Women in Tech Summit 2013 presentation

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Short talk about the need to focus on goals, achievability, and measurability when growing the analytics practice in large organizations.

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Women in Tech Summit 2013 presentation

  1. 1. Anita  Garimella  Andrews,  GM  Analytics  &  Optimization,  Delphic  Digital  
  2. 2. ¡  A  bit  about  me  ¡  The  “Big  Data”  myth  ¡  What  it  takes  to  leverage  data  in  your  biz  ¡  An  approach  to  using  analytics  in  your  biz  ¡  QUESTIONS  
  3. 3. ¡  General  Manager,  Analytics  &  Optimization  §  Founded  Sepiida,  an  A&O  consultancy  in  2009  with  clients  including  Zynga  and  Haymarket  Media  –  sold  to  Delphic  in  2012  §  Previously,  VP  E-­‐commerce  at  Nutrisystem  §  Dir  of  Program  Management  at  Ingenio,  sold  to  AT&T  YellowPages.com    ¡  MS  Computer  Science  –  Stanford  University  ¡  BA  Politics  –  New  York  University  ¡  Love  numbers.    Hate  endless  (and  needless)  discussions.    Constantly  iterating.  
  4. 4. Multibillion  dollar  companies  who  didn’t  look  at  their  Google  Analytics  until  this  year    Angel-­‐funded  start-­‐ups  who  are  tracking  everything  with  innovative  reporting  software    
  5. 5. ¡  Size  of  company  has  little  correlation  to  size  of  dataset?  ¡  Size  of  company  has  little  correlation  to  facility  with  data  and  analytics?  ¡  Size  of  company  has  little  correlation  to  current  status  of  analytics  activities?  ¡  Size  of  company  has  little  correlation  to  where  future  efforts  should  be  focused?  
  6. 6. ¡  Large  company  bureaucracy  §  How  many  stifled  data  geeks  do  you  have?      §  How  much  lost  revenue?  §  Lots  of  boxes  checked.    But  how  many  smarter,  more  efficient  decisions?  ¡  Data  mania  §  Don’t  lose  sight  of  the  forest  for  the  trees  §  How  does  all  the  data  actually  connect  to  the  steps  needed  for  growth?  §  More  data  doesn’t  mean  more  revenue  
  7. 7. ¡  Using  data  to  create  à  Creative  Marketing  §  Big  new  opportunities  ▪  Loyalty  program  creation,  Geo-­‐targeting,  etc.  §  What  data  to  look  at  is  often  unknown  ¡  Using  data  to  optimize  à  A&O  §  Often,  the  metric  that  is  suffering  is  known  §  The  data  subset  is  typically  easier  to  identify    
  8. 8. ¡  Goals  ¡  Team  capabilities  ¡  Sources  of  data  ¡  Tools  for  reporting  ¡  Opportunities  
  9. 9. ¡  What  specific  metrics  or  KPIs  do  you  want  to  improve?  ¡  What  are  the  formulas  for  these?    §  Need  consistent  definitions!  ¡  What  will  move  your  Analytics  practice  forward?  §  Think  of  A&O  as  sales  and  evangelization  §  If  you  do  it  right,  you’re  the  source  of  improvement  for  other  parts  of  the  business  
  10. 10. Bet  you  have  LOTS  of  data  §  Web  traffic  data  §  Transactional  databases  §  Internal  toolsets  (often  different  DBs)  §  Third  party  (email,  CRM,  etc.)  Key  questions  1.  How  accurate  are  each  of  these?      2.  How  much  of  what  you  need  are  you  actually  tracking?  3.  Which  of  these  has  the  answers  to  your  goals?  
  11. 11. ¡  Fight  the  impulse  to  “track  everything”  §  Technically  painful  §  Painful  for  business  people  §  You  don’t  need  it  to  drive  your  business  forward  §  There  is  no  glory  in  having  lots  of  data.    Size  does  NOT  matter  here…  
  12. 12. ¡  Collecting  Data  &  Reporting  §  GA  vs.  the  rest  (KISSMetrics,  MixPanel,  Omniture)  §  GoodData,  Domo,  RJ  Metrics,  WebTrends  §  Excel!  ¡  There  are  no  good  analysis  or  analytics  tools.      Yea,  I  said  it.      Stop  looking  for  them.    It’s  about  people  and  practices.  
  13. 13. ¡  What  should  you  do  NOW?  People  Low  KPIs  Tools  Good  Data  IDENTIFY  THIS  
  14. 14. ¡  It  may  not  target  the  largest  pool  ¡  It  may  not  even  be  web-­‐based  ¡  It  may  not  be  obvious  ¡  It  may  FAIL  ¡  Goal  is  to  experiment  with  process,  prove  value  and  get  data-­‐driven  results  quickly  ¡  Data  driven  culture  will  come  from  doing  data  driven  things  
  15. 15. ¡  Have  perspective  about  the  process  ¡  It’s  all  iterative.    It’s  not  sexy,  but  it  drives  the  numbers  UP.  §  And  that  gets  teams  excited,  grows  your  capabilities,  increases  confidence,  and  so  on.  ¡  Two  approaches:  §  Funnel  optimization    §  Russian  Doll  optimization  
  16. 16. Decent Users“Grade D”Good Users“Grade C”Great Users“Grade B”Best Users“Grade A”1.  Determine  differentiating  characteristics  of  “A”  2.  Use  that  to  move  more  “B’s”  into  “A”  3.  Repeat  4.  Lessen  the  Delta  =  Widen  the  Base  
  17. 17. The  right  data,  from  the  right  places  –  accurately  &  actionably  reported  Harness   Synthesize   Optimize  D            A              T            A  Intelligent  Interpretation    &  Insights  Iterative,  measured  execution  of  prioritized  data-­‐driven  tactics  Faster,  Better,  Decision-­‐Making  to  Improve  KPIs  
  18. 18. Q&A
  19. 19. Anita  Garimella  Andrews  GM,  A&O  Delphic  Digital  @agarimella  aandrews@delphicdigital.com  

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