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Exposing Non-Traditional Metrics To Run Your Business
            Sam Yagan & Christian Rudder | OkCupid
Who is OkCupid?
•   4 math majors take on the
    dating industry
•   Reinventing business model,
    user experience, approach
•   Most innovative dating site
    (iDate); Top 5 corporate blogs
    (Mashable)
Data Drives Daters & Dollars
•   We’re an underdog; 14 people
•   Need to compete for consumers with Match &
    eHarmony – sans 300 employees, sans TV budget,
    sans Dr. Phil
•   Need to compete for ad dollars with everyone
•   Our secret weapon: math
Sources of Data
Demographic data   Stated preferences   Revealed
•   Age, race, &   •   Hundreds of      preferences
    gender             millions of      •   Billions of real
                       questions            personal
                       answered             interactions
The Optimal First Message
•   Question: How can users construct messages
    that are more likely to attract a reply?
•   We parsed and analyzed 500,000 messages
•   We created user messaging guidelines
!"#$%"&'()(*(+(,&-.(
                       3,-#




Netspeak = u r fail    2,-#


                       1,-#
                              !"#    "#     !#    $%#    &%'# "(%)$#   )!*#   +%'#
                       0,-#


                       /,-#


                       .,-#


                        ,-#


                      !"#$%&'&%()'"*$+,'"#-*&'-('!"#$%&'##()'#%.(/-#0/0/1'-%*'+0&-*2'
                         3*,)("2&4'$+(5*2'#1#0/&-'-%*'#6*"#1*'"*$+,'"#-*'(7'*+,8'
!"#$%&#'()*+,-"./,0*1,2'3*"(4%&)/,

Compliments:           ;97%                                                   '3"!-4"%

                                                                      *'!/01')12%

Body vs. Personality   :67%                                      /--+%

                       :97%



                       867%
                                                         /()"%
                                                  ,-.%
                       897%
                                      &"'()*(+%
                              !"#$%
                       567%
User Attraction
•   Question: How does a person’s attractiveness affect his or
    her success on the site?
•   This study led us to make some algorithmic adjustments
!"#$%"&'()$*+,"-"..&/&0$%"&0"..$12-1&

Female Attractiveness     '!"#


                          &$"#



& Male Messaging          &!"#


                          %$"#


                          %!"#


                           $"#


                           !"#

                        !"#$%&#'(#)*+"&                                    ,-$%&#'(#)*+"&

                                 ()*+,)#-.-/,+0.1#2345637/0.18#+4#6+5)2#79#5:)#*)1#
                                 *+,)#;#()*+,)#*)44+<)#2345637/0.1#
!"#$%&'(")*+$,$--%.%/$0"#$%!$--"12,1%

Male Attractiveness   '$"#

                      '!"#




& Female Messaging
                      &$"#

                      &!"#

                      %$"#

                      %!"#

                       $"#

                       !"#

                        !"#$%&                        ,"-./,&                          ,0$%&
                      #'(#)*+"&                                                      #'(#)*+"&
                                  ()*+#),-)./0+1+22#3425-467/819#)2#-)5+3#6:#5;+#<8(+1#
                                  =+()*+#>#()*+#(+22)?+#3425-467/81#
Wooing Marketers
•   Dating rarely contextually relevant
•   Two weapons
    •   Demographics (singles vs. married)
    •   Data
!"#$%&$'()#*(+,"&-,.(!&$'/#(012(3,**&#%(456(7&%18(+6$19:#*(;$&-(
                <9*#,9(6=(>,?6*(!-,@1@A1.(+6$19:#*(BC"#$%&-9*#(!9*0#D(4EFFG8(
J.S9BB.%T%7E.2456%7/338410%
        J1813;.51%71=R4B10%
                     G8B.;.8%
             I15F0%G339=18%
   P.5!98B.;.84B%Q1R1=9610%
                  K>/B9O.5%
                 J1B;5.8.6M%
          N1;4B81%A50/=95B1%
             ?..>%9:%-.E1%
    ?..>%GH9M%?=.E%-.E1%
         J.:98%KL315>4:/=10%
    @1=0.598%<9=1%@=.>/B:0%
             I40B188951./0%
                   ?..:H19=%
          D.E15F0%G339=18%
                    C19>456%
                        @1:0%
        @1=0.598%A50/=95B1%
     -./01;.8>%?/=540;4560%
                -198:;%<9=1%
    -./012113456%7/338410%

                                !"#$% !&#$% #$%   &#$% "#$% '#$% (#$% )#$% *#$% +#$% ,#$%
Q: Do you spend
more money on food
or clothes?
                     Age progression, 18 to 48
Q: Do you spend
more money on food
or clothes?
                     Age progression, 18 to 48
Wrapping Up
•   For many Fortune 1000 companies looking for social media
    strategies, data-driven approaches can be easy, effective
•   We’d love to help you think about your data!
You’re Welcome!
@okcupid | sam@okcupid.com & christian@okcupid.com

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07 samyagan

  • 1. Exposing Non-Traditional Metrics To Run Your Business Sam Yagan & Christian Rudder | OkCupid
  • 2. Who is OkCupid? • 4 math majors take on the dating industry • Reinventing business model, user experience, approach • Most innovative dating site (iDate); Top 5 corporate blogs (Mashable)
  • 3. Data Drives Daters & Dollars • We’re an underdog; 14 people • Need to compete for consumers with Match & eHarmony – sans 300 employees, sans TV budget, sans Dr. Phil • Need to compete for ad dollars with everyone • Our secret weapon: math
  • 4. Sources of Data Demographic data Stated preferences Revealed • Age, race, & • Hundreds of preferences gender millions of • Billions of real questions personal answered interactions
  • 5. The Optimal First Message • Question: How can users construct messages that are more likely to attract a reply? • We parsed and analyzed 500,000 messages • We created user messaging guidelines
  • 6. !"#$%"&'()(*(+(,&-.( 3,-# Netspeak = u r fail 2,-# 1,-# !"# "# !# $%# &%'# "(%)$# )!*# +%'# 0,-# /,-# .,-# ,-# !"#$%&'&%()'"*$+,'"#-*&'-('!"#$%&'##()'#%.(/-#0/0/1'-%*'+0&-*2' 3*,)("2&4'$+(5*2'#1#0/&-'-%*'#6*"#1*'"*$+,'"#-*'(7'*+,8'
  • 7. !"#$%&#'()*+,-"./,0*1,2'3*"(4%&)/, Compliments: ;97% '3"!-4"% *'!/01')12% Body vs. Personality :67% /--+% :97% 867% /()"% ,-.% 897% &"'()*(+% !"#$% 567%
  • 8. User Attraction • Question: How does a person’s attractiveness affect his or her success on the site? • This study led us to make some algorithmic adjustments
  • 9. !"#$%"&'()$*+,"-"..&/&0$%"&0"..$12-1& Female Attractiveness '!"# &$"# & Male Messaging &!"# %$"# %!"# $"# !"# !"#$%&#'(#)*+"& ,-$%&#'(#)*+"& ()*+,)#-.-/,+0.1#2345637/0.18#+4#6+5)2#79#5:)#*)1# *+,)#;#()*+,)#*)44+<)#2345637/0.1#
  • 10. !"#$%&'(")*+$,$--%.%/$0"#$%!$--"12,1% Male Attractiveness '$"# '!"# & Female Messaging &$"# &!"# %$"# %!"# $"# !"# !"#$%& ,"-./,& ,0$%& #'(#)*+"& #'(#)*+"& ()*+#),-)./0+1+22#3425-467/819#)2#-)5+3#6:#5;+#<8(+1# =+()*+#>#()*+#(+22)?+#3425-467/81#
  • 11. Wooing Marketers • Dating rarely contextually relevant • Two weapons • Demographics (singles vs. married) • Data
  •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
  • 13. Q: Do you spend more money on food or clothes? Age progression, 18 to 48
  • 14. Q: Do you spend more money on food or clothes? Age progression, 18 to 48
  • 15. Wrapping Up • For many Fortune 1000 companies looking for social media strategies, data-driven approaches can be easy, effective • We’d love to help you think about your data!
  • 16. You’re Welcome! @okcupid | sam@okcupid.com & christian@okcupid.com

Editor's Notes