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Nathan Stoll
nathanstoll@gmail.com
nstoll@google.com
@nathanstoll
Our Product
Web search is great for objective questions, but subjective
questions generate majority of revenues
  Queries     Search quality
  X00B/yr                  RPMs    Examples

                                  •  What is Einstein’s birthday?
    45%        Great       Low
  Objective                       •  How much is the cheapest iPhone?

                                  •  What’s a decent housekeeping service in
    30%                              SF? How much should it cost for a small
  Research                           two bedroom place?

                                  •  What’s a good book to read about
    25%         Ok        High       Romantic Poets?
   Opinion
The Aardvark pitch
Improving a familiar experience
Friends already answer subjective questions, but...
 •  Unreliability of a small number of friends available to you in the moment
 •  Hard to keep up with what your extended network knows about
 •  Social cost of asking for a favor

Why Social Search?
•  Users want personalized responses to questions
•  Most content is still locked in peoples' heads
•  Each individual’s network is growing exponentially
•  Social intimacy makes information actionable
•  Questions about how to spend your time and money are subjective
Our Principles and Process
Minimize risk by maximizing trials
Conception:
•  Picked a problem area
•  Prototyped in serial
•  Abandoned many attempts along the way

Implementation:
 •  User-centered agile engineering
 •  Wizard-of-oz research leading algorithmic automation
 •  Recruited a core team, contracted to fill gaps
 •  Focused on continuous acceleration
Early experimental Aardvark
User-centered agile engineering
Wizard-of-oz research leading algorithmic automation
Team and process learnings
Experiment with process as much as with product
•  Assume you are wrong
•  Make continuous improvement the goal
•  Hire and indoctrinate to support process

Exploit collective wisdom
•  Task people to collect and disseminate learnings
•  Set aside regular time for discussion
•  Make transparency the default
Additional Aardvark reading/viewing
•  White paper on Aardvark Social Search - (
   http://vark.com/aardvarkfinalwww2010.pdf)
•  How we do qualitative user research at Aardvark (
   http://blog.vark.com/?p=314)
•  Our approach to design and development processes (
   http://blog.vark.com/?p=49)
•  Preaching User-Driven Design (
   http://ventilla.posterous.com/preaching-user-driven-design)
•  Startup Lessons Learned Aardvark Case study (
   http://www.justin.tv/startuplessonslearned/b/262666882)
•  Why Machines Need People (
   http://blurkerlab.blogspot.com/2010/04/video-damon-horowitz-at-
   tedxsoma-why.html)

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Users and Algorithms: Incorporating User Centered Design and Advanced Algorithms into Agile Engineering - Atlassian Starter Day 2010

  • 3. Web search is great for objective questions, but subjective questions generate majority of revenues Queries Search quality X00B/yr RPMs Examples •  What is Einstein’s birthday? 45% Great Low Objective •  How much is the cheapest iPhone? •  What’s a decent housekeeping service in 30% SF? How much should it cost for a small Research two bedroom place? •  What’s a good book to read about 25% Ok High Romantic Poets? Opinion
  • 5. Improving a familiar experience Friends already answer subjective questions, but... •  Unreliability of a small number of friends available to you in the moment •  Hard to keep up with what your extended network knows about •  Social cost of asking for a favor Why Social Search? •  Users want personalized responses to questions •  Most content is still locked in peoples' heads •  Each individual’s network is growing exponentially •  Social intimacy makes information actionable •  Questions about how to spend your time and money are subjective
  • 7. Minimize risk by maximizing trials Conception: •  Picked a problem area •  Prototyped in serial •  Abandoned many attempts along the way Implementation: •  User-centered agile engineering •  Wizard-of-oz research leading algorithmic automation •  Recruited a core team, contracted to fill gaps •  Focused on continuous acceleration
  • 10. Wizard-of-oz research leading algorithmic automation
  • 11. Team and process learnings Experiment with process as much as with product •  Assume you are wrong •  Make continuous improvement the goal •  Hire and indoctrinate to support process Exploit collective wisdom •  Task people to collect and disseminate learnings •  Set aside regular time for discussion •  Make transparency the default
  • 12. Additional Aardvark reading/viewing •  White paper on Aardvark Social Search - ( http://vark.com/aardvarkfinalwww2010.pdf) •  How we do qualitative user research at Aardvark ( http://blog.vark.com/?p=314) •  Our approach to design and development processes ( http://blog.vark.com/?p=49) •  Preaching User-Driven Design ( http://ventilla.posterous.com/preaching-user-driven-design) •  Startup Lessons Learned Aardvark Case study ( http://www.justin.tv/startuplessonslearned/b/262666882) •  Why Machines Need People ( http://blurkerlab.blogspot.com/2010/04/video-damon-horowitz-at- tedxsoma-why.html)