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I’ve had a couple of discussions recently about the general state of prediction markets; where the industry is going, where companies are finding successes and growth, etc. Some of these discussions can be difficult since there are several dimensions to a prediction marketplace, and thus the dynamics of what makes each successful (or not) varies.

So I am proposing a categorization method for prediction markets. Together, the criteria should be exhaustive and mutually exclusive. This could hopefully help describe the wide variety of potential prediction marketplaces.

Without further ado, here it is:

ICROP Criteria

• [I] - Interest: Horizontal, Vertical

  Horizontal indicates a broad focus, such as the exchanges run by Inkling, Newsfutures, and Hubdub. Vertical prediction markets are focused on a particular industry or sector, such as the Hollywood Stock Exchange, the now-defunct Storage Markets, or many enterprise prediction markets.

• [C] - Currency: Real-Money, Play-Money

  This should be straightforward. Is real-money or play-money used to trade on the marketplace?

• [R] - Rewards (maximum): Real (tangible) rewards, Intangible rewards

  Rewards should be classified by the maximum reward available on the exchange. It only distinguishes between tangible rewards, such as cash or gifts, and intangible rewards such as names on a leaderboard. So even Newsfutures and Hollywood Stock Exchange would be classified as Real Rewards sites, since you can win prizes on both exchanges. This is a fundamentally different potential motivation than a site like Inkling or Hubdub, where there are no tangible rewards available at all.

• [O] - Objective: Community, Market Results, or Background data

  This refers to the objective of the market creator. Is the purpose of the site to build or leverage a community, such as Inkling or the Chicago Sun-Times?

  Is the purpose to generate unique market results, such as Intrade or an enterprise prediction market?

  Or is the purpose to gather both the market results and information about the traders to generate additional background data known only to administrators, such as HSX?

• [P] - Privacy: Private, Semi-Private, or Public

  Again, this is largely straightforward. The semi-private case accounts for sites like Betfair that is generally public, but needs to limit participation for legal reasons.

Examples

These are how I would classify a number of prediction marketplaces based on the ICROP criteria.

Betfair:
I - Horizontal
C - Real-Money
R - Real rewards
O - Market results
P - Semi-Private

HSX:
I - Vertical
C - Play-Money
R - Real rewards
O - Background data
P - Public

Hubdub:
I - Horizontal
C - Play-Money
R - Intangible rewards
O - Community
P - Public

InTrade:
I - Horizontal
C - Real-Money
R - Real rewards
O - Market results
P - Semi-Private

Newsfutures:
I - Horizontal
C - Play-Money
R - Real rewards
O - Community
P - Public

[Acme] Enterprise prediction market:
I - Vertical
C - Play-Money
R - Real rewards
O - Market results
P - Private

Some initial thoughts

A few things strike me immediately. First is how so very few prediction marketplaces really utilize what I’m calling “Background data.” (This is something that Bo has mentioned before in reference to his prediction market work at Google.) There is a tremendous opportunity both in public and private markets to analyze and use trader data such as demographic information in conjunction with market data to create a completely new dataset. The only public site that I know of that has really done well here is the Hollywood Stock Exchange. But like Bo, I believe that this information could potentially be invaluable for corporate prediction markets. Unfortunately, none of the software solutions available have good feature-sets for this.

Second, there is certainly a link between Currency and Rewards. Real-money markets result in real tangible rewards, where play-money markets can result in either tangible or intangible rewards. Perhaps they shouldn’t be somehow combined, but I think they are important enough to list separately.

Summary

This isn’t a fully-formed classification scheme, and would certainly appreciate any comments (publicly in the comments or privately via e-mail) to refine it.

I want to begin with a pet peeve: I really don’t like the word “innovation.” I think that many people and corporations have begun turning an important concept into a buzzword.

However, innovation is still a very important concept. An organisation can grow two different ways: through market size and share, but also through innovation, generating new products and services. I firmly believe that while both are important, only innovation can keep a company healthy in the long term. But innovation happens on multiple levels. Jeff Bezos of Amazon.com said this in an interesting article in BusinessWeek:

Q: Academics say Amazon excels at different kinds of innovation — from creating new ways of doing business to making small changes that improve the online store. [...] How do you balance these approaches?

A: There is a ton of fine-grained innovation that happens on a daily basis. That kind is super important—things that make our operations more efficient and lower cost so we can afford to offer lower prices to our customers. But there is a spectrum, and at the other end is large-scale innovation like Kindle and Web services and Amazon Prime. With large-scale innovation, you have to set a very high bar. You don’t get to do too many of those [initiatives] per unit of time. You have to be really selective.

I’ve talked before about prediction markets and using them for innovation. Specifically, using PM’s to rank ideas and innovations is a horrible idea. However, I do believe that once you have a short-list of good ideas, prediction markets are appropriate to get a numerical comparison of how each could affect a business or organisation.

But so far I’ve been distinctly unimpressed with how most corporations approach “crowdsourcing” and innovation. There are two approaches, and both have a similar flaw:

• External: Companies ask their customers or the public at large to propose new ideas.
• Internal: Companies ask their employees to propose new ideas.

All of the software solutions to each of these approaches follow a similar pattern; each is essentially a Digg for ideas. But an idea is only the first step in innovation, and it’s by far the easiest step. Selecting the best ideas is only a further marginal step forward. While a “crowdsourced” method of finding and ranking ideas is great, those ideas then go through a typical corporate process to be approved, managed and implemented. What starts off so promising (getting a group of people to start the innovation process) then becomes a standard organisational process. This implementation are where great ideas go to be killed off.

Great ideas that lead to great innovations inspire passion in the people that believe in them. This passion is what is lost when an idea gets put into an standard process. While the idea still may be implemented, I think that it will generally lose momentum if the people making an idea happen weren’t those that were passionate about it.

Side note: I mentioned the difference between external and internal crowdsourcing above because there are important differences when you go beyond a Digg-style ranking mechanism. Namely, an internal approach can take advantage of great ideas without any real intellectual property concerns, where an external approach needs to be sensitive to this issue.

There are some success stories, however. When a true marketplace for ideas and innovations is established, there is a flourishing community. For example, InnoCentive is a marketplace where companies can post technical problems they’re having problems solving, and anyone can try finding the best solution. This is a great model, but only works when a company has a well-defined problem that can be addressed by the innovator. The innovators can never initiate an innovation, they can only react to a request for innovation.

Summary

Innovation is NOT about ideas. Innovation is about putting great ideas into practice. While ranking systems are appropriate and a good start to the process, they are just the easiest first step.

I am dissatisfied with the current approach to crowdsourcing and innovation because current solutions just attack a small portion of the innovation process. I have some things I’m working on that I hope will eventually bridge this gap. This is a topic I expect to write about much more in the coming months, and would appreciate any and all comments, public or private.

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