Building on our success with Genentech, we are looking forward to learning more about this opportunity and how Crowdcast can help bring drugs to market more efficiently.

Did you know:
- The average drug costs over $1B to get to market?
- Every day of delay costs approx $1.4MM (assumes $500MM revenue per year)?
- Only 6% of clinical trials are completed on time, and 72% of trials run over schedule by more than one month?
- Clinical trials last 42% longer than expected in Phase I, 31% longer in Phase II, and 30% beyond planned deadlines in Phase III – all because of recruitment delays?

If pharma companies could accurately assess and manage their clinical trials, especially around enrollment of patients, we assume that they could deliver their drugs to market faster and significantly increase profits.

The problems they face are myriad. They work with many sites (clinics, hospitals etc) with few patients per site. The trials are often international with varying regulations. Much of the work is outsourced to CROs, making a direct line of site with the clinic harder. In addition, the conditions change with alternative trials overlapping and competing for the limited number of potential patients required. Finally, the leader of the project and the CROs may have reason to be optimistic to ensure funding and selection of their particular site.

What they need is a way to aggregate accurate, real time forecasts, from a diverse set of players and reward for accuracy and insight :)

Planned date = Jan 15, Crowd Forecast = Feb 10….. DANG!

The realistic crowd forecast is important for managing the project and shaping customer expectations. However, this sensitive information needs to be handled well otherwise it could cause issues around employee morale or with partners or investors.

This issue around transparency of the results has really restricted the use of prediction markets in the enterprise, but Crowdcast has developed and patented a solution.

We have now enabled 3 transparency settings to allow our admins to share as much as they want with the participants.

Three Settings:

You can share everything, share comments, or share only the question.

Share with your team: This is the default setting and shares the Crowdcast Curve, the comments, the beliefs behind those comments. Use this for metrics which are ok to be shared with your team.

Share comments and anonymous individual bets: With this setting, the Crowdcast curve no longer appears. Use this setting for metrics where the commentary and social side is important, but where the metric may not be shared.

Share the question only: This shows only the question. Payoffs are ‘location invariant’ — a user can’t determine the shape of the Curve by moving their bets around. Comments are hidden. Use this for your most important metrics.

For all the settings, the Admins and the users with “Executive” access can still see the crowd forecasts on the Dashboard page.

The US Intelligence Agencies have thousands of highly-trained analysts with access to a vast store of privileged information.  However, they have great difficulty efficiently synthesizing and surfacing this info actionable information.  Often the “a broken watch is right twice a day” phenomenon encourages analysts to stick with the status quo rather than flag new conditions.  These kinds of forecasting behaviors have real costs in dollars and in lives.

Despite its importance in modern life, forecasting remains (ironically) unpredictable. Who is a good forecaster?  How do you make people better forecasters?  Are there processes or technologies that can improve the ability of governments, companies, and other institutions to perceive and act on trends and threats?  Nobody really knows.  The goal of the Good Judgment Project is to answer these questions.

We will systematically compare the effectiveness of different training methods and forecasting tools in accurately forecasting future events.  We also will investigate how different combinations of training and forecasting work together.  Finally, we will explore how to more effectively communicate forecasts in ways that avoid overwhelming audiences with technical detail or oversimplifying difficult decisions.

Over the course of each year, forecasters will have an opportunity to respond to 100 questions, each requiring a separate prediction, such as “How many countries in the Euro zone will default on bonds in 2011?” or “Will Southern Sudan become an independent country in 2011?”  As the project evolves, we’ll iterate the mechanisms and the training, arriving at an idealized and verified system.

The Good Judgment research team is based in the University of Pennsylvania and the University of California Berkeley. The project is led by psychologists Philip Tetlock, author of the award-winning Expert Political Judgment, Barbara Mellers, an expert on judgment and decision-making, and Don Moore, an expert on overconfidence. Other team members are experts in psychology, economics, statistics, interface design, futures, and computer science.

If you’re interested in participating, we’d love to have you.  Head on over to our registration site to sign up.  We’re looking forward to sharing the results with you, and leveraging the valuable insights of The Good Judgment Project with our clients.

http://www.technologyreview.com/business/32397/

This recognition reinforces our proposition that corporations have the information they need to make decisions.  However, current methods of gathering and transmitting data up the hierarchy can lead to biases and mistakes.  Collective intelligence can solve this, and Crowdcast is at the leading edge of making this happen.

Congrats again team.

Mat

• Thursday, December 9, 2010
• By Chris Taylor

The need to predict the future, as exciting as it sounds, crops up in corporate life in terribly mundane ways. Case in point: large videogame companies need to know where to put their marketing dollars many months before they complete their games. Inevitably, some games will be stinkers, hardly worth the investment of an ad campaign. But how do you know which ones?

Here’s how one very large videogame company used to guess the answer: its marketing people would predict the score their games in progress would garner on the website Metacritic, which aggregates game reviews. But why would the marketing people know more than the game’s developers?

Three years ago, a startup called Crowdcast suggested a different tactic. Why not take hundreds of your lowliest employees, the ones in the trenches who are actually making and testing these games, and ask them what they think the Metacritic scores will be? Better yet, why not give them each $10,000 in play money and ask them to bet on the outcome? Let them accumulate a pot of pretend wealth if they’re right. Turn game marketing prediction into, well, a game.

To the executives’ delight, the employees’ Metacritic predictions turned out to be 32 percent more accurate. More disturbingly, their accuracy was inversely proportional to their place in the hierarchy. The closer you got to the C-suites, the less of a clue you had—and the lower your pretend wealth in Crowdcast’s game. 

That embarrassing factoid might explain why this particular videogame company, like many Crowdcast customers, wants such stories to remain anonymous. “It’s kind of experimental,” explains Mat Fogarty, Crowdcast’s sardonic British CEO, “and it may undermine the credibility of their awesome management.”

Indeed, anonymity and uncomfortable revelations in the boardroom are Crowdcast’s stock-in-trade. The San Francisco startup already boasts clients and partners as diverse as Hallmark, Hershey’s, and Harvard Business School. It is built on the back of years of research into how internal prediction markets work. In such a market, managers ask employees questions about the future of their product and let them bet on the answers, without knowing who bet what. The results can be scarily accurate.

In September, Crowdcast ran a prediction market for a large American car company, one that normally runs its designs for new autos through a car clinic—a lengthy and expensive kind of focus group of buyers. Crowdcast’s project involved asking engineers and factory supervisors what they thought the outcome of the car clinic would be.

Forty questions were in front of these ground-floor experts at any given time during the market. For example: What percentage of buyers will list this car’s dashboard as its most important feature? The trial market was so accurate that the car company will be trying another in January. The auto giant now has a new way to cut costs: use these predictions markets instead of expensive car clinics at least a third of the time.

Crowdcast calls the space it’s in “social business intelligence” rather than crowdsourcing. “Within your organization, there are people who know true future outcomes and metrics,” says Leslie Fine, Crowdcast’s chief scientist. “When is your product going to ship? How well will it do? Normally, crowdsourcing asks for creative content. We’re asking for quantitative opinions.”

Put that way, it sounds a lot more respectable than “get your employees to play a kind of fantasy football with sales and shipping dates.” But make no mistake—that’s actually what Crowdcast does. That used to be a hard sell, Fogarty admits: “It seemed weird to be talking about playing games at work and using Monopoly money.”

But the gaming approach has gotten easier as more executives have heard about large-scale prediction markets like InTrade, which accurately forecasted 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 by gifting every employee an extra $10,000, say.)

What crowdcasting proves is that even play money talks—and losers walk. Studies show there’s no practical difference between using real money and play money in this context—both equally 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.”

Jay Margolis will lead the engineering and operations groups at Crowdcast.  Previously, Jay was Senior Director of Engineering at Jaspersoft, where he ran product development of the Jaspersoft Business Intelligence Suite, bringing powerful yet easy-to-use web-based ad hoc reporting and analytics to everyday users.  Before that, Jay held leadership positions at Primavera Systems and Evolve Software, developing Project Portfolio Management solutions used by large professional services and IT organizations to improve decision making across large numbers of projects and resources.  Jay has over 20 years experience in enterprise software development and holds a BS in Applied Mathematics from Carnegie Mellon University.  (he also plays the guitar real well!)

http://www.wiredvc.com/alsop202-louie-33close-fund-ii-at-nearly-100m/

Stewart Alsop was our main investor in our first round of financing.   We now have Joe Addiego (ex In-Q-Tel) on our Board.

ALP team – thanks for your support so far.