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From Idea To Innovation
Companies are using online voting tools and prediction markets to conceive new products. So why are most of them still in testing mode?
By David Greenfield
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.
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.
Beauty Contests Vs. Markets
Dell’s launch of IdeaStorm about 18 months ago was one of several steps the company took to shake its image of not innovating and not understanding fast-changing consumer markets. With IdeaStorm, people submit and vote on new features and options for Dell products in an online forum, and as ideas gain popularity, a moderator forwards them to product managers for consideration. The company has received more than 10,000 ideas, implementing about 200 of them.
Besides the Linux laptop, those ideas include Dell’s decision to continue offering Windows XP when Vista was launched and to do a Dell-sponsored small-business makeover show, launched last week on the A&E TV network. Six features in the Latitude Series came through IdeaStorm, including business laptops in different colors, battery life up to 19 hours, and a backlit keyboard. “All were on the radar, but from the 130 ideas these were the ones that resonated the most,” Pearson says.
Harrah’s Entertainment, the 50-casino chain known for close customer ties via its loyalty cards, rolled out its Innovation Portal, based on Salesforce.com’s Ideas software service, to get employee input company-wide. The innovation team seeded the portal with about 60 ideas, and then asked employees for theirs. The portal presents the top 10 vote getters. Next up? Possibly opening it to the public.
But voting doesn’t provide much insight, since there’s no penalty for backing a bad idea, and popularity may not reflect the likelihood of market success. “The incentives for the participants aren’t about finding the truth and correcting the market if it’s going in the wrong direction,” says Chris Hibbert, a consultant and author of Zocalo Open Source Prediction Markets.
That’s why a small number of companies are combining beauty contests with prediction markets.
It was 2006, and Ricardo dos Santos, Qualcomm’s senior director of business development, was charged with developing a program to encourage and identify innovation within the company. Qualcomm had top-down processes for rewarding innovation but wanted to tap employee ideas that had no outlet.
Santos had a vision for certain elements of what would later become the Qualcomm Innovation Network. He wanted to let innovators shepherd ideas from inception to implementation. The model should be similar to venture capital, with the company funding the best ideas. He wanted to let innovators behind the 10 best ideas pitch them in a high-profile, company-wide event, attended by CEO Paul Jacobs.
But Santos was stumped by one problem: filtering the thousands of ideas that overwhelmed the company suggestion box. “Without any kind of filtering, the list was overwhelming—like Craigslist,” he says. Santos came up with a two-tier filtering system. Ideas first face a simple voting process that would cut the thousands of ideas into a manageable 30 or 40 most popular. Those ideas move into a Consensus Point prediction market, where employees could buy and sell shares in them.
The 10 highest-valued ideas are presented to CEO Jacobs at an annual competition, VentureFest. Before the event, presenters get a two-month mini-MBA education on business plan development and selling, and “are even bought a new suit,” Santos says. About 30% of the ideas receive some funding. Santos declined to detail the ideas, other than to say a product Qualcomm will release this fall came from a VentureFest winner—a product manager who got excited about an idea but couldn’t interest his bosses.
Like Qualcomm, Motorola was looking for a way to spark innovation. While the company had an online resource for suggesting patents, there was no place to submit product and feature ideas. Rami Levy, a leader on Motorola’s open source technologies team, was turned loose to create what became ThinkTank Idea Exchange (TIX), a forum for generating new ideas.
Levy and his team first developed an online suggestion box, and ideas flowed in—by the thousands. Also problematic was that people offering ideas weren’t collaborating. So they added a way to vote alongside discussion forums, using an existing collaboration system. That setup provided a glimpse of an idea’s popularity, but the team had a hunch it wasn’t a good reflection of its market impact. “We might have 10 votes for an idea but like another idea even more,” Levy says.
So like Qualcomm, Motorola promoted the most popular ideas to a prediction market, what Levy calls a collective intelligence market, that’s also part of TIX. Motorola has generated 15,000 ideas since 2003 from some 5,000 employees, and 1,000 people actively trade. Ideas from the TIX process include a way to reduce bacteria risk on phones, rapid phone book name search, and a toy car controlled by a phone.
Herbert Remidez, a consultant and management professor at the University of Arkansas at Little Rock, argues that these tools address a problem with more rigid innovation efforts. When he was hired by a financial services company to help improve its innovation program, employees across 20 departments picked three ideas, which were vetted by 11 regional committees, each of which brought three ideas to the annual planning meeting. Holding public votes in small regional groups violates Surowiecki’s criteria for tapping collective wisdom. Remidez addressed the problem with a prediction market from Inkling Market, letting employees from across the country analyze one another’s suggestions. Remidez says it has shortened the innovation process.
WHY NOT MORE?
At HP, a number of companies, including Swiss telecom carrier Swisscom, are piloting its product, called Brain, for Behaviorally Robust Aggregation of Information in Networks. That’s typical: Amid all this interest in tools for nurturing innovation, they’re not mainstream.
Why? For one thing, prediction markets are something of a leap of faith. “Predictive markets use a science that is not intuitive from the start,” says Jonathan Reichental, director of IT innovations for PricewaterhouseCoopers, which has deployed prediction markets internally and for clients. Robin Hanson, an economist at George Mason University who pioneered the use of prediction markets inside companies and became chief scientist at Consensus Point this summer, notes that no one has published the kind of definitive case studies that detail how much money was saved or made based on a given outcome from a prediction market.
And it’s natural for people in a conventional hierarchy to doubt the validity of these markets. That’s why early adopters all point to executive sponsorship as critical to the success of any collaborative decision platform, be it a prediction market or a beauty contest platform. Dell chief Michael Dell personally backed the IdeaStorm effort.
The technology itself has been a barrier to adoption, since it takes time to learn the software and understand idea trading’s nuances. Vendors are making their platforms simpler to understand, but that sacrifices the sophistication experienced traders want, like the ability to short sell an idea that traders think will get less popular.
Companies also need to figure out how to reward employees, and govern the process. Most companies institute small prizes, and many just use recognition. Cash is a nonstarter, because of gambling restrictions. There also are concerns about prediction markets sharing information too widely. If a prediction market price convinces a pharmaceutical company employee that a certain drug is going to be a success, “is it illegal if she trades on this information in the real stock market?” asks Todd Henderson, an assistant professor at the University of Chicago Law School, in the McKinsey Quarterly.
But early adopters say all these problems are manageable. Qualcomm’s only reward for the best idea pickers is to recognize them online and give out crystal-ball trophies for being a “Wizard.” Others rely on people’s inherent competitiveness. Worried about employees wasting too much time? None of the companies in the article had that problem. Some limited access to lunch breaks and after hours, but others relied on common sense. “People understood the market is a small thing, a way to help the company and have some fun,” says Levy. “On average, participants will spend 10 minutes per visit and will average one visit a day.”
Of course, these platforms are hardly replacements for other engines of innovation and market understanding. Motorola credits TIX for millions of dollars of revenue, but having prediction markets didn’t help it deliver a timely follow-up to its blockbuster Razr. Questions and potential answers still must be framed well to work effectively. Today’s markets can’t sort through combinations of questions, such as “What outcome will happen if conditions a, b, or c exist?” says Hanson.
What they can do is help cull through the mountains of responses that pile up in suggestion boxes. And perhaps most important, they can challenge status quo thinking and make people think twice about ideas that might be dismissed through standard innovation channels.