The Exchanges

Q So what, what are the metrics that reveal true customer love in that time period? If you have a smart practitioner who can make anything look good, how do you determine between the true good versus the fake good?

A The, the ultimate metric that, that, that I try to focus on is churn. Because if I have a leaky bucket in the early days, I can, I can fill it up because I got a billion daily users I can pour into it. But You will never fill that bucket if it's got a bunch of holes in the middle, in the bottom. And so churn is the ultimate metric. And then you'd start looking for what are the leading metrics for churn. And that's where things like the Sean Ellis survey and even NPS, which we tend to use pretty maniacally and pretty religiously. And if we cannot get a Sean Ellis survey score, that's actually above what, what would Sean himself recommends well above. We're not focused on scaling it. We're focused on iterating it. Again, for love, not for scale. Um, and that's, that's, that's our attempt. That's, you know, back to one of your first questions. That's our attempt to turn art into science. I also, we've started going back to, you know, having leaders of our, of business product managers, just go call 10 customers. 10, call 10 customers who are using the product and just get a, a visceral sense. And don't ask someone else in the customer experience team to call them. You call them because there's no other way. And by the way, bring your engineering lead with you in, in that call and just listen to what they have to say. And that's part, that part is some of the art. Let's just listen…

AI assessment note: “The, the ultimate metric that, that, that I try to focus on is churn.”

Q Um, why? What's led you to lose confidence? Mishires?

A Not hires? Some, some, some, some, some mistakes in hiring, some great understanding that, again, and some of this is commonplace understanding, but first of all, understanding that credentials matter less than I thought, um, that someone who had Gone to the right school, the right program, the right grades, the right company, and any combination of all of those things isn't necessarily right for your situation at your time. One. Two, a gradual understanding, and I know you're a, you're a football fan and I'm a huge football fan, uh, and I'm really happy this morning because Manu is back after a decade. Um, but, uh, Team matters. I think we've seen that in the world of football since Pep came along, 15 years ago. The team matters. And so, no matter how, how many Galacticos, how many All-Stars you've got on the team, it doesn't mean that the, the whole is going to be greater than the sum of the parts. And so, recruiting someone who is going to fit the need, fit the team, fit the problem And you don't have the like normal signposts of credentials necessarily as like a definitive guarantee. I, you know, I, after consulting, I joined Google and Google was famous for the fact that Larry, uh, literally looked at every hiring packet, including resume. I, what, 12 years after graduating college had to provide my SAT scores to Google. I had to go find them. I had to provide my college t…

AI assessment note: “Some mistakes in hiring, some great understanding that, again, credentials matter less”

Q there. There's two ways, which is full trust from day one and it's yours to be lost or you gain trust over time. I inherently don't trust people, which is why I will die alone with my cat. Um, I need to actually get the cat. I'm just alone now. Um, but like, you know, do you trust people from the start or is it to be gained over time?

A I, I tend to default to trusting people from the start. We had a mantra at Facebook and speaking of lessons from there about assuming good intent. It was built into the cultural training there. Um, I think assuming good intent, trusting people. I, I don't trust people at the recruiting stage. When I'm recruiting someone, my default answer is they're not good enough to, to, to join the company. But once they've proven that, uh, and we've decided to extend an offer, I'm going to assume that you are new bank, Quality. We try to keep a very high bar. And then I'm going to try to give you as much room to run and show me what you can do. And look, if you make mistakes along the way, we'll, we'll correct them. We all do. And if you make too many mistakes, then we have to have more difficult conversations. But I think the right starting point is to give people that trust and give them the room to run.

AI assessment note: “I tend to default to trusting people from the start.”

Q You mentioned dead cat bouncing there. You mentioned it to me earlier, and I had no idea what you were talking about. Um, uh, and in the notes, I was like, bizarre. I haven't heard this. What is the illusion of dead cats bouncing before I forget to ask this?

A Sure. So, you know, from, from your world, from the world of, of investing, there are lots of dead companies that, you know, Have a nice rally at some point on their way to zero, and that can often trick investors. The way that tends to happen in, in massive and largely successful technology companies is, is sort of the following arc. Company launches a product, great entrepreneurial energy, great customer focus. That product takes off. Maybe there's a second one of those that are really, really successful. Then the, you know, the company gets big, the founder becomes the CEO, cannot be as close to driving that, that stuff quite as, as tightly. And a product, a professional product team takes over and sort of forgets the inherent, you got to get customer love before you can explode out. On top of it, you've got, you know, the CFO and the CEO saying our next wave of products has to be really big to really, to matter. And so you start focusing on making early launches really big before they are genuinely loved. And then all of the incentives can go haywire. And I've seen this at, at pretty much every company I worked with. It's one of the things I obsess about and I'm paranoid about it. NewBank bank, as we start to get really big is making sure that we aren't bouncing dead cats, that we aren't launching products that customers don't actually like, where we're milking the NewBank …

AI assessment note: “we aren't launching products that customers don't actually like, where we're milking”

Q So what, what are the metrics that reveal true customer love in that time period? If you have a smart practitioner who can make anything look good, how do you determine between the true good versus the fake good?

A The, the ultimate metric that, that, that I try to focus on is churn. Because if I have a leaky bucket in the early days, I can, I can fill it up because I got a billion daily users I can pour into it. But You will never fill that bucket if it's got a bunch of holes in the middle, in the bottom. And so churn is the ultimate metric. And then you'd start looking for what are the leading metrics for churn. And that's where things like the Sean Ellis survey and even NPS, which we tend to use pretty maniacally and pretty religiously. And if we cannot get a Sean Ellis survey score, that's actually above what, what would Sean himself recommends well above. We're not focused on scaling it. We're focused on iterating it. Again, for love, not for scale. Um, and that's, that's, that's our attempt. That's, you know, back to one of your first questions. That's our attempt to turn art into science. I also, we've started going back to, you know, having leaders of our, of business product managers, just go call 10 customers. 10, call 10 customers who are using the product and just get a, a visceral sense. And don't ask someone else in the customer experience team to call them. You call them because there's no other way. And by the way, bring your engineering lead with you in, in that call and just listen to what they have to say. And that's part, that part is some of the art. Let's just listen…

AI assessment note: “The, the ultimate metric that, that, that I try to focus on is churn.”

Q Can I, speaking of the product, you have so many different ways you can take product with new products, with features. How do you decide what to do next? How do you prioritize product decisions, Jag?

A Again, it's aligned with what we think allows us to become a leading player aligned with the mission of the company. We want to transform Services, including financial services across Latin America. So a couple of things become obvious about that. We didn't say Brazil, we said Latin America. So launching in Mexico and Columbia three years ago was, I wouldn't say a no brainer, but it was an obvious thing, obvious mountain we needed to climb. We started three, four years ago, call it about four years ago, as essentially, and I'm overstating for simplicity, a Brazilian credit card company. We knew, and David was very clear, that we were on a journey from a Brazilian credit card company to being a full solution Latin American financial services company. That's a mouthful, but it's clear. That means You have to have investments. You have to offer insurance. You have to do lending. Um, in Latin America, consumers tend to consolidate their financial lives with one primary institution. And we had made a lot of headway in deconstructing financial services in Brazil. And the reconsolidation with the traction we had was the next play. And so We've been climbing two mountains simultaneously over the last three to four years. One, proving that the model extended beyond Brazil, which was a question three years ago. And second, proving that we were more than a credit card company, which was a…

AI assessment note: “aligned with what we think allows us to become a leading player aligned with the mission”

Q Why was, why was it a question that you were more than Brazil? The cultures are relatively similar. I mean, it's not, you know, Southeast Asia compared to Latam, which is obviously bigger. Like, they are relatively aligned in terms of cultures. Why was it a question?

A Yeah, well, for me, Mead was obvious, and it was back before I joined Nubank. To me, it was an obvious next move, and the timing felt roughly right, but the, this was a question investors were asking. Is Brazil, are there unique aspects of Brazil that make this a one-off? Can you actually do it beyond the country? And there are, there are some, you know, technical specifics. The, the working capital cycle for credit cards in Brazil is very favorable for a credit card company for what we're trying to do, which is Almost, almost compared to anywhere else in the world, Mexico and Colombia are much more typical. So there were some, some technical reasons why, but we firmly believe that Mexicans and Colombians were roughly as poorly served as Brazilians were, and that the formula we had come up with, ah, both starting with the culture and leading into the products was, was something that we could extend, but we had to prove it. We had to prove it, which gets back to execution, you know, to, to go full circle.

AI assessment note: “The working capital cycle for credit cards in Brazil is very favorable”

Q That is so kind of you, but I, when I spoke to David, he asked, um, the first most important question. You went from a cushy job at Meta, working in the Valley, to working for a Brazilian fintech that no one really knew about at the time. Why did you do it? He asked.

A Well, as, as he well knows, David is an extraordinarily persuasive guy, so that's the first thing. The first conversation I had with David, when he asked me towards the end, would you ever consider moving to Sao Paulo? I thought, I said, David, the obvious answer is no, and this is a crazy request, but he's a very persuasive guy, because when I gave him that answer, he said, well, there are people working here who are far more definitive than you are, so we should keep chatting. Doug Leone played a part in, in, in persuading me, but the, the larger, the larger reason is I had spent the previous almost 15 years at that point working in, at Google, at Quantcast, at Facebook, basically in media tech and ad tech as it was the center of what was happening in Silicon Valley. And Had come to realize that I had a ringside seat to the great disruption story of the previous 15 years, and that was amazing, intellectually, incredibly interesting. But I was, I had developed a toolkit that I was applying to serve what is effectively the richest billion people on the planet. The US, Europe, Japan, Australia, New Zealand. And what David and Newbank offered Was the chance to take that same toolkit and apply it to roughly speaking the bottom half of the global income pyramid. I had grown up in the Caribbean. So roughly speaking, depending on how you count the, the English speaking Caribbean, I'm…

AI assessment note: “take that same toolkit and apply it to roughly speaking the bottom half”

Q Can I ask what would be an example of this? Just so I understand it more clearly. So we, you said there about kind of actually strategically being clear about knowing where you're going or what you're wanting. What would be an example of that in product?

A I'll give you, uh, let me give you an example. So Which is one we have wrestled with. She fairly recently at new bank. Um, if you've, and I'll speak a little bit generically, if you've got a hypothesis for a product and you launch it out in the market. And you're not careful about who you've launched it to or how you've positioned it. And you get a bunch of signal that says, Hey, as we did at new back, Hey, the NPS on this product is actually quite mediocre. And you just look at your average NPS and says, Hey, this product just doesn't seem to be resonating. You might iterate in any number of directions, kill the product, iterate the product in some, uh, Incorrect direction. What we ended up finding was, hey, peel underneath the data. There is a set of customers, which was actually the set of customers we meant the product to be for, who love the product. They're actually a small minority of the people who we've given the product to right now. And there's a bunch of customers who don't like the product, but you know what? It turns out this product wasn't designed for them. It wasn't built for them. Of course they don't like it. There's no reason why they should like it. So that's in part a story about the averaging, but it's also a part in story about saying, and this is what strategy is. I am building this product for this specific set of customers. The specific group segment …

AI assessment note: “I am building this product for this specific set of customers.”

Q This is me sitting down now, because it's, it's got serious. Uh, my question to you is, um, what makes a good question I think the majority of customer development actually sucks because people don't ask good questions. What's a good question?

A For my, for my money, and look, I've made, I, I've learned this mostly by making, by doing the opposite and making the mistake. It's, it's about exploring the customer's problem rather than the solution, and even when you're exploring the You don't ask about the problem directly. You ask about the customer's life around whatever area you're, you're interested in. And customers don't even realize when they are employing workarounds, when they are, when they are making do, when they think everything is fine, but if you somehow showed up with something completely different, uh, you can, you can change their lives. And so I think where we tend to fall short in the industry today, even still, is we spend a lot of time trying to pitch the solution, ask about the solution. We spend not nearly enough time talking about the problem. And when we are talking about the problem, we're mostly asking about it rather than asking around it and observing around it, because it's our job to synthesize.

AI assessment note: “You ask about the customer's life around whatever area you're interested in.”

Q new banks, I'm adding that. He didn't use that. And, uh, you know, you're, you're fighting off Uh, eventual mediocrity, um, and you're fighting off eventual slowness. Um, my question to you is, when you think about Facebook operating in that execution speed, how do you retain that speed and agility when you are new bank today with such large teams and very real processes and bluntly heavy corporate structures?

A Yeah. Yeah. Well, a few things. Um, we have a CEO who still, who's a founder and he fights the, the, the, the, the natural, uh, creep of corporate process, even including ones that I try to introduce at times. And I think that's, that's a helpful counter, counter force, counter gravity. Um, but I think there are two things. And again, these are, these things are over the last 10 years getting more and more Codified amongst the really great companies. Um, my wife, my wife used to work at Amazon, so we, we lived and I heard in the early days about, you know, two pizza box teams. At Google, we used to talk about a team should be like a, like a, like a family. You should be able to, you know, feed it around the table. Um, so we at NewBank, we do a couple of things fairly religiously. We try to keep teams Very small, actually. You might have many of them focusing on many things as the company grows, but we tried very hard to keep the size of teams small. That's a really important thing. And the second thing, which we just talked about is when you get stuck, don't stay stuck. Escalate. Someone will make a call, a bad decision, which can be iterated on is better than no decision at all. Cause you start learning from the market and from customers. And, uh, and very few decisions are as, as Bezos would say, one way doors. So escalate and decide and keep your team small. And at NewBank, …

AI assessment note: “So escalate and decide and keep your team small.”

Q utilizing that toolkit. I think there's this kind of common question in, in product, which is like, to what extent is it art or science? Incredibly difficult question, which, you know, is an utterly shit question really, but I thought I would ask it anyway. Um, is product more art or science? And what would you say it would be if you had to like rank it? 70, 3080, 20?

A Yeah, I, you know, I've heard you ask this question of a number of product leaders over, over the last year or so, from Gustav to Kevin Weil and others, um, and I've liked their answers. I'll give you my take on, on the answer. I have tried, and we have tried at NewBank, especially over the last three years, to make it as scientific as we possibly can, and we pushed the envelope, and I think there's still further to go. I think you can make it 90% science. The thing about product management is it's a pretty new discipline. When an engineer shows up at a tech company or a salesperson shows up at a tech company, they kind of know what the job is. When I showed up at Google in 2006, No one really had a definition for what a product manager was. They showed you where the bathroom was, and then they sort of dropped you in a team and said, go. And over the last 15 years, and you've had Marty Kagan and others on, it's become gradually more scientific. And so I think we can make it, and we're on the cusp of making, I think we're quite there, but I think we're on the cusp of making it, 90% science. What is the science of understanding product market fit? How do you make it Metrics driven more than like gut feel. Having said that, I think the last 10% and the most important 10% and the 10% that makes all of the difference is still art. And once you get the science, you get like to the st…

AI assessment note: “I think you can make it 90% science.”

Q What is in the art? If we're in the science and we're like determining product market fit, um, you know, all of the kind of metricized elements that we now have with the data tools that we have, what is in the 10% of art still?

A Yeah, and I'm still figuring out the answer to that question every day, um, learning from my team every day, but I think the heart of it, and it's the hardest part to make scientific is the empathy with the customer. What is that specific thing that we're going to focus on that creates the magic for them that really taps into a deeply unmet need? The thing is, and the pitfall is, you can do customer research. You can go ask customers, but I, I tell, uh, the product team at Newbank all the time, we are not in the business of taking dictation from our customers, because if we were, we would just send the engineer instead of us, and we'd no need for the middleman. We have to listen to the customer, better yet, observe the customer to interpret What their unmet need is and what the hierarchy of those unmet needs are and where the magic is. And then you apply business filters and everything else to see if there's, you know, profit to be had. Um, but that empathy and that intuition for where the real unmet need is, is something that is very hard to make scientific and is something that, you know, you can be called with the official title of product manager, or you can be a guy named David Velez. Who isn't technically a product manager, but came up with a magical idea 10 years ago, uh, and just had that intuition for where the pain was, that was sharp, and that would create this visce…

AI assessment note: “the hardest part to make scientific is the empathy with the customer”

Q How do you do it? You spend time on it with time. You have commitment and with commitment, you have love. Fuck me. This is a dating show, Jack. But like, you know, when, when you've sacrificed other things to dedicate time and commitment and energy and passion to it, you, it's human nature. How do you detach yourself from it?

A Yeah, you know, one, the one step, which is, which is very hard, but is it, but is not even enough is to at least fall in love with the problem, not the specific solution. David, David, you know, just to take that new bank example was in love with the potential of unleashing the Latin American consumer who was being really badly served. He wasn't in love with, I need to build a credit card for Because of X, Y, Z reason or, or business case. Um, and so first you start with at least being in love with the problem. And then when you're in love with the problem, the solution can come out in any one of a dozen or two dozen or a hundred different ways. And you're just keeping your ear to the ground to see how can I unleash this bigger idea, this bigger opportunity. That, that's, that's the key. Whereas falling in love with a specific idea before you've proven, proven it just means you're going to end up leading the witness and you're going to hear what you want to hear. And, uh, and that's, that's always, always, always a trap.

AI assessment note: “fall in love with the problem, not the specific solution.”

Q Jack, before we dive into the meat of the show, I do just have to ask, you know, you spent several years at Facebook as a director of product management. It's a pretty heavy role at a pretty important time for the company. What are one or two takeaways for you that really impacted how you think about product today?

A You know, Facebook has its share of pitfalls and how it operates, but it has a few things it does extremely well, which In at least a couple of cases dovetailed with things that I really needed to learn, especially at the time. One of them, and this sort of builds actually ironically off what we were just talking about, is I, I had a tendency to, A, enjoy the argument, and B, get entrenched in my position. Not with the customer, but internal. And one of the things that Facebook does incredibly well, especially at the senior levels, is I have a strong point of view as Director A, and you have a strong point of view as Director B, and we need to To collaborate and Facebook is built for it. You have to collaborate across business areas to, to make something big happen. And Facebook was incredibly good at saying, and I had, I had to learn this from a number of people, but they were incredibly good at simply saying, you think X, I think Y. By the end of this week, the latest, we are going to either agree, disagree, and know why, and escalate. And the speed of escalation, it wasn't like, I think X, you think Y, let's come back in a week, discuss it some more, and then in a week after that, we're going to escalate. It was like, I think X, you think Y, by the end of day, we're going to agree if we can come to a consensus or escalate. And that speed of escalation I think ties to Faceboo…

AI assessment note: “that speed of escalation I think ties to Facebook's historical, always impressive speed”

Q there. There's two ways, which is full trust from day one and it's yours to be lost or you gain trust over time. I inherently don't trust people, which is why I will die alone with my cat. Um, I need to actually get the cat. I'm just alone now. Um, but like, you know, do you trust people from the start or is it to be gained over time?

A I, I tend to default to trusting people from the start. We had a mantra at Facebook and speaking of lessons from there about assuming good intent. It was built into the cultural training there. Um, I think assuming good intent, trusting people. I, I don't trust people at the recruiting stage. When I'm recruiting someone, my default answer is they're not good enough to, to, to join the company. But once they've proven that, uh, and we've decided to extend an offer, I'm going to assume that you are new bank, Quality. We try to keep a very high bar. And then I'm going to try to give you as much room to run and show me what you can do. And look, if you make mistakes along the way, we'll, we'll correct them. We all do. And if you make too many mistakes, then we have to have more difficult conversations. But I think the right starting point is to give people that trust and give them the room to run.

AI assessment note: “I tend to default to trusting people from the start.”

Q You mentioned dead cat bouncing there. You mentioned it to me earlier, and I had no idea what you were talking about. Um, uh, and in the notes, I was like, bizarre. I haven't heard this. What is the illusion of dead cats bouncing before I forget to ask this?

A Sure. So, you know, from, from your world, from the world of, of investing, there are lots of dead companies that, you know, Have a nice rally at some point on their way to zero, and that can often trick investors. The way that tends to happen in, in massive and largely successful technology companies is, is sort of the following arc. Company launches a product, great entrepreneurial energy, great customer focus. That product takes off. Maybe there's a second one of those that are really, really successful. Then the, you know, the company gets big, the founder becomes the CEO, cannot be as close to driving that, that stuff quite as, as tightly. And a product, a professional product team takes over and sort of forgets the inherent, you got to get customer love before you can explode out. On top of it, you've got, you know, the CFO and the CEO saying our next wave of products has to be really big to really, to matter. And so you start focusing on making early launches really big before they are genuinely loved. And then all of the incentives can go haywire. And I've seen this at, at pretty much every company I worked with. It's one of the things I obsess about and I'm paranoid about it. NewBank bank, as we start to get really big is making sure that we aren't bouncing dead cats, that we aren't launching products that customers don't actually like, where we're milking the NewBank …

AI assessment note: “dead companies that, you know, Have a nice rally at some point on their way to zero”

Q Why was, why was it a question that you were more than Brazil? The cultures are relatively similar. I mean, it's not, you know, Southeast Asia compared to Latam, which is obviously bigger. Like, they are relatively aligned in terms of cultures. Why was it a question?

A Yeah, well, for me, Mead was obvious, and it was back before I joined Nubank. To me, it was an obvious next move, and the timing felt roughly right, but the, this was a question investors were asking. Is Brazil, are there unique aspects of Brazil that make this a one-off? Can you actually do it beyond the country? And there are, there are some, you know, technical specifics. The, the working capital cycle for credit cards in Brazil is very favorable for a credit card company for what we're trying to do, which is Almost, almost compared to anywhere else in the world, Mexico and Colombia are much more typical. So there were some, some technical reasons why, but we firmly believe that Mexicans and Colombians were roughly as poorly served as Brazilians were, and that the formula we had come up with, ah, both starting with the culture and leading into the products was, was something that we could extend, but we had to prove it. We had to prove it, which gets back to execution, you know, to, to go full circle.

AI assessment note: “the working capital cycle for credit cards in Brazil is very favorable”

Q utilizing that toolkit. I think there's this kind of common question in, in product, which is like, to what extent is it art or science? Incredibly difficult question, which, you know, is an utterly shit question really, but I thought I would ask it anyway. Um, is product more art or science? And what would you say it would be if you had to like rank it? 70, 3080, 20?

A Yeah, I, you know, I've heard you ask this question of a number of product leaders over, over the last year or so, from Gustav to Kevin Weil and others, um, and I've liked their answers. I'll give you my take on, on the answer. I have tried, and we have tried at NewBank, especially over the last three years, to make it as scientific as we possibly can, and we pushed the envelope, and I think there's still further to go. I think you can make it 90% science. The thing about product management is it's a pretty new discipline. When an engineer shows up at a tech company or a salesperson shows up at a tech company, they kind of know what the job is. When I showed up at Google in 2006, No one really had a definition for what a product manager was. They showed you where the bathroom was, and then they sort of dropped you in a team and said, go. And over the last 15 years, and you've had Marty Kagan and others on, it's become gradually more scientific. And so I think we can make it, and we're on the cusp of making, I think we're quite there, but I think we're on the cusp of making it, 90% science. What is the science of understanding product market fit? How do you make it Metrics driven more than like gut feel. Having said that, I think the last 10% and the most important 10% and the 10% that makes all of the difference is still art. And once you get the science, you get like to the st…

AI assessment note: “I think you can make it 90% science... the last 10%... is still art.”

Q Can I ask what would be an example of this? Just so I understand it more clearly. So we, you said there about kind of actually strategically being clear about knowing where you're going or what you're wanting. What would be an example of that in product?

A I'll give you, uh, let me give you an example. So Which is one we have wrestled with. She fairly recently at new bank. Um, if you've, and I'll speak a little bit generically, if you've got a hypothesis for a product and you launch it out in the market. And you're not careful about who you've launched it to or how you've positioned it. And you get a bunch of signal that says, Hey, as we did at new back, Hey, the NPS on this product is actually quite mediocre. And you just look at your average NPS and says, Hey, this product just doesn't seem to be resonating. You might iterate in any number of directions, kill the product, iterate the product in some, uh, Incorrect direction. What we ended up finding was, hey, peel underneath the data. There is a set of customers, which was actually the set of customers we meant the product to be for, who love the product. They're actually a small minority of the people who we've given the product to right now. And there's a bunch of customers who don't like the product, but you know what? It turns out this product wasn't designed for them. It wasn't built for them. Of course they don't like it. There's no reason why they should like it. So that's in part a story about the averaging, but it's also a part in story about saying, and this is what strategy is. I am building this product for this specific set of customers. The specific group segment …

AI assessment note: “let me give you an example. So Which is one we have wrestled with.”

Q Do you want usage? Sorry, I'm, I'm just so interested now. Do you want usage? Cause like on a bank, maybe, you know, they have the card. You don't want them to log into the app. They probably log in when there's a problem or when they, you know, aren't sure about something. How do you feel about usage when maybe it doesn't correlate to customer love?

A Yeah, look, we, we are keyed first on customer love. You know, the first value of Newbank when David, Chris and Ed founded the company, it's one of the reasons, frankly, I joined back to your earlier question, was I, I visited Sao Paulo and in the lobby was this very odd statement. Uh, uh, we want, if value number one, we want our customers to love us. Interesting word, fanatically. That's the word we use. Um, we want our customers to love us fanatically, and that is exactly how I think about it. So we are keyed off on that from day one at Newbank, value one at Newbank. We believe in the context of what we are trying to do in our strategy, that intensity of engagement with our app is actually a leading indicator of that customer love, and we, and we've proven that to ourselves. Um, you're right that in concept, Someone could never use the app or almost never use the app and still love the product because, you know, they're using the credit card or what have you. But for us, what we found is intensity of usage is a lead indicator for customer love. Ultimately, what we care about is customer love, but we track both because we believe one's a leading indicator of the other.

AI assessment note: “intensity of engagement with our app is actually a leading indicator of that customer love”

Q What is in the art? If we're in the science and we're like determining product market fit, um, you know, all of the kind of metricized elements that we now have with the data tools that we have, what is in the 10% of art still?

A Yeah, and I'm still figuring out the answer to that question every day, um, learning from my team every day, but I think the heart of it, and it's the hardest part to make scientific is the empathy with the customer. What is that specific thing that we're going to focus on that creates the magic for them that really taps into a deeply unmet need? The thing is, and the pitfall is, you can do customer research. You can go ask customers, but I, I tell, uh, the product team at Newbank all the time, we are not in the business of taking dictation from our customers, because if we were, we would just send the engineer instead of us, and we'd no need for the middleman. We have to listen to the customer, better yet, observe the customer to interpret What their unmet need is and what the hierarchy of those unmet needs are and where the magic is. And then you apply business filters and everything else to see if there's, you know, profit to be had. Um, but that empathy and that intuition for where the real unmet need is, is something that is very hard to make scientific and is something that, you know, you can be called with the official title of product manager, or you can be a guy named David Velez. Who isn't technically a product manager, but came up with a magical idea 10 years ago, uh, and just had that intuition for where the pain was, that was sharp, and that would create this visce…

AI assessment note: “the hardest part to make scientific is the empathy with the customer”

Q This is me sitting down now, because it's, it's got serious. Uh, my question to you is, um, what makes a good question I think the majority of customer development actually sucks because people don't ask good questions. What's a good question?

A For my, for my money, and look, I've made, I, I've learned this mostly by making, by doing the opposite and making the mistake. It's, it's about exploring the customer's problem rather than the solution, and even when you're exploring the You don't ask about the problem directly. You ask about the customer's life around whatever area you're, you're interested in. And customers don't even realize when they are employing workarounds, when they are, when they are making do, when they think everything is fine, but if you somehow showed up with something completely different, uh, you can, you can change their lives. And so I think where we tend to fall short in the industry today, even still, is we spend a lot of time trying to pitch the solution, ask about the solution. We spend not nearly enough time talking about the problem. And when we are talking about the problem, we're mostly asking about it rather than asking around it and observing around it, because it's our job to synthesize.

AI assessment note: “You ask about the customer's life around whatever area you're, you're interested in.”

Q How do you do it? You spend time on it with time. You have commitment and with commitment, you have love. Fuck me. This is a dating show, Jack. But like, you know, when, when you've sacrificed other things to dedicate time and commitment and energy and passion to it, you, it's human nature. How do you detach yourself from it?

A Yeah, you know, one, the one step, which is, which is very hard, but is it, but is not even enough is to at least fall in love with the problem, not the specific solution. David, David, you know, just to take that new bank example was in love with the potential of unleashing the Latin American consumer who was being really badly served. He wasn't in love with, I need to build a credit card for Because of X, Y, Z reason or, or business case. Um, and so first you start with at least being in love with the problem. And then when you're in love with the problem, the solution can come out in any one of a dozen or two dozen or a hundred different ways. And you're just keeping your ear to the ground to see how can I unleash this bigger idea, this bigger opportunity. That, that's, that's the key. Whereas falling in love with a specific idea before you've proven, proven it just means you're going to end up leading the witness and you're going to hear what you want to hear. And, uh, and that's, that's always, always, always a trap.

AI assessment note: “fall in love with the problem, not the specific solution.”

Q Jack, before we dive into the meat of the show, I do just have to ask, you know, you spent several years at Facebook as a director of product management. It's a pretty heavy role at a pretty important time for the company. What are one or two takeaways for you that really impacted how you think about product today?

A You know, Facebook has its share of pitfalls and how it operates, but it has a few things it does extremely well, which In at least a couple of cases dovetailed with things that I really needed to learn, especially at the time. One of them, and this sort of builds actually ironically off what we were just talking about, is I, I had a tendency to, A, enjoy the argument, and B, get entrenched in my position. Not with the customer, but internal. And one of the things that Facebook does incredibly well, especially at the senior levels, is I have a strong point of view as Director A, and you have a strong point of view as Director B, and we need to To collaborate and Facebook is built for it. You have to collaborate across business areas to, to make something big happen. And Facebook was incredibly good at saying, and I had, I had to learn this from a number of people, but they were incredibly good at simply saying, you think X, I think Y. By the end of this week, the latest, we are going to either agree, disagree, and know why, and escalate. And the speed of escalation, it wasn't like, I think X, you think Y, let's come back in a week, discuss it some more, and then in a week after that, we're going to escalate. It was like, I think X, you think Y, by the end of day, we're going to agree if we can come to a consensus or escalate. And that speed of escalation I think ties to Faceboo…

AI assessment note: “And that speed of escalation I think ties to Facebook's historical, always impressive speed”

Q new banks, I'm adding that. He didn't use that. And, uh, you know, you're, you're fighting off Uh, eventual mediocrity, um, and you're fighting off eventual slowness. Um, my question to you is, when you think about Facebook operating in that execution speed, how do you retain that speed and agility when you are new bank today with such large teams and very real processes and bluntly heavy corporate structures?

A Yeah. Yeah. Well, a few things. Um, we have a CEO who still, who's a founder and he fights the, the, the, the, the natural, uh, creep of corporate process, even including ones that I try to introduce at times. And I think that's, that's a helpful counter, counter force, counter gravity. Um, but I think there are two things. And again, these are, these things are over the last 10 years getting more and more Codified amongst the really great companies. Um, my wife, my wife used to work at Amazon, so we, we lived and I heard in the early days about, you know, two pizza box teams. At Google, we used to talk about a team should be like a, like a, like a family. You should be able to, you know, feed it around the table. Um, so we at NewBank, we do a couple of things fairly religiously. We try to keep teams Very small, actually. You might have many of them focusing on many things as the company grows, but we tried very hard to keep the size of teams small. That's a really important thing. And the second thing, which we just talked about is when you get stuck, don't stay stuck. Escalate. Someone will make a call, a bad decision, which can be iterated on is better than no decision at all. Cause you start learning from the market and from customers. And, uh, and very few decisions are as, as Bezos would say, one way doors. So escalate and decide and keep your team small. And at NewBank, …

AI assessment note: “we tried very hard to keep the size of teams small.”

Q Can I, speaking of the product, you have so many different ways you can take product with new products, with features. How do you decide what to do next? How do you prioritize product decisions, Jag?

A Again, it's aligned with what we think allows us to become a leading player aligned with the mission of the company. We want to transform Services, including financial services across Latin America. So a couple of things become obvious about that. We didn't say Brazil, we said Latin America. So launching in Mexico and Columbia three years ago was, I wouldn't say a no brainer, but it was an obvious thing, obvious mountain we needed to climb. We started three, four years ago, call it about four years ago, as essentially, and I'm overstating for simplicity, a Brazilian credit card company. We knew, and David was very clear, that we were on a journey from a Brazilian credit card company to being a full solution Latin American financial services company. That's a mouthful, but it's clear. That means You have to have investments. You have to offer insurance. You have to do lending. Um, in Latin America, consumers tend to consolidate their financial lives with one primary institution. And we had made a lot of headway in deconstructing financial services in Brazil. And the reconsolidation with the traction we had was the next play. And so We've been climbing two mountains simultaneously over the last three to four years. One, proving that the model extended beyond Brazil, which was a question three years ago. And second, proving that we were more than a credit card company, which was a…

AI assessment note: “aligned with what we think allows us to become a leading player aligned with the mission”

Q Um, why? What's led you to lose confidence? Mishires?

A Not hires? Some, some, some, some, some mistakes in hiring, some great understanding that, again, and some of this is commonplace understanding, but first of all, understanding that credentials matter less than I thought, um, that someone who had Gone to the right school, the right program, the right grades, the right company, and any combination of all of those things isn't necessarily right for your situation at your time. One. Two, a gradual understanding, and I know you're a, you're a football fan and I'm a huge football fan, uh, and I'm really happy this morning because Manu is back after a decade. Um, but, uh, Team matters. I think we've seen that in the world of football since Pep came along, 15 years ago. The team matters. And so, no matter how, how many Galacticos, how many All-Stars you've got on the team, it doesn't mean that the, the whole is going to be greater than the sum of the parts. And so, recruiting someone who is going to fit the need, fit the team, fit the problem And you don't have the like normal signposts of credentials necessarily as like a definitive guarantee. I, you know, I, after consulting, I joined Google and Google was famous for the fact that Larry, uh, literally looked at every hiring packet, including resume. I, what, 12 years after graduating college had to provide my SAT scores to Google. I had to go find them. I had to provide my college t…

AI assessment note: “Some mistakes in hiring, some great understanding that... credentials matter less than I thought”

Q You're a leader of products. You have resource allocation at the front of your mind. You mentioned all those different new products that you have now, but that you had to build and build out over time. How do you think about the right ratio of allocating between core product innovation and scaling and growth?

A Yeah, and a bit of art with a rule of form, you know, I grew up at Google. Um, we used to talk about 70, 20th hand there. It's, it's a, it's a poor heuristic, but it's, it's, it's something we sort of roughly tend to use. We want to make sure we don't lose sight of continuing to invest to improve in our core products and our core handful of products now. And so we put a disproportionate effort on making sure every pixel is, is, is Perfect or as perfect as we can make it and improving. We want to make sure we have small teams going back to something we talked about earlier who can do big things. So our insurance or investments or lending team when they started was a dozen people. And we're like, go change the world. Go, go come up with something fundamentally different, not incrementally better because incrementally better isn't going to get anybody to notice. So we're going to obsess about You dozen people come up with Fundamentally Better. Only when you get to Fundamentally Better will you get the next round of investment, meaning people. And when you launch, make sure there's customer love because only when you get customer love are you going to get more investment at that stage and, and, um, and on and on. And so, we try to intentionally limit resourcing with a small group of creative Hyper creative, hyper driven people who think they can change the world. And that's, and th…

AI assessment note: “we used to talk about 70, 20th hand there. It's, it's a poor heuristic”

Q Do you want usage? Sorry, I'm, I'm just so interested now. Do you want usage? Cause like on a bank, maybe, you know, they have the card. You don't want them to log into the app. They probably log in when there's a problem or when they, you know, aren't sure about something. How do you feel about usage when maybe it doesn't correlate to customer love?

A Yeah, look, we, we are keyed first on customer love. You know, the first value of Newbank when David, Chris and Ed founded the company, it's one of the reasons, frankly, I joined back to your earlier question, was I, I visited Sao Paulo and in the lobby was this very odd statement. Uh, uh, we want, if value number one, we want our customers to love us. Interesting word, fanatically. That's the word we use. Um, we want our customers to love us fanatically, and that is exactly how I think about it. So we are keyed off on that from day one at Newbank, value one at Newbank. We believe in the context of what we are trying to do in our strategy, that intensity of engagement with our app is actually a leading indicator of that customer love, and we, and we've proven that to ourselves. Um, you're right that in concept, Someone could never use the app or almost never use the app and still love the product because, you know, they're using the credit card or what have you. But for us, what we found is intensity of usage is a lead indicator for customer love. Ultimately, what we care about is customer love, but we track both because we believe one's a leading indicator of the other.

AI assessment note: “we track both because we believe one's a leading indicator of the other.”