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Google Case Study

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Sarah Gorman

on 30 October 2013

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Transcript of Google Case Study

Case Summary
Prediction Markets
Crowdsourcing IRL
Making the case for

When prediction markets fail :/
Insufficient liquidity
Liquidity is the degree to which a security can be bought or sold in the market without affecting the price.
Characterized by a high level of trading activity.
In other words, you need people to participate
Ineffective reward/risk structure
Awarding winners with cash prizes w/o punishing losers
When Business Ventures Fail :(
2011: Netflix's Qwikster product launch
CEO Reed Hastings decides to separate DVD business into new, totally separate product
Consumers hate it, stock price tanks
1998: The DaimlerChrysler Merger
Chrysler (think Dodge) decides to merge with Daimler-Benz
Clash of cultures, Chrysler's loses value
CrowdSourcing efforts as early as 1936
Toyota Dream Car Art Contest (2004)
2010 Ideas For Good Campaign
Partnership with Notre Dame
Taking Toyotas crash safety sensors and embedding them in football players helmets to measure concussive force. (HEADS)
'96 Chicago Bulls: Jordan Ford, Bobby Fredrickson, Sarah Gorman and Keith McCloat
Crowdsourcing and Prediction Markets: Practical Applications and Implementation
Thanks for your time! #GoBulls!
Google Background
Google was founded in 1988 by two Stanford graduates, Larry Page and Sergey Brin.
Google created the Page Rank algorithm to counter existing search engines
March 2007 - Google attained market capitalization of $140 billion.
Mission - to organize the worlds' information and make it universally accessible and useful.
How Google's Prediction Markets Works
Behaves a lot like a stock market...
Securities traded by buyers and sellers
Securities are outcomes of future events
Each share is priced between 0 and 1 such that the share price reflects the probability that the outcome will occur
Uses fake currency, called "Goobles"
Shares are purchased at market value, and 1 Gooble is paid out for each winning share
Case Background
Bo Cowgill reads The Wisdom of Crowds by James Surowiecki and learns about the idea of “collective intelligence”
Looked at prediction markets such as Iowa Electronic Markets (IEM)
Believed that Google could benefit from its own prediction market
Joined by 4 other “Googlers”, Google Prediction Markets (GPM) is launched
Used their “20% time” to develop the market

How some companies might use a prediction market
The Gap Inc.
Could help keep an eye on quickly changing fashion trends
Bechtel Corporation
Managing large scale projects, implement project management strategy
Allows for international communication as well as creating an avenue for stakeholder input.
Implementing a prediction market
1. Designing a platform
2. Beta Testing
3. Challenges to avoid - attempts to manipulate the market
4. Set up incentive structure
5. Get people to participate!
Contests limit creativity of individuals.
Doesn’t take into count all individuals that aren’t well versed with technology
Rarely forms a lasting relationship
Open Forum
Implements a lot of ideas and changes
Can be specified down to the region
Ex. Customers in Baltimore really took to the website and asked for Decaf Medium roast, so they just started offering it about a month ago
Very similar to what Mountain Dew and Lays did but on a smaller, more efficient scale
Starbucks employees very active on site and use it as two way communication to get ideas vetted

Idea Storm 2007
Open Forum where customers can post any idea they want
Created back-lit keyboard
The website seems to be losing steam and has devolved into a place where just a few dedicated users post their complaints
Creates and hosts contest in computer programming
Major Corporations hire TopCoder to run these contests for them
Design Competitions are the money makers because programmers give up their rights to whatever they create and TopCoder profits from their innovation
Lots of Jobs offers come to programmers who win competitions
Programmers createcontent for TopCoder as well as finding bugs in their system without TopCoder having to pay salaries.

Predictive power and accuracy
Prediction Market Example
Red Sox in the World Series
Security A1 pays off $1 if the Red Sox win the World Series tonight. $0 otherwise.
Security A2 pays off $1 if the Red Sox lose tonight and win it in game 7 tomorrow. $0 otherwise.
Security A3 pays off $1 if the Red Sox lose the World Series. $0 otherwise.
Lack of diversity
Too few participants
Incentives: The Classic View
Winners are rewarded with cash or prize incentives
• Financial self-interest is a good motivator
• Confidentiality allows people to express controversial views
Problems with this approach
• Complexity of payoffs
• Low expected payoffs
• Traders can’t boast about their accomplishments

Incentives: The Reputation-based system
• Display prominent leaderboard with:
o Rankings and categories of winners
o Best performing teams, functions, management levels
o Localized results
• Allow users to personalize their own profile page
o Prediction sharing
o Posting explanations of trades

May not be a sustainable business plan.
Intellectual Property Rights
When an account is made the user basically gives up all rights to the content they create.
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