#279: Betting on Quality with Sascha Eder of NewtonX

Speaker A: I think sometimes people think, you know, when you say the client is king, it means you gotta say yes to everything the client says.

Speaker B: Right?

Speaker A: Sometimes the client doesn't know what's in their best interest. And so we over the years have actually really learned to push back hard, be very consultative. And there are times where the clients get very frustrated at first, but then later on realize, oh, wow, this actually was really smart. And it strengthens the relationship rather than we consider.

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Speaker B: @paradigmsample.com welcome to another episode of Data Gurus. I'm Seema Vasi, your host. I am very excited to welcome Sasha Eder, who is the founder and CEO of, um, NewtonX. Welcome Sasha.

Speaker A: Thanks for having me.

Speaker B: Thank you. You've had an interesting journey and I can't wait to dive into how you got to nutanx and the pain points you've identified. But can you give before we get into that, just on the personal side, like, what's been your journey since you founded Newton X? Prior to that.

Speaker A: Yeah, of course. So I started Mutinex at, uh, the beginning of 2017. Born out of a pain point that I experienced as a management consultant at McKinsey and Company. And before that I originally grew up in Germany, uh, then lived in Spain, France and Canada for a couple years. And before I ventured into the professional world, I, in my youth was a professional soccer player at the youth level up until I was 16 and then transitioned into running track and field for the German national team, which I'm bringing up because it taught me a lot about entrepreneurship and what values that I was using later on in my life.

Speaker B: I love that. That's amazing. Yeah. I was going to actually ask you, did your roles in sport, like, uh, competitive sport, really influence you as you became a founder of a company?

Speaker A: Yeah, absolutely. I think there's a Lot of parallels. Yeah, As a startup you go up against the big incumbents, you go up against other competitors and you generally want to win and you want to create a culture, you know, where you, you want to win together as a team. Usually in a company you can only succeed if you have a very, very strong team. And so that's where it comes in very handy. And not to say that it's a signal for success, but we have hired other former professional athletes and it's uh, a very well, um, you know, choice for us. Very good choice.

Speaker B: Very interesting. I'm sure the concept of teamwork doesn't need to be explained when you hire somebody who plays for a team versus an individualistic sport. So management consulting is interesting. I've heard a lot of founders have that really access to C suite, be able to deliver strategy. What made you then decide to start Newton X after, you know, your vast experience in consulting?

Speaker A: Yeah, so in consulting I was working specifically in the tech practice, meaning, um, out on the west coast, I was helping companies think about big data, um, cutting edge technologies like virtual augmented reality and AI. And that was over 10 years ago, so pretty early on. And we had to tap into a lot of experts at the time because it was such a new frontier area for so many people. So we were working with market research firms, expo networks, to better understand where these technologies are going. And this was at times a very frustrating experience for me. This was really my first exposure to the market research industry and I was struggling with a lot of things from inconsistent quality to not getting the people we needed to speak to, to also just very manual processes. And then as a consultant, at some point, because it's such a big part of your work and it affects you, you start thinking about why is this so, uh, broken, know what can, can be done and then getting deeper into it over time. Eventually to a point where I said I think there's something there where we can really disrupt this industry. And then made the jump to start Newtonex.

Speaker B: Very cool. And what do you think the value proposition for the participants in Newton X? Obviously you likely would be one of the participants in your own platform, but what's the value proposition that the experts in your network really value or it keeps them coming back to participate?

Speaker A: Yeah, there's a couple of value props. You know, it's a question I get quite often, which is why especially would more senior executives even work with you in the first place? Maybe they don't even need any monetary compensation. And so there's a couple parts to it One, there's obviously a monetary angle and you know something, we focus very early on because there are parts of the industry where it has a better reputation and people do say I participated in a project and I wasn't get paid. So very early on we led with a very transparent process where people get automatically vetted on their contribution at the end of a project and then in real time get paid so you don't have this lag couple of weeks that you might get elsewhere. And this was really to establish this trust. Then there is an aspect of non monetary compensation which is, you know, you really tap into people's expertise that they have acquired through very hard work over 10, 15, 20 years. And so feeling appreciated and knowing they can contribute it to maybe a good cause in depending on the project and working with really the respective industry leaders of a given industry can be very appealing to people. And so it's a couple of those things. We also invest a lot in really creating a brand, not just on the client side, but on the expert side. If you go online, you have thousands of reviews from our experts about the experience. And while it's not always perfect, we really strive to set the highest, you know, example in the industry.

Speaker B: Very cool. I love it. I'm curious, you know, you came from management consulting, you developed the expert network and now how many years are you in, in this journey in terms of entrepreneurship?

Speaker A: A bit more than nine years.

Speaker B: Bit more than. Okay, I was going to say 10. So close to 10. What industry do you think you compete in? Uh, how do you see yourself?

Speaker A: Yeah, this is a really good question. It's a little bit complicated because, you know, we're offering so many different things and so depending on what we offer to our clients, we cut across different industries. We started off in the as a company only offering, you know, a sample. They usually call it the professional community. We would be brought into a survey together with maybe other panel sample providers and just provide good people. And then over time we realized that we can add more and more value the more we vertically integrate it. Which means, you know, if we own the research design, uh, if it's a survey, survey coding, we can go faster into market and then maintain a very high quality throughout the full process. So now we do offer end to end all the way from. If you have a business problem and you don't even know where to start, we help you figure out which research to run and how all the way to the end where it's not only getting the data, uh, but actually getting the insights that Come with it to then make a decision. Because it ultimately doesn't matter so much what the data is or how you got there. What really matters is just the decision you're making. And that's what we're working towards essentially

Speaker B: becoming a little bit more of a fuller service agency.

Speaker A: No, we are a full service agency.

Speaker B: Yeah.

Speaker A: The biggest difference I would say is, and that's what we've done from day one is it's highly tech enabled. Uh, and that's where clients usually see a big difference to working with other companies out there. Where from day one, everything we do with you, whether it's questionnaire design that's based on nine years of understanding of different questionnaire types by industry, by methodology, and everything is automated in a way to get you there very fast without losing precision or data quality.

Speaker B: Very good. It seems like the industries there was used to be very clear lines in terms of you have panel, you have agencies and then agency service clients. Obviously we know that's becoming more disintermediary given extension of value chains for other parts of the industry. So it's definitely a common trend. Did you find it hard to be, let's say, in the research space not having a traditional research background and experience?

Speaker A: Yeah, I would say it was a blessing in disguise. I think there's a couple of things that happen when you enter an industry that you didn't come from previously. The first thing you should enter it really humbly, knowing that you don't know and really going very deep and trying to understand everything and learn. Right. So there were a lot of things when we started doing surveys and had never done it before. There were a lot of things we needed to learn around incidence rate and the quality checks and quotas and all these things which obviously we learned relatively quickly. And the same goes for understanding researchers as an acp. What are their pain points? What are they trying to solve? All of this. If you don't know the industry, you really got to put in the work to get the credibility. The blessing in disguise for it was really, I think, because we didn't know a lot of the things, we came in with a fresh perspective and did not start where maybe everyone else from the research industry would have started, which is, okay, we're trying to solve B2B start. So the first thing we should do is build a panel, which is mostly the default over the last 30 plus years in research. We came in M and since we didn't really know what works, what doesn't work, we actually said what's the problem, the problem is that when people need B2B, they generally don't get the feasibility from the existing providers. In order to fulfill everyone's request, you would need such a broad network that you would need tens of millions of experts. So you solve it. Because we knew there was a problem and we didn't have a panel, we approached it from, how can we, in the quickest way possible, do custom recruiting to fulfill all these needs without being bottlenecked? And this is really what was the foundation of the company. And I think we were able to do this because we just had a very out of the box thinking and approached it from all angles.

Speaker B: And did you control the initial user experience like you're going to do a custom recruit of experts to a survey? In those early days, were you concerned about the respondent experience or was it something that you learned after in terms of looking at participation rates and such?

Speaker A: Yeah, it's a great question. We learned it the hard way, uh, I would say, which is, you know, we invested so much time in recruiting these people in such a short time frame and really making sure they understand the kind of project it is and so on, only to be then hit with frustration which ultimately reflected back on us as a brand rather than whoever was hosting the survey, right? Of like, oh, I entered and got a technical error or I got immediately terminated in this quota. Why do you even send me the survey? And so that's how over time we realized, okay, if we truly want to own the full workflow and be a better role model for these experts and control the experience, we gotta own an end to end. That's the only way we can avoid this. Otherwise all the hard work we put in might somewhere down the line actually be for nothing.

Speaker B: And do clients listen? Like, how did you convince them to? Because I think that's, you know, listen, there's, there's a ton of root drivers that, uh, that really impact data quality. So it's no surprise the respondent experience is one of them. The questionnaire design. So it's not new news, but I'm curious. There's a lot of resistance to change in terms of that questionnaire design to, uh, deal with that.

Speaker A: I think sometimes people think, you know, when you say the client is king, it means you got to say yes to everything the client says, right? Sometimes the client doesn't know what's in their best interest. And so we over the years have actually really learned to push back hard, be very consultative. And there are times where the clients get very frustrated at first but then later on realize, oh wow, this actually was really smart. And it strengthens the relationship rather than weakens it. And so that's where you just got to be very confident in doing the right thing. And so especially clients who have worked with us for years now know this. We always are very transparent. If we think a given quota segment or a combination of criteria doesn't make sense, we're very honest. Sometimes they listen, sometimes they don't. And then the data ultimately shows it in field. Ultimately we all want the same, which is get the number of people, get the highest quality, get it done in time and within the budget. And so, you know, there are moments where the clients say, no, it needs to be done this way. And then you go in field and they realize, oh, I can't get there. So maybe we do need to do something. And it's, it's really a partnership. That's where we work best with clients that think of it as a partnership rather than more of a transaction of like just, just do what I tell you.

Speaker B: Yeah, you brought up a, uh, triangle or three pillars to a stool, which is price, quality and speed. Um, which one do you struggle with the most when you think about your business and being able to manage all three? I don't know if you can manage all three quite frankly, but I'm curious your perspective.

Speaker A: Yeah, I would say until recently, you know, it was almost impossible to do all three. It was always a very deliberate trade off. So rather than saying we struggle with one of them, we have made a very deliberate trade off. And the trade off has been we focus on quality, which is the number one above everything then on speed. And then we're very transparent with clients that, you know, if you want 500 SVPs of it that manage the budget and use these tools and manage a 300 person team, they are not going to take a 20 minute survey for $5. It just doesn't exist. And so we work with enterprise clients and generally industry leaders where the decisions at stake are massive. It's hundreds of millions of dollars at stake and in those cases it should be a, uh, no brainer that you want to invest in the right data rather than spend safe a little bit here and there. It's such a big decision at the end. So that's what we always educate clients and teams on and have done so over the last nine years. And I would argue it's a little bit counterintuitive, uh, what a lot of others in the industry have done. Where it's been this race to the bottom. And we have very much held our course. We understand we are 30% cheaper and if we could be, we would have maybe more options or certain clients could afford us. But you run into points where the experts are simply not interested. So that was always the constraining factor. Now we can go into this in a little bit. You have other through synthetic, through AI automation, you have more levers to get closer to all three than you have in the past.

Speaker B: Very true. And have you seen, because you know, over the last course, the last couple of years we've seen definitely price compression across the landscape. I can't speak for expert networks, but are you seeing maybe not clients trading off on um, a cost per complete, but maybe doing smaller sample sizes? Are they making trade offs for the number one quality driver? That's what they want. And then changing behavior to compensate for the others?

Speaker A: I would say there are definitely cases where clients maybe do a little bit less or they experiment with different formats. But on the top line we grow faster than ever before. Yeah, I can't comment on B2C but on B2B I would argue we're probably at one of the biggest inflection points maybe in 100 years because of what AI presents as an opportunity, but also as a challenge for large enterprises. And so they understand this, uh, and need to invest in research more than ever to understand changing customer preferences, launching new product lines and so on. So if anything, in many areas, especially for the industry leaders across big tech and different industries, we see an acceleration where the conversation is shifting is to say, okay, I, you know, used to run these four large trackers four times a year. Now there's more and more demand towards, I want to have this always on research where I can almost on a weekly basis tap into it. And that requires a different structure. And so it's not that they're spending less, but they are changing the way they want to access research.

Speaker B: Yeah. And let's talk about. I know you guys have launched a, uh, product recently that's around synthetic and we've seen that across the industry where many, many companies are launching synthetic. Tell us a little bit about why you decided that you needed to do it. First and foremost, what, what were the demand signals? And then be great to just hear a little bit of background in terms of how you've constructed it.

Speaker A: Yeah, you know, synthetic is interesting.

Speaker B: Yeah.

Speaker A: Because it's probably one of the most debated topics in research in the last few years. And I think there's a spectrum from people saying this doesn't work. This is absolute nonsense, and I can't use any of it. All the way to the other side where people say, hey, this is going to replace all of research. You will never have to run a study again. I think we fell somewhere in the middle, not only because it sounds nice, but because we actually back it up with real research. To answer your question, we launched our, what we call synthetic Personas because we felt there was, as always in research and initial heavy focus on B2C. And it's not really a dedicated B2B solution. And, and B2B, time and time again has shown it operates very differently from B2C. B2C, while it does benefit from a strong proprietary data foundation, has a lot more data available online digitally, which you can use to train the models. B2B doesn't have this. And so you do need much more proprietary data. US, as one of the leading B2B companies having this corpus, is obviously well positioned to do this in the first place. So that was a little bit one of the reasons. But then the other one, to your point, was obviously customer demand with customers coming to us. And you know, like I just said, on the spectrum, I think what people start to understand is this is not about replacing research. This is also not about saying this will never work. But if you look at it the right way, you understand that for specific use cases in specific settings, this can work really well. When we run a synthetic pilot with a client, we very often run a independent a B test with real research to back it up and really build that trust with the customers. The same way over nine years, we educated people why B2B requires, you know, a, uh, high price for complete. And so it really fuels this. Always on conversation where you run research, especially a tracker, again is a perfect example, right? It's longitudinal data. You run it multiple times a year over many years. But in between, you don't do very much, right? You analyze the results you had from the previous wave, and then you end up doing a lot of decisions with very little to no research where you could argue it's very powerful if you can tap into a synthetic Persona of that audience or those audiences in between, which in certain use cases gives you a directional answer that's still much, much stronger than having finger in the air guest. But obviously it's not the same as the primary research.

Speaker B: Very interesting. And do you do synthetic Personas on a client by client basis, or do you say, hey, I have synthetic Personas for it? Decision makers as it relates to software, and many people have access to it? How do you package it?

Speaker A: Yeah, so what we have done is we have designed so called Persona shells. Uh, they are based on behavioral and institutional insights data. Think of it as there's a siso buyer audience, there is a, uh, data science priorities and so on. There's multiple of those. They are based on our own proprietary data. But the real value really comes in when you customize it for a client. To your point, bring in their own data as well and then make it very strong. It's again B2B. Even more so than B2C. Having a generic Persona out there, it's extremely difficult to say that there's a lot of value in this for what clients are trying to do with it.

Speaker B: Yeah, I agree with you. There's too many people doing different jobs and involvement and decision making can be a little gray. I want to switch to your entrepreneurial journey. I always ask folks that are entrepreneurs, like, if they had to look back or share with others who are thinking about entrepreneurship, what are some of the key lessons that you've learned or what I call truths that you hold dear to your kind of your, your way of thinking?

Speaker A: Yeah, I mean it's been almost 10 years as you said, and I am incredibly grateful for the journey. But looking back, if I had known what I was to expect, I maybe would have thought about it twice. There were certain moments that were just incredibly hard at the same time at this point. They also have given me this tool set where whenever a new situation emerges, it's almost like I've been there in some form or shape. And so that's really helpful. I would say that there's three things that stand out to me over the years. The first one is about people. This goes all the way back to the beginning when we talked about the team, about creating a winning team and culture. People mistakes are the most expensive mistakes you can make. Even more so when the team is small. But it doesn't change that much as the team grows bigger. Whether it's in the interview process and you're sort of unsure, you have a gut feeling about someone or it is about someone's trajectory in a company and you feel this doesn't feel right. You should usually as, as an entrepreneur listen to your gut. More often than not, you're right. And the cost of not making a decision fast and just letting it sort of like play out in the long term can be very costly, whether it's affecting team morale and other, other trickle down effects. So that's, that's my number one.

Speaker B: Yep.

Speaker A: The second one is, as a founder, you have to do a lot of things that nobody will tell you to do. Whether it is to say, should launch a new product now or let's start working towards profitability. Those are all decisions. There is nobody who's be like, hey, have you thought about this? And do this. So you really need to be in this mindset thinking about what's best for the business, but expecting if you don't bring it up, nobody else will.

Speaker B: It's not going to come out. Yep.

Speaker A: And that's a really big one. And one of the biggest differences, I think, to working in, uh, for someone. And then the last one is it's more relevant than ever before. In the AI age, distribution matters as much as the product. Now you hear about Vibe coding tools, right? And even though that often doesn't get you to enterprise grade production, um, yeah, everyone can in some form or shape now, Vibe code mockups and other parts. But distribution is the number one thing where more and more products flood the market. Yeah, consumers and enterprise buyers are overwhelmed by the options. So you got to find a way to stand out, build a brand, build trust and get on people's radars to even be considered when they make a decision.

Speaker B: And those things are almost reverse of technology. Right? Building brand, uh, an emotional connection of some sort, relationship building, trust building. Those are all the kind of the soft type of things. Not that they're not driven by quantitative data, but it is the intangibles that are sometimes hard to measure.

Speaker A: Yes, exactly. It's one of the few things AI hasn't gotten to yet in a way. Right. That's when everyone talks about AI slop. And so AI has done, um, an incredible job in many areas, but this is still one where you can really stand out probably for the foreseeable future.

Speaker B: Sacha so thoroughly enjoyed our conversation. Thank you for joining me today and I look forward to keeping in touch.

Speaker A: Thank you. Yeah, it was great being here.

Speaker B: Thank you.

Speaker C: Thank you for listening to the Data Gurus podcast brought to you by Infinity Square. If you enjoyed this episode, please leave a five star review and be sure to subscribe so you never miss an episode. Tired of market research solutions that put your project in a box? At Paradigm Sample, we approach market research support with customized and consultative solutions. Whether you need help with questionnaire design, survey programming or online data collection, we're ready to assist. Let us know your needs and we can customize a solution just for you. Learn more@paradigmsample.com.