I'm skeptical of this on a very plain basis: If you had technology that could do this for companies, why would you focus on the startup market and not instead on the potentially much more lucrative public markets?
If the technology is as good as is stated in the TC article couldn't they pretty rapidly build up a listing of undervalued companies on Wall Street and buy into them?
There's not really a technology here. They are a research firm. They collected a dataset from many web startups and can sell that data or make a nice ad based website out of it(in the TechCrunch/RWW space). It could be a profitable niche. Or it could turn into little more than a thesis paper. Of course, if it turns into a profitable niche it could always grow into something different and bigger.
But the technology, at least in my understanding at this point, is nothing more than running your standard regression on a proprietary dataset.
This is a social-based startup, not a technology-based startup.
It sounds like technology, because cool-sounding mathematical prediction is the selling point... But it isn't that hard to do (though with data, and time, you can surely incrementally improve it). The story here is a community-known credential. Its value comes from other people relying on it as a baseline. Like any other credential, it doesn't guarantee you'll be any good - but it is a better-than-random predictor.
Because there isn't anything like this available already, and it would be a useful thing to have, they have a good chance. For it to be used as a reference point, it doesn't need to be great - it just needs to be betterer- enough -than random (and get adopted).
A "good predictor" just means "5% better than totally random". If you have a strong team, and good connections... your chances are surely better than if you had a weak team and no connections (though exceptions occur in both ways). If you can formalize a bunch of these factors, and find what they weigh... you have a baseline predictor that's better than totally random.
In the public markets, there's already a ton of competition, mostly because of the availability and quantity of publicly-available data. Instead of competing with thousands of other established companies and investors in analyzing public companies, we've managed to solve the problem of collecting large amounts of data on startup companies instead, into which more than $67 billion was invested last year in cash in the US alone.
Maybe because startups are small enough and therefore simple enough (in terms of variables affecting success) for something like this to work, but large public companies wouldn't be.
But really I'd be skeptical about data in this first batch of tests. I'd bet that YouNoodle used these widely known startups as a training set for its algorithm, or at least as a test set...I imagine if one of their unit tests came back and said "Facebook is gonna be worthless", someone would tinker with the algorithms until it didn't say that anymore. Usually you deal with this problem by setting aside some data points until the very end for you to evaluate your accuracy, but without knowledge of where they got their training set, you can't really do this yourself
An analytics engine / machine learning tool has to begin somewhere. Why not start on a small(er) data set you are passionate about (and that has immediate benefit to your business model and user base), that you can record and measure more easily, and that is not generally obtainable by many others, before unleashing it on the public markets where people will be less tolerant of inaccuracies.
Actually, we're pretty serious about this. Connecting a large number of people in the startup industry -is- a big opportunity in itself (and we've already gotten a high level of traction on that front). However, we've found enough evidence to suggest that data can be used to significantly improve decision-making within it also, which is even more valuable.
There are already a lot of firms researching the public company market, like Renaissance Technologies.
To make a counterpart for startups seems interesting, and worthy. With the growing market of successful startups, the industry that supplies them with services such as this one should become viable.
Comments
I'm skeptical of this on a very plain basis: If you had technology that could do this for companies, why would you focus on the startup market and not instead on the potentially much more lucrative public markets?
If the technology is as good as is stated in the TC article couldn't they pretty rapidly build up a listing of undervalued companies on Wall Street and buy into them?
There's not really a technology here. They are a research firm. They collected a dataset from many web startups and can sell that data or make a nice ad based website out of it(in the TechCrunch/RWW space). It could be a profitable niche. Or it could turn into little more than a thesis paper. Of course, if it turns into a profitable niche it could always grow into something different and bigger.
But the technology, at least in my understanding at this point, is nothing more than running your standard regression on a proprietary dataset.
This is a social-based startup, not a technology-based startup.
It sounds like technology, because cool-sounding mathematical prediction is the selling point... But it isn't that hard to do (though with data, and time, you can surely incrementally improve it). The story here is a community-known credential. Its value comes from other people relying on it as a baseline. Like any other credential, it doesn't guarantee you'll be any good - but it is a better-than-random predictor.
Because there isn't anything like this available already, and it would be a useful thing to have, they have a good chance. For it to be used as a reference point, it doesn't need to be great - it just needs to be betterer- enough -than random (and get adopted).
A "good predictor" just means "5% better than totally random". If you have a strong team, and good connections... your chances are surely better than if you had a weak team and no connections (though exceptions occur in both ways). If you can formalize a bunch of these factors, and find what they weigh... you have a baseline predictor that's better than totally random.
In the public markets, there's already a ton of competition, mostly because of the availability and quantity of publicly-available data. Instead of competing with thousands of other established companies and investors in analyzing public companies, we've managed to solve the problem of collecting large amounts of data on startup companies instead, into which more than $67 billion was invested last year in cash in the US alone.
Or why don't they just become a VC themselves instead of running a startup?
If I could predict the market, I would be investing like crazy! :)
Maybe because startups are small enough and therefore simple enough (in terms of variables affecting success) for something like this to work, but large public companies wouldn't be.
But really I'd be skeptical about data in this first batch of tests. I'd bet that YouNoodle used these widely known startups as a training set for its algorithm, or at least as a test set...I imagine if one of their unit tests came back and said "Facebook is gonna be worthless", someone would tinker with the algorithms until it didn't say that anymore. Usually you deal with this problem by setting aside some data points until the very end for you to evaluate your accuracy, but without knowledge of where they got their training set, you can't really do this yourself
An analytics engine / machine learning tool has to begin somewhere. Why not start on a small(er) data set you are passionate about (and that has immediate benefit to your business model and user base), that you can record and measure more easily, and that is not generally obtainable by many others, before unleashing it on the public markets where people will be less tolerant of inaccuracies.
Mighty oaks begin as small acorns.
I think it is a PR gimmick rather than an expected useful product -- their main product is a social network for investors and entrepreneurs.
Actually, we're pretty serious about this. Connecting a large number of people in the startup industry -is- a big opportunity in itself (and we've already gotten a high level of traction on that front). However, we've found enough evidence to suggest that data can be used to significantly improve decision-making within it also, which is even more valuable.
I wish you guys the best of luck. There is certainly an absolutely enormous amount of money in solving this problem.
There are already a lot of firms researching the public company market, like Renaissance Technologies.
To make a counterpart for startups seems interesting, and worthy. With the growing market of successful startups, the industry that supplies them with services such as this one should become viable.