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How to Create Products for Growth

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How to Create Products for Growth DAN Olsen August 4, 2015


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My  Background   n  Educa=on   n  n  n  n  Experience   n  n  n  n  n    Engineering  background   Stanford  MBA   UX  design,  web  and  mobile  coding   Led  Quicken  Product  Management  at  Intuit   Led  Product  Management  at  Friendster   CEO  &  Cofounder,  TechCrunch  award  winner  YourVersion   Product  consultant:  Facebook,  Box,  MicrosoS   Organizer:  Lean  Product  &  Lean  UX  Meetup  Silicon  Valley   Slides  at  hUp://slideshare.net/dan_o      TwiUer:  @danolsen   Copyright  ©  2015  @danolsen  


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n  Win  a  signed  copy  of  book   n  Tweet  about  this  webinar   n  Include  @danolsen   n  Can  include  screenshots   of  slides  you  like   n  Hashtags:   n  #growthhacking   n  #leanstartup   n  #prodmgmt   n  bit.ly/leanplaybook   Copyright  ©  2015  @danolsen  


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The  3  Phases  of  Your  Product   BEFORE   Product-­‐Market  Fit   BEFORE  your   product  is  live   Phase  1   AFTER  your   product  is  live   Phase  2   AFTER   Product-­‐Market  Fit       Phase  3   #  of  Customers   lots   0   Time   Copyright  ©  2015  @danolsen  


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The  3  Phases  of  Your  Product   BEFORE   Product-­‐Market  Fit   BEFORE  your   product  is  live   Phase  1     AFTER  your   product  is  live   Phase  2     Test  hypotheses,   Improve  product-­‐ gain  confidence   market  fit   before  building         Methods: Qualita=ve   Qualita=ve  &       Quan=ta=ve   Goal:       AFTER   Product-­‐Market  Fit           Phase  3       Growth         Mainly   Quan=ta=ve   Copyright  ©  2015  @danolsen  


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Qualita=ve  vs.  Quan=a=ve   Qualita=ve   Interviews,  User  Tes=ng   Oprah   Quan=ta=ve   Analy=cs,  A/B  tes=ng   Spock  


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Phases  1  &  2:   The  Product-­‐Market  Fit  Pyramid  


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Phase  2:   If  you  could  only  track  1  metric  to   measure  your  Product-­‐Market  Fit,   which  metric  would  it  be?   Copyright  ©  2015  @danolsen  


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Reten=on  Rate   n  Reten=on  rate  tracks  what  %  of  your   customers  are  s=ll  ac=ve  over  =me   ~80% never use app again Curve eventually flattens out


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Improving  Reten=on  Rate  Over  Time=   Increasing  Product-­‐Market  Fit  


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Phase  3:   ASer  Product-­‐Market  Fit   Copyright  ©  2015  @danolsen  


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Lean  Product  Analy=cs  Process   Iden=fy  What   Your    Metrics  Are   Iden=fy  highest   ROI  idea   Measure  Metrics   Baseline  Values   Evaluate  Metrics   Upside  Poten=al   Global   Level   Select   Top  Metric   Brainstorm   Ideas  to   Improve  Metric   Metric   Level   Learn   &  Iterate   Design  and   Implement   Analyze  How   the  Metric   Changes   Copyright  ©  2015  @danolsen  


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Equa=on  of  your  Business:   Subscrip=on  Business  Model   Profit  =  Revenue  -­‐  Cost              Paying  Users    x    Revenue  per  Paying  User                 N       ew  Paying  Users    +    Repeat  Paying  Users             rial  Users    x    C  onv  Rate    Previous  Paying  Users    x    (  1  –  Cancella=on  Rate  )   T                    SEO  Visitors      x    Conversion  Rate   +  SEM  Visitors    x    Conversion  Rate   +  Viral  Visitors    x    Conversion  Rate   +  …   +  Visitors  from  Channel  N    x    Conversion  Rate       Copyright  ©  2015  @danolsen  


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View  Each  Metric  as  a  Gauge   Current   Value   Minimum   Possible   Value   Maximum   Possible   Value   Copyright  ©  2015  @danolsen  


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Iden=fying  the  “Cri=cal  Few”  Metrics   n  n  For  each  metric:  Where  is  the  needle  now?   How  much  can  we  move  the  needle?  Revenue  impact?   How  many  resources  will  it  take  to  move  the  needle?   n  n  Developer-­‐days,  money   Which  metrics  have  the  highest  ROI  opportuni=es?   Metric  B   Bad  ROI   Return   Return   Metric  A   Good  ROI   Investment   Metric  C   Great  ROI   Return   n  Investment   Investment   Copyright  ©  2015  @danolsen  


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Friendster  Growth  Hacking   Case  Study   Improving  Viral  Acquisi=on   of  New  Users   Copyright  ©  2015  @danolsen  


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Case  Study:   Op=mizing  Friendster’s  Viral  Loop   Active Users Invite Prospective Users Don’t Click Click Registration Process Fail Succeed Users Copyright  ©  2015  @danolsen  


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Case  Study:   Op=mizing  Friendster’s  Viral  Loop   % of users sending invites Active Users % of users who are active Invites per sender Invite Invite click-through rate Prospective Users Click Don’t Click Users Registration Process Fail Succeed Conversion rate •   Mul=plied  together,  these  metrics  determine  your  viral  ra=o   •   Which  metric  has  highest  ROI  opportunity?   Copyright  ©  2015  @danolsen  


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Case  Study:   Op=mizing  Friendster’s  Viral  Loop   % of users sending invites Active Users % of users who are active Invites per sender Invite Invite click-through rate Prospective Users Click Don’t Click Users Registration Process Fail Succeed Conversion rate •   Mul=plied  together,  these  metrics  determine  your  viral  ra=o   •   Which  metric  has  highest  ROI  opportunity?   Copyright  ©  2015  @danolsen  


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Case  Study:   Op=mizing  Friendster’s  Viral  Loop   % of users sending = 15% invites Active Users % of users who are active Invites per sender = 2.3 Invite Prospective Users Invite click-through rate Click Don’t Click Users Registration Process Fail Succeed Conversion = 85% rate •   Mul=plied  together,  these  metrics  determine  your  viral  ra=o   •   Which  metric  has  highest  ROI  opportunity?   Copyright  ©  2015  @danolsen  


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The  Upside  Poten=al  of  a  Metric   100%   85%   0   Registra=on   Process  Yield   Max  possible   improvement   0.15  /  0.85  =  18%   Copyright  ©  2015  @danolsen  


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The  Upside  Poten=al  of  a  Metric   100%   100%   85%   15%   0   Registra=on   Process  Yield   Max  possible   improvement   0.15  /  0.85  =  18%   0   %  of  users  sending   invita=ons   0.85  /  0.15  =  570%   Copyright  ©  2015  @danolsen  


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The  Upside  Poten=al  of  a  Metric   ?   100%   100%   85%   15%   0   Registra=on   Process  Yield   Max  possible   improvement   0.15  /  0.85  =  18%   0   %  of  users  sending   invita=ons   0.85  /  0.15  =  570%   2.3   0   Avg  #  of  invites   sent  per  sender   ?  /  2.3  =  ?%   Copyright  ©  2015  @danolsen  


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Is  anyone  feeling  a   sense  of  déjà  vu   right  now?  


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The  Upside  Poten=al  of  a  Metric   ?   Metric  B   Bad  ROI   Metric  A   Good  ROI   Metric  C   Great  ROI   100%  100%   85%   15%   0   Registra=on   Process  Yield   Max  possible   improvement   0.15  /  0.85  =  18%   0   %  of  users  sending   invita=ons   0.85  /  0.15  =  570%   2.3   0   Avg  #  of  invites   sent  per  sender   ?  /  2.3  =  ?%   Copyright  ©  2015  @danolsen  


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ASer  Launching  Address  Book  Importer…   Copyright  ©  2015  @danolsen  


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ASer  Launching  Address  Book  Importer…   Copyright  ©  2015  @danolsen  


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ASer  Launching  Address  Book  Importer…   Copyright  ©  2015  @danolsen  


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Questions? @danolsen   olsensolu=ons.com   slideshare.net/dan_o   leanproductplaybook.com     Book:  bit.ly/leanplaybook      


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