Early this year I posted my predictions for 2009. In the best spirit of Robin Hanson (getting better predictions by simply tracking how close predictions matched reality), I want to see how I did.

• “Prediction markets in 2009 are going to become even more well-known and wide-spread, but there will be no single event that brings them to the attention of the public. It’s going to be a slow, but steady, growth.” – This was spot on. All of the vendors appear to be doing well, but there was no big “event” that brought everyone attention.
• “All of the prediction market vendors will mature their business offering/proposition.” – Not having been on the receiving end of any of their sales pitches, I can’t say this one is true for sure. But from their blog posts and public statements I would assess this as likely.
• “HubDub will continue to only be the only strongly popular play-money prediction market.” – Put me down as wrong on this one. Nigel and the creators of HubDub have focused their time and effort on FanDuel instead. (Rightly, for revenue reasons!) So while HubDub is still active, it’s not the hive of activity it was for a while.

That said, I’m still a fan of niche, public, popular sites like HSX. They managed to turn play-money prediction markets into a real-money revenue stream by analyzing trader behaviour (which can only be seen by administrators) and selling that business intelligence to the studios. This could be replicated in many industries, such as video games or television.

• “While a couple additional software vendors may appear, I get the feeling that the market for prediction market software is largely saturated.” – This was also spot on.
• “I’m looking forward to see how the CantorExchange develops.” – Not so much a prediction, but a hope that it proved interesting. It’s taken a long time to get up and running apparently, and won’t be widely launched for real-money contracts until 2010. (If I read the website correctly.)

A great development from InTrade

Just yesterday John Delaney of InTrade posted on his blog that InTrade will soon be offering some historical market data to the public for free. (As he notes, this means they’re losing a source of potential revenue, as historical data can be quite valuable.) This is a great development, and should be a solid source of data for people to dig in and get interested in how traders operate in a prediction market. Kudos to John for doing this.

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I’m a little partial to these guys, mainly because they come from my alma mater, the University of Michigan. The key people look to be a mix of entrepreneurial students and professionals. The company itself was started a year and a half ago, but has had success with early pilot projects.

In fact, three slides included in their “Learn more” online slideshow are quite powerful statements as to why prediction markets can be useful. They were predicting car sales in the winter of 2008/2009, during what was one of the most volatile months the industry has seen in many, many years. And the prediction market beat the internal forecast made at the beginning of the month, and the expert forecast made at the end of the month.

Here are the slides:

To recap, the prediction market beat the official GM forecast (made at the beginning of the month) easily, which isn’t hugely surprising considering the myopic nature of internal forecasting. But the prediction market also beat the Edmunds.com forecast. This is particularly interesting, as Edmunds would have had the opportunity to review almost the entire month’s news and data before making their forecast at the end of the month.

Examining the numbers

Let’s quickly quantify this error. Assume an average Chevrolet sells for $18,000. After dealer markup, assume that GM/Chevrolet receives $16,000 per vehicle.

Within the first week of November 2008, the prediction market would have warned Chevrolet that they were going to miss their revenue targets by $800 million in the Chevrolet division alone. And depending upon the exact product mix, this could have easily exceeded $1billion.

Now Chris can blather on about corporate prediction markets, but he’s simply wrong. Assume that even with three weeks’ early warning Chevrolet was only able to save 10% of that gap, it’s still $80million in savings. Even if a corporate prediction market for a giant company like GM cost $200,000 a year, that would still be a return on investment of 40,000 %. And again, that’s in the Chevrolet division alone. (Note: It would be a rare prediction market that cost $200k/year to run.)

Now not every problem should be solved by a prediction market. This is where management expertise comes in: are the errors large enough to warrant the cost of reducing those errors? But big problems with big numbers are often very suitable to address with a prediction market.

Summary

I’d like to wish good luck to the CrowdClarity team. It’s great to see Wolverine entrepreneurs working on prediction markets. There are more and more players each year in the corporate prediction market scene, but with case studies like this behind their belts, they’ll be well-placed to pick up some business.

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#1 – Trading UK Housing Prices

CityOdds, based in London, has recently opened a prediction market (currently marketed as a competition) to predict a UK housing market index. While this is a play-money market, there is a £10,000 first-place prize.

CityOdds runs both fantasy markets, as well as real-money markets on currencies and other commodities. Mike Chadney, the founder, is a good guy and experienced “City” man. Check out the housing market here: http://www.cityodds.com/hpitrading.html

#2 – Predictify shuts down

Predictify made a bit of a splash when Scott Adams used them to forecast how many sales of his (then) new book were going to be sold.

It seemed a bit of an odd company; venture-funded, but with people who had no background in prediction markets at all. They offered a small number of markets where accurate forecasting would win cash, while most markets were just for leaderboard position. (My trading was perhaps typical: I’d log it and only scan the markets where I could win cash and ignore the rest.)

Well, they’ve died. According to an announcement on their website:

Due to the tough economic climate, we are planning to cease operations and shut down the company in the near future. If you have an account balance of $20 or more, please visit your account page and enter your withdrawal information by 11:59pm on August 31, 2009 to receive payment.

We sincerely enjoyed building and operating Predictify, and we’re glad that you could be a part of it.

The Predictify Team

My question is this: what does this mean for Crowdcast?

Crowdcast has an absolutely fantastic team who have great experience in prediction markets. But can they thrive as a venture-funded company? I’m hopeful, but perhaps am more skeptical that there are simply enough customers truly interested in their level of solution. Particularly as so many companies need hand-holding and thus a lot of expensive people-time through the early stages of implementation, are the profits sufficient to satisfy investors?

I don’t know, but I do believe they’ve at least got the people to give it a go. (Strangely enough, it was Crowdcast’s founder Mat Fogarty who originally told me about Predictify well before their launched.)

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“In God We Trust; all others must bring data.”

– W. Edwards Deming

There has been more discussion recently on Midas Oracle, here and other blogs about the value of enterprise prediction markets. Part of this is because we don’t have a good measurement of value, and partly because a lack of information.

When it comes to value, there’s one great way to determine if a prediction market is valued by a company: they keep using it! No matter what people “think” the value of an enterprise prediction market may be, if the actual customer is willing to pay for it, the tool is valued.

But another big part of why we’re even having this discussion is because there’s no clear perspective on what’s going on with the industry. Why? Think of the parable of the blind men and the elephant. (Quote from Wikipedia).

A group of blind men (or men in the dark) touch an elephant to learn what it is like. Each one touches a different part, but only one part, such as the side or the tusk. They then compare notes on what they felt, and learn they are in complete disagreement. The story is used to indicate that reality may be viewed differently depending upon one’s perspective, suggesting that what seems an absolute truth may be relative due to the deceptive nature of half-truths.

So the problem is, we’re all talking our of our collective a**es.

There’s no way we can talk intelligently as a community unless we have a shared understanding of what’s actually going on in the industry.

The measurement

I propose that we start by looking at two measurements: retention rate and customer growth rate. Retention rate measures how long a customer stays active. This should be a relatively good indication of the value a prediction market provides to a client; the longer they pay for it the more valuable it is to them!

Customer growth rate is exactly that, how quickly the industry is growing and finding new clients.

While rates won’t provide a total magnitude on the size of the industry, it should provide a good proxy of value to customers and growth of the customer base. It’s not the sum-total of what can be measured right now, but I think it’s a solid first step.

A proposal

I hereby make a public proposal. In order that the entire industry can talk about a common set of data, I volunteer to act as the point of contact for data aggregation. Enterprise prediction market vendors would only need to provide limited data, and would get in return a comparison of their own company’s statistics to the industry at large. The public would get the aggregated statistics (only, no company-specific details) on the retention rate and growth rate of the industry.

The information that would be required of the Enterprise Prediction Markets software vendors is:

A list of clients (who don’t have to be named, code names/numbers okay) and for each of those:

• Date (by quarter) when the client was first invoiced
• Date (by quarter) when the client was last invoiced

I believe that invoicing is a good proxy for measuring the start and end of a prediction market, but if a software vendor would like to use other measures that’s fine, as long as they fairly represent the start and end of a paid prediction marketplace.

Thoughts?

I’d be interested in your thoughts on this. So far I’ve gotten some solid interest from some of the software vendors, but am posting this here to gather some additional interest. (Yes, a little public pressure.)

I think that if we’re confident enough in the prediction market industry, we shouldn’t be afraid of the data!

PS- Because of a recent spate of comment spammers, I’m moderating all comments so you see a bit of a delay before your comment shows up here. But once I approve it, you won’t need to wait if/when you comment again.

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