Fintech for Startups and Incumbents

In this episode of the a16z Podcast — which originally aired as a video on YouTube — general partner Alex Rampell (and former fintech entrepreneur as the CEO and co-founder of TrialPay) talks with operating partner Frank Chen about the quickly changing fintech landscape and, even more importantly, why the landscape is changing now.

Should the incumbents be nervous? About what, exactly? And most importantly, what should big companies do about all of this change? But the conversation from both sides of the table begins from the perspective of the hungry and fast fintech startup sharing lessons learned, and then moves to more concrete advice for the execs in the hot seat at established companies.

Show Notes

• Discussion of how pure growth is not always desirable in insurance [2:17]
• How some companies use complex data analysis to target the best borrowers [14:09]
• Approaches that use social pressure to promote profitable behavior [25:26]
• How incumbents might use multilayered branding to mirror the approaches of startups [33:30]
• Turndown traffic and how incumbents can work with startups [39:04]
• Advice for existing fintech companies regarding management and acquisitions [42:19]

Transcript

Frank: Hi, this is Frank Chen. Welcome to the “a16z Podcast.” Today’s episode is titled, “3 Ways Startups Are Coming for Established Fintech Companies — And What To Do About It.” It originated as a YouTube video. You can watch all of our videos at youtube.com/a16zvideos. Hope you enjoy. 

Well, hi. Welcome to the “a16z” YouTube channel. I’m Frank Chen. And today, I am here with one of our general partners, Alex Rampell. I’m super excited that Alex is here. So, first fact — we both have sons named Cameron.

Alex: We do.

Frank: So, affinity there. And then two, one of the things that I really appreciate about Alex — and you can sort of see this from his young chess-playing days — is he understands fintech, and incentives, and pricing, backwards and forwards. And so, fintech has this hidden infrastructure on — how do credit card transactions work, how do bonds get sold, how are insurance policies priced? And there’s deep economic theory behind all of these, and Alex understands them all. So, you’re gonna have a fun time as Alex takes you through his encyclopedic knowledge of how these things are put together. And so, so excited to have you.

Alex: Yeah. It’s great to be here.

Frank: So, what I wanted to talk to you about is, I’m going to pretend to be in the seat of a — let’s call it an incumbent fintech company, right? So, I’m a product manager at Visa, or at GEICO. And I am looking in my rearview mirror, and there are startups in the rearview mirror. And I’m very nervous that the startup in the rearview mirror — exactly as the mirror says — objects in mirror may be closer than they appear. It’s, like — wow, they are catching up to me faster than I really want. 

And so I want to understand, like, what are startups doing? How would they mount an attack on me, the incumbent? And we’re going to talk about, sort of, wedges they can use. And then that’s sort of the first half — like, how are they coming after me? And then the second half, let’s talk about, like — and what should I do about it? So, that’s sort of the premise for our — so why don’t we start with the attacks? Like, how would a startup come for me? And one way they come for me is they come after my best customers. So…

Positive vs. negative selection

Alex: Well, so this is the interesting thing about financial services, in general, because, you know, there’s a Sharp television hanging on the wall, and Sharp knows that they make more money every time they sell an incremental television. So, more customers equals more money — cause-effect. And the interesting thing is that for many kinds of financial services, that is not true, because what you’re really trying to do is assemble a risk pool. And the best example of this is insurance. 

So, what is car insurance? Car insurance has good drivers, okay drivers, and bad drivers. And effectively, your good drivers and your okay drivers are paying you every month to subsidize the bad drivers. The same thing goes for health insurance. You have people that are always sick, you have people that are always healthy. And if you were an insurance company that only provided insurance for very, very sick people, or if you’re a car insurance company that only insures people that get into accidents every day, there’s no economic model to sustain that. You actually have to accumulate the good customers, and use them to pay for the bad customers. And the interesting thing about this is that from the perspective of the good customer, it’s not fair. And I’m not talking morally or philosophically, but just from a capitalist or economic viewpoint. 

It’s like, okay, I want life insurance — and I eat five donuts a day. I just had a doughnut today. I don’t eat five a day, but I have one donut every Friday, as you can testify. And then I have a friend who goes to the gym five times a day, never eats a doughnut. That guy’s probably going to live longer than me. Hopefully not, but probabilistically, he’s probably going to have a better time than I am, in terms of life expectancy. So, why is it that we both pay the same rate? And that just seems unfair to him. Seems great to me, because he’s subsidizing me.

Frank: Yeah, gym guy subsidizing doughnut guy.

Alex: Exactly. Exactly. And that seems unfair. And then the startups can sometimes exploit that psychological unfairness, like, that feeling of unfairness. And it kind of does two things, because from the big company perspective, if you were to take away — think of it as a normal distribution. So, most people are in the middle, and they’re just going to live, whatever — to the average of 79.6 years, or whatever it is right now. Some people are going to live forever. They’re the ones that, you know, have the olive oil, go to the gym, and do whatever it is that they do that makes them live a long time, great genes. And then some people are going to die early. And from the perspective of the startup, if you can get all of the people that are going to live much, much longer, you’re going to be more profitable. 

It’s the same thing for car insurance. If you can get all the people on the good end of that distribution curve, you’re going to make money. And then the nice thing is that if you’re starting a brand new company and saying, “Hey, I give you a loan, if you can’t get a loan.” Who’s going to sign up for that? People who might be bad. If I say, “I’m going to give you insurance, if you can’t get insurance,” who’s going to sign up for that? The people that are eating all the doughnuts. And that might not be very good. So, it actually has this nice, kind of, symbiosis between — if you do it correctly, you get positive selection bias, in that you establish a new criteria. Part of that new criteria is based on data, but part of it is based on psychology. 

But the psychology is, “I’m treated unfairly, I want to be treated more fairly.” That yields a lower price for people, for [a] pretty demand-elastic product. So I say, “I can get life insurance at half the rate because I’m going to the gym? That sounds great. That sounds fair.” But to answer your question, what the incumbent might be left with, is not half of the number of customers. That could be the case — it could be half the number of customers — but it could be half the customers and all of them are entirely unprofitable.

Frank: Right. They took over all the profits. They didn’t have to take all your customers, they just had to take the good ones.

Alex: Right. So you’re actually — and if you just take — and the funny thing is that because it’s not — it’s not like I want to get, oh, “GEICO has X million customers, I want X plus 1 million customers.” You actually might want 1/10 as many customers as GEICO, because if you can just get the good ones — I mean, what if you give people a 50% discount, not a 15% discount, like GEICO advertises about — but a 50% discount on their car insurance. And these are the absolute best drivers in the country. How many claims do you have to pay out on the best drivers? You might have to pay out nothing, literally nothing. And if you have to pay out nothing — and there are these mandatory loss ratios for different insurance industries. So, I don’t want to get into that. 

But imagine that, unregulated, you can pay out nothing — consumers feel like they’re treated very fairly, they’re rewarded for better behavior. This begets positive selection and not adverse selection — then you’re going to have the most profitable lending company or insurance company in the world, because it really is a unique industry where more customers is actually worse than less but more profitable customers, because each incremental customer is like a coin flip of profit or loss. Might generate profit, might generate loss. And that’s not true for the vast majority of industries. Like, Ford never sells a car saying, “Maybe we’ll lose money on this customer.”

Frank: Right. Right. They just, like — I need everybody to buy a Ford F-150.

Alex: They might…

Frank: If you don’t buy an F-150, I need you to buy — this other thing, that’s the Expedition or whatever, yeah.

Alex: They might lose money on the marginal customer until they hit their fixed costs, but they’re never going to have a coin flip of when they sell the car — “hmm, maybe we shouldn’t have sold that car.” But that’s what every insurance company has when they underwrite a policy. That’s what every bank has when they underwrite a loan, so…

Frank: Yeah, auto insurance companies need to find people like me. I have this old Prius, right? First, it’s, you know — [a] hugely reliable car, and then I drive like a grandma because I’m optimizing for fuel efficiency. So, you know, I rarely go above 65. And so, like, rarely say — if I’ve never filed a claim. They need more customers like me, and that’s what drives the profits, right?

Alex: Yes.

Frank: Because there’s no payouts?

Alex: Well, not only does it drive the profits, it actually subsidizes the losses — because there are a lot of people who are the inverse of you. And you’re paying for those people, and the transfer mechanism is through GEICO.

Frank: Yeah. I saw an ad in my Facebook feed recently for Health IQ. And I think they’re doing something like this too, right? So, I think the proposition was, “Hey, can you run a mile in less than nine minutes? Can you bench press your own weight or something like that?’ There’s all these, like — ooh, healthy people. And is that the mechanism they’re exploiting?

Alex: It’s exactly that. I would say the first company to probably do this on a widespread basis in fintech land was SoFi. And SoFi said, “Hey, you’re really smart.” They actually coined this term — they call it the HENRY — High Earning Not Rich Yet. Because if you looked at how student loans work, it’s like, everybody gets the same price on their student loan. It doesn’t matter what your major is, it doesn’t matter what your employment…

Frank: Prospects.

Alex: …thank you, what your employment prospects are. Everybody gets the same rate. You get this rate, you get this rate, you get this rate, because a lot of it is effectively underwritten by the U.S. government. And that’s not — so, think about it, again, from the twin pillars of psychology. Where, I mean, psychology of the borrower — like, how come I’m paying the same rate as that person who’s going to default? That’s just not fair. I’m never going to default. In fact, I’m going to pay back my student loans early. So, that helped.

And then, again, positive selection versus adverse selection, because — and actually, refinance has this concept, in general. Because I would say, if you’re planning on declaring bankruptcy, or if you’re saying, “I’m going to join Occupy Wall Street and never pay back my loans, and I hate capitalism.” Why would you go refinance? It just doesn’t make sense, because you’re just going to default. So, if you raise your hand — and actually, it’s interesting, even on the other side, there are a lot of companies in what I would call the debt settlement space. And this is something that most people don’t know about. But if you listen to some interesting talk radio, you’ll hear all these ads for debt settlement. 

And what is debt settlement? It’s saying, hey, do you have too much debt? If you call us, we will negotiate on your behalf and pay off your debts, and then you just owe us. And you, kind of, need this intermediary layer, because imagine that you owe $10,000 to Capital One, and you can’t pay it back. And you call Capital One, it says press 1 for your balance, press 2 to get a new card mailed to, press 3 if you don’t want to pay us the full amount and want to pay us less. Everybody is going to press 3, right?

Frank: Everybody press — this is why they don’t offer that option. <laughter>

Alex: They don’t offer that option, nor would they ever. However, on talk radio — and this is very big in the Midwest — like, you’ll hear, you know — Freedom Financial. They call Freedom Financial, and we will settle your debts for you. So, they call Capital One and say, “Look, Alex can’t pay you back. We’ll pay you $2,000 right now, and then you’re going to get rid of the loan.” Like, “Well, we’re not happy taking 20 cents on the dollar, but it’s better than 0 cents on the dollar. Fine. We’ll take it.” And then you owe Freedom Financial the 20 cents. But why do they feel comfortable underwriting that? Because you [raised] your hand, you know. You said, “I want to get out of debt.” And that’s positive selection bias right there. 

Because people who are just deadbeats — because, you know, behind every credit score, if you think about how that works — it’s willingness and ability to repay. And the psychological trait of the willingness is, in many cases, as important as the financial constraint of the ability. So, if I owe $1 million to somebody, and I only make $100 a year — doesn’t matter how honest I am, I can never pay that back. It doesn’t matter how long — I mean, I could live 10,000 years and I guess I could pay it back. But otherwise, I can’t pay that back. But the willingness to repay is interesting. And that’s very important. 

And that’s again, this kind of psychological trait, that’s captured in this idea of positive selection. So, what does SoFi do? They kind of, again, hit this twin pillar, which is — I want to only get the good customers. I’m going to reprice them and steal them from the giant pool, that, again, normal distribution. These are the losers, these are the whatevers, and these are the people that you have no risk on whatsoever. Let’s steal all of these people over here. And it makes them feel good. It’s a better marketing message, because it’s differentiated. Like, how do you compete with everybody? It’s like, “Hey, we’re just like Chase but smaller, and a startup, and not profitable. And you probably shouldn’t trust us.” Bad marketing message. Good marketing message is, “You’re getting ripped off. We’re going to price you fairly. Come to us.” So, SoFi did this for lending, and then…

Frank: And what did Health IQ do?

Alex: So, Health IQ did this for health, really for life insurance. So, they started off with a health quiz. Because, I mean, it seems almost self-evident that healthy people are healthy — I mean, it’s a tautology. Like, healthy people are healthier than not healthy people. But can you actually prove this from a life expectancy perspective? So, they started off with just recording data, and then building a mortality table. And it turned out that, you know, what I would assume is a prima facie case, turned out to actually be correct — which is, these healthier people do live longer than not healthy people. And then they turn that into both a positive selection advertising campaign, which differentiated them from a brand perspective — but also left them more profitable. 

So, what they do is they say, “Yeah, can you run a nine or an eight-minute mile? Can you do these things to prove that you’re better than everybody else?” And why is that important? Well, from their own balance sheet or profitability perspective, they want to get these good customers. Versus, you know, a brand new life insurance company that said, “Hey, life insurance takes too long to get, it’s a big pain, and it’s expensive. We’ll underwrite you on the spot in one minute, no blood test.” That’s gonna be adverse selection. That’s like, ooh, I think I’m gonna die soon. <Right.> I want to get — and everybody rejected me for life insurance. I’m going to that company, as opposed to here, they’re only getting the customers that kind of hit — they think they’re going to hit the underwriting standard, which is great. They think it’s fair. So, it’s a differentiator from a brand perspective, and then it turns out that again, each marginal customer in insurance is kind of a coin flip. They’re getting a weighted coin, because they’re only getting people on the far right side of this normal distribution.

Using data to find the best customers

Frank: So, wedge number one is exploit psychology, right? Positive selection, rather than negative selection. And what you’ll end up with, because of this sort of unique dynamic of the fintech industry, is you’ll end up with the most profitable customers. What’s wedge number two? We’re going to talk about, sort of, new data sources, and what startups can do to sort of price their products smarter than incumbents.

Alex: Right. So, imagine that you have a group of 100 people, and of the 100 people, half of them are not going to pay you back. So, think of this as the old combinatorics problem of, you know, bins and balls. You’ve got this giant ball pit, you scoop up 100 balls in your bin, and half of them are going to be bad, half of them are going to be good. So, what’s a fair rate of interest, if you’re a lender, that you have to charge this whole bin, if half of them are going to default, and you assume that you can’t lose money? The answer is going to be 100%.

Frank: Oh right. Because half of them, you have to make up for all the deadbeats.

Alex: Right. So, half of them — you know, you lose all of your money, half of them, you double your money. So you’re back to square one.

Frank: Now you’re even.

Alex: Now you’re even. So, the problem is that that’s not good. Because well, in the United States, you can’t charge 100% interest. It’s regulated.

Frank: Right, right, illegal, step one.

Alex: It’s called usury. There are other parts of the world, again, illegal. Step one. Europe — so, that’s a problem. But, what if you can use different data sources to — again, it’s not positive versus adverse selection, as in for some of the insurance companies, but it’s saying, can I collect more forms of data? So that instead of saying the only way that I can make my operation work is to charge an interest rate which actually turns out to be illegal — can I come up with more data sources that effectively — even though discrimination sounds like a terrible word, and it’s certainly used in that construct. If you discriminate against criminals, that’s fine. I mean, some of the people that try to take advantage of lenders are actual, like organized crime. You don’t want them in your bin, you want to throw them out. 

How do you take more data sources and actually start measuring this? And the interesting thing here, and it’s somewhat unfortunate — but you have a giant market failure happening in many different regions of the world. Because in the United States, like, the top interest rate that you can charge — it’s regulated on a state by state basis — but Utah has a 36% usury cap. So, a lot of people export that cap. That’s a lot less than the 100% that I was mentioning. And there are lots of ways of, kind of, gaming that system. You charge late fees, and you charge this fee, so it actually might end up looking more like 100% or 200%. So, you can’t charge more than 36%. 

And then you actually can’t use certain types of data, if they are prone to having an adverse impact. So, if you think about how machine learning works — I always kind of describe it somewhat over simplistically as linear algebra, where I have — here’s every user that I’ve ever seen, here’s every attribute that I’ve ever measured. And what I’m looking for is strange correlations that I can’t even explain. So, I’m going to ask you — I’m not even asking you a lot of these things. It’s like, how long did you fill out this field for on my loan application? Did you enter all caps or not all caps? Like, just all of these different things.

Frank: Did you take the slider on “how much do you want?” and jam it all the way to the right. All of these things.

Alex: Right. I can ask you, do you have a pet or not? That might be interesting. I don’t know if that’s a leading indicator of defaults or not, but I want to collect all these different variables. And then at the end of the day, I’m going to see “default” or “not default.” That’s the output. And then I’m going to see what’s correlated with that. And it’s a little bit of this, it’s a little bit of that — I can’t explain it, but the computer can. Now, the problem is that in the United States you actually can’t do this, because it might have an adverse impact. And what does an adverse impact mean? There actually was outright and terrible discrimination in lending in the United States. Well, there’s, unfortunately, terrible discrimination in many things in the United States — but lending was one of several, or one of many. 

So, imagine that I said, “Are you married or not? Oh, you’re not married? I’m not going to make you a loan.” Well, that’s illegal now. “Are you this race? Oh, I’m not going to make you a loan.” Well, that’s illegal now. So, what did people do to get around — the people that were actual racists? Or actual, like — maybe they weren’t racist or discriminatory at heart, but they were picking up on cues. They’d say, “Oh, what part of town do you live in? Oh, you live [in] that part of town. Well, that’s like 100% correlated with this race, or this gender, this, that. I’m not going to make you the loan.” So, the law was strengthened. So, there’s a law called Fair Lending in the United States. And then one of the components of it, is this idea called adverse impact. It’s different than adverse selection. It’s saying, I don’t care what you said you did for why you rejected Frank for a loan. If it turns out that everybody in your reject pile has a disproportionate, you know, gender ratio, race ratio — something like that — I’m going to assume that your underwriting standards are having an adverse impact.

Frank: So, you as a bank, couldn’t say, “Hey, look, I asked him if he had cats, and I’m using that to make the loan decision.” If it turned out that having cats was correlated with being a particular race, they couldn’t use the “cats” answer to deny you a loan.

Alex: Correct. Because that was — and in all fairness to the law, this is what people use with your geography. “What zipcode do you live in? Oh, you live in that zip code?” One hundred percent you are a member of this particular race, and the intent all along was to discriminate against people of that particular race. But now, instead of using loan officers that use — you know, God knows what to decide — do I want to make you the loan or not? You’re using a computer, you can look at the code. 

So, I think there is a lot of — there are some anachronistic laws that have to catch up here. But let’s take an area outside of the U.S. to answer your question, where perhaps you don’t have interest rate caps. Because, you know, the thing that a lot of people say, “Oh, you know, 200% interest is terrible. Five hundred percent, that sounds awful, you should go to jail for that.” But what does APR mean? APR stands for annual percentage rate. And what if I’m giving you a four-day loan? So, I say, okay, I’m gonna loan you nine dollars right now, you don’t look very trustworthy. I want you to pay me back $10 on Monday.

Frank: Yeah, that doesn’t sound so bad. It’s a buck, right? Yeah.

Alex: Yeah, it’s like, you’re gonna pay me a dollar. But what is that on an APR basis? That’s like 9,000%. I made that up. But it’s probably about that, right? Because it’s 10% every four days,or every three days, 10% every three days — and that accumulates. Like, that’s a lot of money or a lot of interest on an APR basis. But it’s the wrong metric because, effectively, it’s like trying to figure out what your marathon time is based on your 100-meter dash. Like, the winning marathon time would be an hour, and that’s not true. We know that nobody…

Frank: Nobody can do that.

Alex: …can do run a two-hour marathon, right now. Yeah. So, maybe Angela can.

Frank: Maybe Angela.

Alex: So, there’s a company that we invested in called Branch. And what they’re doing is, they just collect every form of data possible, and they look for these strange correlations. And the interest rates on an APR basis might be high, but they’re really charging, like, a dollar to the lenders.

Frank: And these are small loans, right?

Alex: They are very, very small loans. So, I loan you — and actually the other interesting — like, one of the nice data points that they’re accumulating over time that is a really interesting idea, I think — it’s not new. In fact, it’s almost “Back to The Future” old, where they loan you a dollar, if you pay it back, they loan you two dollars. If you pay it back, they loan you four dollars. If you pay it back, they loan you $10. And they ladder up your credit, and they keep that information proprietary to them. Because induction turns out to be a pretty good formula for figuring out not so much the ability to repay, but the willingness to repay. You’ve established a pattern of willingness to repay. But they also look at “where were you today?” And again, you provide all of this information in order for them to crunch this — in order for them to give you a loan at, ideally, a lower rate. Because the more information — because it’s kind of twin pillars, right? The less information we have, the higher the rate that we have to charge. Not because we’re evil, but because otherwise, you’re going to have a market failure, like you have in lots of the…

Frank: Yeah, the bin and ball problem, right?

Alex: Exactly.

Frank: Because you have no idea how many deadbeats.

Alex: Exactly. And if I don’t have any idea, I either have to charge a high rate or not charge anything at all. And “not charge anything at all” doesn’t mean, like, everybody gets a 0% loan. It means I don’t make any loans. And like both of those are bad outcomes. The better outcome is, you accumulate more data, and you figure out “here are the good people — let me not accept the bad people.” Because again, the way that the good people end up paying more money is if the company starts accepting more bad people, because it goes back to what I said at the beginning — which is, more customers, in this unique industry, often is bad if you don’t understand how to select them correctly. And for many of these new-fangled lending and insurance companies, the default customer is going to be adversely selected. Because if you’re a new lender, and you have no underwriting standards, basically, you’re advertising free money — never pay us back. And those are the people that will be attracted to you, both the criminals and the non-criminals in droves.

Frank: Yeah. So, this is, sort of, startup attack wedge number two, which is — I’m going to generate a new data source that allows me to price my product in a way, or reach a customer that a traditional company would never even try, or they don’t have the data source, so they have the bin and ball problem. So, what are the types of data that Branch went to go get to try to figure out — should I give you a loan of a dollar or two?

Alex: Well, the other type of data — so, Branch was somewhat unique, in that they said, “We’re going to get data from your phone.” And it seems odd — it’s like, most lenders in the developed world — or not developed versus undeveloped. It’s really, like — with developed credit infrastructure. If they look up…

Frank: If there’s a credit bureau.

Alex: They look up your credit report. If it’s good, they make you a loan. If it’s bad, they don’t make you a loan. It’s actually not that hard. And there are all sorts of nuances that you can layer on top, but this is how it’s been working for a long time in the United States as an example. Whereas there, it was like, “Okay, where did you work today? Did it look like you worked today?” So, it was stuff like that. And even like, how many apps do you have on your phone? Like, weird stuff that you would never assume actually has any kind of indication of willingness or ability to repay, but in many cases, it does. Like, are you gambling? Well, if you have a gambling app on your phone, you’re probably gambling. Maybe that’s good. Maybe it’s bad. It’s actually not making human judgments — and it’s also not looking at any one of these unique variables as a unique variable. It’s looking at them in concert, and then correlating them with these outcomes, so really observing the outcomes and then linking them back to all of these different inputs.

Frank: Yeah. I remember talking to the team when I was researching my last machine learning presentation, and the fascinating things that I found were — if you’ve got more texts than you sent, you were more creditworthy. If you had the gambling app, you were more creditworthy, rather than less — which is not kind of what you would expect. If you burn through your battery, you were more likely to default, right? So, like, all of these things where a human, or loan officers, would never really guess, right? And they probably would guess the wrong way because they wouldn’t guess…

Alex: Because many of them are counterintuitive. And then many of them, they’re not unilateral. Like, so it’s not just — I mean, I don’t know. But it’s not just the battery thing. It’s the battery thing with this, with that, with that. If you think…

Frank: Right, different combinations, right?

Alex: It’s like, you know, humans can only really observe three dimensions plus time — so I guess four — and these are, you know, 9,000-dimensional problems. So, it’s much, much more challenging for humans to really grok.

Influence of social pressure

Frank: Yeah. Got it. So, that’s the — sort of the second category of attack. Which is, you generate a new data source, and then that allows you to price or find customers in sort of a more cost-effective way. Let’s talk about the third, which is around, sort of, fundamentally changing behavior. So, why don’t you talk about — maybe Earnin is a good example of this?

Alex: Yeah. So, if you assume that humans are static — so they’re born — both of our Camerons were born, and their DNA is set upon birth. Maybe it changes a little bit with some mutations from some gamma rays here and there. But it’s set upon birth, and then human behavior never changes. And that’s one way of looking at things. Then you think about adverse selection versus positive selection. Good drivers are always good drivers. Bad drivers are always bad drivers. Let’s just get the good drivers. 

So, the other category — and it’s not just that these other two groups don’t do this. But if I look at a company like Earnin, most payday lenders are reviled, because they charge high fees, they don’t educate their borrowers very well. Now, it actually provides a valuable service, because if I’m getting paid next Friday, but my rent is due today, and I don’t have money, do I want to get evicted? No. I want to get paid right now, and the only person that does this is the payday lender. But the payday lender is competing with other payday lenders for advertising in the local newspaper, or something. And if they’re able to rip me off more, not because they’re evil but because they have to afford the advertising spot — they’re now incented to do so. So, it’s a vicious cycle. 

So, let’s talk about Earnin. So, what Earnin does, is they say, okay — we know that you’ve worked this long. So, again, new data source — because the phone’s in your pocket, and you work at Starbucks, and you’re getting paid hourly, and we’ve seen the phone in your pocket, or in your locker in the Starbucks office, you know — by the barista counter, for eight hours. So, you worked, we saw your last paycheck hitting your bank account, we know that that’s where you work. We’re not taking your word for it. We have real-time streaming information about this. And now we will give you your money whenever you want. Not money that you haven’t earned yet, but money that you have earned, but you actually haven’t gotten paid for yet. And then you can tip us. There’s no cost. If you want, you can tip us.

Frank: No interest, no fee, no — huh.

Alex: Nothing. If you want to pay us nothing, that’s fine. I mean, we would appreciate it if you pay us something, because obviously, we’re providing a valuable service for you. And then you can even give tips for your friends. There’s this community that’s really emerged of people on Earnin. And actually, if you look back at different business models — but this idea of microfinance, in general. So, if you think about Muhammad Yunus and what he did — this idea of, can you encourage people to pay back loans using social pressure? So, again, not adverse selection versus positive selection, but actually trying to force everybody down positive behavior.

Frank: Yeah. Let’s get the community to encourage repayment.

Alex: Right. Because then, saying — or, like, let’s get the community to encourage people actually driving safely, because there’s underwriting at the time of admission. There’s underwriting based on ongoing behaviors. So, like, many of the car insurance companies that are brand new are saying, “We will re-underwrite you, like — yeah, if you drive like Frank when you signed up, great. But now you switched into, like, race car driver mode, and you were trying to hack us, but we’re actually monitoring your speedometer at all times. So, guess what? You got a higher rate now. So, that might encourage you to drive safely.” 

If I’m Frank, and I drive safely in my Prius, but then I decide — and then I got a really good rate on my car insurance as a result. And now I’m like “Aha, I gamed the system, now I’m going to drive like a maniac.” Well, the nice thing is that you can make underwriting dynamic, and you can say, “All right, we’re actually going to re-underwrite you every day.” So, we have the positive selection to try to attract the Franks. We have the continuous evaluation to try to encourage the right behavior, post-Frank signup — and also to stop the gamification of — it’s like, I’m going to pretend to be safe and then be like a maniac. But then how do you actually get — what if Frank was a bad driver initially? Doesn’t fall into my positive selection loop, but I still want to try to make Frank a better driver.

Frank: Yeah, if I can turn him into a good driver, he’d be profitable. So, what can I do?

Alex: Right. Because that’s the flaw with, kind of, wedge one and wedge two, of like, creaming the crop. Really wedge one, which is we’re going to cream the crop. We’re going to do what SoFi did, we’re going to do what Health IQ did. I mean, it’s a great strategy, but the rest — again, if you assume that it’s all nature and there’s no nurture, then perhaps there’s nothing you can do. But if you can actually try to nurture better behavior, you actually see better — you do see better behavior, and then the profitability goes up. And the interesting thing there is that you’re still finding mispriced customers, but you’re actually helping turn them into correctly priced customers. 

So, you know, somebody, like a bank would turn away that customer, and say, “We don’t want them because they have a 500 FICO,” which is really bad. And then you have to figure out — and as with all of the new startups that are saying, “We only want the best customers — we want to leave the banks with the bad customers.” But it’s kind of the twin pillars of — can you identify something that’s below that credit score, or below that driving score, or something? And then can you encourage positive change? And if you can, then you can start actually creaming the crop of the bottom half of the customers. Not even the bottom half, it’s the customers that are just neglected, because nobody wants to underwrite them. And then you do that, you take them on, because you have a secret to change their behavior.

Frank: Right. You’re seeing a lot of companies that, sort of, are using behavioral economics research to figure out, “How do I nudge people into better behavior?” And so, this would be an example of how you’re trying to change behavior to get the profitable customer.

Alex: Right. So, you know, there is one company in the lending space a while ago called Vouch — I think ultimately, it didn’t work. But when you apply for a loan, it actually, kind of, taps your social network, and it requires that they do a reference for you. Either a reference, in terms of like, yes — Frank is a good customer, you can trust him. And even kind of a co-commit. So, I’m getting a loan for $1,000, and you say, “Yeah, Alex is okay.” Or I’m saying, “Frank is okay. And if he doesn’t pay you back, I will put $100 in, because that’s how confident I am.” And it’s not all $1,000, but it’s $100. 

And then you’re my friend — I go bowling with you. We go take our Camerons out together. And if you don’t pay back this $1,000 to this, kind of, faceless, large, evil corporate entity — not really — but if you don’t pay that back, I’m on the hook for 100 bucks. I’m not going bowling with you anymore. So, there are other things that are really interesting to try to encourage the correct form of behavior, when — and, actually, part of it is just making it personal. Like, this was the whole Yunus theory. Which is, if you are, kind of, held accountable by your peers, that is so much more powerful than getting a collections call from Citibank. Like, you’re like, “Ooh, that’s the collections number?” iPhone block. Done. But how am I going to block my friends out?

Frank: Right. If Alex calls me and said, “You really got to pay that loan back, otherwise, I’m out 100 bucks,” right? That’s much more powerful. I mean, this has worked great for Omada Health in a different domain, right? Which is, if you are trying to get a pre-diabetic patient not to get diabetes, the most effective thing to do is lose something like 6% or 7% of your body mass. And the way they do it is they get you into a group. They mail everybody a scale. Everybody sees your weight in the morning, right? Like, that’s a powerful motivator.

Alex: Yeah, I mean, this stuff — psychology is very powerful. So, there are a lot of tricks that you can use here. And if you understand the impact of them, you actually have to reassess your entire branding and customer acquisition strategy.

Frank: Right. Right. All right. So, remember, I opened up pretending to be the product manager at Visa. And now we’ve gone through all of these three categories of how the startups are coming for me — and like, I’m starting to sweat here, right? They can come and get my best customers, they can generate new data sources that I would have a hard time doing. They can actually even go after sort of worst customers, change their behavior, turn them into profitable customers. I’m scared now. Like, what in the world should I do? Like, you’re in my seat — you’re the head of innovation, or head of strategy, or head of digital at one of these big fintech companies — what should I do with respect to startups?

Alex: Well, I think it’s actually very hard for a company that’s trying to be all things to all customers. Because, if you look at what SoFi is — look at SoFi’s brand. Brand is, you know, we are the high — like, if you’re great, you’re good enough for us.

Frank: If you’re HENRY, right?

Alex: If you’re a HENRY, you’re good enough for us. Health IQ. If you’re healthy, you’re good enough for us. So, on that sector of the curve, you know — how does GEICO say, “Hey, if you’re a good driver, go to this special part of GEICO. If you’re a regular driver, you still save 15%. If you’re a bad driver, and you had a DUI, well, we can cover you over here.” It’s lost in this, kind of, giant GEICO gecko marketing message. So, in many cases, it actually helps to have sub-brands and divide this up, which is somewhat anathema to a lot of companies that want to say, “How do we get as much efficiency and synergy as possible? We’re going to have one overarching brand.” And you know, one of my favorite examples of this — kind of, different industry — but the highest end of the highest end of jewelry is Tiffany & Co. Or, one of the highest and the highest end.

Frank: Beautiful, beautiful rocks.

Alex: And for a long time, it was owned by Avon.

Frank: No, really?

Alex: You know, the Avon lady, Avon. And if Avon bought Tiffany, which they did, and they said, “Okay, we’re gonna rebrand Tiffany & Co. as Avon,” like, that doesn’t work. Like, you’re not going to get 80% gross margins on whatever they sell at Tiffany & Co for…

Frank: Breakfast at Avon’s just doesn’t have quite the right ring.

Alex: It doesn’t work. And then for Avon to say, “Okay, you know, the door-to-door salesperson or sales lady with the pink Cadillac that’s going around, like — we’re now going to have her push, you know, $2,000 bracelets, as opposed to the normal $10 fare.” Like, that’s not going to work either. But it actually can make sense, if you want to just appeal to more customers, you have different brands, and you don’t want to all suck them together. So, you can imagine instead of having, you know — GEICO could be your generic brand, but then you could have — I think I mentioned this to you once before, a friend of mine is Mormon. Doesn’t drink alcohol, and says we should have Mormon Insurance for cars, because it’s just totally unfair. Again, going back to the psychology point, like — why is it that I’m paying for the drunk idiot that goes through the stop sign? I don’t drink, I can prove that. I will never drink, I have a million friends just like me that will never drink. We should all get car insurance — we should all get a 40% lower rate. 

Do they think of GEICO when they go there? Maybe they could. But it could be like, Mormon Car Insure- — sorry, I’m not good at branding. But you could have a separate brand for all of these separate subgroups, and have the same underlying infrastructure behind all of them. But, again, part of this is just how do you brand and how do you market effectively? Because if you look at the efficacy of Health IQ ads, or the efficacy of SoFi ads, there are so much higher — because again, you have this large group of people — or in many cases, small but valuable groups of people — that feel like they’re being treated unfairly. So, yeah, GEICO is save 15% on auto insurance, click here. Mormon Car Insurance, advertises to LDS members in Utah, shooting fish in a barrel — that’s going to have a dramatically higher click rate. And then many of these products are also very demand-elastic. So, I’m not saying save 15% on car insurance, I’m saying save 80% on car insurance. It’s very easy to do. Click here, positive selection bias. That’s going to work better than GEICO, but we also have something for Mormons, too.

Frank: Right. Yeah, the goal is to find the LDS’ers and the hypermilers who are really safe, etc., etc., right? And so it’s very counterintuitive, because if you’re at a big company, you’re thinking scale — how do I get the next increment of revenue, growth, or profit? And you’re saying, actually go the other way. Don’t try to make your single brand bigger. Try to think about a dozen sub-brands, each going after sort of the perfect market for them. How do you positively select into a sub-market?

Alex: Well, the other side effect of this is that, you know — part of the asymmetric warfare that some of the startups have is that, if you wanted to kill GEICO, you wouldn’t steal 100% of their customers. Because if you did that, that would almost be too obvious. You’d steal 20% of their customers, but only the good ones. So, imagine that GEICO could actually devolve, or evolve — depending on your point of view — into 10 sub-brands. There’s no more GEICO. But it’s just, like, the 10 sub-brands basically select for the right types of customers, or even help judge and improve behavior from other subsets of customers. And then expel the 30% that are just bad news. And if you can expel the 30% that are bad news, you might say, “Okay, well, all of this de-synergy of going from 1 brand into 10 sub-brands — well, that was idiotic, because now I have fewer customers.” But actually, no, it isn’t. Because you might have fewer customers, but it’s not like selling widgets, you’re selling probabilistic widgets — where, in many cases, you have negative gross margin when you sell a widget. So, it’s important to figure out how do I get the good ones, keep the good ones, and then get rid of the bad ones?

Branding for incumbents

Frank: Yeah. So, that’s one strategy, which is, sort of, sub-brands — and, sort of, customer segmentation. What if I’ve been told by my management team, “Go find a bunch of startups to work with,” right? Sort of, somehow figure out a marketing or co-selling relationship so that we can start experimenting with some of these new models, and we can keep an eye on the startup community. So that maybe, you know, we can put ourselves in the best place to buy them if it turns out working? Is there a way to do that?

Alex: Well, there are many ways to do that. Probably the easiest way that is often counterintuitive for a lot of big companies — is I call this the turndown traffic strategy. So, Chase turns down a lot of people for loans, either because —again, it’s the bin and ball problem — where it’s like, well, you might be good, you might be bad. Sometimes it’s not even that. It’s like, we think you’re good, but we just can’t profitably underwrite a $400 loan. But Chase has all the traffic. So, what is turned down traffic? It’s saying, “Okay, we rejected you. Hey, here’s a friend that you might like.” So, this is not cream of the crop — this is the bottom tier on the ingestion point for a big financial institution saying, we don’t want you — which is kind of a mean thing to say. A way to ameliorate that potentially is saying, “We don’t want you because we’re not smart enough to — hey, sorry, we’re working on it. All our systems are down. But here’s a great startup that does.” Now, why would you send customers to a startup? Well, the number one thing — GEICO spends $1.2 billion a year on advertising. It’s really hard to compete with that…

Frank: A lot of spend.

Alex: …from a — so, if I could not spend a dollar of advertising, but give 90% of my net income to GEICO as a startup, I still might make that trade. I mean, we don’t always like this, because we want to see — do you have your own acquisition strategies, your own acquisition channels — you’re not dependent on the big company. But from the big company’s perspective, turndown traffic is often brilliant. Because it’s saying, “Here’s somebody that knows how to underwrite better than we do, or more profitably than we do. We’re going to send our customers” — we said, you know, otherwise, what happens? 

And this is what I think Amazon got right in an area where everybody else got this wrong. Amazon said — okay, you’re on Amazon’s website, and you’re looking at the “Harry Potter” book. And then right next to our “Harry Potter” book is an ad for Barnes & Noble for the “Harry Potter” book. Barnes & Noble is like, “This is amazing! We can buy ads on Amazon’s website! They’re so stupid. We’re buying ads, it’s stealing their customers.” But every time you click on that Barnes & Noble ad, Amazon made a dollar. It’s 100% gross margin — they share that with nobody. There’s no COGS on that. 

And then they can use that dollar of pure profit to lower the cost of their “Harry Potter” book, which actually made more people want to go to Amazon to look for “Harry Potter” than go to Barnes & Noble — that said, we’re locking [you] within our walls. It’s like a casino with no clocks. And we’re gonna pump oxygen in. Because what a lot of big companies don’t get is that Google is just one click away. Like, why give all the excess profits to Google, when I go to Chase, I get turned down for a loan. And then I go back to Google, and I say, “Where else can I get a loan?” Well, Chase should be sending you there. And actually, they’re starting to do this. So, that’s one strategy that I think has a lot of legs.

Frank: Yeah, so turndown traffic. That’s super interesting. Look, you spent all the money to bring them to your site, and otherwise, you would have just lost them, right? That sort of sunk cost.

Alex: Exactly.

Acquisitions and team-building

Frank: So, you get something out of it. That’s fantastic. Well, why don’t we finish this segment out? I want to do a lightning round with you, which is — I want sort of, you know, instant advice for somebody in this seat. I’m an exec at Visa or GEICO. And so I’m going to name a category and you sort of just — of how to deal with startups, and you can react to it. All right. So, category one is, you should always invest super early — as early as you can into a startup.

Alex: So, again, remember adverse selection versus positive selection. So, I would say, the companies — so, this is what you have to get right. Which is, if you take nine weeks to make a decision, and like, you know, we’ll decide within a day — or if Sequoia or Benchmark or some other great venture capital firm will decide within a day — like, you’re not going to get good deals if you take nine weeks. So, it can be very, very important to invest early — but, like, the best things always seem overpriced. Like, this is something that we’ve learned, and it’s the same thing with underwriting your own customers. Which is, like, if something is too good to be true, it probably is. So, some of the best things are actually very expensive.

Frank: Yeah. All right. Just given those dynamics, just wait for the later rounds. Let all the venture guys take all the risk, and then, like, you plow in late, that should be a nice strategy.

Alex: I think, in general, that’s probably a better strategy. But again, saying, like, “Ooh, we’re getting a great deal on this one.” That’s probably — then, you know that you’re the adverse selection source of capital, as opposed to — okay, here’s something I can’t believe we’re paying this much money for it. We have to fight our way in. There are 10 other people that want it. You probably know you’re onto a good customer, if you will, or a good investment.

Frank: All right. Partner with as many possible startups as you can, because you don’t know who’s going to win, so let’s open up a marketplace. A hundred startups that I have — either turned on traffic relationships or something.

Alex: I think actually that does make sense. I mean, there should be some kind of gating item to make sure, like — maybe not 100, but how do we stay close to different models that are working well? Because the main advantage that the incumbents have — again, it depends on lending or insurance — but it’s typically something around cost of capital and something around distribution. So, if you have both of those, and you’re not using it to the fullest extent — like, you turn down a lot of customers — you should try to find an intelligent way of using this and using — that’s your unique thing. Like, venture capital firms don’t have that. I can’t fund somebody and send them a million customers tomorrow, but GEICO could. But you can’t do that 100 times, you can probably do that some sub-segment of times, according to how much, you know, additional traffic — or whatever it is that the unique advantage that you want to bring to bear.

Frank: All right. Now, on M&A strategy. M&A strategy one — buy super early before it’s proven to work — because, presumably, the prices are lower. So, M&A strategy early — focus on early-stage companies.

Alex: I’m a big fan of what Facebook’s done with M&A, and I encourage everybody in pretty much every other industry to do this. So, Facebook has two formats for M&A. One is, we buy the existential threat that could kill us, and we price it probabilistically. So, surrender 1% of our market cap to buy Instagram. That was way overpriced.

Frank: Instagram, that’s a good example. Everybody said that, they said, “Why are you…”

Alex: But one — like, there’s a 1 in 100 chance that this is going to be bigger than Facebook. We should probably surrender 1% of our market cap. WhatsApp, 7% chance, or whatever it was. I think it was 7% of Facebook’s fully diluted market cap — was spent on WhatsApp. These were brilliant acquisitions. Oculus. I mean, Oculus hasn’t turned out the same way that WhatsApp has, perhaps — but, like, same idea. It’s like, this could be the new platform. If we don’t buy this, and Apple does, we are subject to their random whims and fancies. So, that’s category one. Category two — and this is super counterintuitive for a lot of companies — buy the guys that failed trying. Because they had the courage and the tenacity to try to go and build something new. And that’s what you want in your company as well. And then — this is the most counterintuitive part is — like, take the person that failed and put them in charge of the person that was successful. And that’s breaking glass.

Frank: For a big company, that’s so hard. You reward your execs on success, not on failure.

Alex: Right. But in many cases, it’s like, you have a big company that’s been trying to build this thing for 10 years. And if they build it, they will get 1 billion customers, because they — I’m making that up. They have the distribution. Then you have the startup that actually built the thing in, like, a week — and they built it for $1 million. And that would take the big company, like, $1 billion dollars and 10 years to do. But like, “Oh, the company failed. Oh, that’s a bad company. These are bad managers.” But actually, you want to take them and put them in charge.

And the joke that I always make is, like — if Amtrak buys Tesla, the worst thing that Amtrak could do — because Amtrak is probably more profitable than Tesla, at this point. But if Amtrak were to buy Tesla, the worst thing they could do is say, “Okay, all of you Tesla bozos, you work for us.” But the whole point of a lot of this other form of M&A is — you’re really trying to buy products that you can push into your distribution. And you’re trying to buy talent that wrote the products, that built the products, that understand that. And the only thing that they needed, the only gap between them and actual huge success is distribution, which these big companies have in droves.

Frank: Yeah. So, that makes perfect sense. Maybe just a piece of advice on how to actually make that happen. Because you have this dynamic, where you’re a big company, you just bought a failing startup, right? You have all of the execs inside that have earned bonuses consistently, over years, for awesome performance, right? You’ve rewarded success. And now you’re going to say, “I’m going to take this guy that kind of failed. And, like, you work for them.” Like, that’s hard to do inside a big company.

Alex: It’s very hard. But I mean, in some cases, you just want to do it early. I mean, I think it actually — where it works best is where you say, “We need this product, we need this product to exist. We don’t have it right now, we haven’t spent eight years trying.” Rather than saying, “Let’s go assemble a team, and I’m going to rely on something that’s just not in our core DNA. Here’s how we’re going to go shopping. We’re not going to go shopping and value this.” And again, this is not a self-serving comment, because if somebody buys one of our failing companies for $10 million, and we have a billion-dollar fund, it doesn’t matter, right? Like, we want the companies that actually beat the incumbents, but the incumbents — the way that they could actually do great is to adopt more of this Facebook mentality. 

And, like, the key thing is that many of these acquisitions, these kind of acqui-hire — that’s the portmanteau of acquire and hire — these acqui-hire acquisitions that Facebook made, these people now run big swaths of Facebook. So, I agree, it’s hard to do if you already have a leader in place. In that case, it just requires a very strong-willed leadership team, and an actual overt strategy that this is what we do. It becomes easier if it’s like, “Okay, we’re trying to do this new thing. Rather than assemble our own team, and they don’t know what they’re doing but they’re well-intentioned, let’s go buy a company. But let’s buy a company that hasn’t already done the thing — but a company that tried and failed to do the thing, but we’re pretty sure that these are the best triers and failers in the business.” That’s the hard thing to really measure, because most people are used to measuring outcomes and not process.

Frank: Exactly.

Alex: And the key thing to make this strategy work is, you actually want to over-allocate on process — and you want to weight outcome to almost zero, because you’re buying the outcomes that were, in fact, zero.

Frank: Yep. The market is about to interview Annie Duke — “Thinking in Bets” — and this is, sort of, the essential “Thinking in Bets” notion. Which is, don’t confuse a bad outcome with, sort of, a bad bet, right?

Alex: Right. Right. Exactly.

Frank: Awesome. Well, thank you so much, Alex, for coming in and sharing your thoughts. For those of you in YouTube land, please like and subscribe. And for the comments thread on this, I’d love to get your input on what you thought of Alex’s idea — that what you really should do is not go after more customers, but instead go after only the best customers. So, what are examples that you’ve been trying in your own startup, where you’re trying to implement that idea? So, see you next time, go ahead and subscribe to the channel if you like it, and see you next episode.

Looking for more episodes?
Find them wherever you listen to podcasts.

Subscribe

• Apple Podcasts
• Spotify
• Stitcher
• Deezer
• Google Podcasts
• iHeartRadio
• Pocket Cast
• RadioPublic
• Amazon Music