Lemonade, Inc. (LMND) Earnings Call Transcript & Summary

Good morning, and welcome to Lemonade's first ever Investor Day. My name is Yael-Wissner-Levy, and I'm the VP of Communications here at Lemonade. I am thrilled to be with all of you here this morning in our New York SoHo offices, with all of you joining remotely online from around the world. I'm going to quickly read through some necessary disclosures, and then we'll hear from our Lemonade leadership, answer some of your questions and get on with this day. So the recording of today's investor presentation that we will discuss today is also available on our IR website, investor.lemonade.com. I'd like to remind you that management's remarks on this call may contain forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. Actual results may differ materially from those indicated by these forward-looking statements as a result of various important factors, including those discussed in the Risk Factors section of our Form 10-K filed with the SEC on March 1, 2022, and other filings with the SEC. Any forward-looking statements made on this call and today in these presentations represent our views only as of today, and we undertake no obligation to update them. We will be referring to certain non-GAAP financial measures during today's presentation, such as adjusted EBITDA and adjusted gross profit, which we believe may be important to investors to assess our operating performance. Reconciliations of these non-GAAP financial measures to the most directly comparable GAAP financial measures are included in our investor presentation. Our presentations also include information about our key operating metrics, including in-force premium, premium per customer, gross loss ratio and net loss ratio and a definition of each metric, why each is useful to investors and how we use each to monitor and manage our business. So in today's presentation, we hope to demonstrate to you 3 main points. The first that Lemonade has a structural technological advantage that will manifest as a superior loss cost and expense load over time. Secondly, that we have an inbuilt edge in acquiring and delighting new entrants, and their full value will unlock as we grow with them over time. Finally, that we have an advantaged long-term business model and a path to profitability. And it's really precisely the structural technological advantage that begets us this inbuilt edge when acquiring new entrants. And it's the combination of both, both the structural advantage and the inbuilt edge, that leads us to an advantaged long-term business model. This morning, we're joined today by our Co-Founders and Co-CEO here in the room, Shai Wininger and Daniel Schreiber. Daniel will be kicking us off and sharing our long-term strategy for running our business [ and ] the technology that we're deploying. Maya Prosor will then follow, our Chief Business Officer, and give you our product by product runway and practical applications in how we run our businesses product by product. Finally, Tim Bixby, our Chief Financial Officer, will share the financials and some of the modeling work that we've been doing. We're going to conclude, of course, with Q&A. And while each of the speakers are coming from different perspectives, it's really the combination of those 3 points that I just laid out that are a common thread throughout each of the presentations. So I ask that you keep that in mind today as you listen in and tune in from around the world. And with that, Daniel, the stage is yours.

70 years ago, Alan Turing, the inventor of the computer, already fathomed fully its implications, its ultimate implications. Writing in 1951, he spoke of how the forces that he had unleashed would inexorably lead to superhuman intelligence. In his own words, it is customary to offer a grain of comfort in the form of the statement that some peculiarly human characteristic could never be imitated by machine, but he added, "I cannot offer such comfort." A lightning review of how AI has developed since the days of Turing until today and a snapshot of where we stand, with a little bit of a propensity to look forward to where we're going, will form a great backdrop for us addressing the question of how technology and AI will impact insurance writ large and the prospects of Lemonade in particular. Well, AI, as I say, is a theoretical framework kicked off in the 1950s, and there were various starts and full starts and attempts and disappointments, winters by different names. And then the 1990s AI started becoming genuinely interesting, 40 years after it began. Perhaps the seminal event is in 1997 when Kasparov loses to Deep Blue, IBM's supercomputer, 3 games to 2. And suddenly, AI has beat the best of humans as something that we thought was quintessentially human. Now the AI that beat Kasparov was what we today refer to as good old-fashioned AI. It couldn't do anything that it wasn't explicitly programmed to do, and it bested Kasparov because of its brute force. In fact, it was Grand Masters who programmed the computer. Now unless you are a world champion at Jeopardy! or a grandmaster at chess, AI wasn't really a feature in your life in the 1990s. And it really wasn't until 10 years ago that AI started getting to levels of applicability that affected all of us. And there really big machines were met by big data and suddenly all frameworks, theoretical frameworks like machine learning and deep learning, revealed their true power. So that today, Google can translate from any language to any language without having been taught how to do that. Google Images can recognize me in some obscure photo far better than my mother can. And when Deep -- when AlphaGo beat the world champion at a game of Go, it did it without any grandmasters teaching it anything. It played against itself, 1 billion games in a day. And by the end of the first day, it was better at Go than all of humanity's accrued wisdom over 5,000 years of playing the game. So pretty powerful stuff. But machine learning and deep learning, as they've come to be familiar to us, are fairly narrow in their scope. They do what they're being set out to do. They have specific tasks, and they perform them at a superhuman level. What's happened in the last, in theory, a few years, but in practice, only a few months is what some people are referring to as a third generation of AI, or generative AI as it seems to be emerging and being called. And these are AIs that are trained on absolutely staggering data sets known as foundation models. And what's amazing about them is that they have emergent intelligence. They are proving themselves able to do things that nobody taught them how to do, that they weren't even intended to be able to do. With some fun consequences. I'll show you a couple of examples. So I gave an instruction to one of these AIs. It's called GPT-3. I said write a Shakespearean sonnet about AI and insurance. Now bear in mind, this AI has never been told who Shakespeare is or what AI is or what insurances is, or what a sonnet is. And you give me until my dying day, and I couldn't produce anything like what it produced in the 3 seconds. I'll read it out just for fun. "When I do think of insurance, 'tis but to ponder how AI doth make it better. With nimble speed and keenest eye, the claims and coverages do it spy. And in a trice, with little fuss the benefits and the best rates thus are found and given with but a click. So, fairest insurance, I do thee praise for being made more excellent with AI! Now maybe you fancy yourself as somebody who could write that, maybe you don't. Let's switch gears for a second. A sister AI of GPT-3 was given this task: draw an impressionist painting of an insurance adjuster denying coverage after a fire. Now maybe like me, you lack the skills to draw impressionist paintings. That's okay. But let's pit our powers of imagination against the AI and take a second and in your mind's eye, paint what you would do if you were given this prompt. Well, this is what AI produced. Pretty sophisticated, pretty subtle, pretty complex. And if we're pitting imagination against AI, let's take it one step further. I asked AI to render a photo of an insurance agent made out of a salad on a table. You want to try that one in your mind's eye for a second. Pretty cool. And finally, though I could do this all day, I asked it to create a 1960s photo of a despondent insurance broker in a red dress sitting at a desk full of paperwork with fluorescent lighting, taken using a 50-millimeter lens, expired CineStill 800t film and an oversaturated filter, and it came up with this haunting image of a woman in fluorescent lighting in the 1960s in a red dress, and she looks pretty despondent. Hence, nobody told this AI what despondent means or what insurance means [ and I say and ] the CineStill film that I put in the prompt was only invented a few years ago. This image never existed. This woman ever existed. It's pretty awesome what AI is able to do today. And it's really against this backdrop and that's kind of in the front of our minds that I'd like us to start considering how Lemonade has approached the world of insurance. Because as we've stated from our founding day, Lemonade has been established as an AI doing insurance. That is the foundation upon which our company is built. Those are the fibers that run through our company. So we gave ourselves a prompt a couple of years ago. I said we're going to look at 2 pictures from our IPO prospectus. Well, draw a schematic of insurance as a neural network. And this is one that we produced, not the AI. And the picture that formed part of our prospectus, that was 2.5 years ago, was making the following point. Lemonade has but one brain. It is vertically integrated. All of its systems are interconnected in powerful ways. The same intelligence that sells policies, handles claims, answers customer inquiries, identifies fraud, it's all one centralized intelligence. I want to put it to you as we go through the next few minutes together that this kind of AI, this kind of platform, this kind of insurance company could mark the dawning of a new age in insurance. Hyperbolic as that might sound, I'd like to try and make the case for that. The closest analog that I could find to what I think may be going through -- what insurance may be going through today happened a while ago in the scientific revolution in the 17th century. In the 17th century, a lot happened in the world of statistics. Pascal and Fermat framed the basics of probability theory. Jacob Bernoulli formulated the famous law of large numbers. And suddenly, statistics was put on a firm footing, in a way that it never had hitherto. Now, insurance is fundamentally at its core the business of monetizing statistics and data. And it would therefore be no surprise if there was a discontinuous innovation in the field of data and statistics for that to expose a rift to create a disruption in the world of insurance. And for it to advantage people and companies formed post that rift. And so it was. Not a single insurance company, and they've been around for many, many, many millennia -- not a single insurance company that predated Pascal and Fermat survived the scientific revolution, whereas the new founded companies in the late 1600 and early 1700s that use these newfangled ideas about how to handle statistics and data, well, they have not merely survived, but they have thrived for these 300 years. But let us not confuse longevity for immortality. Because what we are living through now, the digital revolution, is akin to the scientific revolution in the changes that it is creating in the world of statistics and data. Let us dive in a second and see what this schematic looks like in the real world. 2.5 years ago, it was conceptual. Let me show it to you in action. But beforehand, 2 data points as we consider what I'm about to show you. One is just a sense of the dimensionality of what we've built. We were -- went live just 6 years ago. We were a young company. And as you'll soon see, our data and statistical capabilities, our machine learning has been building at an exponential rate, but it is still in the early innings. Nevertheless, there's a lot to look at today. We now have several hundreds of millions of customer interactions in that centralized intelligence. That intelligence has digested and analyzed some 160 terabytes of pretty textured and predictive data. That's a lot. And all of that has resulted to date in about 15 machine learning models, which are, at first approximation, AIs unto themselves. So we have different AIs trained to do different things using these data sets. But while the sheer magnitude of the data is impressive and important, it actually misses the most important point. It is not the tonnage of data that matters. It's the interconnectedness. And this is where traditional insurance companies falter. Systems built higgledy-piggledy over years and acquisitions and decades in different geographies that are not connected are very poor at doing what we're talking about. You have data lakes that are a mile wide but an inch deep. By the way, talking about intelligence, this is how the human brain works, right? We have something like 100 billion neurons in our brain, but intelligence doesn't lie in the neurons but in the connections that they make. Each neuron has some 10,000 synapses, which are the connections and therein lies intelligence. And this is where the traditional model falters. From the schematic, I'd like to shift gears and give you a live demo of some real stuff. On the screen now, what you're seeing is a live feed of a visualization of Lemonade's actual customer cortex and its systems. And I'll talk you through what we're seeing. It is beautiful but it is also a little bit confusing at first. And I can manipulate this and we'll zoom in and out and enjoy ourselves as we go about this. But let me try and show you a little bit of what we're looking at. So I'll white them out, and I'll go through the different colors and explain myself. At its core, you can see several pink balls, relatively big. And these are applications. Some of these will be more familiar, some will become familiar as we go through the day. So for example, AI MAYA if any of you have met Lemonade before, or played with our bot. You'll know Maya is the bot that sells policies and insurance. What you won't have known is that she calls on many, many machine learning models, and that's the next set of balls that you're seeing there. All these blue balls are those 50 machine learning models or so that I spoke about a minute earlier. Each of those machine learning models in turn is fed by hundreds of features, features or data points that the machine learning has found to be highly predictive out of a plethora of potential data points. And then the yellow balls each represent a data set. I mentioned 160 terabytes of data, hundreds of millions of interactions. You're only seeing a few yellow dots because each one contains within it millions or tens of millions or hundreds of millions, sometimes billions of data points. So we've got rid of all the data and just shown you the structures, so that it doesn't get overwhelming. And one of the points I really want to drive home now is if we stay on AI Maya here for a minute, if I can find her dot there. So you can see she relies in real time. This isn't stuff that is cached. This is every time she sells a policy, this happens again. she's calling on all these different machine learning models. And if I click on her, you'll see all the connections within the cortex, within this system that happen as maya lights up and does what she does. There's another model I'd like to spend a minute on here, let me darken out Maya for a second. This is the LTV model. You'll hear a fair bit about this during the course of the day. Our LTV model is a series of AIs. You can see all the blue dots there that are lit up. And a stunning amount of data and other stuff behind the scenes, you can see they're lit up now, I'll zoom out of it and you can get a vantage point. But you can see again that a big chunk of our brain lights up. In fact, once we light up Maya as well, you see that between them, they all are interconnected in powerful ways. But at any rate, staying with LTV for a second. The LTV model is a series of AIs making real-time predictions. Every single prospect, every single customer that comes into Lemonade, these machine learning models light up, the whole system here lights up and makes a series of predictions. The ones that I want to highlight to you, are it will make a prediction about how likely are customers to churn. In other words, talk about lifetime value, what is going to be their lifetime? Are they going to be here for a month or for 50 years? How likely are they to claim? And what will be the severity of their claim? And how likely are they to cross sell and buy another policy? These are some of the most important variables in our business. So those 3 Cs, other things as well. But if you think about it, once I'm able to predict those 3, and I know what I'm paying to get the customer, the CAT, the customer acquisition cost, I can start making highly pointed decisions about how to allocate resources. Because what LTV is really doing is it's showing me -- and it will produce a dollar number at the end of the day, which is all the dollars I'm going to spend on this customer paying claims and otherwise, all the dollars I can expect from this customer in premiums, apply a discount for the time value of money and produce a dollar amount and say this customer's lifetime value, collapsed to today, is $1,300, $5,000, negative $2,000. Insurance is a business; for some customers, you don't want it for free. So we now run our business in tremendous reliance and increasingly using these LTV models. So we've seen a little bit about how all these systems interconnect. And I put it to you that there isn't another insurance company in the U.S. for sure, in the world most probably, who has something akin to what I'm just showing you right now. Let's try and look at some of the applications and implications of what I'm showing you. Again, for the first time, sharing here real time data from our systems. This isn't connected to the web, which is why I'm on a different computer, showing you a real feed from our business, although we are starting at a time-lagged spot. If you look at the bottom of the screen, you'll see the dot there is on Q4 '19. So this is the very first LTV model that we had 3 years ago. We're now in Q4 '22. Every dot here is a homeowner's policy. So we're just going to look at homeowners right now. As it stood in 2019, and this is the machine's estimate of the value of each of those policies, color-coded. Light green is marginally positive, dark green is very positive, as it gets into the red that's negative. A couple of things as I step through this. By the way, at this stage, we had so few policies. And think about it, homeowners make claims to sell them. So we had many fewer claims data. So the machine couldn't yet make predictions based on claims. It was using proxies. We could use churn, and we're using churn as some kind of a proxy. This was not yet a reliable model, and we knew it and we didn't use it yet in production. As we step through, you'll see that, a, the dots become more numerous because our business is fast growing, and hopefully, you'll start seeing some separation. The important thing to see with machine learning models isn't whether the business is getting better or worse, that's to do with the business. If the machine is getting better or worse you'll see by its ability to discern differences, more lift, more nuance, less monolithic, more precise. But the real breakthrough for us was in LTV 4. This is just 18 months ago. This was the first time that the machine said it had enough claims data to start making the kind of predictions like I shared with you. And it's the first time that we allowed it into production. Until that point, it was still just learning and being trained. But 18 months ago, we started using it. And by the way, at a glance, you can see a lot about our homeowners' business. Those of you immersed in insurance will be less surprised perhaps than others, but California is looking pretty red here. Texas, bright green; the Northeast a mixed bunch; New York, pretty good; Virginia, less good. And I'll click through a little bit more. You can see LTV 5, more separation, more nuance, more color. And LTV 6, which is our most recent model, again, things are getting better. But beyond the fact that they're getting better, what I want to draw your attention to is the fact that they're getting more nuanced every time monolithic groups break apart and reveal their subgroups. So let's zoom in -- let's go back for a second to a place that was looking bright green, which is down here in Texas. Let's go to Houston. I won't get so close that you can actually tell individual addresses, although we could. LTV 1, you can see the sparsity of data. And as we step through, more and more people in Houston are buying Lemonade, by LTV 3 it's looking pretty good. And then LTV 4, the predictions say actually we were underestimating the value of our customers there. Everything turns dark green. The machine is saying, Houston, we have lift off. Everything is looking really good. Now that doesn't change at a zoomed-out level, Houston continues to look good. But here's the power of the machine as it goes through its iterations. I go through generation 5. You start seeing some more shading. And in generation 6, at a zoomed-out level, everything is green. But you can start seeing that not everything is equally green. In fact, down here there's a patch of red. Friendswood isn't looking so good. We are mispriced in Friendswood. Now we couldn't tell that until very recently, but we know that now, and Fresno, just 15 miles out to the west, is looking bright green, so we can start analyzing what's going on in these 2 markets. Very similar populations, very similar range, something is wrong with our pricing, and we can go and fix that. Now stepping through that map hopefully gives you a really powerful sense of how the machine learns and improves and changes and, in some ways, how recent all these capabilities are. So I'll try and quantify it for you a bit, this exponential pace of innovation. A couple of years ago, those features that I spoke about that the machine learning models call upon, we did about 250 million calls to them in a year. This year, we think it's going to be over 3 billion calls, so more than 10x in the space of a year. And this is important. How many predictions did all of these different machine learning models, our totality of this intelligence that were built? How many predictions was it able to generate in a year in a way that helped our business 2 years ago? 8.5 million predictions. That sounds pretty impressive. But it pales into insignificance next to the 110 million that are happening this year alone. Why do we care about all of this? It's cool. And we like cool stuff. But what are its business implications? Why is this really, really, really important? Why might this herald the kind of change in insurance, a dawning of a new era that I was speaking about earlier. Well, because these kind of capabilities result in more for less. More is for the customer, more delight. Business Insider did a review of Lemonade and they concluded that the cheerless customer service representative at Liberty Mutual had about half the personality of Lemonade's chat bot. Now we've seen that AIs are able to produce beautiful pictures of insurance agents out of salad on a table. It's not shocking that they can also sell insurance in a trice. And pretty much any way that you care to measure customer satisfaction, whatever your word salad is, whether it's NPS or CSAT or BBB or whatever, Lemonade is performing at a level on par with companies like Tesla and Apple, and really on a level that is unfamiliar in the insurance space. Well, it's becoming more familiar in the sense that the mainstream arbiters of customer satisfaction have woken up and paid attention. So now J.D. Powers and Forbes and U.S. News and reviewers, all these guys are now ranking Lemonade when they're running comparisons at the very top of their rankings. So I'm not going to delay too much on this. Hopefully, you don't need a lot of convincing that in terms of customer satisfaction, our AI and our system is outperforming incumbents by some margin. Let's talk about the last part. And one of the central themes that we want to convey to you today is our conviction that the system that we have built is going to show savings, crush costs in every single line in the P&L, every single part of our combined ratio, and I'll use that to talk you through it. What you're seeing here is the expense ratio, the loss ratio. Unimaginatively, they are called the combined ratio. Let's step through a few ways in which this intelligence is affecting all of these. Some are invisible to the outside world. So for example, we have an AI internally, a bot called Cooper, and Cooper runs loads of errands for us. Loads of tasks, tasks that are usually done by humans; pretty sophisticated tasks. He runs big chunks of our engineering. While he doesn't write code at the moment, he does do what's known as DevOps, which is establishing server environments, instantiating environments, giving them to developers, moving things into production. Last year alone, Cooper pushed about 12,000 production builds of software into production, 12,000. He saved us, we reckon, something like 10,000 human hours by doing that. And at the other end of the spectrum, you have Maya, with whom you are familiar, AI Maya and her associated APIs sell all of our insurance, 98%. and she does that in a trice. 90 seconds, and you're done, no hassle, no commission. 95% of homeowners' policies in America, give or take, are sold by humans. And they command something like a 15% to 20% commission, not just for the year they sell but in perpetuity, and not just the policy they sell but on all subsequent policies that their customer buys. Maya is much more forgiving in that sense. We spoke a fair bit about the LTV model. Our conviction and trust in this model, we're putting our money where our mouth is. Quite literally, $0.86 on the dollar that we spent on marketing this past quarter was at the direction of the LTV model. The graph on the left shows 2 overlapping graphs. One is showing what the model says would be optimal. One is showing what we actually did. You can see that they're almost entirely on top of each other. And the bars on the right being so clumped together gives you a high conviction that all the scenarios that it's running, which are all of those little lines, are coming out to the same conclusion. So very high trust level. A big chunk of our customer engagement post acquisition and preacquisition is done without any human involvement. We have an intelligence called CX AI. The users actually interact with Maya, but what we call it behind the scenes is CX AI. And now days 1/3 of all customer inquiries, no matter what they're asking some of these things are pretty complex. Are you writing in that you're moving home? Well, we have to figure out when are you moving and when do you want your policy canceled and then what coverages do you need on the new home and which date and all of these kind of things, and which scheduled items do you want to add and which family members, and all that's done by the AI without any human intervention. 1/3 of all of our inquiries, 1 in 3, is handled this way with an incredibly high level of customer satisfaction. Loss adjustment expense, as we switch to loss ratio for a second, loss adjustment expense is a big part of the loss ratio. That is the bureaucratic overhead of managing claims. 98% of our claims, the first notice of loss is taken by a bot. And in almost half of those claims, everything is done by a bot, start to finish, right through to asking you any clarifying questions, asking you for documents, you uploading a video, analyzing, fraud detection, asking you all the questions and wiring money to your bank account, all done without any human intervention. By the way, as I write, it's now at a level of accuracy -- we audit this -- AI Jim is performing better than humans in most of these tasks. Again, a big chunk of loss ratio is fraud. It should come as no shock that the kind of intelligence that I've described, the kind of AI is getting pretty good at detecting any kind of deviant behaviors. What you're seeing here on the left is a graph. The machine learning is scouring through all these data to look for connections to suspicious behaviors, something that, given all the time in the world, humans could never find. It's just much too much data to handle that way. And finding these connections in this graph is actually a graph of these connections. We now have 9 million connections that we've found within our data sets of suspicious behavior. And this, as you can see, is growing at an exponential rate, which means we're getting better and better and better, more and more and more precise at identifying these kinds of threatening behaviors. The financial impact is profound. These systems to date have identified about $100 million worth of fraudulent claims that we then inspected with humans and found to be fraudulent. It flagged $12 million worth of claims just from the fraudulent documents. We are now getting pretty good at identifying the fingerprints on a document of it being doctored in any way. You'd be shocked at the kind of things we see. But humans miss these things. AIs don't. We have something that we call Watchtower. Watchtower is a system that gets satellite feeds in real time from NASA to look for catastrophic events. It gets other data sources as well. And if it knows of something happening, it will take preventative measures. A pretty terrible hurricane hit Florida 2 weeks ago, Ian. As Ian was approaching Florida, 6,400 Floridians turned to Lemonade for insurance. Watchtower kicked in and offered all of those people a policy so long as the start date was 10 days forward. 31% of them bought. So you get all the benefits of the fact that catastrophes do remind people that they need insurance, without the associated risks. We're going to delay on the next few slides in Maya's presentation, so I'll fly through them. But telematics are a game changer, profound, profound. And in use at Lemonade at a scale that is simply being missed by the industry at large. In our homeowners' business, we are using computer vision and natural language processing to identify incredible signals about the state of properties, and we'll delve into that more in a few minutes as well. And finally, and this too will come back to you, for the very first time, we've now had a machine learning model adopted by regulators as the foundation of pricing for homeowners. And what you're seeing here on the image is what's known as a boosted tree. This is a form of machine learning. And each of those threads is a different path in which customers might go, and you end up with incredibly precise and nuanced outcomes at a level far beyond anything that traditional filings can get to. And this calls on the machine learning model in real time. Okay. There are a few buts that you might have. I have none. I'm going to try and anticipate them. And the first one is, but everybody is doing this. Cool, but this is now stuff that surely the incumbent, surely everybody can do what you're showing. And my monosyllabic answer to that is no. I'll elaborate. Anybody can pay a third party for a suite of software. Anybody can pay a consultant to run some analysis. What we are showing you today is different not in degree but in kind. The kind of connections, the kind of systems upon which we are built, nobody else can do. Unless you were built this way from the ground up, you just can't make the connections, and it's through no fault of anybody else. If you founded your company in the era of the horse-drawn carriage, you optimize for that era, and you find yourself flat footed going into the digital era. We have the good fortune of being founded in the digital era, and therefore, we built it the way we built it. But no, I say this with some confidence in having spoken to many of my colleagues, CEOs of large incumbents, other companies cannot do this. In fact, what I'm showing you is a sustainable competitive advantage that is already manifest if you look at the right places and will manifest ever more powerfully over time. I hope you take my word for it, but just in case you're suspicious, I want to share with you the viewpoints of 3 people, all members of Lemonade's team, who joined Lemonade from incumbents with senior positions and some of the best and brightest in the industry. And let's hear in their own voice, how they compare their experiences at those companies and at Lemonade. [Presentation]

Hi, I'm Scott Fisher. I'm Lemonade's General Counsel.

I'm John Peters. I'm the Chief Underwriting Officer for Lemonade.

I'm Sean Burgess, Lemonade's Chief Claims Officer. And a few years before I came over to Lemonade, I was the insurance regulator for the State of New York.

Prior to Lemonade, I was the leader in McKinsey's global underwriting practice. And I was also a Chief Underwriting Officer at Liberty Mutual.

Previously, I was Chief Claims Officer at USAA, a best-in-class company. But I can say without hesitation that the tools and technology that we're deploying here at Lemonade are simply unmatched in the industry.

I don't know of another insurance company has the capabilities that Lemonade has to use AI.

And I can honestly say, I don't think there's another company with the vision or the DNA on innovation that Lemonade has. Its people, its tech, its general approach are second to none in the industry.

I think one of the powers of having built our technology from scratch and literally from end to end. The moment a customer hits our website to the time we pay a claim, all of that is integrated and connected together.

And those facts are a material competitive advantage in an otherwise really challenging marketplace. In fact, I don't see a scenario where incumbents will be able to get here from where they are today. It's a true structural advantage to Lemonade. And the best news is we're just getting started here.

Okay. To my second part. So why aren't we seeing it in the financials? Lemonade is losing money and has a high loss ratio. How do you square that with everything that we've just seen? I'd like to make 3 points and try and explain that disconnect. The first is that we are seeing it in the numbers. It depends which numbers we look at. So we had a 94% loss ratio in Q3. But based on all the data we have, the business that we wrote in Q3, we'll have a lifetime loss ratio of 61%. So the first point to understand is are we looking at lagging indicators, which are the financials that we report quarterly, or leading indicators. And in a fast-growing business, you just saw literally on a map how much has changed in the last couple of years for Lemonade. But so much of our current financials is a result of what happened a year, 2, and 3 ago. So look at leading indicators rather than lagging indicators, and you'll get a diametrically opposite picture. I want to delay on this one for a second. I spoke about the LTV model. Part of that model, it produces LTV, but on its way, it produces an expected lifetime loss ratio. Lifetime loss ratios are important because loss ratios tend to be front-heavy. Customers have the most claims, the worst loss ratio in the first year, get to be better in the second year. In our experience, they tend to be around their lifetime average loss ratio in the third year, and then they go below it in years 4 and 5. When you're selling business, you want to think about long-term profitability of the business, you don't care if they are loss-making in the first few months. You want to think about will they be loss making in a meaningful way over their lifetime? So you want to be forward looking, not looking at a snapshot right now. And the machine learning model does all of that. And what it is showing you on the screen here right now is that in Q1 of '21, the cohort that we onboarded -- not the totality of our business, the business that we sold in Q1 '21 -- on aggregate across all of our products, the machine forecasted a lifetime loss ratio of 86%. And you can see how quarter on quarter on quarter, it has dropped precipitously to the low 60s, to 61% in this past quarter. So there's nothing at all inconsistent with saying that our actual reported lagging loss ratio was one number, and the new business that we are writing was something quite different. The loss ratio of the people that we sold policies in Q3 didn't impact our Q3 loss ratio almost at all. Now I don't want to vouch for the decimal points here. Actually, I'd prefer to write out the numbers entirely. What I will vouch for is the trend line. Because this is an apples-to-apples comparison. This is using the same methodology to evaluate the lifetime loss ratio. And you white out the numbers, and you can see that quarter-on-quarter-on-quarter, we are bringing in better and better and better business. To fully understand why there is such a difference between leading and lagging, I want to make my second point. Insurance in general, but Lemonade specifically, is front loaded in its expense, in its losses. And the near-term financials do not provide a helpful metric, a helpful signal for long-term profitability. There are 4 ways in which we're front loaded. The first is that as a company that is building all of its own technology in-house, that is getting all of its licenses itself, writing on its own paper, vertically integrated to its core -- as we launch more and more products, and we've launched a lot of them over the course of the last 6 years, you are incurring all of those costs upfront, all of the development, all of the engineering, all of the regulatory work. So you have all the building costs upfront. You start off with a data disadvantage. You saw that beautifully on those maps and how sparse the data was and how long it takes years for those cycles to run through and for the data to accumulate. You have what's known as the new business penalty. Insurance insiders know this that new policies, new business always performs worse than it will over time. And guess what, when you're new, all of your business has a new business penalty. For incumbents, maybe 10% of their business is new. For us, it's like 70% or 80%. And finally, I made this point fleetingly earlier. We are a direct-to-consumer company, which means that we incur all of the customer acquisition costs on day 1. Broker-based businesses, they don't have that. There's no onetime expense upfront. They have a partner taking a commission in perpetuity. So you add up all of those dollars. It may be much more than what we're paying, but it's spread out its peanut butter over the life of the customer. It's not front loaded in the way that it is for us. But here's the good news. All of these resolve themselves over time. When you finish building products, you sell them and you start recuperating the investment that you made. Data disadvantages, as I hope I've demonstrated, at Lemonade turn into data advantages pretty quickly. I will tell you that today, 6 years in market, there is not an insurance company in the U.S. that we would trade data sets with. New business penalty, well, business seasons all on its own. If you're growing super fast, you'll still have loads of new business, but you know where it's going. And finally, the beauty of acquiring customers through CAC is that, year 2, you're not paying anybody anything. So you end up with no commission sales for the rest of the life of the customer, which brings me to my third point. Lemonade is built for scale. Our ambitions are expansive. I just spoke about how we're getting all of our licenses, launching our own new regions, building our own technology. You don't do that if you want to stay at the size that we're at today. You do that because you're envisaging many, many years of profound growth and you're preparing the infrastructure for the company you intend to be, not for the company you are today. That means that you have an expense load that is disproportionate in the early days of the business, but that's the right thing to do if you're thinking about the long term. And using this image, you can see the denominator is what matters. The expense load isn't the problem. The problem is that we still have a small denominator, and an expense ratio is all determined by the denominator. The numerator matters, of course, but you will see our expense ratio resolve itself as our business grows. In fact, it already is. Let's just look over the last few quarters, Q3 to Q3. This is our adjusted expense ratio. Our expense ratio of Q3 last year was 103%. This year, it was 80% and the trend line, I think, speaks for itself. And this happened while our business grew at 76% during that same period. And it's no coincidence, the growth of the denominator is what resolves the ratio all by itself. We are on a path to having a fabulous expense ratio. It's just going to take time for the business to grow into all the stuff that we have built. All of that brings me to the second image in our prospectus that I wanted to talk you through this morning. And the prompt here is growing with customers, and this is the image. This was a picture that we included. It's a picture of a young woman at age 25 starting out on life -- in life. She has a bike. She has a backpack. She has maybe a laptop in it. That's what she needs to insure. We offer a policy at $60 a year, $5 a month. She joined us. She gets a renter's policy. She insures the few things that she cares about. And then she sets off. She steps onto the conveyor belt of life. And if we've acquired the customers that we think we've acquired, she's going to go through pretty stereotypical, predictable life cycle events. It's a bit of a caricature, but you know how this ends. It ends with her going from $60 a year to $600 a year to $6,000 a year. And we made it very clear that we plan to be there with her for life. In general, when you talk about lifetime value in tech, you typically think in terms of 3 years, 4 years. In insurance, it's literally for life. Age 80, she will still be paying insurance for somebody, and she will be paying perhaps 100x more than she pays when she starts off. Now when we threw this graph up on the prospectus we were a monoline business. 2.5 years ago, we had [ bought ]t homeowners, homeowners or renters. Since then, we've filled in pretty much all of that hill, but renters are still in this story of starting with the young consumer, this low-end disruption of finding consumers that incumbents want the least, delighting them and then growing with them was the intention all along, as I think this graph speaks clearly. How are we doing with that? Last month, Google ran a survey of asking Americans across the nation if they bought renter's insurance for the first time in the last 12 months, and if they did, who they bought it from. And you can see at a glance that State Farm came in tops, #1 brand that people bought renter's insurance for the first time last year across the U.S.A. was State Farm. Brand #2, Lemonade. Ahead of GEICO, ahead of Progressive, ahead of Liberty Mutual, ahead of everybody else. By the way, we were handicapped in this survey because we're not live in all of the U.S. and the survey was conducted across the U.S. but let that be. When you then splice the data one level further and you say, "Okay, show me those same results, but just for under 35-year-olds," Lemonade bests State Farm and becomes the #1 brand in the nation for Americans buying insurance for the first time. I put it to you that this is powerful and strategic, that nothing foretells ultimate market share more than new cohort market share. Now it's not enough to just acquire these customers, we then need to grow with them. The second part of that sentence, acquire them when incumbents want them least and then grow with them. Well, at IPO a couple of years ago, we were doing that already, but we were limited. The only growing they could do with us is when they're ready to get rid of their rental and move into a condo, typically your homeowner's policy. And we track that pretty closely. At the time of IPO, 11% of our condo business was sold to existing renters. Today, 2 years later, that has steadily improved. It's twice that today. More than 20% of our condo business was sold to existing customers. Now think about that. Your car renters, the cost of renters acquisition, it's a profitable business in and of itself. And then they grow with you, typically expanding their premium about 5x with no associated cost to acquire the incremental premium. But we've moved a long way in those 2.5 years because we have since then launched pet and life and car. And folks, look at these numbers, this past quarter, roughly 1/3 of all policies of car that we sold, roughly 1/3 of all pet policies, roughly 1/3 of all life policies and roughly 1/3 of all home policies were sold to our renters. This has the making of a very, very powerful business. But I also want to tell you just how early we are in this process. Let's put some dimensionality around this as well. For incumbents, the best and the brightest, something like -- they don't publish these numbers, but as best we can gather, something like 60% of their customers are multi-line customers. They've gone through this. They've bought more than one policy, not just multi-policy but multi-line. And Lemonade is less than 4%. And if that sounds like bad news, it's fantastic news. It means that we have tremendous opportunity ahead of us. And we're on track. So let's look at some of the trend lines in this regard, just looking at the multi-line customers. 2.5 years ago, we had 0. We didn't have multi-lines, so 0 customers. July of 2020, we launched pet, and then we started our journey in the multi-line business. Followed by life, and then you get to the 3.7% that we're at today, a nice up and to the right trajectory. And then something happened. One year ago, almost to the day, we launched car. That was in Illinois. We've launched it subsequently in other markets, and the trend lines are identical. Look at the angle of the curve in Illinois, where we have car available as well, and extrapolate that forward. Illinois a year later is already up 5.9% and shooting right up through the screen. I want to wrap up my presentation and say the following. These 2 images that I've used as an anchor, as a visual aid to talk you through the building blocks of our strategy, when we drew them out 2.5, 3 years ago, they were ambitions. They were a statement to our investor community of what it is that we intend to do. Today, they are a reality. I've shown you the data supporting this young woman's journey. I've shown you literally our back-end systems and how they reflect the schematic that we drew those 3 years ago. And I put it to you, and as we go through the morning, we should return to this point again and again, that they represent a highly differentiated business model from what the rest of the industry does. Neither of these graphs could have been really presented to you by anybody else. They are highly distinct, highly differentiated, and I put it to you, highly defensible because they are structural in nature. Now we're just getting started. We have built the flywheel, and I hope I've shown you it's beginning to spin pretty quickly and accelerating. And I'd like to tell you where at least we hope it's taking us, and this will be my final slide. We're tiny. This is drawn to scale. GEICO towers over us 50:1, more than; State Farm, more than 100:1; others, even more. We find it exhilarating that we could 10x our business and 10x it again, and State Farm would still be bigger than us. There's just that much headroom. And that flywheel spinning, that's exactly where it's intended to take us to go. With every spin, our business not only gets bigger, it gets better, smarter, it learns from the data it generates. And that moat that I spoke of gets taller and thicker. To show how all of this impacts our business day to day and line by line, let me hand over to Maya, our Chief Business Officer.

It's great to be here. We've done a lot of great things, but we're just getting started. We spent the past 6 years building 5 incredible products. Each of these products represents a total addressable market of billions of dollars, if not hundreds of billions of dollars. My job at Lemonade is to manage our portfolio. And so it's great that we now have such a rich portfolio. But that's pretty recent. Up until 2020, we were a mono-line carrier offering insurance in just 28 states. Today, we have all major personal lines. We have customers in 50 states and even Europe's 3 largest markets. The question we ask ourselves every day is where should we be spending that incremental dollar: which product, what geography, which campaign would yield the best return to our investment. Not being able to look into the future, insurance companies oftentimes relied information from the past. And as the warning goes, past performance does not guarantee future results. Take our loss ratio, for example. It tells you a lot about the business we sold 2 years ago, but it doesn't tell you a lot about the business and policies we sold just 2 days ago. You're investors. What if I told you that you need to manage your own portfolio today, deciding which stocks to buy and sell, using only the information available from the Wall Street Journal of January 2021? At Lemonade, we do things differently. We report lagging indicators, of course, but we use leading indicators to manage our own business. Our LTV model looks at every policy we sell and predicts the possibility of that policy to churn, the predicted loss ratio both in terms of frequency and severity, and even the potential of that customer to buy other products from us. Like with any probabilistic model, when you look at it one customer at a time, the model might be off. But aggregated together, it provides a reliable and powerful set of tools, and this is how we manage our business. Let me show you how that looks like product by product. I'm going to get through renters, pet and life fairly quickly, and then I'm going to spend a little bit more time on how we deal with the challenges that we have in home and car, as they represent the biggest potential for our growth in the future. We launched renters 6 years ago. And the short gist of it, it's doing great. It's profitable. We've been growing this product for the past 6 years consistently. And according to Google, we now have 9% market share. We've sold $223 million of IFP, which is exciting. But compared to our aspirations, the more exciting number is 1.4 million customers. As Daniel talked about, these are customers who are first-time insurance buyers, have never purchased insurance before, and these are future homeowners insurance buyers, life insurance buyers, car insurance buyers. All of them are going to graduate into those products with 0 additional cost of acquisition. But we haven't even shifted our focus there because many of these products are new to us. And what we're seeing is a lot of these renters are buying all of these products all on their own. This graph represents the contribution of IFP just from renters to our other product lines in the last few quarters. In Q3 of 2022, if you can squint, you can see $3 million of IFP coming to our car business, which is our newest product, just from renters. Mind you, we're available in just 3 states. The most bundled product for renters in the U.S. is car insurance. We expect that line to overtake all other products and contributions. In Q3 of 2022, $25 million of our IFP for our pet insurance business came just from renters. Speaking of pet, we think about pet insurance very similarly to how we think about renters. These are younger customers. Many of them are first-time buyers of insurance. But what's more interesting is that this really creates a different relationship between us and our customers. It's moving us from insuring the stuff you love to insuring the ones you love. Our customers look at their pets as part of their family. And every time we engage with them, and we pay out a claim, we create a deeper connection with them, which creates stickiness and more opportunities for us to cross-sell them and offer them other products. We're excited about pet insurance also because of its market, which is essentially a blue ocean. Only 2.5% of households with pets have pet insurance. There's a lot of room to grow. One of the reasons why pet owners don't buy pet is because of awareness and education. We've built a phenomenal product that has been able to be broken apart, and so every customer can buy exactly what they need for their pet and understand the value of what they're buying. This market is growing 25% year-over-year, and we're growing 4x faster. In the past year alone, we've doubled our pet business. And we just announced a strategic partnership with Chewy, offering Lemonade pet insurance to its 20 million online customers who are already used to buying pet insurance online. And the numbers show this. Numbers for pet insurance are looking really great. But the number that I want to focus on might be less obvious, and this is the number of claims. We've had 450,000 pet claims in the past 2 years. The reason why we love pet insurance and why this number is great for us is because pet is one of those products that has a really low severity and high frequency. This is a product that is meant to be used. You're meant to be taking your pet to the vet a few times a year. This is how we price the product. And so every time you do that, we learn more about you and your pet, and we're able to price and underwrite these customers better. Let me show you how our pet claims look in comparison to all of our other products. The pink line is all of our pet claims, and the gray is all of our other products combined. It took us 7 months to reach the same number of claims we had in home in pet. Just think about that volume. So it helps us with loss ratio. It helps us with pricing. It helps us with underwriting. But it also helps us, might not be as intuitive, with the cost of handling claims. The more claims we get, the more opportunities we identify to inject technology and automation in the claims handling. And so when we just launched pet, 36% of our premium went to the cost of handling claims. We're already down to 14% with a clear path by the end of next year to reach below 10. Life insurance is one of our more profitable products, but it's small. We haven't managed to crack the cost of acquisition in the current market. And so we've shifted a lot of our resources and marketing spend into other products. This is part of the power of what I mentioned at the beginning of having a portfolio. We focused mainly at cross-selling this product to our other customers. And so it's not surprising that 35% of our life customers have other products, the rest are coming to us organically, and it's reducing churn for the customers who are adding it. We're going to continue to monitor market conditions and push the marketing into this product when things change. Homeowners insurance is one of the cornerstones of personal lines. This is the product that our 1.4 million renters are going to graduate into. This is the product you stay with for many, many years and the product that you bundle and add other products to, but very -- almost mirror opposite to what I shared with you about pets in terms of the claims experience. It's mirror opposite. So this is a product that has very low frequency and very high severity. And so for our data sets to be able to learn and be able to price and underwrite these customers, it just takes longer. And you can see that in our numbers. Our Q3 loss ratio was 119%, highly inflationary year, some catastrophic events, not where we want it to be. But what I want to focus on and highlight is the gap between our ultimate loss ratio and our predicted loss ratio for the new cohorts for homeowners. Or more so, the trajectory of how far we've come and how much better we've become in underwriting and pricing new homeowners coming into our book. The way we use predicted loss ratio, and this is important, is in 2 ways. As Daniel mentioned, we want to look at the trend. We want to make sure that we're getting better and smarter with every new cohort that we're bringing in. And also, we want to find correlations between initiatives and decisions that we're making with that trend line. So you want to see that impacting new cohorts coming in. This is a very powerful tool to manage a business today versus managing your data from the past. A lot of the things that we've done are going to take time to see them in our ultimate results. Rates are a really good example. This past year, we've rerated 100% of our book and then started over again. Only 85% of the rates that we've submitted have been approved and implemented into the markets. But just 4.5% of it was [ still ] earned. It takes time for all your customers to get through the renewal process and then, month-over-month, start accruing the premiums that they're paying us. Another benefit that we're going to see in the future comes from our LTV. Our LTV doesn't know that more rates are coming. It just knows about the rates that were implemented. And so we expect the predicted loss ratio to continue to go down as the rates get approved and implemented into the different markets. I want to share with you a little bit of what we've done in just the past 12 months when it comes to underwriting and pricing for our customers for homeowners. When it comes to homeowners insurance, geography matters. California has been a tough state for insurance companies. We've seen a lot of insurance companies dropping business and discontinuing business in California. The combination of wildfires and a tough regulatory environment that doesn't allow you to raise rates as fast as you would like has really created a challenging condition. In the beginning of 2022, 30% of our new homeowners business was coming from California. Not surprising, it's a large state. And as other carriers are leaving, you're [ becoming ] and you're attracting more customers to Lemonade. But these customers were mispriced. And so by utilizing and leaning on the fact that we're a direct carrier, we're able to shift marketing dollars as well as change some of our product flows and request more data from customers and lower that percentage all the way down to 6%. Today, we discontinued our direct business in California, that's pretty recent, because we're not in the business of selling unprofitable business. And we will turn that back on once our rates get approved and we can rate our customers appropriately. Let me show you some of the things we've done in the past 12 months around underwriting. We've made a huge leap in the types of data and the data sets that we're using in order to underwrite our customers. To orient you, as Daniel showed you a little bit the cortex, you should be familiar with this already. This blue dot is really the model that decides on the different underwriting flows and questions and data sources that feed us and make a decision on whether or not we should be underwriting, we should be binding that customer. We use many data sources. Some of them include the age of the house, the age of your boiler, did you do any renovations in the past year. But it's how we use these data sources that really differentiates us, because this feeds not only our underwriting review team, but this also feeds into our LTV model that helps us make marketing decisions. And so every dollar we spend, we know that we're making it on customers that are eventually going to get through our underwriting filters, and so making sure that we have a lot more efficiency in how we spend money. We don't stop there. We also look for additional sources of information [ that ] not be as obvious but definitely makes sense. How customers describe their homes on real estate websites is pretty telling. This home, for example, says that this home needs TLC inside. Our models in AI identify that the word TLC is usually correlated with disrepair. Disrepair is outside of our underwriting guidelines. And so we're able to flag this automatically to our underwriting review team, make sure they don't miss anything, but also make sure that we're underwriting the right policies. Inspection reports are a really useful tool for us to know the condition, everything you need to know about the home. I don't know how many of you have ever read an inspection report. It can be as long as 120 pages. On average, it's 60 to 70 pages of very complicated language, but really has everything you need to know. More than 60% of our homeowners are first-time homebuyers. And so for them, this inspection report, and for us, is really telling about the condition and what's happening in the house. But we do not want our underwriting associates to be reading through all these inspection reports all day long. We have thousands and thousands of homeowner quotes a day. What size of a team will we need to hire in order to review all these inspection reports? Our [ N ] AI through NLP is able to read through all the inspection reports, cross-reference this with all of our approved underwriting guidance by state, and then flag any mismatch to our underwriting review team, saving us hours and hours and hours of human work as well as eliminating human error. This looks like a great house. Who wouldn't want to insure this house? When I just joined Lemonade, our Chief Claims Officer told me, "Maya, when it comes to homeowners, it's all about the roof." And so looking at this picture, you might miss a dimension which is really important in how we understand the risks for different homes. Through computer vision, our AI is able to look at the rooftop, not only identify any sources of disrepair and flag this to our customer and make sure that they fix that before they become our customer, but also the type of the roof, which might suggest even the type of the structure, and other nuances that we're able to learn through that. Let's move to pricing. Pricing is important in 2 ways. One, obviously it's important for profitability. You want to make sure that you're pricing your customers right. It's also important for competitiveness. And the more granular you become, the more accurate you become for each customer, then that impacts those 2 elements dramatically. And so we've been focused in the past 12 months to increase the sophistication of how we price our customers. And a lot of that has only been able for us to do in terms of the data sets that we have really in the past 1.5 years or so. And so this is pretty fresh. Let me give you some examples of how the transformation we've done and the sophistication of our pricing. When we started Lemonade, and this was still true for about 1.5 years ago, we were using territory factors of counties. California has somewhat of 58 counties. Try to imagine the different types of risk that you have in one county, pretty wide. In our recent California filings, we used 200 square meters, which gives us about 1 million territory factors, which is 17,000x the granularity of what we had before. We also care about the catastrophic exposure we get for our customers. Before, we were using wide zip codes and sometimes even streets level in terms of catastrophic exposure. Think about a street that is built on a hill. Should the house at the top of the hill receive the same score as the house at the bottom of the hill? We've now moved to address level catastrophic exposure. So every single home receives the appropriate score both in terms of pricing and, going back to what I said at the start, feeding into our LTV, making sure that we also manage our aggregation in a good way when we're spending marketing dollars. Daniel shared this with you. We've completely changed the structure of how we file our pricing. Most insurance companies use lineal (sic) [ linear ] regression, which basically takes 2 different factors, correlates them together over time for your risk. We've now transformed into a machine learning model, which allows the model to decide and find correlations all on its own and create much more accuracy in how we're pricing our customers. There's some things in terms of the correlations that I can't expose but they were pretty surprising. We spent hours with a Texas regulator reviewing the machine learning models, making sure that they're both accurate and fair for our customers. And to the best of our knowledge, we are the first insurance carriers to use machine learning models in approved pricing for customers end-to-end. So going back to the predicted loss ratio. I said we use it also to find correlations between the initiatives and decisions we're making, and we want to see that impacted in predicted loss ratio. So when we launch these rates, we would hope to see an improvement in the new cohort predicted loss ratio. So it's great that this is really what we saw. We launched these rates in Texas September 19, and we saw a drop of almost 20 points of the predicted loss ratio for all these new customers buying through these new rates. But the best news is our conversion rate slightly increased, and the total volume of sales we received from Texas remained the same. So this is really telling you that it's not that we reduced the number of policies we're selling, it's just the granularity and sophistication of the pricing became better. I just showed you some of the work, not all of it, that we've done in the past 12 months. How much is it reflected in our actual loss ratio? Very little. This is going to take time. That lagging effect that Daniel spoke about as well is really in full effect here. And so it's going to take time until you see it in our actual loss ratio for homeowners. But we believe that we're on the right path. We're making the right decisions. We're pricing our customers appropriately, and it will show in the next few quarters. I like to joke and say that the best thing that happened to home is car, but it's pretty true. In the past 6 years, we were selling renters and homeowners essentially with one hand tied behind our back. The way homeowners insurance is sold in the U.S. is bundle, and we didn't have that. And so launching car was instrumental and very impactful for all of our business. It's a huge market, and from where we stand right now, essentially unlimited. And we're seeing huge pent-up demand from our customers and from people who are not our customers for us to come and launch Lemonade car in their state. In fact, the #1 most asked question for our customer care team for car is, when are you coming to my state. We have over 300,000 customers in our car wait list. So growth is not top of mind for us. We believe that, that will be something that we'll be able to grow with. We want to make sure that we're growing right and profitably. And in order to do that, we have to make sure we have the right data and that we're able to price and underwrite our customers in a good way. We also don't want to wait 6 years like we've waited with home. And so not surprisingly, and this is something we shared quite a lot about, the acquisition of Metromile was really essential in helping us building these data sets, billions of miles driven, 10 years of driving data that we received from Metromile that we're still in the process of integrating into our systems. In fact, next month we'll be launching our first LTV model for car. It took us 4 years to do that for home, so really crunching time and being able to get faster to the area of accuracy for our customers. The second thing that Daniel touched on is telematics. We have 90% telematics. Across the U.S., there's just 4% adoption of telematics and 2% who keep it on ongoing. So at first approximation, every Lemonade customer is driving with telematics and no one in the industry is. And this is very powerful. Let me show you why. The way the industry prices customers for car insurance is by proxies. It looks at your age, your marital status, your credit score, to try and anticipate what type of driver you're going to be and how well are you going to drive and what are the chances that you're going to get into an accident. We don't use proxies. We use how you actually drive in order to determine your driving quality, in order to decide what type of risk you pose and in order to understand what is the probability of you having an accident. That is dramatically different. Now I said we're still in the process of digesting a lot of the Metromile data, integrating that into our systems. We also have had the car insurance product just 1 year on the road, in just 3 states. So this is early days. But I want to show you some examples of the things we're already doing with this data. We know when our customers have had an accident. This allows us to initiate the first notice of loss automatically without waiting for the customer to reach out to us. We don't have to build, like other carriers do, huge teams that need to reach out to the customer, chase after them and try to fill in the gap of data of what happened between you had the accident until you actually decided to file a claim. We're also able to be there for our customers when they need us. We prompt a digital prompt for our customers when we know they had an accident. We ask them if they're okay. And if they don't respond, we automatically trigger emergency services to reach out to them and be there for them when it matters. We're able to identify if other people from your household are driving your car as well, people that might have never gone through the quoting process and have never been priced. Let's think about a household with a 47-year-old mom with a perfect driving experience history and a 17-year-old kid getting on a car for the first time. Who do you think got the quote? It's no surprise though when we interview people through our claims team for car, they actually say this is one of their biggest problems they have to deal with. This creates huge premium leakage and obviously, you're mispricing your policy. So [ by ] being able to know that, we're able to better price our customers. We're also able to know and identify if you're using your car for something other than personal use. We're also able to know and identify if you're using your car for something other than personal use. Rideshare is a good example, and that triggers for us underwriting reviews. Very similar to home, pricing is a huge focus for us when it comes to car insurance. I spoke about sophistication for Home. Let me speak about the volume and velocity when it comes to car. You want to make sure that every time you learn something new, when you have new insights, new data, that it is reflected in your pricing. Some of the regulatory process of pricing is out of our control. It takes time, different regulators, different states, to approve rates. But whenever the ball is in our court, we want it to go as fast as possible. We don't want to have any lingering. And so we've developed a tool for our insurance team to be able to, with a click of a button, update the entire rates and flush them through the book. We used to require back-end engineers coding, it used to take us months. Now it takes us literally days to update rates. And so every time we learn something new, we're able to reflect that in our pricing. This is really building us for scale, as Daniel mentioned, making sure that we have all the tools, platforms and infrastructure that we need so that the volume of these products grow, we're able to price them, underwrite them as accurately as possible to make sure that we hit profitability. And Car is going -- is already, but will become, more and more, one of the most important levers for us to increase our bundle rate and cross-selling for our customers. I'm glad I've had the opportunity today to share with you in more depth the types of decisions and the sophisticated technologies that we're deploying. And I hope that you now share or at least understand our convictions that the systems and the processes that we've built internally are setting us on the right trajectory. With every turn of that flywheel, our predictive data continuously grows, our machine continuously learns and our pricing and underwriting become more refined and precise. This is putting us on a path to reach parity with some of the more established players in our industry and then go beyond, which will have far-reaching implications for our business. To get a better handle of how this trajectory looks like from a financial lens, let me hand it over to Tim Bixby, our CFO. But before that, here are 60 seconds about a topic which is an immense source of pride for all Lemonade employees, the Lemonade Giveback. [Presentation]

I'll take you through some of the metrics and the numbers and some of the modeling we put together that underpin much of what we've talked about today, but also much of what we're thinking about in the days and quarters to come. I'll walk you through a 5-year model and a couple of variations with some sensitivity that we think will be very interesting and useful some of -- for most of you. We'll talk about cash, capital availability. We'll talk about some loss ratio detail. We talked a bit about our predictive loss ratio, our predicted future loss ratio. We'll talk a little bit about our -- dig into our actual a bit more. We'll talk about our path to profitability. We'll also touch a bit on our surplus strategy, one of the areas of capital use for us. And I hope through the next few minutes, it will give you a better feel for some of the numbers and metrics underpinning what we're planning and what we've shared today and why we're confident about the next phases for the business. Now a thing to think about is the environment we're in, the model that we're going to share a path to profitability, getting to a place where the capital that we have on hand today gets us to cash flow positive to breakeven. We have to be aware of the market that we're in. The market that we're in has certain conditions. We plan to put ourselves on a track where we are not forced to raise additional capital in a market where that is pretty expensive. Two years ago, the world looked very different. Today, we have to be working within the reality of the market that's in front of us. Now this is not a market of our choosing, and someday it will change. But today, we're basing our decisions and the model we're sharing with you on that market. 3 main levers to think about in the cases and the scenarios that I'll walk through. Growth is a key one. So we've modeled out a 20% compound annual growth rate over the course of the 5 years. Gross loss ratio, a key lever for us. We talked about the predicted loss ratio and the cohorts coming in, looking quite positive, seeing that nice improvement quarter-over-quarter. But we've targeted over the 5 years in the models that we're going to share with you, us getting to a 70% loss ratio by year 5. And then if we think about that multi-line customer rate, what percent of our customers have more than 1 policy, we showed you 3.7%. A huge opportunity ahead of us in that line. Incumbents, best-in-class performers, see a number more like 60% of their customers with multiple policies. So in the modeling, I'll show you getting to a point of about 25% over the course of 5 years. So based on these assumptions, here's a base case view, and I'll walk you through the key parts of this step by step and then we'll look at some sensitivities on this case. So we'll end the year with roughly a little less than $1 billion in cash, cash equivalents and investments. Now some of that is restricted. Some of that is set aside for surplus as is common for every insurance company. But think of that as the starting point going into next year. We have noted a number of times over recent quarters that we expected Q3 this past quarter that we just reported, to be our quarter of peak losses in terms of the EBITDA loss that we reported, and we continue to believe that, that is the case. Over the course of the 5-year model, this base case, you can see those next 5 bars is that EBITDA loss declining consistently year-over-year until in year 4, you see a pretty small bar and then flipping positive in this base case. So what this tells you is 5 years out, you'll see a cushion, a sufficient cash to get us to that point. Now there's a couple of other factors to keep in mind that we factored into this modeling. There's capital expenditures along the way. That's fairly nominal, but more than 0 for us, that's a use of cash. There's also the opportunity for interest income, investment income, and that's a nicer environment today than it was not too long ago. We factored both of those in and the net of all of that is a bit favorable to cash, and that's that sixth bar that you see there. The last piece that I think is worth noting is working capital. Historically, the change in working capital has been favorable to us. We collect premiums upfront, we pay out claims over time, pretty straightforward. That tends to give you a source of cash. We've modeled that at 0 in this forward-looking case. So over time, that could be an additional source of cushion for us. So again, that's the base case. 3 key drivers, think of the growth rate. And again, the growth rate is at a pace of our own choosing. We deploy capital to acquire new customers and we can adjust quickly, and we can manage that in a way that enables us to be thoughtful about how we deploy capital. So for this model, in this environment with the goal of getting to that point with just the capital we have on hand today, moderating that -- with a 20% growth rate, 70% gross loss ratio and that 25% multi-line customer rate. So this is that base case. Same case, a little different view, sort of a dashboard view, so we can compare a couple of other cases that we did. Again, over the course of that 5-year period, getting to a point where the trough is about $100 million minimum level of unrestricted cash. So think of that as sort of the answer to the base case. And then about 4 years out, mid-2026, turning from a cash EBITDA loss to a profitable position. So at the end of year 4. Let's look at a case now where things go a little bit better. We get a little bit more of a tailwind in the model that we've put together. Let's keep the growth rate about the same at 20%, but then let's moderate that gross loss ratio, let's say that we're able to get to a 65% gross loss ratio. You saw numbers better than that in the predictive numbers we looked at just a few minutes ago. And let's say, the multi-line customer rate tracks a little more towards that industry best practice of 60% at 35%. Lots of room yet to grow, but showing some nice upside in that number. What does that do? Well, the $100 million of trough cash almost doubles, $175 million now in this case, with these additional tailwinds. And turning profitable almost a year earlier, 2025 versus 2026. So obviously, this is an easier case to manage when these things start to happen. We won't see these at the end. We'll see this as the years go by. And in this case, we could obviously consider accelerating growth beyond the 20% with that additional cash cushion. Now let's think about if things don't go quite as well as the base case. Maybe there's a little bit more of a headwind on some of the metrics. Again, keeping the growth rate consistent at 20%, so we can have an apples-to-apples comparison. Let's say the gross loss ratio is not tracking to where we expect it to be. And it caps out at, say, 78% versus 75%, 70%, 65% in the other cases. And let's say that multi-line customer rate continues to improve, we're on a nice track but it doesn't quite get as far as the prior case. Say it gets to 20%. The result of this case now, you see what was a $100 million cash cushion becomes a deficit, a $125 million unrestricted cash deficit at the end of the 5 years. Now, the good news here is it's relatively small sensitivity, $100 million is not a $0.5 billion, it's not $1 billion. But we would unlikely watch these metrics unspool and do nothing about it. We would see this coming over the course of the earlier years, and we'd be able to make choices. Perhaps the market environment is a bit different. We're able to raise more capital, that's an option. Or perhaps we moderate growth. And let's show what that looks like. If we pull back growth even further, let's say we were in a market where we chose, for the cost of capital reasons, not to raise more capital, we could pull growth back to, say, 12%. And the result is a transition back to that $100 million cash cushion that we saw in the base case. And again, profitability coming back in to about the same point at 2026. Now to be sure, this would be a smaller company if the growth rate is dialed back a bit. But the big picture, the kind of takeaway I'd like to leave you with from these various cases is that we have ample capital to control our destiny. There are -- because of what we've done in the past couple of years, we were -- we moved quickly to raise capital in a market that was conducive. And so we have the benefit today of not thinking out 3 or 4 or 5 or more quarters, but thinking out 3 or 4 or 5 or more years. So underneath these assumptions, we have some confidence and some metrics and we'd like to share a little bit of that with you. Why do we think we can navigate this path? Well, a couple of things, some of which you can see externally. This is a view of our in-force premium over the course of the last 5 years. Steady and smooth, looks like a straightforward business tracking nicely over the course of the years. But think of the macro trends happening during the course of this period of time. A pandemic, shifting a company from private to public, significant inflation we haven't seen for decades, a bear market, a war in Europe. Yet, with these macro headwinds, Lemonade steadily moving upward and improving and growing over time. Some suggest that insurance is recession-proof. That may be a little bit extreme, but Lemonade in particular, within the world of insurance, I think has shown terrific and impressive resilience during this period, particularly when you think about some of the macro headwinds. We have a fair amount of visibility. We can see what's coming, not forever, but pretty far into the future. In fact, in just the time since we went public about 2.5 years ago, each quarter, we come, as most of you know, and we give our view of the coming quarter. All of our key metrics, how we see them spooling out in 10 out of 10 quarters since going public, we've been able to hit our own guidance or exceed our own guidance every quarter. Now this is the kind of thing you don't want to brag about too much because it's a record that may not last forever. But the reason that we have this visibility is just the nature -- visibility is the nature of our business. We are large enough, we are multiproduct, and so we can see into the evolving book of business with good confidence. We talked a bit about our predicted loss ratio, the loss ratio of the cohorts that we're acquiring today and how we expect them to evolve, and our models continue to get better and better. But we're also seeing this in the actual loss ratio, the lagging indicator. The forward indicators are super important. That's how we manage and run the business, but the lagging indicators are facts, and we have to deal with the facts. Now the facts are pretty good. This is our view of our Home, Pet and Renters loss ratio. So by product. Over the last 4 quarters, actual results, fourth quarter is obviously not actual yet, but we have a feel for how that might play out. Now what's not here is notable. So life is not here. We don't write those policies on our paper. So we don't manage those claims. Life is not on the chart. And Car is relatively new, relatively young. The product we launched a year ago is still relatively small and evolving. So not quite at this mature stage of these products. And Metromile is certainly brand new to us. We haven't had much of a chance to impact that yet, and those numbers are public and available for all of us to look at from the first part of the year. But a consistent trend, even in an interesting year from an insurance perspective. Home, tracking from 132% loss ratio, heading towards 117 estimated in Q4. Pet 93%, heading to the mid-80s, 86% by Q4. And Renters, the most mature aspect of the book, a significant piece of the book, 61%. A little uptick there because of weather conditions in the recent quarter, but tracking to the mid-50s by Q4. So good progress, a consistent theme and continued support for the fact that our predicted loss ratios are something that we can also rely on. I want to talk a little bit more about path to profit. Path to profitability is on the tip of everyone's tongue. Everyone wants to see it, everyone wants to feel it, ourselves included. Now we're not here to just get profitable. We're not here building Lemonade to just break even. We're here to do something much bigger. And I think a couple of the charts that Daniel showed, showed the potential future vision of what can happen if your business grows and continues to grow at a pace that we have. But we want to show you our path and how we're tracking in this environment. And the path is really here in front of you. EBITDA is a good proxy for cash flow. It's a number we report every quarter. It takes out the non-cash charges and the significant onetime charges from operating income. So here you see EBITDA divided by our gross earned premium. So the earned number, but it's before the impact of reinsurance and quota share reinsurance that we have and others have can move some of those other numbers around. So this is sort of an apples-to-apples comparison of how our EBITDA investment, our EBITDA loss is improving consistently over the past 5 years. And what you can see here is our -- and I think you've heard it a couple of times today, our most significant investments are behind us. Not all of our investments are behind us. Our most significant investments are behind us. The platform we've built, the multiple products that we've now launched just in the last few years, the integration of our first acquisition, significant investments now in the rearview mirror. Now let's look at those same numbers from the right-hand side, let's blow out the right-hand side of that last chart. Same similar trend. Now you see a little bit more volatility quarter-to-quarter, and that is the nature of our business. That's the nature of insurance. But look at that trend line. Even in this market, going public, launching Pet, launching Car, first ever acquisition right in the middle of integrating with a wholly additional public company, steady upward progress up and to the right. We also invest a fair amount in what I consider our most valuable resource, is our employees. So the people who build incredible products. The people who create a customer experience that we believe is second to none in the industry, even that investment is becoming more efficient. So for a long time, we were investing ahead of the curve, ahead of growth because we had to build these things. We had to build a regulatory environment inside the company. We had to build an infrastructure. We launched the business in Europe. We had to expand in states all across the United States. That has now shifted. And just in the past year, you can see what was a pre-investment is now starting to show significant leverage. And what is on this chart here is in-force premium, so that very, very top line, the size of the book of business, divided by our number of employees. Over the last 4 quarters, 25% improvement in that metric alone just in the past year. So our customers are amazing, yet our average premium per customer is $343. That's an incredible achievement. 5 years ago, a little bit after I joined the company, our average customer paid us $138. Some of our first customers paid us $60 a year. So in just 5 years, 2.5x greater premium per customer. In the most recent quarter, a bit of an uplift from the Metromile combination is in there, of course. But even before that, consistent upward growth in that number. So the glass is -- I'm not sure if it's half full, but the glass is going in the right direction on this page, significant improvement. But now let's back that up and look at the entire market potential. That same chart from the last page is now shrunken down to the left side here, and it's barely visible, that $343 of Lemonade average premium. It's there, you can see the arrow, but you can barely make it out on the page because this is the market we're in. This is the market potential that we're going after. The U.S. average, maybe a home policy and a 1 car policy, that's a greater than $3,000 premium per customer. That's the average. The top Lemonade customer -- this is interesting, the top Lemonade customer today pays more than $10,000. So we're there today. Now there's only a couple at that level, to be fair. But we're there today, we have the product, we have the portfolio, we have the support mechanism for customers to pay us $10,000. If we dial back just to the point of our IPO, that number was $6,000. Let's have a magic wand. Let's wave our magic wand. Let's say we added no more customers, and we moved everyone to the average. That's a 10x business, a $6 billion business. We want to talk a little bit about surplus. There's a lot of insurance experts in the audience, and we want to talk a little bit about how we think about capital management. A reality of the assets that we have is that some of them have to be set aside. They stay in our bank account, but they have to be set aside and restricted for capital surplus. Every insurance company has this regulatory requirement. Important to note that it is still our money. It sits in our bank account, we can earn income on it. It's still on the balance sheet, it's an asset of the business, but it is restricted. We can't go out and acquire new customers with it. So we've simplified down some basic metrics to help you just get a feel for how we are thinking about it. How we've modeled it -- how we factored it into the model that we share today and how we're thinking about it going forward. Because there are some things that are evolving. So there's a simple ratio I'll use to kind of take a little bit of the complexity out of surplus requirements. There's a deep complex model for every company that drives what your actual surplus need is. It's got a lot of moving parts. But at a very high level, with no real thoughtful management of capital or surplus, think about a 3:1 ratio. And what that means is for every $3 of premium that we go out and write, $3 of in-force premium, we would have to set aside $1 of surplus into this restricted account. It's ours, but it's restricted. We can't use it. Now we're going to be a little more thoughtful as others are, and we're actually in the process of setting up what's called a captive structure. This will be new to Lemonade. It's not new to other insurance companies, it's a commonly used approach. It's a legal entity, a regulatory structure that enables you to get more leverage out of this capital surplus ratio. And it's a little more common at companies that are growing a little bit quicker. If we employ this captive in the way that we expect we'll be able to, we can expand that leverage from a 3:1 ratio to a 5:1 ratio. So think about instead of setting aside $1 for $3, you set aside $1 for every $5 of premium that we write. There's also quota share reinsurance, which is a structure we have in place today, and we've had in place for some time. Today, we seed or share about 55% -- exactly 55% of our book of business with our reinsurance partners. That's been as high as 75% in the past. Reinsurance is a market, just like any other. We expect that reinsurance will be available to us. We don't always know what the cost will be, what the price will be, what the terms will be. And if we find them to our liking, that is a lever that we will take advantage of. And if it's not, we have other opportunities and other ways that we can manage our capital. So think of the reinsurance lever as something that if we find terms attractive for as much as 75% of our book of business, as we have in the not too distant past, that ratio becomes 8:1. So think of a 3:1, a 5:1 and an 8:1 as potential ratios for this surplus structure. Now the reality will be that it will be a combination of these. And we don't know exactly what that combination will be. We'll be able to augment and adjust over time as makes sense and as terms dictate. But I would think overall is sort of a mix of those potential structures of about a 6:1 ratio, and that's what we factored into the models that we have shared today. Now underneath the detail, there's more in the surplus world. There's things like risk-based capital, RBC. For those of you who are experts, that adds a little bit more to the surplus, and we factored that in. There's a little bit more requirement for companies who are fast growing or for companies who have had historical losses. These are all true of Lemonade, and so we've factored that into the modeling that we've shared today. So back to one of my first comments. We're not here to break even, although we expect to break even. It's a checkpoint along the way. We're here to build something big. We've already built something great. We've built a structure and a product and a user experience that we think is second to none on the planet and the world of insurance. But we're on track to build something that's much bigger. And if you maybe do a little what-if experiment with me, I think it would be a little bit fun. Today, we have 1.8 million-ish customers, $343 on average, about a $600-plus in-force premium business. Let's imagine we double our customer base. We've done that in just the last 18 months or so. Let's imagine we double our customer base again. And let's imagine that we take that average premium just up to half of the national average, 50% of the national average. That's another 10x business, 10x where we are today, $6 billion in-force premium. And let's roll that tape forward a little bit further, and let's say we double it again. So we double once and we double again. And let's say we take the average customer with multiple products that we're able to offer up to just 100% equals the average in the U.S. All of a sudden, that $6 billion is now a $23 billion premium business. Let's apply a 12% operating margin. Let's apply a couple more points of investment income. That looks a little better today than it looked a couple of years ago. And I think that can give you a feel for why we're here, what kind of value and potential value we think that we can create. And if we double and if we double again and if we move that average price point up in the way that I described, we'd still barely be breaking into the top 10 of insurance companies in the U.S. and we'd have a less than 5% market share. This is why we're here. Breakeven is nice, path to profit is even better, but this is why we're here. Next up is Q&A. And before we get there, we have 2 minutes on something that is very near and dear to the hearts of all Lemonaders, and we hope you as well. [Presentation]

All right. We'll be taking questions from here in our offices and also online. So if you'd like to ask a question, please raise your hand, and 1 of our team members will come over with a microphone. I'll start us off online as we get warmed up in here.

Tim, I'm getting a question from Darren Stark and [ Pierre Quasmi ]. Both are talking around our G&A spending. Asking if it's possible to cut it down by 50% and still support mission-critical operations. And in general, what measures are we taking to improve it?

G&A spend, a topic that's near and dear to my heart. So one thing I should note is that we have talked a fair bit over the recent past about peak losses. Q3 being our period of peak losses, and what a period of peak losses means is you're continuing to invest during that period. G&A is no exception. Now Q3 was a bit of an anomaly. We merged formally on July 28 with Metromile. So in Q3, you've got 2 things happening of note. One is the transaction itself closed. And in the numbers of Q3, there's $7.4 million of expense related to the transaction, onetime expenses. So that's detailed in our disclosure and our shareholder letter. And so for more detail, I would encourage you to look there. And it's also the quarter of -- again, bringing the companies together. So you get sort of a double penalty. We've done quite well, I think, in optimizing some of those expenses in the folks that we brought on, the synergies we're seeing between the businesses. So I would think of Q3 as sort of that maximum point because we're bringing those 2 companies together. And then over the coming period, we'll see things optimized. And our guidance in Q4 indicates our kind of first next look at how we see the expense lines evolving.

Okay. I'm going to remind that you can raise your hand and a member of our team will come over. Maya, a question from [ Thomas Layton ], who is asking what kind of actions we are taking to reduce loss ratio?

So I think I shared quite a lot in my presentation in terms of the progress of both sophistication and a little bit of the granularity that we're now able to do in our pricing. And also, each of our product lines needs different attention and different actions that we're taking, Renters is already profitable. But a lot of the focus that we're going to have is around rates both in terms of velocity, but also in terms of the sophistication and granularity. And there's a lot of that happening already and will happen in the next few quarters.

Okay. Tracy?

Tracy Benguigui, Barclays. Tim, you mentioned the possibility of setting up a captive. And I recognize New York, your lead regulator, is a pretty tough one. Have you begun conversations with your regulator? And I'm just wondering, would the captive be offshore?

So it's relatively early in the evolution of the concept. And so we're at the point where we wanted to introduce the fact that's something we have been working on. We're not yet to the point where we're disclosing any of the particulars around how the formal structure will be put in place and that will, I would expect, evolve over the coming months. And we'll -- to the extent we can share those details, we will. But at this point, we're just stepping into the structuring of it.

Okay. If I could just ask another one. On your predicted cohort lifetime loss ratio, you gave that slide from the progression from the first quarter of '21 through the third quarter of '22. I'm just wondering what the retention rate is of those customers. Like what should we typically expect in terms of cycling out those customers so you get the fruits of LTV?

Yes. A couple of thoughts. One, Tracy, just coming back to your earlier point, you're understandably focusing on New York. Do remember that as of last quarter, we have 2 regulated entities. One of them is in Delaware. So we do have more optionality around that as well. We disclose our retention on a dollar basis. In the last quarter, you saw actually a nice spike in that. So that is our dollar retention. If memory serves, we're at 84%, which means customers that were acquired during that cohort, their dollars now represent -- or the dollars that they pay us today represent 84% of what they paid us on day 1.

Okay. So that's not blended by cohort. That's...

It is a 12-month rearview mirror. So you're looking at the same people, what they're paying you today. So in that sense, it is cohort.

Yaron?

Yaron Kinar with Jefferies. Maybe a couple of questions on the reinsurance side. So first, when you talk to the reinsurers and talk about the terms for the upcoming renewal, do they factor in the predicted loss ratio? Or are they looking more at the rearview mirror?

Yaron, some do, some don't. The reinsurance market is going through unusual tumultuous times right now, and it's certainly been hardening. But I have to say that we have, at this point, pretty deep and meaningful relationships with our reinsurance partners. Our largest reinsurer today is Hannover Re, with whom we do a lot of interesting things. The video that you just saw before was of the Crypto Climate Coalition, Hannover Re is our partner in that. So we do have deep relationships. And our anticipation is that they have -- well, I know that they have a deep understanding of our business. The stuff that we've shared with you, they understand deeply, and our anticipation is that the partners who have been with us will want to continue to be with us.

And maybe a follow-up to that. Given that we are entering a hard reinsurance market and potentially means higher reinsurance costs, I understand you have the leverage to maybe scale back on reinsurance, but how ultimately does that affect your gross premium growth and the path to profitability into cash breakeven?

Yes. So we haven't entered renegotiations around our reinsurance yet. That contract falls due at the end of June. And as you probably know, it's customary to do those things 30, 60 days out at the utmost. So it's still premature for me to answer with any specificity the question that you're asking about what would the terms be? And the market has been shifting. So I want to be cautious. We do approach reinsurance, though, going forward slightly differently to how we did early on. And you heard us talk about it as a tool for surplus optimization rather than as risk management. We feel pretty good about the risk management piece of this. We feel like our business is now stable enough, diversified enough, both geographically and across product lines, and we have a handle on what we need to do in order to manage the loss ratio. So we are less interested in paying, in margin stacking our business, if you like, in order to shift risk. We're much more interested in doing that as a way of optimizing capital. And that's why we'll come down to a calculation near our midyear 2023 of what are the actual quotes that we're getting. There will be -- we'll have to give up margin in order to get more capital efficiency. And then it will just be up us in an Excel spreadsheet figuring out the optimal mix.

Let's take 2 more here, and then we'll go back online. Jamie?

Jamie Inglis, Philo Smith & Company. In your slide, you had a predicted loss ratio in the first quarter of '21 of 86%. At this point in time, what did it turn out to be? I mean, if you were to look back on those policyholders now, where would that have been?

It's a great question. Thank you, Jamie. I don't have the precise number with that particular cohort but of course, the actual results are what train the model itself. So I'll answer you kind of in a more theoretical framework because I don't have the specific number to hand. And I'd also remind you that, that cohort is still only 1 year old and the first year, as I explained, is going to be above its average loss ratio. The average loss ratio generally materializes in year 3 of our customers. So I wouldn't expect that cohort to have an 86% loss ratio over the last 12 months. I would expect the model would have predicted a higher loss ratio over the last 12 months but trending downwards. And because the model trains itself on the actual historical data, I would imagine that they tracked pretty closely to that. But I can't give you a specific answer to your question.

Over time, has the predicted loss ratio -- the ultimate loss ratio become closer to what you predicted? Or is it about the same?

Now, over time, it's definitely asymptoting in the right direction. Maybe, Jamie, I'll add 2 more points about the predicted loss ratio. One Maya touched on and 1 we haven't spoken of. The predicted loss ratio does predict claims and churn and all the things that I spoke about, and it does understand that we are in a high interest rate environment. And therefore, it will apply a discount for the cash flow. It doesn't know how to predict inflation. If we had a model that could truly predict inflation we'd utilize it in all different interesting ways. So this assumes nominal values, and it assumes that our rates will take care of inflation, that we'll be able to stay abreast of inflationary pressures, which are a big deal in our business. But I just want to highlight that, that is 1 factor that could impact actual results and how they might vary from modeled results, and that's not included in the model. The other piece that gives us tailwinds rather than headwinds Maya did touch on, which is the model, while it is providing a leading indicator, it is fed by historical data, right? It crunches historical data in order to generate a prediction. It doesn't know what's going to happen tomorrow. We do. Maya already showed you how many filings we have that we have not yet implemented. We know what features we're developing that we've not yet deployed. So there's a lot of tailwinds still coming to the model. So the model is imprecise in at least 2 ways. One is it doesn't know about inflation and that could hurt us. And the other one is it doesn't know about all the hard work that we're doing and that could help us. Which is why I urge us to look rather than at the specific number of 86% to look at the trend line, because then you are looking at an apples-to-apples comparison quarter-over-quarter.

And Jamie, can you hand it over to your left to [ Emmett ] for a question.

Great presentation, by the way. I just had a question about the model for that neighborhood in Houston. That was a great case study, but I was curious, what did it discover about that particular neighborhood that made it so the pricing off? Was it like the sister neighborhood seem to be optimized, but that neighborhood was clearly all off? I'm just curious what it discovered about it.

That's a wonderful question, [ Emmett ]. I don't know. I don't know. I was playing around with the map and finding these interesting things myself. The team will know. But I apologize, I don't know. I will tell you at a high level that the answer is going to be pricing. So it's obviously mispriced. And Maya spoke about moving -- she gave California as an example, we're doing this across the nation, where the precision, the neighborhood precision has moved up 17,000 fold over the course of the last few years. So we're getting much, much, much more granular, which is what you saw in that particular neighborhood as well, that we are seeing the stuff. How that is then fed in and you absolutely need to identify the root cause and typically, in that case, it will have come down to pricing. I don't know, Maya, if you want to add anything to that?

No.

Sorry. May I ask a question?

Absolutely. Go ahead.

Yes. So I'm [ Yukon ]. I'm a retail investor. So I have 2 questions. First question, in terms of the dollar retention there. That reflecting the in-force premium churn rate, right? I'm just wondering like for the actual, the customer churn rate, I think, based on payer [ bag ]. And I'm also calculating myself, it's like between 30% to 40%. So it's relatively high, the churn rate. So I'm just curious like what's happening here. And over the time, are you predicting to see the improvement. Yes, the customers.

So maybe I'll make a couple of comments, and then Tim, if there's anything I'm missing out, you can help me out. So we haven't -- it's not been a number that we've disclosed, but it's not as high as some of the numbers that you're estimating either. But let me give you a little bit of color on that. The reason that people churn is overwhelmingly important in kind of thinking about churn, and we look at that data pretty closely. I'm going to miss the exact decimal point on this, but I'll make the point broadly because I'm working off of memory here, but every single customer that churns gets asked a question of why are you leaving us. Sometimes it's hey, we didn't like how you handled that claim or something like that. That is incredibly remote. If memory serves it's a fraction of 1%, it almost doesn't happen. It happens. But -- that is not what is accounting for churn. What overwhelmingly is being answered there is stuff like I'll be back. It's, hey, I -- it's so much of our book is Renters and people went out to college and they got a policy, now they've moved back with mom and dad, they no longer need a policy. They moved in with their girlfriend, they're now combining policies. They moved to a state in which we don't yet offer it, so they don't have it right now. And I think it's helpful to think about churn as coming in 2 flavors. Throughout the last few years, I've disconnected and reconnected with Netflix a bunch of times because I moved home and moved country and stuff like that. So I probably appeared on some churn chart of Netflix, but I've always kind of come back. The cable company, when I cut my cords with them, that was real churn. They're never going to see me again. And we feel really very good that the customers that are leaving us are alumni. They are people who -- and we see this in the numbers as well. When opportunity comes, when they need us again, they come back to us. So although we have structural reasons that would elevate churn today, a lot of them, for example, are the absence of Car. A lot of people are telling us, hey, I need to bundle because I need to save money and I don't have car. They go [ after ] State Farm and State Farm says, hey, I'll save you so much if you bundle and the dollars just don't make sense. They're going to a place where we don't yet offer the product. So there are structural reasons, which will resolve themselves as we continue to roll out our product. So although the number, I think your calculations are high, but also understandable. If you pierce 1 level deeper and try to say, well, what are the underlying causes and concerns, we're pretty bullish on those.

Over the time, are you expecting to see the improvement?

Yes. And I think you're seeing it in -- the dollar churn is reflective of that as well

Yes. Yes. And the second question is about the predictive model you are showing. 1 is the lifetime value model, the other is like the lifetime loss ratio model, right? So you have different generations. I'm just wondering like what is kind of observed value? I think it's also a similar question as that gentleman said: it's what's the observed lifetime value that you calculate. The customer that you, say, acquired in 2020 and then 2 years later, you calculate it again based on their lifetime value, is that you're seeing kind of the reflecting your improvement of the model?

Yes. And the models are getting better and better. So 18 months ago was the first time we were able to make a prediction. So the most we have is 18 months and our models have matured a fair bit since then. But the answer is yes and absolutely, and that is the methodology for how the new generations come about, right? They look back on the results and they fine tune and get more and more data, I hope that answers that.

We'll take another question coming online from Arvind from Piper Sandler. Can you talk about the impact of macro in your business? Insurance is an essential product, so it seems like revenue should be resilient. But as customers become more price-sensitive, should you benefit? And what is the impact on pricing with reinsurance in a tough macro environment? Do you want to take that, Tim?

Do you want to take one?

There's a lot in there. To be honest I didn't take notes in [ a real sense ]. Tim addressed some of this, right? This has been one of the big reliefs for us, a young company facing the kinds of tumult that the world has faced seeing so many companies from all different stripes, particularly young ones, suffer quite a beating and fall on hard times. And it's just been amazing that when you look at our financials, it's hard to identify when each of these headwinds kind of hit. It's been a fairly consistent up and to the right, no pivots, no changes of strategy. Investors who saw our presentation pre-IPO or for that matter in our seed round would recognize everything I said today as an expansion and realization of the stuff that we've been saying all along. A lot of you have been with us for a long time, and I hope you recognize that consistency. So we wrote in one of our recent letters. Maybe there was some hyperbole, but not much, that looking at our internal dashboards, we wouldn't know that all this stuff is happening in the world. We read the papers. We're aware of what's happening. But it's not reflected very much in our financials. Other than the 1 thing that we see on the dashboards is inflation. And we've had to pick up, and Maya spoke about this, the pace of filings. In a low inflationary environment, that time lag matters less. And in a high inflation environment, if you're not filing at a rapid rate, then you can follow into the trap of pricing based on a certain nominal value and then paying claims based on an inflated value, and that can be pretty painful. And part of our elevated loss ratio is explained by that. But so is our newfound accelerated rate that Maya spoke about and all the energy that was put into the technology to enable us to move from a 2-month code-intensive implementation of filings to a very rapid 1 where we shift the focus of our energies towards resolving the most pressing problems. So inflation is a real issue. It's not gone, but it is the one that we recognize and contend with. The second one, which you won't see in our dashboards, but you know this as well as anybody is just an elevated cost of capital. So when our share price was much higher, raising capital was much easier and cheaper. And we always want to make sure that if we are in any way diluting ourselves in raising capital, we want to make sure that we can give a positive ROI that the earnings per share will increase as a result of any subsequent fundraising. At this climate, we're not sure we can do that, which is why we're making do with the money that we have. If that climate changes, that will change. That is the other way in which headwinds, macroeconomic headwinds have impacted our business.

I'd add 1 to that, which is actually somewhat favorable, which has been our marketing efficiency. So the environment over the past year to 2 years has been a bit of a step change in the cost to acquire a new business for customers in general or for companies in general who are doing primarily digital acquisition, online acquisition of customers. Yet our value, our long lifetime value of those customers has increased faster still. And so even though the absolute cost of acquiring that new business has risen in that period, the -- our net impact has improved somewhat. So that's 1 macro trend that has impacted us, yet we've been able to overcompensate by more efficiency internally.

All right. I'll turn it back here to the floor. Few people back there and -- [ Brenna ]?

Could you give us a little more specifics about the rate, rates that you're actually going after by product. You talked about the backlog of rates that you have filed for. What are they? What percent increase in rates have you filed for, by product? How much of it has been implemented, so we kind of know what's in the hopper? And then in conjunction with that, I mean, all that is to just keep up with loss cost. So where do you think the loss costs are really running? In an auto book or a homeowner's book, they're definitely double digits now, so you almost need double digits just to stay even, not even improve.

You want to start? And then I will.

Sure. The Car, Tim touched on this in passing, but our own Car business only launched a year ago, 1 year of data barely seeing kind of annual renewals it's just very hard to get a good picture. We are seeing declining loss ratios. But the quantum of data is such that we're being very cautious in reading too much into it. It is still a very small book, only live in 1 state for 1 year and in other places more recently. So it really doesn't rise to the level of statistical significance. And therefore, I just want to be cautious. We are filing, I think Maya mentioned, in Illinois, where we've been for 1 year, we've already rated 3 times. So we're monitoring the data, quickly fixing, short cycles, rapid iterations. But we're not in a position to really zoom out and give you a sense of what is going to be needed over the long term. In most of the states, we are still doing our initial filings. In Metromile the picture is clearer, but not so clear to us yet. In other words, the numbers have been published for some time. They've been under a loss ratio that has been in the triple digits. 65% of that business is in California. And as you well know, they've just not been able to take rates in California. This is an important point to perhaps underline, which is we have absolute clarity on what they need in terms of rate. The data is very revealing in terms of what is needed. It's just a regulator that is unwilling to allow for that. So there's no disparity between what is needed and what we know. There's a disparity between what we know and what we are able to implement. And we respond to that by throttling back on growth. We have spent no dollars promoting the Metromile business since the acquisition. We said that on the day that we acquired them. We will change that the day that the rates are approved. So this is exactly what the portfolio management that Maya spoke about before, the fact there are multiple geographies, multiple lines allows us to do. In Home, maybe Maya, you can [ speak to ] Home.

So I'll just say, obviously, this differs by product and by state. So some of our states require very little rate change, really just to keep up with inflation. The business was profitable even before we went out of all of these rate changes. But just to give you a little bit of understanding of the velocity. So we grew our filings from last year for Home 3x. So we're doing 3x the filings that was done last year in our homeowner business. And some of the states that were seeing bigger impact in terms of inflationary up to 20%, 30% in terms of the cost of rebuilding and changing in the RCE, then we're taking as much as 35%, 40% rates in some of these states, and California is obviously a good example. We have a 35% rate standing.

And if I can just follow-up. You talked -- you gave the example of Texas and using the AI models getting approved there. What is the start to finish time frame that takes to educate a regulator in that kind of a model? Yes.

So I think, again, it depends on the regulator, depends on the state. And I think in many ways, this is a huge focus of ours to really be at the front of it, sit down with regulators and make sure that we're taking the time to make sure that they -- the way we're thinking about implementing these models is in a framework that they feel comfortable with. And with some states like Illinois and Texas, I would say we've managed to make huge strides with in implementing that. And now we also have data to make them feel even more comfortable and almost use that as a case study as we move into other states and get more of that approved in other places.

Yes. Sorry, just to add maybe 1 dimension to what Maya already said. It's an ongoing discussion with the regulators, but it's been ongoing for a while. And we do feel that if we want to be leading the industry in terms of AI, it is a burden that we have to lift to educate the regulators and work on this. And we do a lot in this. I've met with many, many regulators, spoken to them many, many times. We were joined quite a while ago now by Tulsee Doshi, who is our AI Ethicist, who really is meeting with regulators with incredible frequency to try and help them understand the issues and concerns that they have. It's a bit of a gulf they need to fill because regulators are not trained in AI. And therefore, their caution is understandable, but we are putting a lot of resources into overcoming that and explaining. We have a very strong thesis that I've written about and spoke about as well, that ultimately, properly applied, this is a massive boon to fairness. As you break monolithic groups up, as you stop using proxies, what you're getting is to a much fairer result as opposed to the state of the industry today. So it's not that there's a fundamental difficulty here. There's just a lack of familiarity and we're trying to overcome that. We've also been joined recently, he's in the room with us today, by Scott Fischer, who is in charge of our Government Relations and was the former head regulator in the state of New York and is putting a lot of his energies into exactly what you're talking about.

Josh Shanker with Bank of America. As far as I can tell, your customers are having outstanding experiences, and they're very pleased to stay with Lemonade, and their habits are changing and evolving. And to what extent are you finding that may be the case that customers are like, I'd love to stay with Lemonade and they go look at your product and they say, and not only that, the price is the best. And then your loss ratio is quite high at 50% higher than competitors. To what extent if you got your pricing in line where you were delivering 60% loss ratios in Home and Pet, would that same customer say, look, I'd love to stay with Lemonade, but Lemonade doesn't have the best price anymore. And they're making an economic decision that they haven't had to make so far.

I love the question. Thank you. 2 or 3 thoughts on that. One is before inflation kicked in, we reported a combined company-wide loss ratio of 69% just a couple of years ago. So we've been there. And in certain pockets in certain states and certain products, we're there today as well. So we can say with some confidence, our customers love us and buy at better loss ratios, not just at higher loss ratios. So I think the empirical data speaks for itself. Let me expand on that, though. What I tried to do in my presentation was take you through the expense ratio and the loss ratio and try to make the case that we should ultimately be able to best the industry at pretty much every line on the P&L, every part of the combined ratio. But the point to highlight is that the ratio part means it is affected by price. What I mean by that is, we could raise prices and have a fabulous loss ratio and lose our competitive advantage. The art that we're trying to apply here is how to be a price leader in most places for the right risks that you're trying to get, identify those risks better than others, be a pricing leader for them and not for others. The telematics example that Maya touched on, I think, is a fabulous illustration of that point. When you're pricing based on proxies, much of humanity looks very much alike. You say, okay, these are men and these are women and men, this is a fact, have twice as many fatal accidents per mile driven than women. So I'm going to use gender as a pricing factor. But when you use telematics in the way that Maya expanded or expounded, you start seeing that I can see so much more nuance. Because while men as a category may be worse drivers than women, there's more variation within those groups than between those groups. And therefore, it's actually a pretty crude measure, but it's the 1 that the industry uses. So suddenly by getting continuous data streams from the vehicle, I can actually price people based on them being a human being and how they drive rather than being a husband or a man or a woman or having this kind of job or this kind of credit score, which touches back on my fairness point earlier. But it touches on your point as well because what happens then is, you take this big cohort of people, and you can see instead of this big chunk of monolithic people, you start seeing all the shades of gray in between. Suddenly the better drivers, the better risks, get a better price than they do than when they go to the competitors pricing based on proxies. And they will find us very attractive. The worst risks would prefer to pay average rates next door, then pay their true rates with us. So you'll start getting positive selection happening. Loss ratios remain very, very healthy, prices remain very competitive for the risks that we want and unattractive for the risks that nobody really wants, but that our competitors are unable to identify with that level of precision.

I might add, just I think Texas is also a great example that I gave the new rates. The total impact of that created a rate increase. On average, if you look at the impact that we had in terms of the rates that we took. And so we increased prices but we managed to lower the predicted loss ratio for these groups as well as keep conversion the same in the amount and the volume of business that we were selling. So you should expect to see that more state after state as we become more sophisticated. It doesn't mean that we're not raising rates. We're just raising them in a way that is a much more sophisticated and customized for each of the customers, depending on the risk that they have.

We have a question from Katie. I know, Marco, if you could. Alex, great.

Katie Sakys, Autonomous Research. I think this is a question for Tim on the base cases that you outlined. Annualizing this quarter's dollar increase in organic IFP ex Metromile implies something like a 25% growth rate next year, which seems aggressive to me given that messaging on marketing spend will be decelerating and that 3Q is a seasonally high growth quarter. So I'm wondering what's giving you the confidence in today's 20% CAGR outlook and what marketing spend assumptions are you including in your model?

So we, of course, have not yet given guidance for next year. Today was a bit of a preview, and we have indicated the growth rates of 20% in the model and -- 20% to 25%, that is correct. And so I think the connection between the amount of capital that we spend, the growth budget that we allocate and the amount of business that comes in is pretty direct. It changes from quarter-to-quarter and channel-to-channel, but it's a fairly direct connection and fairly predictable. Now we're having to deploy fewer growth dollars to grow because we now have multiple products, and we're seeing more and more customers cross-selling, upselling without the additional customer acquisition cost. So in the modeling that we put together, we've assumed that our efficiency is about what it is now, which is higher in Q3, this past Q3 and going into Q4 than it was at the beginning of the year. And so as I noted a few minutes ago, we're seeing an improvement in that efficiency. We've also seen a bit of a benefit in the acquired Metromile book of business, where our assumptions as to the level of retention that we would have, we're actually a little bit better in the third quarter than we anticipated. And so that's giving us a little bit more of a benefit in terms of the anticipated churn there. And so I think the combination of those and the sensitivities we've done, give us that confidence going into next year. Now in Q1, we'll come with more formal guidance as -- for the quarter and the full year as we have. But between that sort of thinking and the sensitivities we built, really, that gives us confidence in that 5-year view that even if year 1 and year 2 look a little different in real life, that over the course of the model, the results are pretty reliable.

Awesome. And then as a follow-up, I just wanted to ask about some of the variables that might impact those assumptions. What's driving your confidence in the multi-line outlook, given that California homeowners is no longer a cross-sell option? And can you point to any specific bundle types that are propelling that growth rate and might be the stickiest in terms of your customers?

Yes. So again, it's a tricky one to predict, although we do have a track record now. We can sort of draw a line through 2 points. We know what that number was a year ago and 2 years ago. One of the items, one of the slides we presented today showed that uptick for the state -- for Illinois, where we have the state with all of the products available. And that gives you that sort of hard indicator that where the products are available and we're able to optimize and enough time has passed for the models to start to learn, we saw that nice steady uptick. Now again, can we predict with precision what it will be each quarter along the way, that's a little trickier. But I think the benchmark of best-in-class as 60% is something that's attainable. We're assuming we're less than half of that. over the modeling period. Net Promoter Score, customer experience, all of those indicators suggest that we should be able to track towards those best practice levels.

Great. We have time for 2 more questions. I'll take them from the floor. Yaron?

Maybe a couple of questions on bundling. I think if we look at the industry, we haven't seen a lot of success in Auto and Home bundling through the direct channel. And I think that's going to be an imperative part of the growth opportunity at Lemonade. Maybe you can talk a little bit about why you believe that you can succeed there.

Time will tell. We don't want to kind of boast of any successes that we haven't yet had. And you'll note that in Tim's modeling, he showed quite a range of potential outcomes. So we're not giving guidance to a particular number. We're making -- taking a stab in the dark about what sounds reasonable to us over a 5-year period. And you'll note that we didn't in any year, even in our most optimistic things, we didn't suggest that we're going to get to parity with just the incumbents. We speak about all our advantages, but the most aggressive model we showed. So we'll get to 35% when they get to 60%, and we showed more conservative models still. And our optimism, such as it is, is drawn from the last 12 months. And really the experience that we've had in Illinois and now repeated over 2 other states, where we see the trajectory change angle in exactly the same way. So it's repeated 3x over. And that is about car bundling, all that lift is coming from car. So the early indications are positive. The trend lines are in exactly the right direction. You can straight line out from that and apply whatever discounts you want to that. And we know that there's something of a ceiling in the industry at 60%, and we've kept a very wide berth from that out of conservatism. But let's talk again in the coming years, and we'll tell you how we're doing.

And then maybe just to level set, the 60% industry average that you were talking about in terms of multi-lines and the premiums per customer, are those for personal lines carriers only?

Correct. Including direct-to-consumer, personal lines. Yes.

We have another question in the back. Sorry, couldn't see you.

Sure. Two questions, actually, 1 for Dan, 1 for Tim. Dan, just you made a pretty strong case of AI being the future of insurance. And clearly, it's the brains of Lemonade right now. Can you give us a sense of how much you're investing in AI on an annualized basis in growing it? The second part of that, I think into your seesaw where you were suggesting it's front loaded as an expense. Where are we right now in terms of the AI-specific expenses on that seesaw. DO you think we are kind of becoming more neutral? Or it's still a lot of front-load expense in the next few years that we should expect?

Tim? I can take a [ best ] at it, but you might be better for this question.

Well, I think the way to think about it is it's...

About tech spend in general...

It's essentially being built by us. So everything that we're investing in, whether it's AI specifically or tech and engineering more generally, of which a significant proportion I would think of as AI or intelligence, is significant. So it's about 25% of our overall head count of the business. The costs of a tech expert are obviously higher than average costs, so the dollar amount is higher still in terms of the scope of investment in the 30-plus percent range. I don't have a hard number of what subset of that is AI-focused, but I think of it as a significant focus of that team. We're at the point now where we're not launching significant new products, so most of the efforts of those teams previously was focused on the new product: building and launching Pet, building and launching Car, now building and launching a pay-per-mile product to match Metromile's product. There's not large products like that coming. There may be additional future products coming, but that significant investment is behind us. And that really opens up the potential to expand that team's focus on things that are working, particularly in AI and machine learning.

And maybe I'll just add 1 kind of vantage point. You saw the visualization of our system that I showed earlier. We can break out how many data scientists we have and what we're paying them. But the deeper thing that I wanted to get across is that actually everything that we build in technology is part of that same digital substrate that is feeding and is fed by that machine. So this isn't just about how many data scientists. We've got a lot of world-class data scientists, of course. But all the data engineering that we do and all the applications that we build out and all of the product flows that we do are being fed by and feed into that same thing. So it's really the totality of our engineering and product expense that is driving the cycle that I was talking about. I wouldn't want to isolate it just to data scientists, although we've got many and fabulous data scientists as well.

Great. Thank you. We have a lot more questions coming in online, then we'll have to tackle them at another day. Daniel, some closing remarks?

Thank you. It's been wonderful to spend these past few hours with you all. And we hope that we've convinced you of some of the things that we believe in. At a bare minimum, I hope we have convinced you that we believe in them. We believe that we have built Lemonade on an unrivaled technology platform. And we believe that if you know where to look, and if we share the data, you can see a lot of the impact of that today, but that it will be hard to miss in just a few years' time. So that trajectory is 1 that we firmly believe in. We believe that we are winning the battle for tomorrow's consumers, that our market share among first-time buyers of insurance is strategic. For the reasons that I touched on earlier, it is highly predicted perhaps the single most predictive thing of ultimate market shares, market share among first-time buyers of insurance if you are a long-term investor. And nothing is as profitable as growing with those customers. And we believe that the combination of those and the structures that we've built around those are highly differentiated, highly distinct and highly defensible. They are not akin to anything that any other insurance company is doing, and we've been architected very much to support them. And therefore, we believe that as we roll this movie forward, we will be generating an increasingly valuable business, an increasingly large business to the benefit of Lemonade, its customers and its investors, those who join us on this journey over time. I want to kind of temper that by saying we know we're not there yet. We know that we are up against massively entrenched and formidable competitors whom we have nothing but respect for. And our belief is not at odds with that statement, nor is it mere hubris or mere wishful thinking. And I'd like to try and explain why our optimism, what it's grounded in. Lemonade is sometimes refer to as a disruptor. It's not a moniker we often use about ourselves. We have mixed feelings about it. And the reason that we have mixed feelings about it is because -- not because we have any issue with disruption, but the way it's conjugated suggests that we are causing the disruption. We're not. The disruption that is coming to the insurance industry is coming because of seismic changes in the way humanity is organizing itself. And one of the consequences of that is that insurance companies are losing supremacy over the most important factors of production: statistics and data. It's as simple as that. One thought experiment to kind of drive the point home. If you stopped a Joe Public in the street in the year 1700, and you said to him, who are the bastions of the world's data and who is home to its finest statisticians? He might have said an insurance company, if he understood those words. In the year 1800, he would definitely say an insurance company. In the year 1900, no question, his top 10 would all be insurance companies. Quite possibly, in the year 2000, if you stopped somebody in the street and you asked them that exact question, who has the world's data, who has the world's best statistics. They might have still named an insurance company. Today, towards the end of 2022, not a single insurance company would make the consideration set. Their question would be entirely answered with a catalog of Silicon Valley-style companies. Google might top that list. State Farm won't appear on it. That is the disruption that is coming to the insurance industry. It has lost dominion over statistics and data, and it's in the business of monetizing statistics and data. Lemonade is not causing that disruption. We can't take credit for it, but we can absolutely take advantage of it. And that, that seismic shift, that secular trend is what's creating the opportunity into which Lemonade is entering. Because companies like Google that are built on an engineering culture are eating the lunch of insurance companies when it comes to data and statistics. And guess what, that's how Lemonade is built. Now this isn't a quick turn. You ask us, will this reflect in Q1 results. That's not how we think. We're in this for the long term. We want to build something of sustained value for decades to come. I mentioned earlier that some insurance companies that were at this or a similar intersection 300 years ago, are now doing $100 billion a year, 300 years later, the prize is huge and worth fighting for. And that is really the perspective that we take as we think about what we're building here. And that is the root cause of the optimism that we exude and I hope we do. I want to thank our investors, present and past and potential. And I want to do it in a roundabout way. I've been fortunate over the course of my time at Lemonade to meet a lot of the CEOs and leaders of a lot of great insurance companies around the world. And to a person they are smart and focused and analytical. And the analysis that I just shared with you about the seismic changes and the disadvantages and the structural advantages, they know that stuff cold. They tell me that stuff. You get them in an honest unguarded moment, they'll tell you that stuff as well. It's what keeps them up nights. And they share with me the challenges that they have, because having seen the writing on the wall -- that would be 1 thing if that business was doing terribly, you can reinvent a business when it's doing terribly. But they're doing great. They just see the impending stuff. They won't just ride out their tenure before they all hit the wall. They see the impending stuff and most of them want to do better than just run out their tenure. So they are thoughtful about what should we be doing about it and they struggle. And this is the stuff of books, Innovator's Dilemma was written all about this stuff. And I remember 1 particular conversation with the CEO of 1 of the largest insurance companies in the world, and he starts cataloging all the issues that he faces. He says, I've got an amazing team of executives who are groomed for legacy preservation, not for business transformation. I have a culture that is risk-averse, even though I'm in the business of risk management. Don't think I don't invest in technology. I invest billions in technology a year, but I don't produce a black box. It all goes into a black hole because it's spaghetti codes dating back to the 1980s, written by people who have died and code that nobody knows how to maintain. And the list goes on and on. He talks about distribution. I want to go direct to consumers. Apps are amazing. But I'm not going to give up all my business and my 40,000 brokers in order to make that transformation, et cetera, et cetera. Listening to him go through this litany of woes. I was reminded of a joke. It's a Scottish joke, and it's about a guy who finds himself in some remote village in Scotland. And he goes into the local pub and he stops the first bloke he meets and he says, tell me, how would you get to Aberdeen from here? And the guy looks at him and he says, If I was going to Aberdeen. I wouldn't start from here. The biggest news to me when he was cataloging the mismatches between what he needs and what he has was his investors. This one I hadn't seen coming. He said to me, I have an investor base who want no volatility on a 4% dividend every year. And if I did the bold things that I know I need to do, they would oust me, because I don't have the right investor base. That was striking to me. The importance of having an investor base who understand and agree with you about what it is that you're trying to do. Which is why for all of us and for China, in particular, since day 1, being aligned with our investors has been of paramount importance. We don't want anybody buying Lemonade shares who thinks they're going to get something different to what we think we're building. We want to open the proverbial kimono. We want you to understand what's working and what isn't and how long things will take. And Lemonade is not everybody's cup of tea, it isn't. And if you like what you hear, and this all makes sense to you, then hopefully you'll be drawn to Lemonade. And if not, sincerely we hope you seek your fortunes elsewhere, because we plan to keep doing what we're doing, and we want fellow travelers who see the world in the same way. And in that vein, I want to thank you all for your consideration and for your time. And for those of you who are fellow travelers, for your continued support and encouragement. And I want to just end the day by thanking our spectacular team of Lemonade makers. These are the most outstanding professionals who I've ever had the privilege of working with: bold, customer-obsessed, detail-driven, big-hearted group of people and building Lemonade over these last few years has for Shai and I been the thrill of a lifetime. Thank you all so much. This wraps our day. Thanks, everybody. Have a great one.