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But the gaming approach has gotten easier as more executives have heard about large-scale prediction markets like InTrade, which accurately forecast the results of the 2008 and 2010 elections, and the Hollywood Stock Exchange, which predicts the success or failure of major movies. The current craze for “game dynamics” in apps like Foursquare and Scvngr, which let users rack up points for various tasks, also helped drive the idea home. Crowdcast differs from other prediction markets, however, because users don’t get to bet on any outcome they can name. Instead, the company runs closed markets where top executives get to pose the questions.

Part of what’s fascinating about Crowdcast’s approach is how wildly inequitable it is—much like capitalism itself. The democratic, politically correct thing to do would have been to hand those auto employees survey forms, and count all their voices equally. But that would not have given the more prescient ones a louder voice. “We’re trying to create a meritocracy of information,” says Fine, who spent more than a decade studying prediction markets at HP Labs and holds several patents in the field. In theory, if you make bad bets, you go bankrupt. (In practice, these virtual bankruptcies happen rarely, and Fine can provide a back-end bailout—say, by giving every employee an extra $10,000.)

What crowdcasting proves is that even play money talks—and losers walk. Studies show there’s no practical difference between using real money and using play money—both represent represent a person’s intention. It turns out you put your money where your mouth is, even if it’s Monopoly money. And as much as the best players like racking up millions of fake dollars, here’s the answer participants most frequently give as the reason they enjoy the game: “I believe management is listening to me through this tool.”

Management, however, doesn’t always like what it’s hearing. The biggest blow of Crowdcast’s young life came when it ran a trial market for a consumer goods company, one that makes a popular household lubricant. The market was asked about sales figures, new customer acquisition, and the price of oil (vital for lubricants) at the end of the month. In every metric, the market was more accurate than the company’s official forecast. “We nailed it,” says Fine. After presenting their results, “we were high-fiving each other.”

But Crowdcast didn’t win the contract—because it had failed to connect with the head of sales, a 20-year veteran of the company who simply ignored the evidence. As much as it believes math should win any argument, the startup is learning the importance of the personal touch. If the marketing department says a product will ship on time, but engineering is more bearish, some boardrooms may prefer to cling to the marketing fantasy. Crowdcast’s next task, therefore, is figuring out how to make an eminently disruptive tool look less threatening to “awesome management.”

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Credit: Crowdcast

Tagged: Business, analytics, Predictive Modeling, Crowdcast

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