Case Study: Coverage Cat
Coverage Cat is a consumer-optimized insurance broker for property and casualty insurance.
Introduction:
Coverage Cat is a consumer-optimized insurance broker for property and casualty insurance. Their customers are individuals who have, or are first-time buyers of, insurance products like renters, auto/car, homeowners, and general liability insurance. Coverage Cat asks users essential, but often ignored, questions about their risk profile and assets to tailor insurance portfolios to their financial needs. The product leads to savings of hundreds of dollars a year ($700+ on average) and provides ongoing peace of mind that they have the right coverage.
Max Cho is the co-founder and CEO of Coverage Cat. After a serious accident in high school that involved him losing many of his teeth at a party, Max found himself reading his insurance contracts in the ER only to discover his accident was not covered by his insurance. Since then he’s become obsessed with insurance and committed himself to applying his expertise in software, data-driven analytics, and advertising science built over years of working in tech and quantitative finance at Microsoft, Google, and Two Sigma.
Gabriel Botelho is the co-founder and CTO of Coverage Cat. Gabriel graduated from Yale University, where he and Max met while working at the school paper. After college he went to work at Compstak, a prop-tech startup based in NY before he started his own venture building software for lawyers and was accepted to the Y Combinator Fellowship. Since then he was also the first tech hire, managing the operations and manufacturing tech stack, at Ruggable a major D2C rug company, and worked on a variety of other startup projects before Max called him to start an insurance company.
Executive Summary:
Problem: People expect their insurance to cover them. But people suck at buying insurance that actually covers what they need.
Market: Our first product is ideal for 9 million young Americans with household net worth between $100k–$5M. This adds up to a $2 billion initial TAM that will eventually expand to over a $600 billion TAM once Coverage Cat starts to provide its services to non-HENRY households.
Solution: Our users submit unique data about their risk preferences that enables us to save them money on premiums and get them better catastrophic coverage. Coverage Cat then uses optimization algorithms and access to a plethora of insurance products to match its users with the best possible coverage.
Team: Max was a PM @ Two Sigma and later a Senior PM @ Google, where he discovered it is possible to front-run Google for in-market insurance ads. Gabriel has been working with startups for his entire career as a founder (various co’s), a data analyst, and the first tech-hire at Ruggable, a major D2C rug company.
Hiring: The top three roles Coverage Cat needs to fill are:
Software engineering
Data science
Generalist operational
Case Study: Coverage Cat
Tell me about the problem the startup solves.
Consumers buy the wrong insurance and leave money and risk on the table. Our approach to solving this problem at Coverage Cat is that we don’t compare; we optimize.
Inflation is the biggest reason why this problem should be solved now. Inflation, especially in cars and homes, is pushing millions of people to switch insurance in the next year or two.
The wrong insurance can ruin your life. Ask anyone who’s been in a serious accident and had their claims denied because they bought the wrong thing. We avoid this problem entirely and improve the welfare of our average user in the form of, often, better coverage and premiums.
Tell me about the market the startup serves.
Our initial target market is Americans with a net worth of over $100,000 and less than $5,000,000. Our focus will be the five states where most of this group lives in the US: New York, California, Washington, Florida, and Texas.
We spoke to dozens of customers and read through their insurance contracts to verify our hypothesis that we’d correctly identified this hole in the market. As we build the product and the company, we continue to engage in customer interviews and research to falsify our assumptions about the insurance market.
The top three reasons for signing up we’ve seen from users are: 1) they’re frustrated that they’re buying a contract they don’t understand well and that feels arbitrarily expensive. 2) They’re exposing themselves to risks that they often deem unacceptable but don’t realize it. And 3) they strongly dislike thinking about insurance optimization, and just want it handled.
We’re aiming to understand the gap between the things people buy vs the things people say they want. In insurance, and other financial products, people often buy things that are bad for them, generally because they either don’t know better or the industry sells them stuff that harms them.
Traditional insurance brokerages are our competitors! This ranges from captive brokers, i.e. those that only work with one insurance carrier like State Farm, to independents who sell across carriers, but tend to push insurance consumers don’t actually need in order to maximize their own profits.
Once we’ve saturated this initial HENRY (High Earners Not Rich Yet) segment of the market we plan to move on to other categories of users, both up and down market, to increase our TAM.
Tell me about the solution the startup created.
There are plenty of insurance comparison sites, but consumers hate comparison shopping for insurance and generally aren’t very good at actually picking the best policy out of a lineup. We’re the only ones that focus on optimization — getting you the perfect policies just for your needs. That’s how we save our users an average of $800/yr even while improving their coverage by $500,000+.
The novelty of our solution comes from the optimization! We remove the confusion that comes from buying a complex financial product and ensure they get what they need at the best possible price.
Unlike other brokers, we match the best policies to your needs, rather than maximizing our own profits. We do this with software, at scale, rather than relying on human salespeople calling you.
It’s novel because of how we leverage data to provide customers with the best possible policy. The data we gather is fed into a flywheel that enables us to find more customers to benefit from optimized insurance at a much lower cost than competing brokers.
As you continue to experiment you get better and better results that let you help more customers manage their risk without having to think about it. Building the machine that lets you run those experiments properly and connect the data across boundaries that are important for growth can be a really powerful tool. When we’re forced to automate ALL of our operations because we can’t handle the volume of users that want to optimize their insurance then we’ll have found PMF.
Our company’s North Star metric is the number of users whose insurance we’ve fixed.
Our current GTM strategy involves licensing viral cat videos to drive paid advertising growth and reach out to companies to offer a white-glove service.
For the paid model, our sales process is straightforward, compressed and repeatable. The prospective user looks at an ad then moves through to another one and converts or doesn’t. The first version of the b2b sales motion looks like rolls of friendly SMBs targeted for outreach and then moving on to larger and larger companies and partnerships as time allows.
Tell me about the team behind the startup.
Our goal is to create fairly priced and optimized insurance products for Americans across the income distribution. Our plan growth plan is to start small as a brokerage with a focus on selling others’ policies and better understanding our consumers’ risks. Once we have a rich understanding of our customers, and what the best ways to acquire them are, we'll investigate moving down market, with different tailored insurance products, to optimize financial portfolios for these new sets.
We’re both quite technical (Max has a graduate degree in computer science and Gabriel worked as a contract/freelance developer for a number of years). This enables us to lay out individual tracks and just each get the job done. High trust, quick and honest feedback, and tight iteration cycles with empowerment make us move fast.
The biggest personality complement is in relative aggressiveness. Max tends towards finding a sustainable perspective on business decisions. Gabriel tends towards a more aggressive approach aimed at fixing this once-in-a-lifetime opportunity of insurance turmoil happening right now. It’s a nice mix because it leads the team as a whole to feel like they’re leaning into challenges (the aggressive approach) but also have perspective and flexibility to plan/react/change (the balanced approach) when needed.
We’re a remote first team, and very anti-micro-management. We get the work that needs to be done, but on our own schedules and with the understanding that we can lean on one another if we ever feel that we need help or are falling short of achieving the business’s goals.
We’ve seen firsthand a world of bullshit jobs and companies cripplied into unproductivity. We believe everyone at work needs to be empowered to make things happen, and trusted to take care of it in the way that makes sense.
There’s no set decision-making framework yet, but we discuss challenges that come up in depth and trust in the decisions we come to as the team makes even if we don’t individually agree with them. We’re flexible to recommit and change course as needed, but pursue the decisions we make for the company ruthlessly until more information becomes available.
As we start to re-orient again from fundraising to growth, our emergent needs will likely be around data science, design, and engineering so we can build a strong growth engine and support it with the required insurance ops automation.
Who are you trying to hire, and why?
The top three roles we need to fill are:
Software engineering
Data science
Generalist operational
The biggest pain points each role will address:
Data Science - setting up the first experiments and supporting org approach and culture to the testing process.
Growth - data driven culture and strong capacity to execute on ambitious go-to-market goals.
General Business Operations and Automation - There are still significant bridges in insurance automation that remain to be crossed.
All three roles are very early stage startup roles so there will be tons of space to grow, learn, and help establish culture for the company.
The most unique thing about Coverage Cat is option to work on something that is a slam dunk. Few other products on the market can be said to be quite so user positive in terms of their potential for immediate and long term impact to users’ lives. We also have a clear strong growth case of the sort that is not easy to find in the wild.
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