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Will Lansing: Power of Data Standards

July 1, 2021
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About the episode

Will Lansing, CEO of FICO (NYSE:FICO), talks with World of DaaS host Auren Hoffman. Will previously served as the CEO of InfoSpace, ValueVision Media, NBC Internet, and Fingerhut and a partner at General Atlantic Partners, a global private equity firm. He also co-authored the DaaS Bible 2.0. Auren and Will dive into data standards, the relationship between analytics and data companies, and how FICO successfully transformed their score into a currency.


Will Lansing

Will Lansing


Episode Transcript

[Auren Hoffman] Welcome to World of DaaS, a show for data enthusiasts. I'm your host, Auren Hoffman, CEO of SafeGraph. For more conversations, videos, and transcripts, visit

Hello, fellow data nerds -- my guest today is Will Lansing. Will is the CEO of FICO -- a $14 billion market cap data analytics company famous for its “FICO” scores. He is also my co-author for the DaaS Bible 2.0. Welcome Will, really excited to talk with you. As you know, I'm like, I'm super obsessed with like standards. And the FICO scores are really kind of the core standard for consumer lending decisions. What are some takeaways? Do you think other people can learn from FICO if they're trying to develop a standard in their own business?

[Will Lansing] Well, I guess we'll spend a little time on that because that's not a simple question to answer. But I would start with, understand what question you're trying to answer. I can't tell you how many times I've spoken to people who've come to me and said, we want to be the FICO score of safe driving, we want to be this FICO score of health care, we want to be the FICO score this and that. And of course, we're flattered that's what they want to be but you know I usually ask them, what exactly are you planning to predict? What question are you going to answer with the score? Because you have to strike a balance between what you're trying to do being narrow enough that it has utility that people would consume the score, and broad enough to you know, to be interesting and turned into a standard. So know what question you're trying to answer, I think is kind of the first thing I would try to do.

[Auren Hoffman] One thing I really like about the FICO score is that it's just super simple to consume. It might be really hard to make, but super simple to consume, right? It's a three-digit integer. It's kind of very simple to understand, both from like a consumer who's actually seen the score and from let’s say, a data scientist that's using that score. How much do you think of success of creating these standards is in the end is again, complex going in, but simplicity coming out?

[Will Lansing] I think that makes a big difference. And you know, it obviously depends on the application. But simplicity has all kinds of virtues. You know, if you think about the way the FICO score developed, initially, it was as a private score done for individual lenders. And then over time, we developed a, you know, more public generic score that was built across multiple credit bureaus data. And the simplicity made it easier for lenders to standardize on it. And later on, this simplicity helped us to build the brand because the consumers could relate to it. 

[Auren Hoffman] There's this like weird trade-off, right? Because the simplicity means like, it won't have everything or it won't like deal with this corner case or well, it's kind of like a 70% solution rather than a 95% solution. Right? Like, how do you think about those trade-offs?

[Will Lansing] It's a great point. And there are trade-offs. And so if you think like, if you think about the lending continuum, we start with scores, where we're predicting a consumer’s propensity to repay debt. Very simple question, how likely are you to repay debt in the future if we lend you money, and we can reduce that to a three-digit number. And it's down and dirty. And it was the evolution of the FICO score took us from a world where you had an old-time bank vice president who knew Johnny when he had a newspaper route, and knew that Johnny was a good egg and knew that was a bad egg. And, you know, that was a laborious, difficult way to make credit decisions. And the whole idea of the score was to simplify dramatically, you know, let a minimum wage person or machine make that decision. And so certainly for lower stakes lending decisions, it's enough and it does the job. But it doesn't always do the job. Sometimes you need more. And so we have software that incorporates other data that's not built into the scoring algorithm. But we have actually tried to strike a balance with the score to your point about the complexity in the score, but reducing it to something simple. We have a score and the three-digit score that everyone knows about, most people don't know that there are 32 reason codes behind the score. And so, under the regulations, if a lender turns down a consumer, the lender has an obligation to explain to the consumer why he or she was turned down. And so we provide reason codes which explain why the score is what it is, so a lender can say they don't even have to post the score to the consumer. Although nowadays they do, but they can just say we've declined to extend to increase your credit line for the following reason. And the reason number 14 is you're overextended. Or you know, you've been late on your payments or whatever it is. So with reason codes give you that extra degree of subtlety that doesn’t show up in the three digits, but they're obviously built into the algorithm.

[Auren Hoffman]  Okay, got it. And you know, you and I, we wrote this piece called DaaS Bible 2.0, about data standards. And, at least for me, it was one of the most-read pieces I've ever written, that we wrote together. And we kind of call it out, this thing where we said, the perfect is the enemy of the standard. That if you're trying to build like the most perfect standard, then it won't become a standard people won't adopt it. And so if you're advising somebody else to kind of think this through, where do you draw the line? How do you know you're at the right kind of point, the 70% point? Or, like, how do you know when to stop, essentially, stop innovating on it so that it will actually get more adoption? Like the, you know, the QWERTY keyboard is certainly a very imperfect thing, or even, you know, even a certain measurement like Unix time, or some of these other things that we call it out in the paper.

[Will Lansing] Well, so if you're thinking about the steps to adopt to creation and adoption of a standard, you know, we said a minute ago, we think step one is know what question you're trying to answer. And, you know, that's obvious. Second step is, takes many, many steps. And that's become embedded, you know, become the center of an ecosystem. I don't think that you can become the center of an ecosystem, if you strive for perfection, you'll never get adopted, you really do have to work your way there. And so, you know, to use FICO as an example, we started out, as you know, a private score, as I said, private scores for lenders, then more public scores available more broadly to multiple lenders. Then we had the same score work across different data sets. But it was all aimed at lenders, it was one constituency, which we understood, and so that was a step. But it was still in a sense, it was imperfect. It wasn't enough to be an industry, it was an industry standard, but it was a, you know, a fungible industry standard. But how do you get even more embedded than that? Well, you continue to innovate. Try to have more precision around the answer, you know, the obvious kinds of things. But where we went with it was, we tried to build more constituencies and get more usage of the score. So for example, lenders, you know, they often securitize their loans. And the investors want to know, well, what's the value of the loan I'm buying. And there's nothing like a FICO score to just, you know, let you know, in a very quantitative way, exactly what it is that you're buying.

[Auren Hoffman]  When they’re packaging those loans, are they just saying, okay, the average FICO score, or the median FICO score, for this package is x? Is that how they securitize it?

[Will Lansing] They can do it that way. Yeah, exactly. At a simple level, that's exactly what they do. And so, you know, so more utility, right? More utility for the score. And a score or any standard for that matter, is, at some level, it's a network business, and it exhibits network effects, meaning that the utility of the network goes up with the number of nodes on the network. 

[Auren Hoffman] So you started with like, the lenders. Okay, now we've got some lender adoption. And then we've got another stakeholder, which is the investors, these people like buying the security, you know, as these things get securitized. Then ultimately, you'd have like the consumers now all that every consumer knows their own FICO score. And so you've got an, I don't know if there are other states, but maybe you have regulators and people in the Fed trying to understand right, so and the more you have, obviously, the more it becomes a standard. Is that the way you think about it?

[Will Lansing] That's exactly it. And that's just how it evolves. Those are the big four constituencies for the score. But they evolved over time. So you know, so it was lenders first, investors, regulators came next really. Regulators, when they're trying to understand the risk profile the portfolio's of the lenders that they regulate, they often ask, you know, what does it look like, what's the propensity to repay debt of this portfolio is essentially with asking in risk scenarios. And, and then finally, the hardest one to crack, frankly, is the consumer. And what we did in that situation was because I will tell you that 10 years ago, the aided awareness on the FICO brand was around 30%. In the US today, it's over 90%. Everyone knows.. And so, you know, how did we do that? The goal was very much to make the FICO score important to consumers, because that was kind of rounding out the ecosystem and getting us even more embedded. I would start with this idea that provide utility, provide value, and all else wonderful follows. And so, that's kind of been our goal every step of the way. With the consumer, what we did was we gave away this part for free. We said to lenders, if you guys are using our score to make lending decisions, please feel free to share it with the consumer, you can put it on their bank statement, put it on your website, let people understand what a FICO credit score means. Not only that, but let's educate the consumers and help them to understand how to improve their score. 

[Auren Hoffman] There’s so many articles on the internet about how do you improve your FICO score. What do you do, what are some of the things? This is very, very helpful for our consumer.

[Will Lansing] We don't want it to be a black box, we don't want it to be a mystery. And so lenders can provide all kinds of benefits to their consumers by sharing the FICO score and their own mechanisms for making the decision about the loan with the consumer. And they're basically, you know, teaching the consumer, you want a good FICO score, so you can have better pricing on a loan or more credit capacity, or whatever it is. And the way you get there is pay your bills on time and don't extend yourself and so on to all those things. And so the education commodity, it's important. But think, you know, I started this with we made it free. We said let's give it to consumers for free. And not surprisingly, many lenders were reluctant to do that, because they thought, oh, we'll never get out of here. If we give consumers FICO score every month, and they become sensitized to how important it is, it's gonna be very hard for us to stop using FICO in the future. Yeah, you know that that's not a bad thing for FICO. And we're providing a lot of utility for the consumer. I mean, it really is good for the lender, it's good for the consumer, but it's all part of becoming more embedded. So we are very much the center of the ecosystem. And now it's not just the lenders, the lenders demand that the consumers demand that the investor demand it. I wouldn't say the regulator's demand it, but they use it. 

[Auren Hoffman] There are these businesses where they get spread to consumers or end users through their clients. And those become really, really powerful. You know, I got an email recently from a bank saying, you can interact with us better through Plaid, and they're basically marketing Plaid to me, which is so amazing and good for Plaid that they're doing that. And obviously you guys have benefited from something similar from all of your clients marketing you to the consumers.

[Will Lansing] Oh, we absolutely did. We had the lenders do it. And we had bureaus do it too. I mean, it's really been great. That part's been really great. Well, you know, brand building is expensive. And the amount of brand building that has to go along with trying to become a standard is not trivial. And so having a strategy where you can provide utility to partners who will then build your brand on your behalf, that's something to think through because we did that.

[Auren Hoffman] Now FICO is an analytics business, right? You have these data businesses kind of rely on analytics businesses to essentially make that data much more useful. And then analytics businesses rely on data businesses as kind of a raw materials that go into the to the analytics. How do you think these companies should think about these kind of this important partnership? And obviously, there's some tension there. And then how should companies navigate that tension, so they really build a real partnership?

[Will Lansing] Well, so with respect specifically to data analytics, I think it's a symbiotic relationship. And, you know, just as better decisions can be made by adding incremental datasets, you know, we start with a data set with tthe most predictive value and then add incremental datasets until it doesn't pay to do it anymore, diminishing returns set in.The same goes with the analytics, apply analytics until you're kind of wasting your time. It's a level of precision you don't need. And then put the two together, and let's use analytics to look across data to get a better answer than we would if we looked at more limited data. So I think it's very much a symbiotic relationship. And the analytics companies benefit from the data, the more data, the better. The data companies should benefit from strong analytics because better decisions will be made. So I think they work together.

[Auren Hoffman] In kind of the FICO case, you've got these credit bureaus. So in the US, we've got TransUnion, Equifax, Experian, right? These credit bureaus, and they're creating this like incredible data of, you know, essentially a payment history of somebody, did they pay their utility bills on time, etc. And then that's kind of like the raw material, right? Then they send that raw material over to FICO. And you're basically creating a prediction on it. Basically they're sending you the past and you're creating some sort of prediction on the future. Their raw materials are not as valuable without you, you're not as valuable without them. Right? Is this the way you think about it? Like there's kind of a, like a virtuous circle, essentially, a flywheel between the two of you guys?

[Will Lansing] There’s absolutely a virtuous circle and the FICO score would be worthless without that underlying data. It can be as simple as that. Without it, we couldn’t even have the score. We actually don't take the data. And we're not a data company. We don't touch the data, we after creating the score, we turn over the algorithm to the Bureaus and they apply it to their own data and then provide it to the lenders. So it's not church and state, but we definitely we're analytics, pure analytics. We don't take the data. And, you know, they’re tremendous partners and custodians of the algorithm, and then they go and use it. But it is very much a virtuous circle. That's right. And, frankly, we're always trying to innovate with the data with our data partners with the Bureau's how do we get to a better score? How do we score populations that have been previously unscored? You know, their people, the credit card payment history is the cornerstone of the FICO score. And it's the cornerstone of our credit lending system in the US. There's a lot of people who don't have credit cards. And so, you know, but they may still be very responsible, and they may be good credits. We just don't know it, because we don't have their their credit card payment history, because it's a catch 22 they don't have a credit card. Is there other data that we can look at? That is evidence of responsibility that that would suggest to us these are people we shouldn't be lending to. And so we work with the Bureaus to try to innovate and identify other data that can get us to the same place.

[Auren Hoffman] That could be as simple as you paid your rent on time, or other types of things like that.

[Will Lansing] Exactly. The rent utility payments, those phone bills, those kinds of things. But it could also be your checking account, you know, how often you overdraw? If you don't overdraw at all, that's evidence of responsibility. So you know, I think there's a lot of different things you could look at, it'll get you two good answers. And so we do partner with our data partners to innovate and build new scores to accomplish new things around the edges that we haven't been able to do before.

[Auren Hoffman] In some ways, this is a very, very key partnership for FICO. You have these credit bureaus, they are core partners. I'm sure you're on the phone with them all the time talking with the other CEOs of these companies. It kind of reminds me of like the 90, you know, the the classic 1990s partnership between Microsoft and Windows and Intel. And, you know, as Windows did better Intel did better, as Intel did better Windows did better. And they formed a very, very important partnership. Do you think of it like in a similar way? And then where's the tension come in? And how do you guys like navigate? Do you guys just like get on the phone with them? Like, hey, let's deal with this. Because there's going to be tension in any relationship that comes up over time.

[Will Lansing] There's, of course is a little bit, but not really. And then we work pretty hard to take the tension out and the friction out and we work pretty smoothly together. We both benefit through adoption of our solution. The way to think about it is if you think about what the Bureaus do and what FICO does together our system, as a platform as a lending platform, if you think about it that way, you know, Bill Gates, I think is famous for a definition of platform business where the platform provider is much smaller than you know, the revenues of all the participants in the platform.

[Auren Hoffman] Like 20x or something like that? 

[Will Lansing] Yeah, and there's something to be said for that in this case, right? Well, you know, we're partnered with Experian, we’re partnered with Equifax, you know, and constantly trying to improve what it is that we do. More precision score, more people, get more credit out to more people, the best way we know how using science and the benefit to the participants in the ecosystem is much greater. Obviously, it's, you know, it's many, many fold the size of our little world. 

[Auren Hoffman] In some ways do you think of it like as least with data and data businesses, there's one side which kind of where we think of as maybe like truth. This happened. This is true. It's clearly happened. It happened in the past, right? And then there's this other side, sometimes they call it religion, but this is what we expect to happen in the future. We don't know for sure. Right? And so we're going to give you some sort of percentage chance that this will happen in the future. So if you think of it as a, if you have data about the AT&T stock price or something like that, we know the ticks of AT&T going back over 100 years, maybe the tick 100 years ago was like one tick per day now. It's like one tick per 10th of a second or something like that. So we know that what the AT&T stock price has done over the last 100 years. Now I'm gonna predict what it's going to do over the next year. That's obviously a prediction and it may or may not turn out to be correct. You're more in the prediction business, and something like Experian, Equifax, TransUnion is more in the kind of here's what happened in the past businesses. Is that that the way you think about it?

[Will Lansing] I think if you asked them, they'd say they're also in the direction of trying to predict, but I would say that it's a fair characterization. Well, everything we do is built around what we think a consumer will do in the future, what's the behavior going to be? How we're going to guess that we're going to guess that based on what behavior we saw in the past. So we look at past behavior, we look at the historical data, the backward looking data, and then we model it, you know, we build models and say, okay, based on that, what do we think will happen in the future? What's so interesting is how consumer behavior repeats itself in so many different ways. And it turns out that good credits, you know, tend to be good drivers, tend to be good students. There's all this correlation with good behavior. So you can look at a lot of different datasets to get to a place. It's quite interesting.

[Auren Hoffman]  Yeah, at SafeGraph we’re data company, but our motto is that we predict the past. So we are clearly about the past, not about the future. And we have so many clients that take our data and then help use our data about the past to predict the future. But we're clearly about this happened. We're going to put our name behind that this clearly happened. But we don't tell you what will happen. That's not what we're about. And in some ways, it is kind of the difference between a data business and the end analytics business. 

[Will Lansing] Yeah, I think that's exactly right. 

[Auren Hoffman] Now, let's talk a little bit about pricing? How do you think about pricing for standards, right? If you charge too much, then it'd be very hard to become a standard. And, of course, once you're a standard, you have some sort of monopoly power to charge more. But if you start to exercise that monopoly power to charge more then people might be very incented to leave you and go somewhere else. So there's almost a check on the pricing power of a standard. How do you think through that at FICO? And how should other standards think through that?

[Will Lansing] I think you're absolutely right about that. It's we live in a world where it's increasingly easy to change a standard if a standard fails you in some way. So I think you have to be mindful of that. And you have to be very careful with the pricing. I would say that...

[Auren Hoffman] Because when I look at it, I mean, I don't know that much about lending. But when I look at the FICO, how much you guys charge for things? It doesn't seem like a lot, it seems like you charge very little. I mean, I maybe I'm talking your book too much or becoming a FICO salesperson. But I feel like you guys have strategically decided to charge less than you can, because you want to maintain this standard. Is it my getting that right? Are you thinking that way?

[Will Lansing] You're absolutely getting it right. So if you start with the premise of the best standard is free. From an adoption standpoint, yes, the best standard is free. And then you move from that to,  well, I'd like I'd like to monetize it a little bit. And frankly, I'd like to be able to fund R&D and innovation and make the standard better. Okay, so now we can charge a little bit. 

[Auren Hoffman] Somebody has to pay to run the standard. There is some always some cost. So sometimes government can run it. Maybe you can set and forget it. Like the meter, we’ve set and forget it. But usually there's some sort of thing where you still have to run and there's some cost around that. 

[Will Lansing] And it might not be a very high cost. But there's something out there. But also, if you want to make it better, I mean, innovation, research cost money. I guess what has guided FICO, and whether it's right or wrong, I can't tell you, it's worked well for us. We've always charged a fraction of the value that we create. So we look very hard at the value gap, what is our customer getting from using it? What's the scale? What are the stakes in the decision that they're making? How much does our standard help them with that decision? You know, how much is our score helped them with that decision, and then we charge a fraction of the amount of the value that they get. And so there's a huge value gap. That doesn't mean that we won't attract competition, because we will, but it does mean that we don't give our customers an easy reason to switch because it's typically not about price. We do the job as well as it can be done. And then they get tremendous value out of it. So that works pretty well. I think, you know, if you're trying to start a standard, the closer to free you can start the better. And over time, I think you characterize it correctly, which is be mindful of the influence that you have, and don't abuse it. Be thoughtful about it, and I think it's worthwhile for us put it that way. It's not that we don't have competition. It's not that other people couldn't develop scores, but there’s so much value in it.

[Auren Hoffman] One of the nice things about the standard, once you have a standard or something approaching a standard is that your cost for acquiring new customers, it goes down dramatically, right. And so you have these CAC costs start going down. And so in some ways, you can afford to keep the prices quite low. Because you don't have all these expensive costs to acquire a company that somebody like traditional a SaaS company may have, where there's all this competition. And so SaaS companies always have to raise their prices, because they have such high costs to acquiring customers. Am I right about that? Or am I a little bit off on?

[Will Lansing] There’s certainly be a benefit. There’s certainly a benefit. For example, we we don't advertise. FICO’s advertising budget is basically zero. Close to close to zero. And yet, when consumers are interested in their score, they'll sometimes come to my And you know, and buy that service from us, the score monitoring service. We have partners who are tremendous at consumer marketing, who are really strong at it, and they've built big businesses, leveraging the FICO score. And the FICO score helps them to attract consumers, but they pay for that there's, you know, there's a cost with that. They spend the money on the marketing, but you know, it's our brand, they're spending money on the marketing. So that works out really nicely for us, where we have an ecosystem where they can benefit by building our brand. That's pretty great. So yeah, we do that. It definitely helps. In your point about the cost of customer acquisition, it definitely goes down. You know, there's obviously some players that are much better at acquiring customers than others. I wouldn't say that we're, you know, at the top of that we don't, we're not really a consumer company.

[Auren Hoffman] Yeah. Acquiring a new bank or acquiring a new like, all these different customers that you have, I imagine that maybe you have every bank, but like I imagine that your your CACs are so low compared to any other comparable software company that's trying to acquire a customer.

[Will Lansing] Well, I would say that banks know who we are. It definitely lowers the cost that customers might otherwise have, although it's not free to cover banks. I'll tell you that.

[Auren Hoffman] Yeah. Okay, good. Well, now, there's somewhat of a difference between a standard and a currency, right? In some ways FICO is both. There's a standard, but then like, because everyone's using it and securitizing everything, it's also kind of a currency that people use. And I guess you could be a standard without a currency, you could be a currency without a standard. But, gosh, if you're both, it's really, really nice. Where does that change? Or how do you think about that curve? And how do you really push that curve along? So you can become both a standard and currency?

[Will Lansing] I think that's right. If there's there's something of a continuum there. And I think that what drives the you know, if you want to call it a currency, that aspect of it, is really increasing returns and network effects that come from having a lot of nodes on the network. 

[Auren Hoffman] All of these different stakeholders. Yep.

[Will Lansing] Yeah, I mean, the reason FICO sits at the center of the lending ecosystem is it provides tremendous utility, what is provided that utility because the number of nodes on that network are just immense. And there's very high utility for all the participants, you know, everyone participating has very high utility. And so you want to call that a currency, you can call it a currency. I mean, the fact is that the utility to each of the participants is what makes it a currency, right? It's a trade, you know, I'm gonna buy a FICO score from you so that I can make a lending decision so I can sell my paper to an investor, and the consumer cares, too. So you know, it all hangs together.

[Auren Hoffman] What are some additional standards in the financial industry that maybe our listeners wouldn't know as much about? Maybe not as, as well known as FICO that you really admire? Or that you like?

[Will Lansing] Well, there's a lot and then there's the ones you know. Of course, you know, Dow Jones is a standard, S&P 500 is a standard, Russell 2000 is a standard. Those are obvious ones. Then you have standards. You know, MSCI has, ETFs are a form of standard and you know, that you can see how that kind of evolves. Moody's and S&P bond readings are standards that have evolved and have an ecosystem built around them similar, actually similar to the FICO ecosystem.

[Auren Hoffman] Yeah, yeah. Interesting. One of the interesting things about like, you know, if you think about the S&P 500, is because so many things are tied to the S&P 500. So many things are judged on that. There's obviously so much money from like BlackRock and Vanguard in that, that it's kind of this like, self fulfilling thing to like, once enough people buy into it. You know, even as imperfect as it is, and you can read a gazillion papers about how the S&P 500 is incredibly imperfect, but it doesn't matter. Like once enough people start buying into it, it becomes this. It almost has its own legs, its own type of thing. And keeps you moving forward.

[Will Lansing] No, that's absolutely right. Actually, it's an interesting case, because you could argue the Dow Jones standard is less perfect than the S&P 500, which is why people use it to be 500, you could argue that the Russell 2000, or the Russell 3000, provides a different level of kind of predictiveness, and a similar kind of a standard. So it is kind of interesting to see how these things evolve. But absolutely, once you're at the center of an ecosystem, you know, it's a, it's a good place to be.

[Auren Hoffman] Yeah, it is really interesting how like, it does seem like the bigger one now is the S&P 500. Whereas maybe 30 years ago, it was the Dow Jones Industrial Average was the bigger ones that people talked about. And then of course, you have the Russell 2000, but for whatever reason, there's like more money and more hedge funds are tracking the S&P 500 today, they are looking at some of these other indexes. And all these indexes are all they're all imperfect, whether it's GDP or whatever index that we're using.

[Will Lansing] What I would point out about these, all these indices that we just mentioned, is that they're all trying to answer some kind of a question. And if you think about the ETFs, you know, I'm going to, I'm going to select a number of companies put into this ETF, and then it's gonna have certain kind of characteristics, it's gonna move, it'll be correlated with oil, or it'll be inversely correlated with oil. You know, someone wants to answer some kind of a question. So we're going to build an index and a score and a standard that does that. And, you know, it all starts with, there's some question that's getting it answered with this bundle of stuff that we've turned into a standard.

[Auren Hoffman]  Outside of like, FICO and, of course, SafeGraph, what are some data companies are handling is companies that you admire?

[Will Lansing] Well, you know, I would start with close to home. I think Experian and Equifax are to really fine companies that have done incredible things where you know, between the two of them, you're talking about 60-70 billion in market cap. That reflects the value that they're providing to their customers. They've done a remarkable job of taking data that used to be throw away data, and doing something meaningful with it. And in the process, you know, building the foundation of the credit system in the United States, which is really the envy of the world.

[Auren Hoffman] Yeah, and increasingly, globally, right. I mean, they're becoming very experienced, huge in Brazil, and, you know, these companies are all over the world. And one of the things I admire about these credit bureaus, I throw TransUnion in there as well, is that they really increase the credit availability to anyone, you know, used to be that, as you mentioned, like, the credit manager would know, Bob, and they know, Bob's family and stuff like that. But you know, they may have their own implicit biases, whether racial biases or just like, you know, religious or other types of biases that they thought Bob was a person of high character, and maybe Jim was a person of lower character. And one of the nice things that these data companies and FICO has done is it's really just democratized access to credit. And imperfect as everything is, it's really, it's really about creating these like much bigger white lists for people to have access to care, where used to be the whitelist was very, very, very small. Now, maybe the whitelist still could be bigger, but it's probably 100 times bigger than it used to be. And sort of way more people have access to these really important tools in life than did in the past.

[Will Lansing] No doubt about it. No doubt about it. I think they've done a remarkable thing. And, you know, it's not just it's not just credit data, right? It's all data companies should be thinking about how we're going to make the world a better place by putting the data into use, having analytics applied answer questions that create markets that you know, make more customers and provide access to all kinds of things. It's not just credit. So there's a lot of opportunity.

[Auren Hoffman] All right now, I got a couple of more personal questions for you as well. Now, I've known you for a while you're definitely one of the I would say the more relaxed CEOs that I've ever met, and no one could argue with your success. I think the FICO stock is up like over 15x, since you joined nine years ago. You've got a huge global operation that you're managing, but you're always pretty, like, chill and relaxed. Like, what advice would you give? I'm certainly the I'm a CEO of a much less successful company than you, but I'm certainly a lot less chill than you are. What advice would you give other CEOs of how to manage like a global operation?

[Will Lansing] You know, some of this may sound very obvious, but I would say, what I would do is I share with you our three corporate values, because I think the whole game is people. You have great people and a company and the company does great things. And for us, our three corporate values are one think like an owner. So we tell our employees, treat this like a family business, think like an owner. If you're doing something that you think is a waste of time, or you disagree with your boss about it, and you're spending money or your own time on it, or the time your developers or whatever it is, your obligation to speak up. This is your business, treat it like family business. So number one, think like an owner. Two, delight your customers in a way that's not exactly unique to FICO. But I think not every company tries to do that as much as it should. And only wonderful things follow from having that kind of an attitude. And then finally, three, our third value is earn the respect of others. And that's really about participating in a company. It's a team effort. And you want to bring people along and you want people to look up to you, for whatever you want to be that person that people call and say, hey, will you come to this meeting with me and help me close the sale. Well, you know, you want to be that person. Yeah. And that combination of values, which are pretty loose, and so much general and motherhood and apple pie, but that combination of values has produced a culture at FICO that's very family oriented, very constructive. Not a lot of politicking not a lot of turf stuff. And it's been really successful for us. So I you know, I invite anyone listening to steal these values and run with them.

[Auren Hoffman] So another question. Like, I've gotten to know you and your wife, Megan. And you guys have a really amazing relationship. And this is more of a personal like, what is the secret? Like, again, I know there's no secret but are you bringing in data? Like you're a very data oriented guy, or analytics into this relationship? And somehow and like mapping that out? Or how do you how do you guys navigate it in a way? For for the rest of us, how can we learn something?

[Will Lansing] I'm not sure we bring we bring some data to it, I would say what we mostly agree to is game theory. So you know, the game theorists who are here know of the boxing and ballet example, which is he likes to go to boxing matches, she likes to go to ballet. He hates ballet, she hates boxing, but they both love spending time with each other. And the answer to maximize utility in that particular game is remember, it's a multi round game. And why don't we both go to boxing now and next time we'll both go to ballet. And then we'll both go to boxing or time after that. And you maximize the utility for the two of you by doing it.

[Auren Hoffman] Just to stress like, if one person really doesn't like boxing, one person really doesn't like ballet, like wouldn't it be better find like a third thing that you both like, kind of like and go together? Or is it really just like, it's really good to delight where if you really care about this, let's do it together. And I want to support you in doing this thing.

[Will Lansing] Yeah, obviously you've done these you both love to do great. Part of the theory in the in the game theory exercise is that you get satisfaction out of doing things together, even if you don't love the thing. But the point is, right, it's what you want to do. The point is, you know, bend on the things that are important to your partner, and hope that they bend on the things that are important to you, and everyone will be happier. 

[Auren Hoffman] All right. I like that. Okay, last question is the question we ask all of our guests. What would you tell yourself if you could go back to Will Lansing in college or in high school to save yourself either time, or money or emotional well being? What advice would you give to your younger self?

[Will Lansing] Well, this is advice for CEOs and all high achievers because we're all you know, we kind of operate at as close to a 10 as we possibly can. We're super driven. Everything we do, we do flat out. And I'm no different. And I got pretty deep into endurance sports 15-20 years ago and did a lot of marathons and triathlons, that sort of thing. And I overdid it, I blew out a few disks I can't run anymore. And if I had to do all over again, I would probably just keep my running to five miles a day. And I'd probably still be running today and be a very happy person. And so I think there is a lesson in moderation. I think you should think about diminishing returns. I mean, you know, especially for people who are wired to try to do everything at the extreme, it makes some kind of sense to take a step back, think a little bit about the consequences think a little bit about diminishing returns. That's what I would tell myself 15 years ago.

[Auren Hoffman]  Oh, this is really interesting. Let's dive into that a little bit more. So, I mean, maybe if it's like a hobby or something like that, you could say, okay, you have a hobby of, you know, doing like crazy triathlons, and we just go a little bit easier on your body or something like that. But like in work, it might be a little bit harder to know, you know, should I work super hard of this thing? Or should I, you know, should I operate 110%? Or should I operate at 70%? And then survive longer? Like, how does one know when to make the trade offs?[Will Lansing] Please don't interpret my advice is, you know, it's okay to be a slacker. And work life balance. You know, while we believe in it, to some extent, I think most of the really high achievers favorite work over work life, you know, it's I mean, I think that I think you have to recognize kind of the stage of life, you're at the, you know, the opportunity and the company you're working in, and what you need to do to be successful. And there's times and there's times to be flat out, and, you know, elevens on the dial for reason.

[Auren Hoffman] Yeah, but the longevity is important. And also kind of like, in some ways, if you think of the investing, investing analogy, you know, that Warren Buffett like investing analogy, like there are some margin of safety also, that you need to have, and whether in your life or in your career, like, it's a little bit easier to quantify investing than it is in life, like, how do you?

[Will Lansing] I work on the theory that, you know, most of us achievers are, you know, are flat out on everything we do all the time. We're kind of wired to do 10 or 11. And so all I'm saying is ask yourself, if every single one of the things you're applying that to it makes sense. You know, there's probably a time to dial it back. It could be for a family thing. It could be an exercise, it could be on something. 

[Auren Hoffman] Okay, this is great. Well, thank you. This has been awesome. I've learned a lot. Thank you. Well, I really appreciate your time. 

[Will Lansing] Great fun to be with you Auren. Thanks.

[Music playing]

[Auren Hoffman] Thanks for listening. If you enjoyed this show, consider rating this podcast and leaving a review. For more World of DaaS (DaaS is D-A-A-S), you can subscribe on Spotify or Apple Podcasts. Also check out YouTube for the videos. You can find me on Twitter at @auren (A-U-R-E-N). I’d love to hear from you.

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