Easy, Effective, and Engaging Prediction Markets

1. Easy, Effective, and Engaging Prediction Markets Mat Fogarty and Leslie Fine Crowdcast mat@crowdcast.com leslie@crowdcast.com

2. Today… The corporate forecasting challenge EA case study Learning from our customers The Crowdcast Confidence Curve

3. The Forecasting Challenge Process Improvements in Place Most Significant Forecast Challenges More Detail Improving Accuracy 43% 27% Higher Frequency 25% Reducing Costs of Forecasting 20% Accuracy Targets 21% Forecasting Right metrics 20% Forecast accuracy is the biggest problem New Software 20% More Detail, More Frequently 17% Other Other 15% Impact on Accuracy Required Heads (as % of Finance) With hit and miss results Requiring more resources 70% more Negative 49% Positive 51% 39% 23% 10 Forecasts / Year 10 or More Forecasts / Year Corporate Executive Board, 2007

4. Bias Sandbagging Sales forecasts Budget padding Over-optimism Ship dates New product pitching Stickiness Forecast = target “Don’t worry” Decision makers Errors Uncertainty Middle Management Politics Sandbags Front Line Ship Dates Quality Sales

5. Football

6. EA Stock Exchange Worlds largest video game publisher: $4B – 10,000 employees 1% Quality ≈ $3MM revenue per game Overoptimistic forecasts  $300MM of product buybacks annually; mistimed and misplaced marketing spend Quality is measured using the average review from game critics Year 1: 300 employees participating Prizes include t-shirts and consoles

7. Participation by Function Trades per trader Finance are day traders QA and marketing very involved

8. Profit by Rank Profit / trade VP Profit max at junior manager Director Manager

9. Results Average Error

10. Current Customer Use Cases Electronic Arts– 1,200 employees predicting quality, sales and ship dates, 37% less error vs management. Going to 9,000 in Q2. J&J– panel of scientists and doctors predicting regulatory decisions and sales of new drugs, more accurate 86% of the time Sony Electronics – 700 employees forecasting new product sales and ship dates General Motors– 40 employees forecasting technology trends, demand, competitor actions and regulatory changes

11. Lessons Learned Corporations recognize the promise of PMs, but aren’t satisfied with the current state For a PM to gain widespread adoption, it needs to be far simpler, require less participation, and allow richer expression of users’ information Stock markets are for trading stock. Corporate prediction needs a different framework. Stocks don’t allow forecasters to ask the right questions, to get usable results, or to parameterize risk

12. Back the the Drawing Board Continuous Stocks: intimidating to many; requires repeated visits for convergence; hard to go negative Discrete/Bins: need finer resolution to be actionable Pari-mutuels: don’t incent early revelation of information; variable payoffs confuse

13. Applying our Knowledge: Crowdcast Confidence Curve Relies on a betting/watercooler vernacular I bet $1000 that revenues will be between $70MM and $73MM. If I’m right, I will earn $5000.” Payoffs are fixed, but interim values are according to current crowd beliefs. Provides robust reporting and risk analysis Allows a host of forecasts to be accommodated naturally: no problems with negative numbers, dates, multiple choice, etc. No more ‘higher than/lower than’ push around Incents early revelation of information Preserves anonymity, but creates conversations, competition, and group vs. group dynamics Easy Expressive Engaging

14. Crowdcast Launch Premier customers already live on the new mechanism. Feedback is overwhelmingly positive. Demoing to new customers now. Watch for our public launch, May 2009.

15. Q&A Mat Fogarty and Leslie Fine Crowdcast mat@crowdcast.com leslie@crowdcast.com