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Predictive Analytics in Political Campaigns: Obama and Beyond


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In the last decade predictive modeling has changed American political campaigns, especially at the presidential level. Long before Election Day 2012, Obama campaign staffers were confident that President Obama would be re-elected because they had sophisticated modeling predicting wins in many important states. More importantly, modeling helps political campaigns learn which voters to target with particular messages. This session will summarize predictive modeling in American politics, with an eye toward the way it might be developed for international applications.

Published in: Data & Analytics

Predictive Analytics in Political Campaigns: Obama and Beyond

  1. 1. Predic've  Analy'cs  in  Poli'cal  Campaigns:   Obama  and  Beyond   Amelia  Showalter   @ameliashowalter     Predic've   Analy'cs   World
  2. 2. Maybe  you  heard  about  Obama’s  data…  
  3. 3. That  was  mostly  all  true!   The  Obama  campaign  used   predic8ve  analy8cs  to:   ◦ Contact  voters  more  efficiently   ◦ Track  our  real  status  vs.  Romney   ◦ BeCer  media  targe8ng   ◦ Raise  more  money     But  we  didn’t  invent  modeling     There  is  a  long  history  of  analy8cs   in  U.S.  poli8cs  
  4. 4. The  American  voter  file  and  the   advent  of  micro-­‐targe;ng
  5. 5. The  American  voter  file   First,  there  was  the  voter  file   ◦ For  a  very  long  8me,  each  U.S.   state  has  kept  a  semi-­‐public  data   file  of  all  registered  voters   ◦ This  file  contains  each  voter’s   name,  address,  age,  gender,   some8mes  race,  some8mes  party   registra8on   ◦ Also  shows  each  person’s  vote   history  –  which  elec8ons  did  they   vote  in?  
  6. 6. The  American  voter  file   In  the  1960s,  70s,  80s,  and  90s,  poli8cal  campaigns  started  to  use   the  voter  file  to  iden8fy  broad  groups  to  target   ◦ Example:  Send  a  piece  of  mail  to  all  women  over  40  who  have  voted  in  at   least  three  of  the  last  four  elec8ons,  convincing  them  to  vote  for  your   candidate     At  some  point,  people  figured  out  you  could  enhance  the  voter  file   ◦ Example:  Census  block  à  average  income  in  the  neighborhood   ◦ Example:  Commercial  data  matches,  public  records  matches  
  7. 7. Predic;ve  analy;cs  in  American  poli;cs   In  the  2000s,  poli8cal  opera8ves  started   developing  models     The  steps  of  building  a  model:   ◦ Conduct  a  massive  voter  survey  (5000+)   ◦ Ask  about  candidates  or  issues   ◦ Use  voter  file  informa8on  to  make  models   ◦  Age,  gender,  vote  history,  Census  variables,  etc   ◦  Decision  tree  models,  regression  models,  etc   ◦ Validate  on  a  held-­‐out  subsample   ◦ Assign  a  model  score  to  en8re  voter  file   ???
  8. 8. Micro-­‐targe;ng   We  use  models  to  “micro-­‐target”   voters  to  receive  different  types   of  contact   ◦ Encouragement  to  vote   ◦ Persuasion  to  vote  for  your   candidate   ◦ Recrui8ng  volunteers   ◦ Voter  suppression  (joking!)   Likelihood  of  vo8ng   Support  for  your  candidate   GOTV   Persuasion   Volunteer   recruitment  
  9. 9. Issue  modeling  and  other  innova8ons     We  can  model  almost  anything!   ◦ Environmentalism   ◦ Women’s  rights   ◦ Religiosity     We  can  even  model  who  is  easy  to  persuade  
  10. 10. Predic;ng  elec;on  outcomes
  11. 11. Nate  Silver  and  the  2008  elec;on   Campaigns  were  not  the  only  ones  using   predic8ve  analy8cs     In  2008  a  guy  named  Nate  Silver  (and  other   nerds)  started  using  public  polls  to  run  Monte   Carlo  simula8ons  of  the  presiden8al  elec8on,   making  predic8ons  that  were  quite  accurate   ◦ Dozens  of  polls,  each  with  n=400-­‐1000   ◦ Simula8on  accounts  for  each  poll’s  MOE   ◦ Also  accounts  for  each  pollster’s  quality/accuracy  
  12. 12. More  uses  for  Monte  Carlo  simula;ons   I  built  models  to  predict  likely   outcomes  in  state  legisla8ve   elec8ons  in  Oregon  and  Alabama   ◦ Linear  regression  model  at  the   precinct-­‐level,  using  past  elec8on   results  and  other  variables     These  results  were  used  to   channel  money  toward  districts   where  it  would  make  the  biggest   impact  
  13. 13. The  2012  Obama  campaign
  14. 14. Data  and  modeling,  to  the  max!   We  did  all  of  that,  and  more     The  2012  Obama  campaign  had  a  huge  data  and  analy8cs  team   ◦ Analy8cs  department:  50+  people   ◦ Data  team:  20+  people   ◦ Digital  analy8cs:  15  people   ◦ Tech  team:  30+  people  
  15. 15. Data  and  modeling,  to  the  max!   Television  targe8ng   ◦ What  TV  programs  are  best?  What  geographical  zones?     Fundraising  (online  and  offline)     Models  for  persuasion,  turnout,  issues,  etc   ◦ Direct  mail   ◦ Online  adver8sing   ◦ Phone  calls  and  door  knocking  by  volunteers  
  16. 16. Data-­‐driven  volunteers From  2012  Campaign  Manager  Jim  Messina:   “My  favorite  story  is  from  a  volunteer  in  Wisconsin  10  days  out   [from  Elec8on  Day].  She  was  knocking  on  doors  on  one  side  of   the  street  and  the  Romney  campaign  was  knocking  on  doors  on   the  other  side  of  the  street…”  
  17. 17. Data-­‐driven  conversa;ons “…  [The  Obama  volunteer]  was  asked  to  hit  two  doors.  One  was   an  undecided  voter  and  she  knew  exactly  what  to  say.  The  other   was  an  absentee  ballot  and  she  was  told  to  make  sure  they   filled  it  out  and  returned  it.  On  the  other  side  of  the  street,  the   Romney  campaign  was  knocking  on  every  single  door.  Most  of   the  people  weren’t  home,  and  most  of  the  people  that  were   home  were  already  suppor8ng  Barack  Obama.  She  looked  at  me   and  said,  ‘You’re  using  my  8me  wisely.’  That’s  what  data  can   do.”   -­‐  Obama  2012  Campaign  Manager  Jim  Messina  
  18. 18. Our  own  internal  Nate  Silver-­‐style  modeling   On  the  day  of  the  elec8on  in   2012,  we  knew  we  would  win   ◦ Our  internal  modeling  bounced   around  less  than  Nate  Silver’s  
  19. 19. Online  data  and  A/B  tes;ng
  20. 20. A/B  tes8ng:  Obama  2012     Constantly  looking  for  improvements,  large  or  small,  in  every  aspect   of  our  digital  opera8on  
  21. 21. Email  tes8ng:  test  many  flavors!  
  22. 22. Email  tes8ng:  subject  lines     version   Subject  line     v1s1   Hey   v1s2   Two  things:   v1s3   Your  turn   v2s1   Hey   v2s2   My  opponent   v2s3   You  decide   v3s1   Hey   v3s2   Last  night   v3s3   Stand  with  me  today   v4s1   Hey   v4s2   This  is  my  last  campaign   v4s3   [NAME]   v5s1   Hey   v5s2   There  won't  be  many  more   of  these  deadlines   v5s3   What  you  saw  this  week   v6s1   Hey   v6s2   Let's  win.   v6s3   Midnight  deadline   Test sends 6 drafts x 3 subject lines = 18 possible versions
  23. 23. Email  tes8ng:  gexng  results     version   Subject  line     donors   money   v1s1   Hey   263   $17,646   v1s2   Two  things:   268   $18,830   v1s3   Your  turn   276   $22,380   v2s1   Hey   300   $17,644   v2s2   My  opponent   246   $13,795   v2s3   You  decide   222   $27,185   v3s1   Hey   370   $29,976   v3s2   Last  night   307   $16,945   v3s3   Stand  with  me  today   381   $25,881   v4s1   Hey   444   $25,643   v4s2   This  is  my  last  campaign   369   $24,759   v4s3   [NAME]   514   $34,308   v5s1   Hey   353   $22,190   v5s2   There  won't  be  many  more   of  these  deadlines   273   $22,405   v5s3   What  you  saw  this  week   263   $21,014   v6s1   Hey   363   $25,689   v6s2   Let's  win.   237   $17,154   v6s3   Midnight  deadline   352   $23,244   $0   $1   $2   $3   $4   ACTUAL   ($3.7m)   IF  SENDING   AVG   IF  SENDING   WORST   Full send (in millions) ¨  $2.2  million  addi8onal  revenue   from  sending  best  draz  vs.  worst,   or  $1.5  million  addi8onal  from   sending  best  vs.  average   Test sends
  24. 24. Results  of  the  online  campaign   Campaign  raised  over  one  billion  dollars,   half  of  which  was  raised  online   ◦ Over  4  million  Americans  donated     Recruited  tens  of  thousands  of  volunteers,   publicized  thousands  of  events  and  rallies       Did  I  men8on  raising  >$500  million  online?   ◦ Conserva8vely,  tes8ng  probably  resulted  in  ~$200  million  in  addi8onal  revenue  
  25. 25. This  was  also  a  very  nice  result  
  26. 26. Looking  ahead
  27. 27. The  2016  U.S.  Presiden;al  Elec;on   The  Democrats   ◦ Hillary  Clinton  will  probably  be  the  Democra8c  nominee   ◦ Clinton  will  have  a  huge  analy8cs  team,  with  many  Obama  alums     The  Republicans   ◦ Whoever  wins  the  Republican  nomina8on  will  make  a  strong  effort  to   build  a  data  and  analy8cs  team  (well,  maybe  not  Trump)   ◦ In  2012  the  Romney  campaign’s  analysts  and  pollsters  failed   spectacularly,  and  the  Republicans  do  not  want  that  to  happen  again  
  28. 28. Opportuni;es  for  enterprises   Poli8cs  and  social  movements  are   huge  opportuni8es  for  the  data  and   technology  industries   ◦ US  poli8cal  analy8cs  industry  growing   ◦ Other  countries  are  learning  from  the   U.S.  example  
  29. 29. Opportuni;es  for  enterprises   Supply  beCer  data   ◦ In  the  US  and  everywhere  else,  good  models  require  good  data       Supply  the  first  voter  file   ◦ In  countries  where  voter  files  are  not  common,  the  first  par8es  or   advocacy  organiza8ons  to  get  them  will  have  a  huge  advantage       Supply  the  first  micro-­‐targe8ng  model!  
  30. 30. Thank  you! Amelia  Showalter   @ameliashowalter     Predic;ve   Analy;cs   World