay. Yeah, and they have a number of really cool startups here that we started working with and kind of launched the beta version of our product.
We did that for a number of months, and then we realized that in this ecosystem with all the corporate partners around here, in-house, actually here we have amazing folks like GE Appliances and 3M and really Times of India and big corporations. So we realized, hey, there's a pretty good opportunity to condense our sales process here and actually go through this.
go through large companies and actually do some POC that way. And we launched a couple of really early, just bare bones products that were able to capture internal data. And we used our engine basically to look at reviews. We looked at things like internal customer support tickets. And we were able to actually just really easily show insights that these teams really didn't think we can get to.
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Chapter 4: How does Stitched Insights generate revenue from its services?
What we're doing essentially is like automating market research in a sense, where we can understand the entire space of everything that customers care about uh, with our engine, basically the, not just the express things, but the things that make them anxious, the things that make products really sticky.
And this came out of the project, the World Wellbeing Project originally, that looked at psychological states from people rather than the expressed content, they're saying.
I want to dive more into that in a second, but round out the economics for me. So you raised capital, it sounds like, how much total?
Yeah, we're a little over a quarter million right now just from private investments. We're actually raising a 500 convertible note right now, basically to help us finish off some of these larger pilots and go into kind of loud commercial mode.
And how many folks on the team? Is it just you and the co-founder or just you or...?
No, we actually have quite a good team here. We have Dr. Johannes Eichstadt, who's heading up our data science team. We have Andy, who's our CTO as well. We have a team of about 10 or 11 folks now. We actually just had one of our key advisors come in and participate in a more kind of a leadership role as well. And everyone's in California? Yeah, actually most of us in California.
Half the folks in the leadership roles are just doing this out of the goodness of their heart for the past 10 plus months. Some of the more entry-level folks, some of the data scientists and engineers are on a salary.
Okay, got it. But everyone in California there?
Yeah, like 80 plus percent of us. We have, yeah, one of our folks is actually up in Peru right now, I think, or Machu Picchu, somewhere over there.
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Chapter 5: What is the current revenue and growth strategy for Stitched Insights?
And we're able to kind of make some really nice early introductions for us to validate some of our kind of early assumptions. And the very first thing that we did is kind of tried to do a super, super bare bones, just the simplest kind of project that we could do. And we delivered an interactive dashboard basically that had something like 10 broad insights.
And the team that we found was actually recommended to us. through one of our advisors, basically. And we said, hey, what we can do is we can pull external reviews, a sample of about 12,000 reviews from Amazon, and we can go into that product team, the Undersync Water Filter team, and actually give them insights about their own customers that they didn't have.
So what we're able to do is kind of like validate and replace.
so where how are you getting so part of this like feels like you strike me as someone that's extremely well-rounded extremely well-educated there's a kind of doctor component to this right so it feels very like official into the point but like make this like dumb this down for me make this extremely real when you say like capture people's emotions it sounds and feels very pie in the sky to me like give me a very practical example
Yeah. So to set the context, actually, I think it'll be helpful to. So the original point of this of this technology was to look at survey methodologies being used by folks like the CDC Center for Disease Control. And what the CDC would do is if they wanted to predict a region's susceptibility to disease like Miami's risk for heart disease.
What the CDC would do is they would go out and they would survey about a thousand people to get statistical confidence. Takes a bunch of time, a bunch of money. So what Dr. Johannes and his team did is they developed a new set of machine learning and essentially an evolution of natural language processing methods that are able to look at the linguistic structure and the syntax of text.
And by simply looking at tweets, they were able to significantly outpredict the CDC for things like, we were able to predict things like, is the person depressed? Is the person anxious about something? Are they influenced by social factors like their family? Or are they more influenced by social factors like their friends?
And what we realized is there's a really, really beautiful application for this technology in the consumer space. We can look at what customers are telling businesses in customer support requests, in reviews, and we can actually understand understand a whole new different way of understanding how severity works. So if somebody, for instance, reports, hey, these three things are broken.
So for our first POC for this under-sink water filter that we did, a really neat thing came out that people were telling the company that, hey, installation is really critical to us. The water flow is also really important. And how much the water tastes is also super important. As a product manager, you have to figure out, okay, which one of those three do we actually focus on?
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Chapter 6: How does Stitched Insights validate its product with early customers?
It's about actually Tom Kalinske and how he did CEO. What's it called? Console Wars. Console Wars? Console Wars by Blake Harris. It's a story of how Nintendo basically had 98% of the share in the market in the 90s. Now under Tom's leadership, basically Sega came out victorious and gained billions in the US.
Number two, who's your favorite CEO or CEO you're studying?
Fair enough. I'll stick with Tom Kalinske in this space.
Number three, how many, or sorry, what's your favorite online tool for building a business?
Online tool. I like Optimizely. I think that's a really powerful tool, especially for early stage companies.
Number four, how many hours of sleep do you get every night?
At least seven to be fully functional. That's good. And what's your situation? Married, single, kids? Oh, no, single. Focusing on this thing until, yeah.
Not married, no kids. And how old are you?
I'm 30 or 31, one of those.
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