How Leaders Should Use Data with Benn Stancil

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Welcome to Founder Chats by Barometrics where we chat with founders and hear how they started and grew their businesses. My name is Leah and I'm on the marketing team at Barometrics. This week, Brian talked with Ben Stancil, co founder and Chief analytics Officer of MOAD Analytics. Moat helps businesses grow faster by speeding up answers to complex business problems and making the process more collaborative so that everyone can build on the work of Data analysts. Over 52% of the Forbes 500 have turned to Mode for help with making data driven decisions. We were stoked to have Ben on the show to share his story of building Mode and lessons he's learned along the way. Enjoy.

Ben, welcome to the podcast. How are you today? Good, good.

Thanks for having me.

Of course. Yeah, it's my pleasure. Well, let's get started where we usually do. Tell me about where your entrepreneurial journey got started.

I'm one of the founders of a company called Mode. We build a product for analysts and data scientists. If you're familiar with BI tools or tools like Tableau, it's broadly similar to those sorts of things. And so we started it about eight years ago. That was and is my first and so far only sort of foray into being a startup founder or entrepreneur or anything like that. So I got started after meeting some folks at a previous job. I worked at a company called Yammer, which was a SaaS company that built sort of a Facebook for work before. Facebook for work was a thing that got acquired by Microsoft in 2012 and so met some folks there. We ended up having the idea for the product that we wanted to build based on some like, internal tools that we had built at Yammer and then, and then started the company off of that. And so I'm someone who found myself basically in a position that it was an interesting idea, it was really good people I wanted to work with. And so it was kind of like, let's take the leap. I'm not someone who sort of went into it thinking my lifelong dream is to be an entrepreneur or a founder or anything like that. Sort of did it out of interest in the idea and the team and then have been kind of following that path ever since. Cool.

What was it like working at Yammer?

Yeah, so it was my first job in the tech world. So prior to Yammer, I was actually working in DC for a few years doing a very DC job. I worked at a think tank doing economic policy research. And so think tanks are, they kind of operate as this bridge between academia and the policy world. Where they basically are a bunch of people who are experts on different issues. A lot of times are PhDs in these things. Economists, political scientists, experts on foreign policy, that sort of thing. And so what they do is they write essentially research papers that are designed for policymakers to make policy suggestions about. We should do this about US Chinese relations. Or I was there in 2009 and 2012, which was kind of the midst of the financial crisis from 08. And so policymakers should respond to this crisis in these various ways. Our job was to, again, make these sort of policy recommendations, but we were very much doing it from a distance. We were one of tons of think tanks that do this. We write a bunch of papers, they basically get shipped off to congressional aides. Those congressional aides may or may not read them. They may or may not pass them up the chain in the congressional offices or various policymaking offices. Eventually, the people who are the actual policymakers probably never see them. Maybe some thoughts enter into the way that they think about policy, but ultimately, you know, those folks are making decisions on a lot of other motivations and kind of pseudo academic research that we were doing was very rarely like one of them that really mattered. And so I eventually left, in part because of that. Because you're so far removed from ever actually seeing anything get done that the work is interesting, but you're kind of yelling into the void, doing it a little bit for sort of the. It's perfunctory in some ways, where it's not actually making that much of a difference. And so I left to eventually join Yammer. And the role there at Yammer was actually structurally kind of similar. Like, the job is go find problems. I was an analyst, so it was like, solve them with data. Make recommendations to people so they can make better decisions. The difference was rather than me sitting in an office and DC writing things indirectly for hundreds of policymakers that probably don't care about it, I was writing it for or doing this analysis for the PM that sat next to me who was trying to make a decision they needed to make tomorrow. And so it was obviously much faster paced. It was work where you could immediately see whether or not the things you were doing were right or wrong. And so it was academically interesting to me in that it was thinking about problems in the same way, but in a way where you actually could see the impact of that. You could actually see whether or not people would follow through on those recommendations and people actually cared about what you were doing.

That's really cool. Were you actively looking For a switch into the tech world, Was this something that was opportunistic or how did it cross your mind that all this hard work that you were doing for basically no audience was like, hey, maybe I should find somebody who cares about what it is that I'm doing here?

Tech was one of several things I think I was looking for at the time. I wasn't sure what I wanted to do. I mean, I wasn't then, I don't know that I am now. But at the time I knew that I liked the kind of data work. I knew that I in that general galaxy of types of problems, I enjoyed it. I liked the econ way of thinking too. So econ is kind of math, but not hard math. It's more of applied math where great, you have data, you want to try to figure out what it means. You're not just in the weeds of basically solving hard technical problems all day, at least econ at that level, academic econ is becoming the technical thing. And so I was looking for a number of different things. I explored the possibility and came very close to actually doing an academic econ PhD. I was looking at jobs in finance which have some similarities there, though I think that very much depends on the role. And I never actually did it, so I don't actually know what those would have looked like. I had some friends who I had worked with who had left to go to San Francisco. One of them went to Google, had good things to say about it. So it ended up being kind of like, yeah, I was just looking across a bunch of different things that all kind of had basically data in the title and was kind of open to different opportunities. I ended up landing the job at Yammer on good old fashioned nepotism. Basically. I had a friend who I worked with in D.C. whose sister worked at Yammer, which doesn't get you the job, but it basically gets you the interview. And so I think that especially a lot of these startups, and especially the Googles and Facebooks of the world, which Yammer was not, but the very big companies that just get thousands and tens of thousands of applicants a day, it's difficult to crack into that process basically without having some way to sort of get your resume to the top of the pile. And so basically that's what happened is I was looking for a bunch of jobs, had a friend who was like, you should check this one out. So I was like, okay, that again, kind of put my resume at the top of the pile for the interview slates. And so I went through the interview process there later the job and was kind of like, don't have anything better to do?

Why not?

And so took the leap.

Then how was it going through that interview process?

It was interesting. I the way that that interview worked is. So this was in 2012, 2011, 2012. I don't remember any of you interviewed 2011, 2012. It was, this was kind of at the height, I think of the like brain teaser phase of interviews that, that had gotten popular especially in data stuff where it was like, here's a math problem. How do you figure it out? How would you solve this sort of stuff? So it was a lot of that, you know, the phone interview was, was a couple problems like that. The on site was a series of, you know, here's a circle if you draw a line here, what happened? Like kind of how do you think about these things quantitatively? I remember that part being a little strange. The actual part of it though was, and I suspect this is true for a lot of folks who don't work in tech and then kind of get their first view of it. You forget how different it is from so much of the rest of the world and like what those interviews are like and what those offices are like. And the way that sort of tech operates is just very different or was. I think this is like kind of becoming a little bit more normal. But the finance interviews were very much kind of stuffy, old school interviews of you wear a suit, you go into an office, people kind of grill you on various things. The tech interview was much more of like this seems friendly. And so I think I was somewhat taken aback by how different that was, how different the offices were, all those sorts of things which I think for people who work in tech, you come to kind of, you come to, it becomes a thing you expect like you take it for granted that that's how sort of the working world is. And I think having come from a world that wasn't, that it was a bit more of a culture shock, mostly in positive ways, at least through that process.

I don't want to put you on the spot from a memory perspective, but just curious if you remember any of those brain teaser type questions that you were asked. Those are always so interesting to me.

Yeah, so let's see. So I remember three of them. I think there was three or four. There was one about. There was one about. Basically it was something along the lines of one out of every X number of cars randomly is a Prius a probability question. It was like one out of X number of Cars is a Prius that drives by. Suppose that you sit and watch cars drive by for half an hour. How many Priuses are you likely to see? Suppose you sit and watch cars for an hour. How many Priuses are you likely to see? Or like, what are the odds of seeing no Priuses in half an hour? What are the odds of seeing no Priuses in an hour? That kind of thing. There's a question like that there was some sort of like geometry, geometry question about draw a circle. Say I put two points on the edge of it randomly. Now I put a third point. Like what are the odds that the third point is sort of in the, within the angle of the first two. So you know, say like there's create two points and it creates an acute angle. Like what are the odds that the third point is inside of that acute angle versus the outside of it? I guess not acute angle. It was like a, like a, an angle that's less than 180 degrees. So like that part of it, there was some sort of card shuffling question of like you have to shuffle a deck of cards or some sort of probability dealer on that. And I for some reason have some vague recollection of a question that involved close and some sort of probability deal about that, whatever it was. But it was all this kind of probability. Most of it was probability. Most of it was just these questions about like, how do you, you know, how would you try to solve this thing? That's like some probability. And I think a lot of the things people are looking for and obviously having sat on the other side of the table for a while, it's not necessarily like, how quickly do you just like get through this question? But it was more about how comfortable are you talking about this stuff, how comfortable are you? And kind of this kind of quantitative gymnastics to see if you're, when you get a hint, do you respond to it well, do you kind of see the things that you're trying to see? And so they're trying to get you to see. And so a lot of it was I think more of like comfort with math and sort of dexterity in it rather than can you win a Mensa award?

Well, that's actually I think much more reasonable, at least in some of the experiences or different questions that I've heard people go through. And I think those questions would have been excellent at weeding me out of the candidate pool if I were, if I were in there. I don't think I could answer any of those questions reasonably well. So certainly effective from that Standpoint of getting any non math person out of the way.

Yeah. And they weren't. It wasn't the. I mean, the finance questions, you know, for those interviews, to the extent that they did these sorts of things tended to be more of straight up puzzles of either something of like, you know, you've got a fox and a chicken in a boat and you have to cross and they can't do this, or like, how do you get them out?

Right. Yeah.

Or the kind of classic, you know, how many ping pong balls can you fit in an airplane? How many manholes are in New York? All that kind of stuff of just like seeing how well you kind of reason through these unreasonable questions, which, you know, I don't. I think there's a. The question itself, I don't know that is a bad thing. I think there's ways that those things certainly can be valuable for assessing folks. I don't know that. I think it depends on how you judge it. Essentially. If you're judging it of just, can you be a calculator and crush this thing? I think that's usually pretty bad. If you're judging it, how do people respond to the situation? Do they work on it? Well, can they talk through it? That kind of stuff? I think there's maybe something a little bit more reasonable there.

Yeah, I think there's. I guess, and I've kind of already been guilty of this, a little bit of. There's a certain inclination to make fun of these sorts of questions because they are kind of like, they're obviously silly on the surface level, but it is kind of a difficult challenge as a business owner to be like. Especially if you're in tech and you are hopefully doing things that have never been done before. It's like, well, how else are you going to be able to say, like, okay, this person's gonna be put in an environment where there's no prior knowledge and they need to kind of figure it out. How good of a job are they gonna do at figuring it out at this, you know, future state that not even like, if we knew what that thing was gonna be, we would just test you on that, but we don't even know. So, yeah, I think it's a really good point that you make of like, yeah, just put somebody in this unreasonable situation and see how they do. And I certainly agree that maybe one of the reasons why this is like a little bit of, at least for me, I don't want to speak broadly, but the reason why, you know, how many manhole covers are There in New York City is kind of like, can be something that you can make fun of is like if somebody's judging you, kind of misusing that tool and judging, you know, how close. If they know the answer and they're judging you based off of how close. Okay, we only hire people that get within 5% of that number. It's like, I think you use the tool a little bit incorrectly there in trying to assess somebody's ability.

Yeah, and I think that's true. And certainly among. I mean there are things that data folks tend to do probably across the board for people who kind of ask these questions. I think that they can be useful tools if used appropriately. I think these questions do kind of lend themselves to. To being misused partly because they become kind of tests for horsepower and people like want to use them that way. Where it's kind of like the interviewer will. There's a lot of times where it seems like the interviewer wants to basically prove how smart they are. And part of the process is like to make. I mean, it's not obviously what the interview should be, but interviewers will like ask questions with the intent of seeing someone struggle and be like, I know how to do this and I feel clever about having asked it, which I think is like a pretty problematic way of doing it. The way that this has evolved and this is how we interview and it's like how my interviews have evolved. And I think it's something that's probably a bit more of an industry wide practice is trying instead of asking the unreasonable question that is a brain teaser, like how many ping pong balls in a plane started to ask questions that are kind of similar in their unreasonableness, but are much more open ended and are much more about the business itself. So like, an example of this that I would ask for somebody is how much do we pay for a billboard? Where it's like, it's really hard to actually figure that out, but it's more about, okay, how would you try to solve this problem that is a problem that we may actually encounter on the job, that it's difficult to measure, that you have to kind of like balance both quantitative problems and kind of figuring out how do you actually assess the value of a billboard? What does that look like? How do you decide, you know, people are buying your product because of it? How do you actually connect that back to the billboard? All those sorts of things where it's a mix of kind of quantitative and qualitative problems in a way that there's no clear answer In a way that I can't sit there as the interviewer and like, know the answer is this and actually trying to wait you to see if you can get exactly there. It's more of like, let's work on this thing together and feel like we get to a point where we actually came to a place that we feel like is good and if we can get there, then then great, we should work together. And if it's like, it's a real struggle, then I think that's a. That's a, you know, a different. Then it's like, maybe this isn't a fit. And so I think those are. Tend to be better questions where they're kind of structurally similar, but in a way that, that protects against the, like, how smart are you? How fast can you think about stupid problems? And more of like, how can you work together on something that's kind of ambiguous and vague but actually reflects the way that the job will actually be done?

Yeah, that's really great too, because if you can kind of farm the. It also means that you are being thoughtful about what the role is and what their responsibilities are going to be, which I've seen a lot of times, and I've been guilty of this. Hiring somebody without really having a crystal clear picture of like, well, what value do I want this person to bring? Even what is their job? How are we evaluating them? What are their goals? So if you know what those goals are, and if you're hiring a marketing person and you want them to work on distribution strategies, these are open questions. And it's almost like you almost get to the point where it's like, well, man, if you can fool me in the interview, if you say something, I'm nodding along, being like, yeah, that sounds great. It's like, well, that's good enough. You got into the ballpark from there. And, you know, if I were to hire you and you came to me with these proposals, I would be like, yeah, you know, that that seems as reasonable like that that follows as much data as we have. And, you know, I don't think you're crazy and I think you did your research, so let's go for it. I mean. Yeah. What more could you want other than that? Yeah.

And data roles, I think in particular are an interesting kind of case study in that because as an analyst or a data scientist, the thing you were saying of like, what's the success in the job look like? It's actually kind of hard to define. This is sort of a recent rant that I've been on and been written about some, but like, it's actually hard to figure out what a good analyst or data scientist looks like. Not in the sense of, like, what are the attributes they have, but like, say you have 10 of them in a job. How do you know which ones are better, even seeing their performance and like, how do you measure that performance? Because ultimately their job is to kind of be an advisor, to recommend things and stuff like that. And you could kind of say it's the quality of the recommendations they make. But sometimes because it's in effect, it's not sort of formally, but it's in effect kind of probabilistic of, well, we should do this. And it probably work out and sometimes it doesn't. You can't really use those judgments either necessarily as like a direct measure of how good folks are. And so a lot of it comes down to I think, what you're saying, where it's basically how convincing is it? It's basically like, if you do some analysis and I see it and I'm like, sure, seems right to me, then it's probably good. And that's probably about as good of a judgment as you can have. And so, yeah, I think in some ways that makes the interview process easier where it's like, give people a problem and if you leave it feeling convinced about their answer, then they're probably pretty good at the job because that's basically the job. So, you know, I think that's not necessarily the way a lot of people approach it, but. But to me, that's kind of the, the ultimate job of the role anyway. And so. So, yeah, if people are good at that, then they're probably good at what you ask them to do.

Yeah, it feels like a really, really hard job to say like, who's the best analyst? Because you're right if you just say like, well, of all the analysts, which. Which of their recommendations, like, made us the most money or whatever, you know, made us the most of target metric. And the answer to that would be like, well, the analyst who did that was the analyst who got the best questions, like the questions that were most likely to yield a lot of money. So it's like there's like that whole upstream, you know, that whole upstream effect of like, well, the person who was asked the question that would lead to the thing that makes us the most money is likely, if everybody's within plus or minus 20% in skill, the inputs is what's going to affect what their performance is going to look like. Right.

And there's no counterfactual either. Where it's like, well, they recommended us do this thing. We were weighing between five options. We chose option A, we didn't choose option B, C, D or E. Okay, well, we don't actually know how well those would have done. If A did pretty well, was B great? Did we actually miss a lot of opportunity? Did we dodge a bunch of bullets where B through E were all terrible? Those are things you don't really know. And so, yeah, so it's all kind of a subjective thing to me. And a lot of that subjectivity is around how convincing they are and the things that they put forward to the decision maker. Yeah. Wow.

It's really interesting. One thing I'm thinking of is too, is like sort of thinking of your journey through and going from the think tank to Yammer. I'm curious, kind of to rewind back there of like, how does your skillset need to change? Because it seems like you're very thoughtful about being this role and what the requirements are. How did you feel like you needed to change if you needed to change to fit in at this new, faster, higher paced organization?

So to go from the think tank into the startup, like as an analyst, there's obviously a lot of change that has to happen to go from being an analyst at a startup to founding one. But the first change in that, of going from the think tank to the analyst, there were probably two big things that were different. I mean, it was still trying to use math to solve problems. And so in that, the skillset wasn't that different. The thing I think that was the biggest change was one, there was some technical need that I didn't have. So things like SQL, basically, that's the technical skill that you really need. You're not having to become a rocket scientist by any means. I didn't know any SQL before I started the job, and after a month of using it, I felt fine with it. And so it was like, okay, you can learn that, but you did have to adapt to an environment that wasn't downloading Excel files from a bunch of World bank and IMF websites and instead using something that's a little bit more sort of technologically scalable. And so I had to learn those pieces. And so I think that was somewhat of a change where it wasn't just kind of everything was done, throw it together and you're doing it for this particular paper. And once you're done with it, who cares? Just have a bunch of Excel files on your computer. You're never Gonna actually use them again. It was all done. We're like, okay, we need to repeat this over and over again. We need to have dashboards and things like that. So there was some sort of change to that mindset. The other thing that I don't think was a change for me because it was the thing that I was looking for, but it probably would be for some people, or depending on how long you'd come out of, like, the academic world is the pace and kind of the things associated with pace. So it's not just like, hey, we have to go faster, that that's there. It's that in order to go faster, we are more accepting of kind of directional pieces. We are more accepting of. You don't need to cross all the T's and dot all the I's. The job here isn't to write a paper. The job here isn't to do any kind of, like, formal math. The job is to do enough work so that we can decide what we need to do, and that's good enough. And so I think, like, there was some. Some change around if, for instance. And again, this wasn't my case because I wasn't in it for that long. But if you come out of academia and you could ask a question, there's a lot of art. Like, the thing I'm ultimately trying to move towards is write a paper. So I need to have some ideas, but then I need to start formulating, like, more formal expressions of those ideas. I need to have stuff that's. That's a little bit more buttoned up. Whereas in this case, it's kind of like get to the point where you have the idea, do enough work to feel like your idea is probably right, and then move on. And so there was like a change in approach, I think, with that, that different people from different backgrounds kind of had to adapt it at different degrees. In my case, it was relatively mild. In some people's case who were academics or PhDs or postdocs and had been doing that for 10 years, I think it's a harsher change.

Interesting. And that's actually something I've been thinking a lot about. And I'm wondering if you have any lessons learned that you can share there. As we become increasingly more data driven and data informed on our side, I've noticed the same thing. And I'm very much a feelings guy, which is awful. I'll go into a meeting and I'll present the data and I'll go, yeah, but it doesn't feel like this. I see what the data's saying. But as a person, that's the operator, it doesn't feel like this. And usually that means sometimes I can dig deeper and actually find the slice that shows, or perhaps I just didn't do a good job the first time around presenting the data. But I'm trying to find that balance between letting experiments run to statistical significance versus getting that direction. And then my approach would be like, well, just try it. If you have an idea, just try it and see what happens. Which I think is too cowboy to operate at scale doing that. But I'm kind of feeling like maybe the second step is to swing to the other side of the situation and say, well, we're going to run experiments and we're going to run them until statistical significance. And then we are going to feel pretty good that that's going to work. Whereas maybe, maybe we need to be somewhere in the middle. So I'm curious if you have, you know, you might be uniquely qualified to speak to this.

Yeah, I have a bunch of thoughts on that. So one on the. I think both ends in the sort of boring answer, both extremes aren't really workable. You can't be fully data driven in everything that you do and just make decisions because data tells you to do it. But if you do everything by gut, you'll have some misses too. I think that the more nuanced version of that to me is there is a tendency among data people and anybody who's data inclined, so it doesn't have to be a data analyst or data scientist, but also the executives that put some faith in data in some ways to use it as a crutch, to use it as say, we want to run this test. Well, we're not going to make a decision until we get the results on this test. And really to me, and this was another thing that I sort of been on a rant about recently, to me, the effect of that is that people are in essence trying to offload the responsibility of making that decision, saying, well, I will just do what the data tells me. And it's like the data is not going to actually get you an answer. Sometimes the best thing it's going to tell you is we don't know what to do. Or it sort of things seems like it's pointing in this direction, but like, I don't know if it's better and we can't run this experiment longer to figure that out. You know, we can't just do more analysis to figure it out that we're just not going to know that we are trying to predict. I mean, in effect, what an A B test is doing is trying to predict the future by saying, what is the effect now? We think that effect will continue. And there are times where you're like, we cannot predict the future and we don't know. And I think people sometimes use data too far in that way where they're uncomfortable making a decision or just like taking a leap on saying, this is the thing we're going to make a bet on without being able to say like, well, the data says this, therefore it's the thing we have to do. So I think in those cases, like, yes, do the analysis to kind of get a sense of the direction. Do the analysis to keep yourself honest. But that analysis isn't sort of ground truth in the sense that there's a lot of things that go into the way that you run the test. There's a lot of things that go into the ways that you measure things that that stuff isn't going to be like, at some point, you have to make decisions based on some amount of uncertainty, and I think we need to be more comfortable doing that. The other side of it of, like, when you're looking at data and it doesn't feel right, I think that's an important thing to pay attention to. There's like, this Jeff Bezos bit about something where it's like, you know, when he looks at data and it's something that goes against his gut as often as his gut being wrong, as the data is wrong, or like, the data is incomplete, really, where it's like, I'm looking at this thing and it seems like it's not reporting everything or there's some element that's not captured. I think that that is also, like, a reasonable stance to take. I don't think that you can just say, like, well, I'm going to trust my gut over what the data says, sort of every time. But to me, what, like, a gut feeling is, particularly from an executive or someone who's in the business. Say, for instance, we make a recommendation to the sales team and they say, like, that doesn't feel right. I can't quite identify what it is. It doesn't feel right. What that really is saying is there is some experience that they've had that they don't quite know how to quantify or they don't know how to describe, but it doesn't jive with that. It's not like some nonsense feeling. It's just that it's hard to articulate what it is that is off to them. And that doesn't mean it's not real. That just means it's hard to articulate. And so I think, like, just because something, we shouldn't basically only take evidence that we can present in charts. If it's something that's not easy to chart, that doesn't mean it's any less important. It just means that, like, doesn't fit into a chart very well. And so to me, it's like, there has to be a balance here, and we have to be careful about erring too far on the side of overusing data for everything. But I think obviously it has to be a check against what's actually happening in the world. In some ways, it's the senses of an organization and what the business is doing. But that doesn't mean those senses are perfect. It doesn't mean those senses are always to be trusted over any other instincts that people have.

That's really interesting. And I guess I feel Jeff Bezos is a pretty good, pretty safe example of someone that seems like they're doing a relatively good job of running their business. It is interesting. And as you went through that description, sort of talking through that, it does feel like in those scenarios of when the feel is off, the vibe is off, that you're missing some sort of data. And one thing, when you brought up the sales team, I think that's really interesting because sometimes when we're going through these experiments and I've had that weird vibe about the data, sometimes it's a week and a half later and I'm in the shower and I realize, oh, geez, we forgot about this very important piece. We talked about, well, how many more trials are we going to get? But we didn't think about the amount of time it's going to take those trials to convert or something like that, a piece of data that's like, oh, well, if you double your trials, but you quadruple your trial length, then that's not actually really helping you. It'll help you eventually. And that puts you into the. You know, you can make a decision on if that's actually better or worse. But I also think too, sometimes there's like an experiential thing as well, right? It's just like the reason why it doesn't feel good to the sales team is because, you know, it's more. It's a higher conflict or, you know, this is going to be a hard, more difficult, more challenging conversation to have with somebody. And I noticed that myself as well, certainly it's like, you know, the thing that feels off is like, ah, I'm going to have to, I'm going to have to be for the. Especially if the data that we're looking at here is correct, you know, I'm going to have to. For the best of the team and for the best of the business, we're actually going to need to be a little bit more confrontational with whatever group of people you're talking about. So I think it's certainly a challenge between, like, well, are you just missing the data or is it actually correct? But you actually don't like the fact that like, I'm going to have to talk to this customer and tell them they're not paying enough money or tell this customer that they are actually not a good fit for the product and we're breaking up with them or something like that.

Yeah, And I think those are the ends up. Like if the argument for listening to feel is like, this thing represents something that is real and that data is never a perfect measure of what's happening. I think the argument for it is kind of two things that you alluded to. One is, yeah, people will make decisions based on emotion, not emotion of like, it's not irrational, it is emotion in the sense that I don't want to do that thing. Or like, this is the path that just is the one that is of sort of least resistance to me or whatever. And so I think people will sort of find ways to back into those arguments of, hey, we decide we need to focus on this new market and there may be people who just don't want to do it. And so, okay, they'll find ways to make that argument and data gives a way to make that argument. Or, you know, these kind of experiences give a way to make that argument too, in a way that doesn't seem like it's an opinion. It seems like it's like, look, this is my reasonable and rational argument for this thing when obviously it's like the end goal came first and everything else is sort of just trying to, trying to get to that point. The other thing I think on the, on the gut part, and this is particularly prominent the higher you move up an organization, that people's guts get worse. So for instance, say that we're trying to figure out the problems that our customers have, like, what are the biggest sort of pain points our customers have with our product and the things we need to solve most urgently. A salesperson's sense of that and their gut on that is probably pretty good because they talk to tons of people. They are kind of assessing across a whole bunch of different inputs. Yes, it is anecdotal, but it's like a wide range of anecdotes, and they kind of pick up on what are the trends there. For a sales manager, they hear less. So their gut is probably worse. For a CEO, they basically hear nothing. A CEO is sort of pulled into a couple convers, and those anecdotes become like, they. People will latch onto that as like, I have this one story. I tell it all the time. It is the proof point I have for this thing. And so in, like, the CEO's case, it may be that they got pulled in to the biggest customer, because that's the customer the CEOs gonna get involved in. Those people were upset about this thing. They said, like, this product is too slow or, like, it doesn't have this one feature. And then that becomes a thing that they latch onto as well. This is the only thing that matters. Everybody's saying this. I've heard this, I've heard this, I've heard this. And like, that is a gut that is based on a lot less. And so I think in the data is like a better counteract for that. Like, better sort of counterbalance for that. Because the anecdote there is that, like, gut feeling is based on something that's much more limited. So I think it depends on where those things come from. But there's certainly, like an executive tendency to, when you're more removed from the things you're trying to get a sense of, to attach yourself to a couple anecdotes, to kind of get emotionally invested in those things and to see that as reality. Whereas, like, the folks on the ground that hear this stuff day in and day out are less inclined to get attached, like one anecdote in emotional ways.

That's really interesting. And that totally makes sense. There's certainly like a. There's a volume of exposure. Like, the volume of exposure goes down the higher up you get. Like, if you're like a frontline support rep, you're talking to just the pissed off customers all day, every day. And you're probably speaking to a large volume of them. And, you know, salespeople, you're talking to all the fresh faces, people who aren't using the product or very new to the product. And so, you know, but you have a ton of touch points there. So, you know, certainly you can, you know, if you. It's like the classic story of, like, you know, you get the head of sales in the, you know, roadmap planning, and you get the head of support and, you know, head of sales. Like, we need these new features. We need to compete with our competitor. They have this thing we, you know, it's so obvious to me that we need to build towards that. And then the head of support is like, we have these bugs that customers are frustrated with or, you know, like performed or whatever the case is. And it's so obvious to me that we need to go in that direction. So, yeah, it's really. It's interesting. And it almost feels like certainly as you roll up then now as the CEO, you're hearing these two separate arguments, and those are basically the two data points that you have. It's almost a challenge of differentiating between why. It's almost like understanding the motivation of the data and the anecdotes, or especially if we're falling back on that gut feel like, what's the motivation of that? Which feels like almost impossible problem to solve for. But it's like the CEO might make a recommendation and there might be pushback from the sales team because the CEOs effectively saying, well, we need to sell more, you need to do more. You were comfortable hitting your quota before, now we need to increase that. We have a bigger team, we have bigger appetite, whatever. And so the team might be like, well, no, I don't want to do that. Like, that sounds like I just finally got my feet under me for where we were setting the numbers before. Or it might be like, well, we're already kind of hitting our limit. Like, there might actually be like a valid. A valid. You know, like we were saying, like moving into this other market. You know, the team might be like, they don't want to do that. They might just be comfortable or they might be like, they might have some experience that would indicate that that's not going to especially. Sales is really easy to pick one because they are compensated directly for how good they do. So they might be like, well, no, I don't want to go sell to this other market. I don't want to go sell to the education market because that's going to take forever and I want to get paid and it's going to be really hard. And the feature set's not going to line up and it's going to be months and months before I get any result from that. And that's actually kind of what the company cares about too. So it's kind of interesting to try to figure out, well, what's the. What is that switch on the Motivation. And maybe this is where the data can actually come in and help. Maybe that's the true application of the data, that you have to help kind of differentiate the different feelings that people might be having.

Yeah, I think that's true. And this, I think, gets into what leadership ultimately is, is you're not going to have an answer to those questions, that there will be salespeople who say, well, this is why this won't work. There will be data that says, this is why it does. Probably there will be other people on the product team that will say, this is why this is what I want. And there isn't an out there. There is no solution that will tell you what to do. Like, it's not, well, great, we'll return to the data and just tell, like, get the data to eventually solve this problem. It won't. The salespeople will just not agree. They will have reasons that may not be articulated in the data, or there are reasons that will be articulated in the data. But the point is, like, you will just have a bunch of different things that are telling you different things, and you have to make a decision despite that. And so I think that that is one of the things that particularly people who come from data backgrounds that are kind of growing into leadership positions have to let go of, is this sense that if only we go further with what we look at, the answer will emerge. And I think it won't. In those cases, you have to just make a call, and you have to make a call under uncertainty. And your job as the person in charge is to do that. Your job is not to be the clever calculator that figures this out. Finally, your job is to say, we can't figure it out. Somebody still has to make a call. Somebody still has to own that decision. Somebody still has to tell the salespeople that we're doing it the way that they don't want to do it. And I'm the one responsible for that. And they still have to be motivated to do it. And there's not going to be data that gets you out of that.

Yeah, I found myself in almost that exact scenario before where I kind of had this feeling, and I kind of call it caveman mode. We were working really hard to get the data that we needed to answer a question appropriately. And it just kind of wasn't coming together. It was kind of challenging. It was one of those ones where the deeper we went and the more data that we had, it's like it wasn't clearing up the question. It was just Sort of making it more complex. And eventually I kind of got to the mindset of like, all right, if it's this hard to answer if this thing is working or not, then it's not working. And additionally, number go down bad, number go up good. And I feel like you just get to a certain point of you have so much data and it creates such a complex argument that you get to the point where it's like, okay, well, I kind of even repeating it now. I don't necessarily know that this is true and works in all scenarios, but it's kind of like if this was working, we would know, and if it was really not working, we would know. So that means it's probably somewhere in the middle. And I don't think we want to commit to a big change that puts us basically back where we are now or worse. So it is interesting that maybe I'm reading too far into what you're saying to say, like, yes, that is correct. You must do that eventually. But that's kind of how I'm hearing it of like, yeah, like, eventually you're going to just have to, like, once you get to the point, once you kind of go back to the data well, two or three or four times, eventually you have to be like, okay, there's no more data water in the data. Well, you're just going to have to make a decision and see what happens and move from there or try something else. Yeah, exactly.

It's just going to be. I think there is this sense of like, well, if we keep going back to it, if we return to this, enough will find a solution. Right? Like, there will be a way where in some ways it is. It is to me, the same as consensus there. We have to make a decision. And I think that some. Some leaders view consensus as their job where there's a sales leader that doesn't agree with the decision and the product leader who thinks we should do, you know, a product leader who should do a, sales leader thinks we should B.

And it is. It is the.

The executive job then to say, all right, how do we get people on the same page? And I think that's wrong. And I think that is similar to, like, continuing to dig through the data well, or fish through it or whatever to get to a point where there is an answer that everybody says, okay, yep, finally, this is the thing we agree on. Like, they're not going to agree. Your job is not to get them to agree. Your job is to figure out what to do despite them not agreeing. And so, yeah, you have to give it up at some point and just say, this is what we're doing. I'm sorry, product leader. We're not making a decision you want to make tough. That's the job. We got to move on and make sure they're, like, you know, committed to that thing, not look for a way so that they. They ultimately look the decision and say, yep, I would do the same thing. Sometimes they will look at a decision that you look at, and they'll say, I mean, I want to do something different that's. That's going to happen, and you have to get over that. You can't. You can't make it where your job is to get everybody to say, like, what would you do? And we're all going to do the same thing.

Yeah, absolutely. Yeah, I've experienced that as well, and I think that I've at least found a little bit of value in. It's like going to that person who's not on board and being like, hey, yes, this is the decision. This is the direction that we're going. And I'm very well aware you've been heard, and I understand that you actually want to go a different direction, but here's why we're going that way, and here's kind of what I think. Certainly, any decision that we make is not made in the realm of infallibility. It's like, well, this is what we're doing. This is what we're expecting to see. This is what we're expecting to happen. And here's our out. Like, if we try this and we try it for three months and it doesn't work, here's what our next steps are gonna be. So I don't need you to actually, I don't even need you to agree with me. I just need you to get on board and let's try it together, and hopefully you actually agree with what we're hoping to have happen. But if it doesn't happen, I'm not asking you to commit to this for life. I don't want us to get married to this idea. We're just gonna try it, and we're doing it for the greater good. That's probably a weird. A weird, potentially dangerous phrase to use. And, yeah, let's just kind of like, you know, you've been heard, and I understand where you're coming from, and you have. Hopefully they have, but maybe they haven't. But you can say, like, you know, you've materially affected the direction that we're going, but this is. This is the way we're going, and we're gonna try it and let's give it a shot. And we're all gonna be like, obviously, I'm gonna watch it. And if. If it's not working, I'm. I'll be the first to say it, and I'll take responsibility for that. And usually that's. Usually that's enough to get people, you know, going in the same direction. Hopefully you didn't. Hopefully the team didn't start off, I get. I guess, actually it depends. I was about to say hopefully the team didn't start off that far apart to begin with, but maybe just depends on your team and what sort of personalities you have of how strongly. How strongly people have different opinions. And, you know, that might actually be a really valuable thing for an organization, too, to have a bunch of people that view the world in a very strong way and view it in a different way. So you're always getting a kind of a melting pot of different ideas, but it's probably wartime when it comes to actually make the decision and get going in a specific direction.

Yeah. There's no one path through it, for sure.

Yeah. Cool. So I want to be thoughtful of your time, but I do want to sort of talk about, as you were transitioning out of Yammer. I'm just sort of curious, like, if you'd just share a little bit more on what was it like starting your business and kind of rotating out of the company and how did that. You also mentioned that you didn't have this plan. Your life goal wasn't to become an entrepreneur. It kind of happened almost accidentally. So just sort of curious, like, how did that happen? And sort of, what was the process you went through there?

Yeah, so basically there was a point. So I worked with the team at Yammer, and it was with the data team, me and other folks, a couple other folks on that team. After the acquisition by Microsoft, we ended up. A few things started happening. We started, like, talking to more people around Silicon Valley and stuff about, like, the data tools that we built, because once you get acquired, people are kind of like, what'd you do? And they want to learn more from you and stuff. And so we showed them some of the internal tools and they were like, these things are really cool. Either we want to buy it or we have built some internal version of it ourselves. So basically we started to see, like, the things that we had built for ourselves that we kind of originally had thought were, you know, a special tool for a special team that was unlike anything else. Was actually just a kind of a growing norm in Silicon Valley and potentially a growing norm broader than that. And so, like a lot of the. For us, it was kind of a, well, if people are building this and a bunch of people want to buy it, maybe we should just build a product like this and that'll be, you know, the thing that, like, why not just build it as a. As a product? So to me, like, the decision to do it was. It was kind of like, what's the downside? It was that, yeah, okay, I'd be walking away from a job at Microsoft, but I wasn't super thrilled about that anyway. It was an opportunity to start a company as an analyst, which frankly, like, you don't have the opportunity to join early startups usually as a data person that they don't have data for you to do anything with. And so there's no, like, you wouldn't hire an analyst as your fifth hire because there's nothing for them to analyze. You know, hiring engineers and designers and eventually salespeople and marketing. But the data people come later. And so for me, it was like, oh, this will be an interesting opportunity to do something that I wouldn't have a chance to do otherwise. It's a product I believe in that could be successful. It's with people that I enjoy working with. Why not? I think one of the benefits of the way in which it happened was we were coming out of this acquisition. It was for a bunch of money at the time anyway. It was for a billion dollars. Yammer got bought for a billion dollars. So it made a bunch of people some money. In Silicon Valley, when people make money, the thing they immediately do is turn around and invest in. And so we had sort of an easy access to seed money essentially to start it that we didn't have to go out and sort of bootstrap it for a long period of time. We basically, like day one, could go out and raise money for salaries for us, essentially. I think there are good and bad things about it. If I were to do it again, I don't think I'd quite do it that way. I think there's reasons why it's good to kind of bootstrap it for a while and all that kind of stuff, but it made this sort of road for it easy. It made the decision where it's like, there wasn't a lot of. A lot of downside. And one of the things that there is a lot of, like, in the sort of political world a lot of. And Silicon Valley sort of promotes this, too, the. The boldness of entrepreneurs. And, like, these are the people who are taking risks and going out there to do stuff that's, you know, going to reinvent various things. Okay. Like, in some cases, yes. In other cases, like, it's not that bold. Like, it's. It's a job. It's a job that you can immediately get a paycheck for. You're risking your own money. And so that's kind of the position we were in, where it's like, what's. What's the downside here? And so, yeah, so I was like, all right, yeah, why not? And so that was kind of my approach to it, is I was excited about the product. I was excited about the opportunity. But a big part of it was that. That it was. It was something I wouldn't have a chance to really do otherwise. And so I figured. Figured out. I think there's obviously things I've learned from that after having done it for a while, things that were right and things that were wrong.

But.

But, yeah, I think that was, you know, sort of making the leap. It didn't feel like a particularly bold leap. It felt like something where it was just like, this will be kind of fun.

That's cool. Because you sort of. It's almost like you did the roadshow for what the product would be already. Did you find it easy to get your first couple of customers, or did that wind up being a little bit more challenging than expected?

It was easier than we expected, probably. So we knew people in the space, and so. And a lot of people's first customers are that they're kind of friends that you have. They're the types of people who are like, oh, yeah, they know you're starting the thing. They want to try it out. Someone likes it. You kind of end up building it a little bit for them. You know, I think breaking out of that, where we started, like, getting to the point where you're getting customers from people you've never met. I think, like, you know, the first 10 customers or something are all people that are friends of friends type of thing where it's like, oh, you get introduced because your friend knows somebody who works at this other company that's, like, interested in the thing you're trying to solve, and they're willing to try it out. And, you know, you can sit next to them for a day and try to do it with them, and they'll like it and all that kind of stuff. Getting past that point is always a little bit hard. And I think for Us in particular, the thing that we struggled with was kind of the marketing side of it. Honestly, it was a product that in some ways was like, a little early for its time. And so the problem it was solving was not a problem that people sort of immediately understood. I think there's. If you're in this position, it's like, very easy to sell. It's much easier. Not very easy, but it's much easier to sell products where you can give sort of the elevator pitch to people where they immediately understand what it does. And Mode was never one of those products where. Where it was a little bit like this. Seems like it's in between a couple things. What exactly is it? And we didn't have great answers for that. It was something we knew was valuable. And once people started using it, they're like, oh, I get this. But it wasn't something we had the 30 second elevator pitch for. And so I think that was always the struggle for us for finding customers then was it worked when people tried it. It didn't work when people pitch it well to somebody who didn't understand it at all. And so over time, you kind of figure that out and you figure out what works and what doesn't, and the market evolves and things like that. But, yeah, for the initial phases, I think that was the bigger struggle was not sort of marketing per se. It's not like our marketing was bad. I think marketing was very good for what it was. I think it was more just that we were trying to sell something into a market that wasn't quite there yet. And so it was a. It was more of a mismatch around, like, kind of the product in the market and the timing of that market than it was around the messaging that we had.

Yeah, it kind of sounds like positioning. It's like once people used it and they're like, okay, cool, I get it. But when they were trying to. Just trying to explain it in words, they're like, yeah, like, how exactly does this plug into? Like, I have this thing over here and I have this thing over here. Like, how does this thing fit in the middle? And why is it better? Is that kind of the experience that you were having?

Kind of. I mean, I think those are. The conversations went like. I think it was less about positioning and more about the problem that we were solving was not a problem that people had quite experienced yet. So we could have positioned it in all sorts of ways and they wouldn't have quite gotten it. But that. This is where I kind of said, we're early where that problem came around. And I think that it became. And part of this was because we were solving a problem that basically was a problem that we had at Yammer, which was a very sort of data heavy startup that was thinking about these things at kind of the bleeding edge of the way companies thought about data. And so we're right in the bet that the world would kind of eventually follow that path. We were just kind of early to it where we were trying to sell people a product that were like, this isn't our problem. And so yeah, it was less to me about the positioning and a little bit more about. I don't know what an analogy for this would be, but any product that, that arrives a little bit early where it's like, why would I need to do this? And three years later people are like, oh, I understand why I need to do this. If you're selling cost management software for AWS in 2010, you're not going to be very successful because not many people are using AWS and people don't understand why it's useful. We're selling cost management software to AWS people now. They're like, please, I need this. And so we were in some ways on the early end of that where it was like we were trying to sell something where there wasn't that big of a market for it yet. But. But again, I think the bet for where that market was headed was right. If the timing was still so off.

Do you think it was a benefit? Do you think the timing was a benefit from the perspective being a little bit earlier maybe than other competitors would have shown up?

That's a hard question to answer. I think you get good things and you get bad things. It is good in the sense that we learned a lot of things early that are mistakes that we now that the market is in a place that's where we want it to be, we're not going to make those mistakes again. When I talk to early stage founders in this market, they often say like, here's the way we're thinking about it. And it's like, yeah, we tried that six years ago. Let me tell you why that was a bad idea then. It may work now, but like, let me tell you why it's a bad idea then. We have at least gone through some of those things where we can hopefully not repeat those mistakes. You also have like you build stuff like in terms of the things you built, you have a head start on the things that you've built, which matters. The downside of it I think, is that you. So much of Silicon Valley, this is like sort of a Silicon Valley problem. So much of it is momentum and kind of pace, essentially. And so to be in the position, like if we were to start Mode last year and say, hey, here's this new product, capture this new market, the pace of the company would be growing more quickly. It'd be much smaller, but it would be growing more quickly. And I think a lot of Silicon Valley is just like that hype. And so there are benefits of that now you can mess that up and that by no means a success. And there's all sorts of stories you can point to of things that were super hyped and then fell apart. But you kind of want to be on that hype train for a number of reasons. It makes it easier to raise money, it makes it easier to hire. It makes customers more willing to try it out. You want to be the thing that people are talking about and trajectory is what matters rather than position for that. Oracle makes more money than all of us. But people. I don't talk about Oracle because Oracle's make more money for all of us forever. And it's not growing that fast. The startup that went from $0 to $10 million despite being a rounding error on Oracle's balance sheet seems very exciting because it's growing really quickly. And so Silicon Valley is enamored by that shiny new thing. And so being early basically means that obviously not Oracle, but it means that you're not in that conversation about the shiny new thing, even though if you were to launch it today, you potentially would be.

That's funny. I wonder. It almost feels like that's something that might drive you crazy a little bit. As somebody who's super skilled with data, for people to look at this like, you know, insufficient data, and they're probably looking at like, oh, well, they're growing so quickly and they're just extrapolating that line. You know, it's like, okay, well they went from 0 to 10 million in a year, so that means they're going to be at 100 million next year and then they're going to be at a billion the year after that. And then, you know, 10 billion. And you're like, yeah, no, the line doesn't just extend out into eternity on the same trajectory.

Yeah, for sure. And, and you know, it's, it's certainly, I think the, yeah, people aren't, people aren't so crude about it, for sure, but there definitely is a lot of. There definitely is like a lot of Just like Hype in. In Silicon Valley, I think, you know, it's. Is it frustrating? Is it not? I don't know. You know, it is what it is.

It's. It's.

Sometimes it's great for you, sometimes it's not. It's. It's just the way that it works. And I think it's. It's. You have to learn how to play it, basically, and not get too caught up in. Ultimately, the job is to build a business that. That you have a product that people want to buy, and Hype can certainly help you on that path. But at the end of the day, you just got to deliver on the product, you got to deliver on the service, you got to deliver on building the machine that tells a good story about it, that can sell it, all those kinds of things. And that's hard work. No matter what the Hype's like, Gotcha.

Yeah. It's like hype is a tool, and if you can get it, use it. But just remember that it is just a tool and it can't be everything. And so I was just sort of curious, too, what's kind of with our timeline. I feel like we're pretty close to current day going on today. And what are you paying attention to? What are you looking at? What are your plans over the next couple of. Whatever length of period you plan over.

The industry that we are in? I think, like I said, when we started it, we were a little bit early for it. There was somewhat of a settling in probably three or four years ago around new standards, around the data tools that people used, and a settling in the sense where the foundations are now kind of agreed upon, where it's like, this is what these things should roughly look like. This is how people should think about data in their organizations. The sort of early debates that people had about, like, what's the role of data? And do you use this type of database or this type of database? Those sort of things feel like they are. They're kind of the battles of the past. And so there's a new kind of explosion of different tools in the space and us trying to figure out, like, where data goes and all that kind of thing, but it's around a. A more settled foundation. And so for us, I think, like, that settled foundation is a foundation that fits us very well. And that's kind of what I mean by it was early. We're like, Mode is well positioned within that new kind of landscape. And so our focus is basically like, all right, how do we take advantage of it? I think that it's, you know, it is an opportunity that is a really good one, is an opportunity that again, we are sort of in a good spot to capitalize on what I think is a very big opportunity. But, but you have to execute on it. You have to make sure you're not getting too far ahead of yourself. You have to make sure you're still listening to customers, that even if you see this sort of path forward, that you're continuing to validate that it's actually the right path forward. So for us, I think it's about kind of, all right, now is the time to really make this the thing that it could be. And a lot of that is one continuing to just confirm that the ideas that you have are right, continuing to get validation from the market. And if you don't like being humble about, you may be wrong about what this is and being willing to adjust. And a lot of it's just execution. You know, like good companies are not just built on good ideas. They're built on people building good products and sort of the day to day operations of our sales team runs and whether or not you can actually get your message out there. And that doesn't take, you know, the. It's not a Mad Men speech from Don Draper that makes that good. It's hard work. It's just the, the daily process of executing well on marketing operations. It's the daily process of having a good support team that responds to hard questions and does a good job of it. It's a daily process of doing the diligence of reaching out to customers and making sure they're happy. And if you've got customers that aren't engaging with them and talking to them, there's just a lot of stuff like that that has to happen to make it grow and to make it successful. And so I think we are in a position where we feel good about where we're at the market. We want to make sure that that belief is right. And so we're going to kind of continue to follow that. But a lot of it is all right. How do we make sure that the machine that needs to power all of this, which is really the much bigger part of the job than the idea of what the shape of the machine should be, actually performs awesome well.

Ben, I think that's perfect advice to end on. I think we missed that, we missed the operational excellence point a lot of pretty easily because it's not very exciting. It's not very. Sometimes it's not very fun either. But yeah, Totally right. And totally agree. Yeah.

Awesome.

Well, thanks so much for, for joining us. We'll, we'll obviously have all of your, all of your links and everything, you know, connected to this episode. But, you know, is there anywhere particular that you would, you would recommend sending people to if they want to learn more about you or about the business? Sure.

So if you want to learn more about, about Mode, it's just mode.com so you can, you know, check it out there. You can sign it up for free. Try the product if you're a data person, want to try it out so you can, you know, the various resources and things like that there. For me personally, most of the things. So I, I basically like most of my, some of these I've talked about and most of the stuff that I do is basically like writing something. Once a week I have like a blog in effect, it's on a substack because it's 2021 and you're supposed to have substacks and not blogs now I guess. So that's just bin dot substack. And then yeah, it's, you know, there's the usual stuff on Twitter and things like that though that's probably much less interesting than the substack.

Cool. Awesome. Ben. Well, thanks so much for joining us. I really appreciate it.

Thanks for having me.

It's been great.

That was our conversation with Ben Stancil, co founder and Chief Analytics Officer of Moat Analytics. If you need a collaborative way to visualize and user data, you know where to go. Mode.com that's M-O-E.com. if it's business analytics and growth tools you're looking for, check us out@barometric.com we hope you enjoyed this episode and invite you to check out our other founder chats. And if you're able to share with a friend or leave a review, it goes a long way. Thanks for listening.