How Companies Are Using IT To Spot Innovative Ideas
Tools like prediction markets and online voting can't replace internal innovation processes. But they can open a new channel.
In a three-week experiment, GE Research turned its 85 employees into day traders, letting them watch market movements on their screens to decide whether to buy or sell any of 62 stocks. Only the stocks were product ideas in which the company had the option to develop. At stake was $50,000 in research funding that GE would allocate to the highest-valued project.
When the markets closed, GE ended up with a prioritized list of ideas that the collective wisdom of the market thought would best help the company. Topping the list was an algorithm in the area of machine intelligence, an idea pitched directly by a researcher, not through the normal hierarchy of lab managers and senior management.
Dell looked to an even broader market for new product ideas, using Salesforce.com's online voting service called Ideas and launching Dell IdeaStorm, where anyone can submit and vote on new features and options for Dell products. Perhaps best known of these ideas is a Linux-based laptop Dell introduced in May 2007. Starbucks uses the same voting platform, at MyStarbucksIdea.com, and took an online suggestion posted Oct. 7 by BillMac to offer a free cup of coffee Nov. 4 to anyone in the United States who voted.
The use of these collective decision-making technologies, both sophisticated prediction markets and simple voting tools, is spreading, and they're increasingly being paired together as a component of corporate innovation programs, helping companies sort through reams of ideas--from new products to customer service to productivity improvements--to find that handful of blockbusters.
Few things matter more to a company. Think of the impact a single product, whether the iPod or New Coke, can have on a company's fortunes. IT needs to make itself part of that process, and one way is by providing tools to help their companies make better decisions. Like blogs, wikis, and other social software, these tools tap into a free exchange of ideas. Unlike other social software, they lead to a definitive outcome and measurable results.
Still, prediction markets aren't money in the bank. That GE Research experiment that pushed the algorithm idea to No. 1? That was back in 2005. And while the group has run nine markets in all, including one for GE Healthcare that led to its filing for patents, it's still evaluating a product from Consensus Point, and it's not an everyday part of its innovation process. The algorithm itself was basic research, not a product.
Dell remains a believer in the community's intelligence after more than a year of using the voting technology and thinks that of the 200 or so ideas it has implemented out of the process, 4% are "potential game changers," says Bob Pearson, Dell's VP of communities and conversations.
Markets have proved their worth in predictions. Hewlett-Packard has used faux markets to predict the cost of DRAM and found them to beat official HP forecasts six out of eight times. (It's now marketing its own prediction product.) One major software company says prediction markets accurately predict project completion times as often as 95% of the time. In The Wisdom Of Crowds (Anchor, 2005), James Surowiecki laid out four conditions under which the crowd tends to be more accurate than experts: a diverse population; a decentralized population, so no one dictates an answer; an independent population, so voters focus on what they know, not what others think; and a summary of opinions into one verdict.
This article, the third in a four-part series, is just one element of a special multimedia package on business innovation. For links to related stories and additional editorial content, go tobusinessinnovation.cmp.com.
It's this last factor, effectively summarizing opinions into a decision, where companies face a trade-off between the two broad types of collective decision-making tools, prediction markets and vote-counting beauty contests. Beauty contests such as those used by Dell and Starbucks offer only a measure of an idea's popularity. However, their simplicity makes it practical to open the innovation process to thousands of employees, or even the general public.
The more complicated approach is to create markets, making each idea a stock, which players buy and sell to accumulate as much virtual cash as they can. Players are likely to give the decision more thought than when simply voting, since they're trying to win, not just throw out an opinion. And since participants choose how much to invest, the price reflects intensity of expectations, providing a better projection of a given outcome (see table, "Vendors, And Some Questions To Ask", below).
Vendors, And Some Questions To Ask
There are seven major vendorsof prediction markets: Accept, Consensus Point, Hewlett-Packard, Inkling Markets, NewsFutures, Spigit, and Xpree. Here are issues to explore with them.
Much of the success of a prediction market relies on understanding customer requirements. Consensus Point lists Best Buy, General Electric, and Qualcomm among its clients. Inkling cites General Mills and CNN.
Many companies want to move from simple voting to more complicated prediction markets, which provide more insight into an idea's market potential. Consensus Point and Spigit provide both capabilities.
If idea trading requires a buyer and seller, markets need thousands of participants for liquidity. So you probably want software with an automated market maker, which can succeed with a few dozen traders.
Within companies, where individual strengths and weaknesses can be assessed, companies may want to factor those in when determining the importance of contributions. Spigit is the only one to offer that feature.
Some cater to sophisticated users comfortable with market data such as price histories, while others target novice users, providing simple forecasting interfaces.
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