The industry is now based on prediction marketing.

The first, and perhaps most powerful, is project management.

Every project has various key milestones, and those on the critical path are even more visible to and closely watched by managers and executives.Most forecasting to date is largely self-reported by team leaders.They are largely meant to be as honest as possible in reporting project status.In reality, this is highly dependent on the company’s culture, and in far too many businesses, project status is a commonly-known lie.(Unfortunately, this also means that it can be a politically dangerous kind of market to implement, since the project managers that tell these lies usually don’t want to be exposed!)

The business case for a prediction market in project management should be calculated based on the effects of slips in a project’s schedule.For projects or products where sales are front-loaded (such as films, major software releases, ground-breaking new drugs, and the like) a projects slip into the next fiscal quarter or next fiscal year could cause a serious impact in potential earnings.If that information was available weeks or months earlier, how would that affect sales, reputation in the marketplace, and other factors important in your industry?How much does it cost to plan for a major product roll-out in January, only to have to cancel it all and do it all over again in April?These are the questions that can build a business case for a project management prediction market.

The second type of market quantifies your industry.

In many industries, this means defining the cost of goods needing to be purchased, the quantity of goods or services to be sold, or the price potential of the goods or services to be sold.In some industries, this could mean quantifying the user growth of a given product.(Such as Google’s prediction markets, How many users of [Google product] will we have at the end of Q3?] This is the classic case written about by HP’s research center, when they asked their traders to predict sales figures for various products for the next quarter.It turned out they were measurably better than the forecasting system they had been using.

Quantifying a business case here is generally more computational, and therefore more straightforward.What are your costs associated with inventory of products that you can’t sell because you thought there was more demand?How much less revenue do you earn because you have to discount products to move them out the door?Alternately, what are your costs associated with paying extra to ramp up capacity because you didn’t realise the demand was as big as it was?How many lost sales did you experience because of empty shelves?These numbers are concrete and can hit your company’s bottom line.They can sometimes be the easiest way to prove your business case.

The third type of prediction market I want to discuss quantifies risk.

Based on my conversations, there are a number of obstacles at present re: prediction markets in corps:

- most execs I talk with aren’t familiar with prediction markets. Whereas everybody’s heard of social networks, blogs, and wikis (even though a lot of people still aren’t quite sure what a wiki is).

- prediction markets are a more difficult concept to understand (vs social networks, blogs, wikis). Thus significant education is required for execs and end-users. More education = bigger time suck thus lower ROI & opportunity cost.

- the prediction market biz process is more complex and more integrated into existing decisions/processes, than is the process required to implement/support social networks/blogs/wikis. What outcome will be forecasted; what end-users will participate; what’s the timing/schedule/due date; who will report the results and to what audiences; how will results be integrated into existing biz processes; etc. Again = more work, greater time sink, greater risk, lower ROI.

- there aren’t yet enough corporate examples of successful prediction market use. Google and Microsoft have shared their experiences publicly; but those two are not seen by most companies as a realistic example for their own organizations.

We’re definitely pre-chasm on prediction markets for corp use (whereas social networks are post chasm — much wider adoption).

And here’s the fundamental problem with prediction markets, IMO: the democratic, collective, participative nature of prediction markets comes into direct conflict with the hierarchical nature and existing communications patterns of most organizations.

Organizations are about the allocation of decision-making rights. Power is based upon who/what group has the authority/ability to make a decision. (Yes decisions can be subverted…but the official decision does matter in a very real way, even when it’s not implemented effectively.)

Also, leadership (formal or informal) in any organization is very much about communication and dialectics: what information is provided to whom, what context and meaning is assigned to that information by the speaker, which points are emphasized and which are de-emphasized, etc.

Well crafted communications are key to productive dialogue and decision-making. Poorly crafted communications can be a major obstacle and even derail issues permanently. There’s an enormous effort in organizations put into crafting effective communications. Yes, some of this is BS/spin….but some of it is legitimate communications work to communicate effectively between different constituencies/audiences.

Prediction markets pose a direct challenge to both of these (decision authority and well crafted communications).

Prediction markets second-guess the official management opinion/decision. And by engaging a wide group of people, the pred market sends a meta-message that everybody’s opinion is just as good as anybody else’s. It also implies that any answer that differs from the collective wisdom is inherently ‘wrong’ in some way.

Also, like all-hands meetings and employee surveys — prediction markets are a collective group experience. And that is different from the ongoing efforts of managers to balance out many complex issues between their organization and others, and tailoring their communications for those various contexts.

For example, there can be many reasons why a manager’s “official” forecast/prediction is modulated and differs from a group collective opinion. But those those nuances of social dynamics aren’t accounted for, with a prediction market.

That is PM’s strength — the direct unmodulated answer — but within an orgnanization/social dynamic, that’s also a problem.

Prediction markets aren’t quiet. They aren’t subtle. They are a blunt instrument — they produce a straightforward, simple answer to a straightforward question — with a big public splat.

It’s not about the specific answer per se. It’s the bigger social context: of collectively and publicly involving a wide group of people in an issue that previously involved a select group only; the expectation that employees now have, for engagement and responsiveness and dialogue, after being asked to give their opinion.

Look at the angst that employee surveys and all-hands meetings can cause for management — and that’s for mechanisms that have been in use for decades and for which there is a lot of knowledge about how to use them effectively. And managers still stress over them.

Anything that stirs up collective action among employees is worrisome to managers. So it’s no wonder that prediction markets, being a brand new democratic/collective mechanism, would cause a lot of anxiety and resistance among managers.

Tom Davenport, a Professor as Babson College, has identified traditional hierarchical organizations as a major obstacle to the use of prediction markets (as well as trader participation once deployed). At BitInsight (www.bitinsight.com), a consulting firm utilizing prediction markets as part of a decision support service, our experience would concur with this organizational resistance. The resistance ranges from disbelief in the process, concerns about data leakage, concerns about the impact on current business processes, and concerns that the process will “undermine management” (to use the words of Microsoft in their presentation on their prediction market, PredictionPoint at the recent Conference on Corporate Applications of Prediction/Information Markets).

By definition prediction markets are highly visible and if not carefully managed can be controversial, note the furor over DARPA’s terrorism futures market. This visibility adds to the personal risk from failure and precludes “skunk work” executions in most cases. The (ideally for many questions) cross organizational nature of the trading pool also adds organizational obstacles to market introduction.

Prediction markets are the data mining tool to access the unstructured data stored in the enterpriseÂ’s distributed human capital (broadly defined). It provides another source of data to be considered and leveraged; it does not, per se, make or even recommend any decisions to the corporation. Defining the tool as such may make it more palatable to senior management whose buy-in and support are absolutely critical to successful trial and on-going incorporation of this technique within the enterpriseÂ’s business processes.