looking forward to hearing what Scott Galloway has to say about this
Lemonade files S1
11–20 of 194 posts
Re: Lemonade files S1
#12In parallel to this growth of topline and increasing efficiencies, our gross loss ratio declined steadily from 161% in 2017, to 113% in 2018, to 79% in 2019 and to 72% for the three months ended March 31, 2020. See "Management's Discussion and Analysis of Financial Condition and Results of Operations — Key Operating and Financial Metrics."
Seems like a struggle to get to profitability. With the ratio of closing the gap slowing, sure looks like it's getting harder. This is what bugs me about these companies, after years of running the business and doing 1 Billion in revenue it's still a coin flip whether they will ever be profitable. How is this any different from the first "Internet Boom" except that they've been floated to a much bigger revenue number by VC losses.
So now the VCs want return on investment. The public is getting a chance to buy, and some will, and the VCs make out, the founders probably already did by selling to the VCs, but where is the value creation? Just a shell game.
Re: Lemonade files S1
#13for an insurance company, who supposedly uses bots, their numbers are horrible. $1M in marketing spend to generate $2M premiums. Revenues are low, losses are ultra high
Re: Lemonade files S1
#14You only make insurance cheaper by charging risky people more. Right now it is mostly laws that protect categories of people that keep insurance companies from charging people more. What’s the plan here, use machine learning in a “hands off” way with a black box algorithm to apply pricing discrimination in a way that a human could not because of regulation?
One interesting approach would be to actually charge less risky people less, and "safe" people more, as a way to equalize various groups in the society. Once this has enough traction, doing it the other way round could be even stigmatized.
but if you charge 'safe' people more, aren't you encouraging them to not be 'safe'?
Re: Lemonade files S1
#15This is honestly the first I'd ever heard of their company; interesting!
Only thing that would keep me from jumping is lack of auto insurance as well, but I understand NOT being in to that business.
I hope they do well.
Re: Lemonade files S1
#16You only make insurance cheaper by charging risky people more. Right now it is mostly laws that protect categories of people that keep insurance companies from charging people more. What’s the plan here, use machine learning in a “hands off” way with a black box algorithm to apply pricing discrimination in a way that a human could not because of regulation?
Insurance can be cheaper if the customers lower their average risk burden. Lemonade tries to explain this, but not clearly:
“We seek to encourage good behavior and build a long-term relationship based on mutual trust by endeavoring to decouple our financial incentives from variability in claims. In our model, we minimize any incentive to deny legitimate claims as we aim to give back, rather than pocket, leftover monies. After our customers purchase a policy, we ask them to designate a charitable cause for us to support with the residual premiums from their policy. Despite there being no contractual obligation requiring us to donate leftover premiums to nonprofits, when a customer embellishes a claim, such customer reduces the total amount available that can be contributed to nonprofits. As a result, we believe customers are less inclined to embellish claims as they would be hurting a nonprofit they care about, rather than an insurance company they do not.”
Re: Lemonade files S1
#17You only make insurance cheaper by charging risky people more. Right now it is mostly laws that protect categories of people that keep insurance companies from charging people more. What’s the plan here, use machine learning in a “hands off” way with a black box algorithm to apply pricing discrimination in a way that a human could not because of regulation?
Or you could lower overhead by cutting costs, hiring less people, changing commission structures.
The nicest business meal I ever had was during a lunch meeting that I tagged along to with a large insurance carrier. They could probably cut out $30 steak lunches to lower costs too.
Re: Lemonade files S1
#18You only make insurance cheaper by charging risky people more. Right now it is mostly laws that protect categories of people that keep insurance companies from charging people more. What’s the plan here, use machine learning in a “hands off” way with a black box algorithm to apply pricing discrimination in a way that a human could not because of regulation?
One interesting approach would be to actually charge less risky people less, and "safe" people more, as a way to equalize various groups in the society. Once this has enough traction, doing it the other way round could be even stigmatized.
"subsidize what you want more of, tax what you want less of"
Re: Lemonade files S1
#19You only make insurance cheaper by charging risky people more. Right now it is mostly laws that protect categories of people that keep insurance companies from charging people more. What’s the plan here, use machine learning in a “hands off” way with a black box algorithm to apply pricing discrimination in a way that a human could not because of regulation?
Not disagreeing that is part of the strategy, but also it is worth thinking how much overhead there is in the insurance industry. How many offices are there nationwide? How many of the jobs are essentially basic data ingestion? Approving of claims? How much is spent on advertising? Probably a fair amount of fat to trim.
Does anyone know what an insurance agent makes? If I go to my local State Farm office to get a home owners policy, what is the cut that goes to the local office/agent?
Re: Lemonade files S1
#20You only make insurance cheaper by charging risky people more. Right now it is mostly laws that protect categories of people that keep insurance companies from charging people more. What’s the plan here, use machine learning in a “hands off” way with a black box algorithm to apply pricing discrimination in a way that a human could not because of regulation?
At most big old and public insurance companies, claims payable represents a significant chunk of expenses, but not even close to 100% (it's closer to 60-70%). The rest is, generally, "administration" (humans processing papers, and managing humans processing papers, in cushy offices).
This is where better technology can result in lower costs. It's a volume/unit-cost game. Their unit cost per person is maybe a few cents or a few dollars cheaper, but at huge volumes it makes a big difference.