Gaming the Forecast

1. FEI IT, Seattle March 4, 2010 Gaming the Forecast Leveraging Collective Intelligence for Real-Time, Accurate Forecasts Mat Fogarty, CEO Crowdcast

2. Speaker Background • London School of Economics, Management Science • Unilever, Finance Manager, CIMA • UC Berkeley, MBA • Electronic Arts, Director of FP&A • Founded Crowdcast in 2007 • San Francisco, Venture backed

3. Crowd Forecast = Crowdcast • Forecasts are often subject to bias, stickiness and gaming • Crowdcast leverages wisdom of employee and partner crowd • Based on a prediction market • Produces unbiased, real time forecasts of key metrics • Valuable for new products, key projects, and external metrics

4. Costs of Poor Forecasts

5. What is a Prediction Market? • Based on stock market or betting market • Aggregates knowledge of diverse group • Players weight forecasts • Rewarded for early, accurate predictions • Anonymous and meritocratic • Accurate players given more weight over time

6. Actionable Intelligence Intelligence Insight Action Crowdcast Platform

7. Actionable Intelligence Intelligence Insight Action Crowdcast Platform When will new product ship?

8. Actionable Intelligence 50% chance late Intelligence Insight Action Crowdcast Platform When will new product ship? “Supplier problems”

9. Actionable Intelligence 50% chance late Intelligence Insight Action Crowdcast Platform Change Supplier When will new product ship? “Supplier problems”

10. Actionable Intelligence 10% chance late Intelligence Insight Action Crowdcast Platform Change Supplier When will new product ship? “New supplier rocks”

11. Examples of Forecasts • When will the product launch? • What will be the product’s quality score? • Sales units in first 90 days? • Sales at $39 vs Sales at $49? New Products • When will the next milestone be complete? • How much will the project cost? • Will the project achieve its stated goal? Project Management • Will a regulation pass? • What will our market share be? • Will our competitor launch a competing product? Industry Intelligence

12. Use cases • 400 employees forecasting success of game projects, 32% more accurate than official • Panel of scientists and doctors predicting FDA decisions & sales, more accurate 86% of time • Diverse team forecasting tech trends, demand, competitor actions and regulatory changes • Employees and customer community forecasting success of new products

13. Screenshots

14. Player – Select Question • Employee scans available forecasts and chooses one where he has special knowledge • When will the Beta of the Tablet Computer launch?

15. Player – Enter Bet • Employee chooses a range of likely outcomes • Weights forecasts with virtual money • If correct, the bet will payoff • All input is anonymous

16. Player – Add Comments • Why does the employee think this? • What is the underlying reason and assumptions?

17. Player – Leaderboard • Leaderboard shows how accurate the players are, also which department is more accurate • Competition, bonuses and other incentives encourage participation and accuracy

18. Manager – Forecast Analytics • How does the Crowdcast compare to the target? • Early warning of issues

19. Manager – Forecast Analytics • Understand most likely outcome • Spread indicates range of outcomes for analysis of risk • Which teams are more optimistic / pessimistic about the metric

20. How has the forecast changed through time

21. Complete solution Software Services Secure hosing Market design Training, deployment User engagement strategy Custom report and analytics