Here’s a brief video about TagTrade, Best Buy’s prediction market.  It’s an introductory video that was produced last year in advance of their market rollout.

A great quote from the video:

“Big companies are like communist countries – we all know how well communist countries worked. At some point they fell apart, not because the leaders were dumb, but because nobody would tell the leaders at the top, who had to make decisions, what decisions to make.”

Jeff Severts, EVP, Best Buy

UPDATE: Here is a video of Brad Anderson, Best Buy’s CEO at the Zeitgeist ‘08 conference.  He starts talking about their prediction market at 9:50.

Ben Kunz just published a BusinessWeek article with several interesting insights about how the Dow Jones industrial average signaled the recent market turmoil - very much like how a prediction market aggregates intelligence about future events.

How did Wall Street know what would happen? It acted like a prediction market, a pool of intelligence that can foresee the future. Prediction markets are simply bets on ideas: What do you think something is worth, and more important, what will it be worth tomorrow? When groups of people bet on something, their combined intelligence is often remarkably prescient.

As you may know, this is something that James Surowiecki discusses extensively in his book, The Wisdom of Crowds.  Ben also talked with Robin Hanson about why the traditional prediction methodologies fail:

The trouble with humans, it seems, is that even when we’re smart, we have access to imperfect information and follow the groupthink of our peers. Because we often disagree with other groups, we band together and end up agreeing too much with our own teams. No single leader can overcome such biases and data gaps to predict with certainty whether an action will succeed or fail. But Hanson suggests markets can do just that.

(Ben Kunz is director of strategic planning at Mediassociates, a media planning and internet strategy firm. He is author of the advertising strategy blog.)

When executives at electronics retailer Best Buy Co. want to know if a new product or idea is likely to succeed, they can seek the opinion of rank-and-file employees by turning to the company’s “prediction market.”

The market, called TagTrade, allows Best Buy’s workers to trade imaginary stocks based on answers to managers’ questions. The market’s judgment has often proved to be more accurate than the company’s official forecasts.

TagTrade is open to all of Best Buy’s 115,000 U.S. employees. The roughly 2,100 of them who choose to participate get $1 million in fake money to trade for a nine-month period. The top trader in the period wins a $200 gift certificate.

Jeff Severts, a 38-year-old executive who is currently chief of the company’s Geek Squad personal-computer maintenance services, spearheaded development of the market. Mr. Severts says TagTrade helps flag potential problems early.

In January, he asked both his management team and the market to predict sales of a new service package the company was offering for laptop computers. A week before the company began selling the product, the market’s guess was 33% lower than the team’s official sales forecast. It subsequently sank further.

When initial sales figures confirmed the market’s prediction, Mr. Severts ended the offer and began redesigning the service package. He listed a TagTrade stock to gauge the revised package’s chances of starting on time, in mid-September. The stock rose, and Mr. Severts says he took “great comfort from that.” On Sunday, Best Buy started offering the new package.

Best Buy isn’t the only company using prediction markets as a way to tap the knowledge of front-line employees. Web-search giant Google Inc. uses them to solicit forecasts on everything from how many users its Gmail service will attract to whether products will launch on time. Other companies that have experimented with them include General Electric Co., Intel Corp. and Microsoft Corp.

Best Buy’s chief executive, Bradbury Anderson, encourages experiments like TagTrade that seek to drive decision-making down the corporate ladder and information up toward the top.

Last year, two marketing employees, working on their own, created an internal electronic bulletin board called Blue Shirt Nation, a reference to the trademark garb of the retailer’s sales force. The bulletin board’s 24,000 users post questions for co-workers, comments on work policies and personal videos.

Tim Sheehan, a senior vice president, watched the bulletin board closely after Best Buy decided to reduce its employee discount last year. Mr. Sheehan, who had participated in the decision, emailed fellow executives, quoting some of the fiery reactions that were posted on the site; the company reinstated the larger discount within days.

Mr. Sheehan now scans Blue Shirt Nation daily. In November, he helped start another site that lets employees with business ideas seek help and funding from elsewhere in the company, bypassing the normal chain of command.

Mr. Anderson, the CEO, says narrowing the gap between management and workers helps to make his Richfield, Minn.-based company more nimble and responsive to customers, while boosting sales and profits. Monday, Best Buy agreed to buy music service Napster for $121 million in what it said was a bid to reach new business.

To encourage more open lines of communication, Mr. Anderson, who says he remembers how distant corporate headquarters felt when he was a Best Buy clerk in the 1970s, invites management gurus like James Surowiecki, author of “The Wisdom of Crowds,” to address his employees.

Inspired by Mr. Surowiecki’s thesis that a crowd, collectively, can make better decisions than individuals — even experts — Mr. Severts decided three years ago to test whether Best Buy’s workers could outperform its bosses.

At the time, Mr. Severts was a marketing executive looking for ways to improve the accuracy of Best Buy’s sales forecasts. He emailed hundreds of Best Buy employees asking them to guess how many gift cards the electronics retailer would sell in February 2005.

As a group, the employees’ guess was more accurate than Best Buy’s official projection. But, Mr. Severts recalls, the forecasting director for gift-card sales said her team felt humiliated.

As a result, Mr. Severts says he learned to keep his experiments from seeming like personal attacks on the competence of his fellow managers. He explains his work at employee gatherings, using PowerPoint presentations that compare Best Buy to a centrally planned economy like the Soviet Union and feature portraits of Joseph Stalin and Mao Zedong. Those leaders, like many managers in big companies, were prone to bad decisions because they were too far from the front lines, Mr. Severts says.

In 2006, armed with Mr. Anderson’s support, a $50,000 budget and some volunteers, Mr. Severts widened his experiment to include a prediction market.

To spark interest in trading, Mr. Severts listed stocks on political contests and DVD sales; one asked how often Mr. Anderson, the CEO, would use the expression “lifestyle groups” during a conference call.

Mr. Severts moved slowly, wary of ruffling more feathers or forcing his ideas on skeptical managers. Seeking more credibility for his results, he enlisted researchers from the University of Chicago to study how market guesses compared with official forecasts. He hopes to get the results later this year.

Other Best Buy managers have started floating stocks. Earlier this year, Rob Rausch, a senior manager two levels below Mr. Severts, created a stock to gauge the likely reaction to a price increase for PC maintenance services. Managers of Best Buy’s private-label business floated a stock to help predict sales of a new digital-TV converter box.

Despite Mr. Anderson’s encouragement, employees say most of Best Buy’s management innovation still emerges from isolated pockets of the company, and depends on the benevolence of bosses who permit work on pet projects.

Mr. Severts says he is glad Mr. Anderson doesn’t force reluctant managers to change their ways. “Nothing would squeeze the life out of TagTrade faster than putting it at the top of a priority list,” he says.

One of Mr. Severts’s latest stocks asked if Best Buy’s information-technology department would finish a planned systems upgrade by mid-September. The market correctly forecast that the department would miss its deadline, but Mr. Severts noticed a glitch: most of the tech work was done by employees of consulting firm Accenture Ltd., who didn’t have access to TagTrade. He proposed opening the market to them as well; Accenture managers say they are interested in participating.

Copyright 2008 Dow Jones & Company, Inc.

What if your boss, rather than dismissing you off hand when you suggested a different way of doing something, let you bet on the fact that your idea was better. And paid you if you were right.

That is the premise of prediction markets – a tool companies are increasingly using to make better decisions by allowing employees to trade in a mock stock market based on information they have about the business.

They work a little like a futures market. Say a company wants to release a new product but is unsure whether a launch date can be achieved. It creates a market for a prediction that the product will launch in a given month. Employees are then given a fixed amount of a mock currency they then use to trade contracts in that future.

Those who think it is likely the product will launch in that month will buy the future, and the price goes up. Those who don’t, sell, sending the price down.

At the end of the trading period, the value of the contracts that have been bought and sold are tallied, and those traders who have been most successful are given cash prizes or bonuses.

The company benefits since the final ‘price’ reflects a broadly based forecast of the likelihood that a given launch date can be achieved.

Several large American firms – including Google, GE, Hewlett-Packard, and Best Buy – have begun using prediction markets to help make assessments about all kinds of projections, including how customer numbers will grow, what demand will be for a product, and when it will launch.

Companies can also set up more complex trading systems which allow employees not only to trade in a given prediction but also to make their own estimates of when they think a product will launch, say, and trade contracts in that future.

Two recent reports – by McKinsey, the management consultancy, and Forrester, the analyst – have suggested that such markets are likely to grow in importance, particularly as the technology which facilitates them becomes cheaper and more widespread.

The advantages, exponents say, are many: the trading system gives employees an incentive to share information they have that may be valuable: although the currency is artificial, the cash rewards are real.

A market also aggregates knowledge more efficiently, as employees can – in effect – give feedback to their boss by trading more or less aggressively on information they have. The social and cultural issues which may have prevented an employee from sharing information are also, in theory, swept aside, because all trades are anonymous.

Companies that have used prediction markets say they provide more accurate information about aspects of their business than could be learnt by more traditional methods, such as polling employees or consulting outside experts.

In one instance, a malfunction in the furnace of a chip manufacturer led employees at the factory to update their positions on future yield more quickly than could employees elsewhere.

Google, the search giant, has been using such markets since 2005 to estimate – among other things – traffic volumes, and when its international offices will open.

About 1,500 employees will trade at any one time, and each has 10,000 ‘Goobles’ – a mock currency – to play with per quarter. The Goobles are issued weekly, “otherwise people would lock up their positions early and there’d be liquidity issues,” Bo Cowgill, an economic analyst at Google, said.

The markets run for anywhere between two weeks and three months. When they close, successful traders are given cash prizes and specially branded T-shirts which, Mr Cowgill said, end up being a greater incentive than the money.

At GE, the healthcare to aviation and media conglomerate, about 40 to 50 predictions – typically about the types of new technologies the company should invest in – are traded at any one time by up to 10,000 of its 330,000 employees.

“We use them as another point in the decision-making process, alongside asking experts and other business leaders,” said Christina LaComb, a computer scientist in the R&D lab at GE.

The window displaying the live trading system sits on the desktop of the employee’s PC. The employee sees the current asking price for any contract and its trading history, and there are simple buttons for buy and sell.

One problem is the inherent biases of such markets, which can make the information they provide less useful.

“The first is a long-shot bias – people tend to overesimate the likelihood of a long-shot paying off, so they tend to overpay to make that bet; conversely they tend to underpay for a sure thing,” said Jeff Severts, a VP of services at the US electronics giant Best Buy, which has used prediction markets for two years.

“The second bias is a home-team bias: employees have been overpaying for ’stocks’ that are based on outcomes that would be good for Best Buy, but we believe we can mitigate the biases through continued refinement of the market.”

The US Commodity Future Trading Commission is also examining prediction markets from a regulatory point of view, and companies which use them have steered away from running markets which predict financial results.

“If we ended up with an application that did an amazingly good job or predicting our results, there would be a concern that anyone who saw it would be an insider,” said Mr Cowgill.

Todd Henderson, an assistant professor at University of Chicago Law School who has written about prediction markets, said that, assuming prediction markets were approved by regulators, the case for using them was compelling, and that the “real puzzle” was why every firm didn’t.

“Companies are the members of society most comfortable with markets as processors of information, and yet when it comes to decision-making, they display these socialist ‘command and control’-style tactics to make them,” he said. “Twenty years from now, prediction markets will be ubiquitous.”

Companies pay anywhere from $25,000 to upwards of a $1 million to run a market, according to David Perry, president of Consensus Point, a business which implements web-based internal trading systems.

David Greenfield over at the ZDNet Team Think blog points out eight pitfalls to avoid when implementing internal prediction markets.  These comments come from a recent recent roundtable run by McKinsey & Company.  Jeff Severts, an EVP at Best Buy and Consensus Point client, participated on the panel.

1. Garbage-in-Garbage-Out – Prediction markets aren’t magic. By their very nature it depends on the quality and quantity of participation. Even smart individuals with in sufficient access to the right data will make poor predictions.

2. Lack of participation – Prediction market initiatives require rich involvement on the part of informed, diverse individuals. More important than lacking the number of people are not providing individuals with the access to available information needed for making informed decisions about their investments

3. Watch the optimism – Positive news and feelings tended to drive outcomes higher. Google found that new employees, for example, who were more optimistic about the company or when the stock performed well traders tended to result in people betting that good things would happen to Google, says Bo Cowgill, product manager at Google who has managed the company’s prediction markets for two and a half years.

4. Avoid extreme events. Google noticed that its traders undervalued extreme events, whether they were good or bad. When Google posed contracts with multiple outcomes, such as forecasts about the number of Gmail users, —the highest and the lowest outcome happened more often than the market expected.

5. The water cooler effect. Google found that that beliefs tend to cluster together. Individuals who sat and worked alongside one another, down to feet and meters from one another, tended to bet in similar ways. Geography was more important than work relationships, socializing outside of work, and language.

6. Watch the enemy - Best Buy found that employees tended to underestimate the competition or to think they knew more about them than they actually did. “Our prediction markets have not had a very respectable accuracy on anything related to our main competitor,” says Jeff Severts the vice president and general manager of Geek Squad, the services arm of Besty Buy, the US consumer electronics retailer

7. Air cover is key –Corporations need to be willing that an important initiative may fail. Too often, that’s not the case, which calls for senior executive sponsorship. “Air cover is key or you’ll find yourself trading on what kind of casserole we’re having in the cafeteria on Thursday,” says Severts.

8. Legal Trouble – Prediction markets deployment broaden the accessibility to sensitive information. How the Security and Exchange Commission (SEC)  will view that matter is anybody’s guess. “Take the employee who sees a prediction market price on her dashboard and realizes, with some degree of confidence, that a certain drug is going to be a success,” says Todd Henderson, an assistant professor at the University of Chicago Law School, “Is it illegal if she trades on this information in the real stock market? Is she an insider because she now has information that only a few top people had before? What kind of disclosure obligations does that put on a US public company? Gambling laws are another issue. Should prediction markets be viewed as an unregulated form of betting? These are enormous question marks for US public companies.”

Robin has been a long-time friend and advisor to Consensus Point, going all the way back to 1994, when he collaborated with Ken Kittlitz, our CTO, on the first web-based prediction market software platform. Since that time, Robin has been credited as the “father of prediction markets” and is responsible for many of the ideas and technologies that have become commonplace in prediction markets today. He has written and spoken widely on the application of prediction markets to business and policy and has been mentioned in over one hundred press articles on the subject.

At Consensus Point, he will continue to develop innovative new technologies and market mechanisms as we continually enhance our market-leading Foresight Server and Foresight On Demand enterprise software platforms. Additionally, he will work directly with customers, advising on market design, quantitative analysis, and best practices.

In addition to his role at Consensus Point, Robin is an Associate Professor of Economics at George Mason University and a research associate at the Future of Humanity Institute of Oxford University. Robin received a B.S. in physics from the University of California, Irvine in 1981, an M.S. in physics and an M.A. in Conceptual Foundations of Science from the University of Chicago in 1984, and a Ph.D. in social science from Caltech in 1997. He also hosts a group blog entitled Overcoming Bias.

CORPORATIONS live and die by ideas, and many enterprises have used Web-based technologies, like blogs, wikis and social networks, to gather thoughts and hasten their way into new services, products and cost-saving steps.

Now executives say they are harnessing a new Web tool, called prediction markets, to transform the idea pipelines inside their companies. Companies like the InterContinental Hotels Group, General Electric and Hewlett-Packard are using prediction markets to try to improve forecasting, reduce risk and accelerate innovation by tapping into the collective wisdom of the work force.

Like blogs and wikis, prediction markets can spur communication and collaboration within a company. Yet they add rigorous measurement to business forecasts, like estimating the sales of a new product or the chances that a project will be finished on time.

Corporate prediction markets work like this: Employees, and potentially outsiders, make their wagers over the Internet using virtual currency, betting anonymously. They bet on what they think will actually happen, not what they hope will happen or what the boss wants. The payoff for the most accurate players is typically a modest prize, cash or an iPod.

The early results are encouraging. “The potential is that prediction markets may be the thing that enables a big company to act more like a small, nimble company again,” said Jeffrey Severts, a vice president who oversees prediction markets at Best Buy, the electronics retailer.

The store chain has experimented with prediction markets on everything from demand for digital set-top boxes to store-opening dates. For example, Mr. Severts said that in the fall of 2006, the prices in a prediction market on whether a new store in Shanghai would open on time — in December 2006 — dropped sharply from $80 a share into the $40 to $50 range. Players made yes-no bets, and the virtual dollar drop reflected increasing doubt that the store would open on time.

Indeed, Best Buy’s first store in China opened late, in January 2007, but the warning signs from the prediction market helped prevent further slippage.

Mr. Severts noted that prices in a current prediction market — betting whether new offerings from its Geek Squad service will be introduced on time in June — are in the $90 range, an encouraging sign.

Best Buy plans to move beyond pilot projects in prediction markets to involve more workers throughout the company, starting next month. “It helps on two fronts, the speed and accuracy of information, so that management can move faster to deal with problems or exploit opportunities,” Mr. Severts said.

For years, public prediction markets have been used for politics, like the Iowa Electronic Markets and Intrade, where buyers and sellers bet on which candidate will win a particular race. And there are prediction markets where people place bets on news events (Hubdub, among others), video game sales (simExchange) or movie box-office receipts (Hollywood Stock Exchange).

These markets have often been more accurate than professional pollsters or market researchers. The idea is that the collected knowledge of many people, each with a different perspective, will almost surely be more accurate than an individual or small group or even experts. The concept has been championed by academic economists and was popularized by James Surowiecki’s 2004 book “The Wisdom of Crowds.”

Robin D. Hanson, an economist at George Mason University, proposes a “futarchy,” a form of government enhanced by prediction markets. Voters would decide broad goals of national welfare, but betting in speculative markets would determine the policy steps to achieve those goals.

Few in the corporate world go that far. An important issue is whether prediction markets are mainly an innovative way to gather information from employees or a font of reliable answers. “It’s still an open question whether the wisdom of crowds is really wise,” said John Kao, a consultant and the author of “Innovation Nation.”

So far, most of the companies using prediction markets are doing so in limited ways, in one or two departments, testing the concept to see how it goes. But in the last few years, corporate experimentation has moved beyond high-tech businesses into other industries, including retailing, consumer packaged foods, hotels, health care, steelmaking and telecommunications.

Today, analysts say, there are dozens of major corporations testing these markets. The companies include Google, Cisco Systems, GE Healthcare, General Mills, ArcelorMittal, the world’s largest steelmaker, and Swisscom, a large telecommunications company.

At the same time, a network of software and service suppliers is developing to cater to corporate prediction markets. The vendors include H.P. and smaller specialist firms like Consensus Point and NewsFutures. The field is attracting start-ups as well.

In 2006, for example, Adam Siegel and Nate Kontny left Accenture, the consulting firm, to found Inkling Markets. Mat Fogarty had been director for financial planning at Electronic Arts, where he experimented with prediction markets, before founding Xpree last year.

“Prediction markets are starting to move into the mainstream, and they will really change the way companies are run in the future,” said Emile Servan-Schreiber, the chief executive of NewsFutures.

At InterContinental Hotels, Zubin Dowlaty, vice president for emerging technologies, decided to create an online market last fall to “harvest and prioritize ideas” from within the hotel’s 1,000-person technology staff. “We wanted to tap the creative class that may not be able to voice their ideas,” Mr. Dowlaty said.

With InterContinental’s prediction market, players were asked to submit ideas anonymously, with a description and the benefit to customers and company. The bettors were given virtual tokens, each receiving 10 green ones to be placed on the best ideas and three red for bad ideas.

There were no limits on the number of times bettors could change their wagers as new ideas came to market, and the market was open for four weeks. The five top ideas (most green tokens), five bottom ideas (most red) and the top five bettors (most accurate, according to market consensus) were listed regularly.

The winners got $500, while second- and third-place finishers received $250 each. The winners, Mr. Dowlaty said, were engineers, analysts and contractors, not managers.

More than 200 people participated, submitting 85 ideas. One person proposed bringing back quarter-operated vibrating beds. “That one got beat down really fast,” Mr. Dowlaty said.

The winning ideas were suggestions to improve searching the company’s Web site to find and book hotel rooms. Two projects have been started as a result of the market, Mr. Dowlaty said.

Next, he said, prediction markets may be opened up to InterContinental’s customers, probably beginning with members of its Priority Club loyalty program. They could bet in markets for improving service and offerings, with points redeemed. “It’s the next frontier and the natural progression for this,” Mr. Dowlaty said.

Setting up corporate prediction markets can be tricky. Public markets for presidential candidates will attract thousands of bettors, but a company may want to run a market only for people with expertise in a certain product or project. At Hewlett-Packard, researchers have been working on techniques and software to make even small prediction markets efficient.

“We want to reduce the wisdom of crowds to the wisdom of 12 or 13 people,” said Bernardo A. Huberman, director of the social computing lab at Hewlett-Packard. Among the techniques, he said, are preliminary tests to assess the “behavioral risk characteristics” of participants to shade predictions from people who are inherently risk seekers or risk averse.

Starting a year ago, a group in the purchasing unit at H.P. began prediction markets on the price of computer memory chips three and six months ahead. The prediction markets, Dr. Huberman said, were up to 70 percent more accurate than the company’s traditional forecasting models. The more accurate predictions, he said, can be used to finesse purchasing, marketing and product pricing decisions.

The H.P. research project has become a service offering called Brain, for Behaviorally Robust Aggregation of Information in Networks. The service is now used in pilot projects by H.P. clients that include Swisscom, which is trying it to predict demand for new services like Internet television on cellphones.

At GE Healthcare, Steven Linthicum, manager for advanced prototyping and innovation, has recently managed prediction markets projects that have generated ideas for software products for the hospital and health care market. Based on that work, patents have been filed and projects are under way.

“These markets bring not only ideas, but what your organization thinks of the ideas,” Mr. Linthicum said. “That’s what leadership needs to know.”

G.E. is also evaluating how broadly prediction markets could be used in the health care division, a $17 billion-a-year unit, and elsewhere in the company.

“We’ll know a lot more at the end of the year how much this becomes part of the decision-making process,” Mr. Linthicum said. “We’re at the crossroads right now.”