Ann Berry (00:00):
I moved apartment recently and one of the first things I did was look for home insurance. So many online search results took me to brokers wanting appointments to discuss my needs, but a few providers were savvy enough to clock. I just wanted to fill in a digital form and be done. While Lemonade is one firm that stood out, an AI powered insurance company providing home renters pet and car insurance entirely online, a whopping 97% of its policies are sold through bots, and 55% of claims handling is automated. Founded in 2015, Lemonade's first product was renter's insurance, and it's still the largest portion of the business at about 35% of in-force premiums with over 70% of customers aged under 35. Its product expansion has been driven by graduating with clients through their life phases. So Lemonade's Launch Pet and Home Insurance as customers have grown to need these with a big new product focus we'll be digging into later.
(00:51)
While Lemonade stock is up significantly in the past month or so, it remains down 80% from its 2021 High. As the market has asked whether the company's revenue growth can be matched by profitability, lemonade has guided the market to EBITDA profitability by year end 2026, which is quite far away. So Lemonade has been trying to prove its profit potential, focusing its Q3 earnings on 71% year over year growth and gross profit driven by two factors. First, a 17% increase in its number of customers reaching 2.3 million and with a 24% rise in enforced premiums to about 890 million, along with that second gross loss ratio improved to 73% down 10 points from a year ago. Now let's look a little bit deeper into what that actually means as it's the heart of how insurance companies make money. Loss ratios represent the total claims paid out to customers, plus expenses divided by the total premiums received by the insurer.
(01:46)
A lower ratio means the company collects more in premiums than it pays out in claims, so it's a key indicator of its financial health and how well its products price in risk. So Lemonade's been now highlighting how its loss ratio has come down over time, and now in their first investor day in two years in November, lemonade also has gone out of its way to explain to shareholders how it plans to grow its business tenfold while getting its profits and cashflow humming. To get to a target of 10 billion of in-force premium means lemonade's about to make a big, big bet on the auto insurance space. We sit down with CEO Daniel Schreiber to find out how cars will drive growth, a secret source of cashflow called synthetic agents and how AI wraps its arms around all that lemonade does. Wow. All right. We are welcoming Daniel Schreiber, CEO of Lemonade. Daniel, let's start with where you guys are going all in and that's the auto space. So in your latest ambassador day presentation, it looked as though you were targeting car insurance becoming roughly 40% of your business over time. Auto is not an easy space, so how are you guys going to win here?
Daniel Schreiber (02:50):
Great to be with you. No, not an easy space. We are today something like a hundred million book of business and you think about the Geicos and progressives of this world, about 50, $60 billion. So they outsize us about 500 to one. So I think your question is entirely fair, and yes, we're approaching in just a few weeks time. We should pass the $1 billion mark in terms of our premiums overall not for car. And we're now talking about how we're going to 10 x that and get to 10 billion. And you are right that we're thinking that car will play a much bigger role in the next 10 X than it did in the previous 10 x, if you like. But even then, even if we see, say 40 50% of our premiums become kind insurance over the coming years, we'll still be a tiny player in a huge, huge ocean.
(03:36)
So in pet insurance and renter's insurance, which we launched before we launched car insurance, we very quickly became 5% of the market for one and 8% for the market for the other. And in both cases, among new buyers, we might be 10 or 20% In some states, if we achieve the goals that you just said for car insurance and get to say a four or $5 billion, we'll be like 1% of the market, one and half percent of the market, of a multi-year horizon. So the market is just so vast that even if we 10 x our business, we will remain like a rounding era in terms of market share. So there's just a real exciting, I think once we 10 x, we'll then 10 x again and we still won't be all that dominant in this space. It's that big.
Ann Berry (04:19):
Let's talk about the actual product we're going to offer. So just to give folks some context, I watched the Lemonade Investor Day video on YouTube, not once I watched it twice. And the piece that really caught my attention, Daniel, was you breaking down how you've used telematics and how you've used AI to design this car insurance product. One piece that really stunned me was where you said if someone applies for car insurance, you can literally figure out if this is someone who texts while they drives. How is AI getting that level of precision to inform how you price and put these products together?
Daniel Schreiber (04:51):
So most people listening to this will have car insurance which is priced using none of that using really fairly crude and actually kind of cringey
Ann Berry (05:00):
Ies
Daniel Schreiber (05:01):
Your gender, your marital status, your credit score.
(05:05)
Nobody's very proud of how the industry prices insurance, but they're somewhat predictive and they've become the mainstream and we're doing something quite different. We're saying enable us to track the usage of your phone. The phone has a magnetometer, it has gyro, it has GPS, it has incredibly sensitive, fine tuned and advanced sensors that tell us a lot about how you drive. And that allows us then instead of proxying the risk that you represent going to the thing itself, actually seeing how you drive and offering you a rate commensurate with not your age or your credit score, your gender, but with you.
Ann Berry (05:43):
Wow,
Daniel Schreiber (05:43):
Kind of pissing through all of that. And rather than treating what are really very heterogeneous drivers as if they're one monolith, breaking that up, averaging and pricing you and the trade that we say to you is you enable permissions on your phones that we can track those sensors and we will never sell that data. We don't use it for any purpose other than your insurance needs. And that then allows us, if you are indeed a responsible driver and two thirds of drivers are better than average, that sounds counterintuitive, but it's true. That allows us then to give you a rate that is appropriate for you rather than for whatever proxy you happen to represent. And if you are driving and the phone suddenly gets picked up and you start texting, we won't read your texts, but we will know that you're messing with your phone while you should be focused on the road.
Ann Berry (06:30):
That's incredible. And then how does this all translate into pricing? This is a price sensitive sector and you've got some really interesting information out there as to just how price sensitive it really is.
Daniel Schreiber (06:40):
Well, if you, as I say, price based on these crude measures, then you're really treating much of humanity as very much alike. And even though the actual risk is variable, you're just peanut buttering them across if you like, as averages. What we do is we separate out all the component parts and we say so-and-so is a much better driver than average. So-and-so is a much worse driver than average, and about a third of people will be worse than average. So not everybody will get a great price. Everyone will get a price commensurate to the risk that they represent for two thirds of people, that will translate into a great price for a third of people. They'll be quite put off by lemonade's pricing and would be much better off being mispriced and treated as if they're average, even though they're actually worse than average by going to somebody else who will just look at their proxies and price them accordingly.
Ann Berry (07:29):
Let's talk about how you're using AI in another part of the business, which is for some people quite tough because Daniel, when I look at your customer base, 70 percentish are aged under 35. Now that customer base is exposed to a constant barrage of digital ads. It's a really noisy area, but you guys have been able to use AI to cut through the noise and go find this customer base. How have you been doing that?
Daniel Schreiber (07:52):
It's a mixture of things. You're absolutely right. And I think among that cohort, we may be actually the number one in terms of market share. Part of it is appealing to them with the right mix of user experience and value proposition. There are people out there who just want to deal with an agent and they're amply served. But if you want to be able to buy a policy in a matter of seconds and be paid in a matter of seconds and you don't want paperwork and you don't want hassle, there aren't that many insurance companies out there and the younger generation, they operate their whole lives that way. Why wouldn't they want insurance the same way? So there's a good goodness of fit there and the digital first buying insurance in your pajamas at 2:00 AM and getting a claim paid in
Ann Berry (08:30):
The same, that's how I bought the issue.
Daniel Schreiber (08:31):
There you go. So there's that, but we actually use AI in other ways. So every person who buys insurance hits our website, clicks on an ad, downloads the app. We actually have something like 50 different machine learning models making predictions about them. How likely are they to make a claim? What kind of size claim are they likely to make, how likely are they to churn? When are they likely to churn, might they buy a second when and what policy? And it actually generates a stream, a predicted stream of incoming and outgoing cash flows, and it then discounts that to the current time and says this customer, the lifetime value of this customer in today's money terms is $1,300.
(09:10)
And that allows us then to evaluate different prospects, different marketing campaigns, different Google ad searches, different regions, and really have almost an algo trading approach to acquiring customers. So we are using that close to 90% of our marketing spend is earmarked this way, is kind of focused exactly on that total lifetime over time. And one of the things that we did show on the investor day is just how prescient these models are. They really do a very, very good job in traditional insurance companies. They'll set a price and then they have to wait a year or two years to see how the behavior, the losses materialize. If you have something that can collapse that time to an instantaneous read on what kind of customer you're acquiring, your ability to acquire customers efficiently, it really grows exponentially.
Ann Berry (09:59):
It sounds as though you're doing everything you can to use AI effectively for factors that not quite in your control, but don't feel quite as random as something else that can hit your business, which is climate related issues, hurricanes, wildfires, we're seeing more and more of those. Daniel, how's that impacting lemonade and how's that impacting your predictive capabilities?
Daniel Schreiber (10:21):
You're absolutely right. So we don't claim any advantage in predicting the weather. That's just not something that we have any great skills at. We are really about understanding the risk represented by a person and for different policies that may be more or less dominant as the thing that you're truly underwriting. So in renter's insurance, car insurance, even pet insurance, you're really not underwriting the way almost at all. You're really underwriting the person or the pet. Homeowners insurance is much more exposed to the vagaries of the weather and we've really avoided the places worse hit. So we don't offer home insurance in Florida at all in California. We avoid fire zones, which is an increasing amount of the country. So we recognize that that's not an area of advantage for us, that we focus on areas that we play best.
Ann Berry (11:10):
Let's talk a little bit about how everything you've just described is translating into financial performance. Daniel, you've had a very strong earnings just behind you in Q3. One of the things that you focused a lot there was cash. Let's talk cash is queen. So let's talk about that. You'll be cashflow positive this year that's ahead of your target of mid 2025, and you've got a secret weapon that struck me as I looked at your reports, that secret weapon is synthetic agents. Talk to us about how those work.
Daniel Schreiber (11:38):
Sure. This is an amazing, quite novel financing structure that we put in place. We call it synthetic agents and cause a fair amount of confusion by doing that, but I'll try and talk you through it.
(11:53)
We are a direct to consumer company, which means that we have to spend the money upfront on Google and Facebook and everywhere else in order to acquire you as a customer. And then based on those predictive models, we will know that you will probably return every dollar that we invested three times over. We have an LTV teac of about three, but that takes time. Insurance is a business that customers can buy insurance and stay for years, decades, so you're buying a very long lifetime value, but the expenses are all front loaded for us. They aren't for others. If you use agents or brokers as State Farm and Allstate and Liberty Mutual and many of the incumbents do, they actually see none of the acquisition costs hit their p and l, they've got an off balance sheet approach, which is the broker, the agent, they will do the acquisition
(12:42)
And we will then give them a fractional stream of the premiums. We call it a commission and that's how they will get compensated. We like that part. We like the cash part of that, how you can close the cashflow gap and not incur these huge costs upfront and how you can spread them over time. But we don't like agents very much and the reason, nothing personal, but there's two things that we don't love about it. One is it disintermediates a relationship that really should be direct. We want to be able to communicate directly with a customer to own that relationship, to sell them other policies when it's
Ann Berry (13:14):
And get their data. Let's be honest
Daniel Schreiber (13:15):
And absolutely, absolutely get high fidelity, high resolution data. Absolutely. We're a data centric industry and company. And the second thing we don't love is having a partner who stays with you for the life these long lifetimes and draws out 15% of premiums in perpetuity. And we sought out to recreate the cashflow dynamics of agents without those two hindrances. So we found what we're calling a synthetic agents. We partnered with a fund called General Catalyst, and what they do is they do finance our acquisition 80% of it upfront, and they do that in return for a fractional stream of premiums. We pay them 16% of premiums, but everything is direct to consumer. We choose the ads, we choose the campaigns. They have no relationship and we don't pay them in perpetuity. We pay them only until they're made whole, which tends to be two to three years. And suddenly the cashflow dynamics change dramatically and it's not debt in any traditional sense. There are no liens, there are no covenants, there's no recourse. All they own is the same kind of fractional stream that normal agents would have a right to as well.
Ann Berry (14:22):
I'm curious. General Catalyst, they're very smart investors over there and they pick their horses pretty well. Did they invest in Lemonade before you went public? Were they?
Daniel Schreiber (14:30):
They did.
Ann Berry (14:31):
They did. So how long has that relationship been in existence and tell me how it continued on to reach this point with this synthetic agent model.
Daniel Schreiber (14:37):
General Catalysts are phenomenal. They, they're just a wonderful, wonderful fund. They've been instrumental in helping us from the early days. They led one of our early rounds and have been investors in lemonade ever since. But typically at IPO that kind of ends, VCs tend to drop off. And Joel Cutler, who was the partner on our board who ideally love stepped off the board in due course and that relationship remained friendly but not professional. And then we discovered this new fund of theirs, which does this kind of financing and we reengaged with them around that. And it's been a parallel relationship to one that preexisted, obviously there's institutional familiarity, but we kind of started from scratch. This fund is phenomenal because they are quants. The way they decide to underwrite is they'll say, all I want is a data dump of your cohorts and their retentions, and they create these data retention or cohort retention triangles.
(15:32)
And really what they care about is I'm now trading in money upfront for a fractional stream. I just need to know that that stream won't dissipate over time. So that's what they're monitoring. That's the only underwriting that they do. If it does disappear, if the China is too high, they don't get paid back. We never have to dip into our other pockets and pay them. So they are very data-centric and it's an amazing trade because nominally they're underwriting and incurring real risk, but in practice they are so diligent in their data collection and monitoring that there's very little risk, they make a good return. And for us, this has doubled our ability to invest in growth and collapse the cashflow from GAP from two years to none. It's really transformative for us as well,
Ann Berry (16:17):
Given the quality of the data that you need for this model to have been a successful hazard seems to be at the moment. And to get the vote of confidence for someone like a general catalyst in your ability to underwrite. I do hear you Daniel, that there's a ton of opportunity for your insurance, but lemme give you a different example. I was lucky enough to interview the CEO of UPSTART last week. That's the use of data in that business for looking at credit worthiness. What about going into adjacent products? What about looking at using your data to try and partner with someone to get credit cards for your customers? For example?
Daniel Schreiber (16:48):
Never say never.
Ann Berry (16:50):
Okay.
Daniel Schreiber (16:50):
And you do see companies, USAA is a prime example that have spread out into banking and other services and across the pond in Europe it's not unusual to see an insurance company Lloyd's to also run high street banks and you do see that. So financial services writ large is kind of within the remit within the sector, but the size of insurance is so staggering that it's also not necessary. You're talking about something that's roughly 11% of GDP
Ann Berry (17:19):
As much as that. Wow.
Daniel Schreiber (17:20):
Yeah, this is bigger than as a sector, bigger than oil and gas, bigger than defense, bigger than ai, bigger than software, bigger than SaaS, the enormous bigger than automotive. This is really one of the absolutely biggest markets. It's considered gray and dull and therefore it's overlooked oftentimes. But just the size of the prize is such that we don't need to look at greener pastures. We've got amazing growth ahead of us. As I say, we can 10 x, 10 x again and we still won't be the largest insurance company around.
Ann Berry (17:50):
Let's talk a little bit about what Wall Street's been focused on. And that has been, again, cashflow and one of the old fashioned drivers of that is expense management. It doesn't sound very glamorous, but it's pretty key. And I know that you at Lemonade have been focused on keeping your operating expenses low relative to your revenue growth in quite a differentiated way. Tell us how you've been doing that.
Daniel Schreiber (18:08):
Yeah, I think it's very glamorous. I do because we're doing something that is gravity defined over the last three years. Our business has more than two XD and our headcount has grown by 2%.
(18:21)
In fact, in this last quarter we reported a 24% growth in headcount and a reduction of 7% in headcount, 24% growth of top line. And we've been doing this quarter after quarter. And you're saying, well, how can that conceivably be? How can you grow a business so dramatically without adding headcount? We have more customers they need to be served. There's more stuff to file, there's more salary, there's more. And the truth is that I think well, human headcount has remained static or human intelligence has remained static. The total quantum of intelligence deployed at Lemonade has grown dramatically. It has to in order to service a growing book of business and the gap is filled by ai. So we are really onboarding ais at an incredible rate, training them up and then deploying them amongst our team members to do the kind of stuff that was once thought of as impossible.
(19:07)
So not only are almost all our policies sold directly to consumers by bots, but most of our claims are settled that way. A lot of our financial planning and analysis is done that way. A lot of our filings are prepared that way. A lot of our HR function is managed that way. A lot of our purchasing and other kind of internal functions are managed by bots. A lot of our r and d work is increasingly being done by LLMs. So we have been built a top of AI since 2015. When we were founded. We didn't discover AI in 2023 along with much of our industry. And it just means that we've been built for this moment and it's allowing us to do so much more with less. So you see this, as I say, gravity defined trend of rapid growth of the top line and a scaling business that just keeps the expense line flat. We had an investor day this week, but we had an investor day two years ago, almost to the day as well. And I just showed investors the before and after and 50% top line growth and a decline in the underlying opex, not at the expense of customer delight, highest NPS in the industry, high retention rates, employee satisfaction. So AI is the short answer to your question.
Ann Berry (20:18):
How big is your team now?
Daniel Schreiber (20:20):
We have about 1,200 people.
Ann Berry (20:22):
And as someone who's been leading in AI for quite a long time, how do you manage a team of that size knowing as you do, knowing as they certainly do that AI is going to do more and more of the doing at your company? Are people looking over their shoulders going, how long is my job going to last and when's AI going to displace me?
Daniel Schreiber (20:40):
I think that is a fear in the industry at large. And we saw Hollywood's writers going on strike. We've seen other examples like that. No, the team at Lemonade come here because rather than being scared by the coming wave, they want to be in a place that's riding the wave. The fact that we're growing fast has helped. We haven't laid off people. When I say we shrunk our headcount by 7%, that was natural churn. So people's jobs are not really being threatened, but they are upskilling dramatically. So increasing numbers of our team who not that long ago were themselves writing answers to tickets submitted by customers are now training ais and auditing their answers and fixing the prompts. We've built a whole system where you don't need to be an engineer to train an AI now. So we are upskilling people. They are now managers of ai. Their team is comprised of AI and it's hugely empowering for them. It's good for their career, it's exciting work and I hope we are able to continuously keep that up, that our growth will always be just ahead of where AI's capabilities are so we never have to do it at the expense of employees. Certainly that's been the case so far.
Ann Berry (21:43):
When we translate all this into your share price performance, Daniel, you look back away, you iPod, you had this big runup that was true in the zero interest rate environment for lots of tech focused businesses and you've really had to do some work frankly persuading everyone or teaching people to understand your business model and what edge it may have. As you look at investors today, whether they're institutional or retail, do you think this is the moment where they go, okay, now we get it. Now we get AI we didn't before. Do you think this is the moment where people are beginning to hear you?
Daniel Schreiber (22:14):
It looks that way. The last few weeks certainly have been reflective of that. So we've seen a very strong run of the stock and I think a couple of things are now true that weren't just before. One is you look back at our S one filing for our IPO, you look at our decks before that. You look at our invested day a couple of years ago, our story hasn't changed, but in the past you'd have to look at it and say, well that hypothesis makes sense, but I'm not sure. And I dunno if they can pull it off.
(22:39)
And the difference now is that we can point to evidence and numbers, some of which I just mentioned earlier. So the leap of faith is much smaller now than it used to be, and I think that's very settling and comforting to investors. It's like the hypothesis makes sense and now I actually see it playing out in reality. So we've got a retrospective rather than just a prospective view on that. The second thing that I think is important for investors to consider, the last couple of years the world discovered ai, and I don't mean that cynically, it's really exploded and transformative in important ways and investors have gravitated towards the building blocks of ai. Nvidia is the poster child of that, the chips, the data centers, the foundation models that a Google or an Amazon or Microsoft are building out. I'm not sure there's much alpha to be found there anymore.
(23:27)
It was good if you discovered it two years ago, but now you're a little late to the party and investors ought to be asking themselves, okay, what is the next place where all of these capabilities will manifest? And I think the application layer, which is where Lemonade is, taking those foundation models and building an entire company that harnesses all of that capability to take on something as monumental as the insurance industry. If you're asking yourself where is the incremental dollar going to be made? It's not going to be made necessarily at the chip level. It's going to be made at places. We saw this with internet as well. It's not at the plumbing. It's then somebody who creates an Amazon based on that plumbing,
Ann Berry (24:04):
The applications,
Daniel Schreiber (24:05):
The applications. And that's really where Lemonade is. We've been positioned for this moment, we've been doing it for the last nine years, and I think investors are suddenly realizing, wow, there's upstream or downstream depending on how you look at these things. There's a lot of value to create and investors are only just lurching towards that. Latching onto that
Ann Berry (24:22):
Before I let you go, something else that you're doing that's a bit differentiated is in the philanthropic arena. Talk to us about how you're using some of the proceeds generated by your company to give back
Daniel Schreiber (24:33):
With pleasure. Appreciate that question you asked me before, by the way, about homeowners and climate and those risks and it's something near and dear to our heart. So I answered you in an underwriting perspective, but let me talk to you from another angle and use that as a way into this question. We are B Corp. We are a public benefit corporation, which means we have a double bottom line. We're absolutely focused on profit but not to the exclusion of all else. And we try to balance the different interests. So for example, when it comes to climate, we've done several interesting things. One is when we launch our car product, which we spoke about earlier, part of our value proposition to our consumers is we will be tracking your driving, which also means we'll be tracking your emissions and we are going to plant trees sufficient to offset your emissions. And our customers can actually see in the app the forest that they are planting.
Ann Berry (25:24):
Oh, fantastic.
Daniel Schreiber (25:24):
They can zoom in on Google Maps and see the trees that we're planting for them. So we build it into our products. We have a program called Give Back, which says that any underwriting profits that remain beyond a certain threshold will go towards nonprofits of our customers choosing. And this is really part of the kind of game theory behind lemonade, which is we never want to be in conflict with our customers In traditional insurance, it's perceived as a zero sum game. You deny my claim, you get to pocket that money, I'm out of pocket. And in order to try and diffuse that dynamic, we say, look, yes, we are going to tell you right now how much profit we're going to make, but anything beyond that, we'll go to a nonprofit. So we are not fighting over that coin, that marginal coin. At the end of the day, if we deny your claim, it doesn't deserve to be paid.
(26:09)
Not because we're trying to enrich ourselves at your expense and a lot of nonprofits that we've given many millions of dollars to these nonprofits including climate related ones, but really of our customers choosing. The third thing that we've done in this area is at the eve of our IPO, we set up a parallel nonprofit to lemonade lemonade.org. You can check it out. What we've done there, we seeded it with a bunch of shares, which it's sold partially and has cash in hand. And it has started offering climate related insurance in Africa, Sub-Saharan Africa. And one of the big challenges there is by Western standards, people that are living on just a few dollars a day and traditional insurance, even though they need it desperately because they are dependent on what they grow for their livelihood, either because they eat it themselves or they sell it at the local market, they're subsistence farmers in that sense.
(27:00)
They're massively climate exposed, but no insurance will cover them because the cost just to get to them and then to settle their claim, traditional methods can't work. So we've used blockchain in order to give them insurance for their crops and for the failure of their crops. Nonprofit, entirely nonprofit where it's automatically paid out using smart contracts, they buy it on their phone, it converts it from fiat money to cryptocurrency, executes a smart contract and they get paid immediately. So there's all different areas in which we're trying to use what we know how to do in order to also do some good in the world.
Ann Berry (27:34):
What's the investor response been to that at the same time as the market said, show us the money. Show us your path to profitability. Why are you giving a cut of that profit away? Have you had to demonstrate to folks here is the direct linear tangible impact it's having in terms of customer stickiness or customer acquisition?
Daniel Schreiber (27:50):
We've not, we've been very upfront about this. It's literally in our incorporating documents and how we're structured. So this is not something that anybody who buys Lemonade knows this or ought to know this. So I've never felt the need to explain or to apologize for this. But I will tell you that I have no doubt that it's accretive to shareholder value. I would not feel comfortable doing this if I felt it was not the case. Giving away shareholder money is no kind of charity that I want to be associated with. I think what we've tried to create here is a win-win insurance is plagued by distrust. It's an industry where perhaps the biggest problem in the industry is distrust. And that manifests that conflicted relationship in consumer's minds manifests as fraud to the tune of tens of billions of dollars a year. People who are law abiding citizens in other aspects of their lives, let the devil loose when it comes to making an insurance claim.
(28:42)
And they'll talk about it over dinner, about how they ripped off their insurance company because they feel they're in a conflicted relationship. And you get this tit for tat and it produces low brand loyalty, high churn, and as I say, high fraud levels. So I think if we're able to neutralize any of that disaster that insurance is by telling our consumers, listen, we're not in the same kind of conflicted relationship as you imagine, because leftover money goes to something that you care about. And then to us, our incentive to screw you over is much reduced. But your mental state changes as well because when you come to make a claim, we remind you of that and we say, and if you are claiming excessively, you're not hurting us, you're hurting. That soup kitchen you've told us is really near and dear to your heart. And we hope that we change the dynamic and bring out the best in both parties and create a more trusting relationship,
Ann Berry (29:33):
As much a case study in psychology as it is in data and data-driven insights. Daniel Schreiber, CEO of Lemonade, thank you very much for joining us here, folks. That's it. Join us next time on after earnings.
