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Origins of the Marketing Intelligence Engine (SXSW 2015)

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Marketing automation platforms save time, improve efficiency and increase productivity. They give companies an unprecedented ability to understand buyers, identify opportunities, track campaign performance and link marketing activities to business outcomes.

But, they do not provide insight into the billions of bits of data being created as consumers move from screen to screen and interact online and offline with brands, and they do not recommend actions to improve performance.

Humans are limited by their biases, beliefs, education, experiences, knowledge and brainpower. All of these things contribute to our finite ability to process information, build strategies and achieve performance potential.

Algorithms, in contrast, have an almost infinite ability to process information. They possess the power to understand natural language queries, identify patterns and anomalies, and parse massive data sets to deliver recommendations better, faster and cheaper than people can.

What inevitably comes next are marketing intelligence engines that process data and recommend actions to improve performance based on probabilities of success.

Published in: Marketing
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Origins of the Marketing Intelligence Engine (SXSW 2015)

  1. 1. origins  of  the  marke&ng  intelligence  engine #SXSW     #MKTEngineMarch  14,  2015@paulroetzer
  2. 2. what’s  possible  when     the  art  and  science     of  markeFng  collide?
  3. 3. “Determining  the  next   field  to  be  invaded  by   bots  is  the  sum  of  two   simple  funcFons:  the   poten&al  to  disrupt   plus  the  reward  for   disrup&on." @paulroetzer www.pr2020.com
  4. 4. poten&al  to  disrupt  +  reward  for  disrup&on
  5. 5. of  marketers  think  markeFng  has   changed  more  in  the  past  two   years  than  the  past  50   ! source:  Adobe  Digital  Distress 76% @paulroetzer www.pr2020.com
  6. 6. the  consumer  is  the  true  change  catalyst @paulroetzer www.pr2020.com
  7. 7. 90% of  daily  media  interac&ons   are  screen  based source:  Google,  The  New  MulF-­‐Screen  World @paulroetzer
  8. 8. B2B  buyers  may  be     up  to  90%  through  their  journey     before  contacFng  a  vendor.   ! source:  Forrester image:  Jayneandd
  9. 9. Source:  Google Every  trackable  consumer  acFon  creates  a  data  point,  and   every  data  point  tells  a  piece  of  the  customer's  story @paulroetzer www.pr2020.com
  10. 10. Image:  Chiefmartec.com the  customer  journey  does  not  follow   a  linear  path  defined  by  marketers @paulroetzer www.pr2020.com
  11. 11. Define  FoundaFon  Projects blog  posts   podcasts   website   video   email   webinars   mobile  apps tailored  markeFng   through  a  deep   understanding  of   buyer  persona  needs   +  the  ability  to   deliver  personalized   messages Image:  HubSpot we  have  entered  the  age     content,  context  and  the  customer  experience @paulroetzer www.pr2020.com
  12. 12. Define  FoundaFon  Projects create  more  value,  for  more  people,  more  oAen,     so  when  it’s  Fme  to  choose,     they  choose  you new marketing imperative
  13. 13. We  need  markeFng  automaFon  tools  to     reach,  engage,  convert  and  delight  customers. Source:HubSpot
  14. 14. Source:  Marketo Understand  buyers,  idenFfy  opportuniFes,  track  campaign   performance,  and  link  marke&ng  ac&vi&es  to  business  outcomes.
  15. 15. Source:  Oracle Capture  lead  intelligence  and  improve     lead-­‐to-­‐sale  conversion  rates.
  16. 16. Source:  Pardot Drive  repeat  purchasing  and  enhance  the  overall  experience   throughout  the  customer  journey.
  17. 17. ExactTarget  IPO  (Mar  '12) Eloqua  IPO  (Aug  '12) ExactTarget  buys  Pardot  (Oct  '12) HubSpot  raises  (Nov  '12) Oracle  buys  Eloqua  (Dec  '12) Marketo  IPO  (May  '13) SF  buys  ExactTarget  (Jun  '13) 0 5 10 15 20 25 $161.5M $92  M $95.5M $100  M $871  M $79  M $2.5  B venture  funding,  mergers,  acquisiFons  and  IPOs   fuel  the  marke&ng  automa&on  space   @paulroetzer www.pr2020.com
  18. 18. the marketing automation we see today is elementary when we consider what comes next . . . @paulroetzer www.pr2020.com
  19. 19. marketing automation platforms save time, improve efficiency and increase productivity . . . @paulroetzer www.pr2020.com
  20. 20. but they do NOT provide deep insights into data . . . @paulroetzer www.pr2020.com
  21. 21. data  >  intelligence  >  acFon  >  outcomes @paulroetzer www.pr2020.com
  22. 22. We  create  2.5  quin&llion  bytes  of   data  every  day  (that’s  18  zeros)     ! 90%  of  all  data  in  the  world  has   been  created  in  the  last  2  years   ! Source:  IBM Infographic:  Domo
  23. 23. on  average,  marketers  depend  on  data  for   just  11%  of  customer-­‐related  decisions.   ! source:  CEB     @paulroetzer www.pr2020.com
  24. 24. B2B  marketers  say  just  9%  of  CEOs  and  6%  of  CFOs  use   markeFng  data  to  help  set  corporate  direcFon.       source:  ITSMA,  VisionEdge  and  Forrester @paulroetzer www.pr2020.com
  25. 25. marketing automation platforms generally do NOT recommend actions or predict outcomes. @paulroetzer www.pr2020.com
  26. 26. marketers  remain  limited  by  biases,  beliefs,  educa.on,   experiences,  knowledge  and  brainpower.  
  27. 27. We  have  a  finite  ability   to  process  informaFon,   build  strategies,  and   achieve  performance   poten&al. @paulroetzer
  28. 28. Algorithms,  in  contrast,  have  an  almost   infinite  ability  to  process  informa&on.   They  possess  the  power  to  understand   natural  language  queries,  idenFfy  panerns   and  anomalies,  and  parse  massive  data  sets   to  deliver  recommendaFons  bener,  faster,   and  cheaper  than  people  can. Image:  Wikimedia  Commons@paulroetzer www.pr2020.com
  29. 29. Turning  data  into  intelligence,   intelligence  into  strategy,     and  strategy  into  ac&on     remains  largely  human  powered.   @paulroetzer www.pr2020.com
  30. 30. What  inevitably  comes  next  are     marke&ng  intelligence  engines     that  process  data  and  recommend  acFons   to  improve  performance  based  on   probabiliFes  of  success. @paulroetzer www.pr2020.com
  31. 31. There  is  a  relaFvely  untapped   technology  that  possesses  the   power  to  change  everything:     ar&ficial  intelligence. @paulroetzer www.pr2020.com
  32. 32. consumer behavior + big data + human limitations = potential to disrupt @paulroetzer www.pr2020.com
  33. 33. the  disrup&on  of  industries
  34. 34. @paulroetzer www.pr2020.com 60%  of  all  trades  are  executed  by  computers     with  linle  or  no  real-­‐Fme  oversight  from  humans.   ! Source:  Christopher  Steiner,  Automate  This
  35. 35. avg  120  stops/day
  36. 36. what  is  the  possible  number  of   alterna&ves  for  ordering  those  stops? @paulroetzer www.pr2020.com
  37. 37. 6,689,502,913,449,135,000,000,000,   000,000,000,000,000,000,000,000,   000,000,000,000,000,000,000,000,   000,000,000,000,000,000,000,000,   000,000,000,000,000,000,000,000,   000,000,000,000,000,000,000,000,   000,000,000,000,000,000,000,000,   000,000,000,000,000,000,000,000,   000,000 Source: Wall Street Journal
  38. 38. “Can  a  human  really  think  of  the   best  way  to  deliver  120  stops?  This   is  where  the  algorithm  will  come  in.   It  will  explore  paths  of  doing  things   you  would  not,  because  there  are   just  too  many  combinaFons.”   ! Jack  Levis     Senior  director  of  process  management,  UPS Source: Wall Street Journal
  39. 39. NETFLIX  uses  algorithms  to  suggest  content   and  manufacture  shows  based  on  subscriber   viewing  habits  and  preferences. Source:  Neqlix  Tech  Blog
  40. 40. 75%  of  what  people  watch  on  NeXlix  is  from  some   sort  of  algorithm-­‐generated  recommenda&on Source:  Neqlix  Tech  Blog
  41. 41. Epagogix  algorithms  analyze  movie  scripts  to     predict  how  much  money  they  will  make  at  the  box  office   and  offer  recommenda&ons  on  how  to  make  them  more   marketable  and  profitable,  including  through  changes  to   plot  lines,  se[ngs,  character  roles  and  actors.
  42. 42. Source:  NASA  Instagram
  43. 43. Source:  NASA  Instagram “enlisFng  the  help  of  machines  to  sort  through  thousands  of   stars  in  our  galaxy  and  learn  their  sizes,  composiFons  and   other  basic  traits.  .  .  .computers  learn  from  large  data  sets,     finding  paerns  that  humans  might  not  otherwise  see.”
  44. 44. Image:  Franck  Calzada/YouTube The AP “writes” 10x more earnings reports using Automated Insights technology
  45. 45. Source: Social Media Frontiers Source:  vicarious.com “We  are  building  a  unified  algorithmic  architecture  to  achieve   human-­‐level  intelligence  in  vision,  language,  and  motor   control.  .  .  .  our  system  requires  orders  of  magnitude  less   training  data  than  tradi&onal  machine  learning  techniques.”
  46. 46. Source: Social Media Frontiers Source:  vicarious.com
  47. 47. Source: Social Media Frontiers $70  million  in  funding  from:     ! Elon  Musk,  Mark  Zuckerberg,  Peter  Thiel,  Jeff  Bezos,  Jerry   Yang,  Marc  Benioff,  Janus  Friis,  Ashton  Kutcher,     Aaron  Levie,  DusFn  Moskovitz  .  .  .     Source:  Wall  Street  Journal,  TechCrunch  and  Vicarious
  48. 48. Source: Social Media Frontiers Facebook  uses  “deep  learning,”  an  A.I.  subfield,  to  filter   your  Newsfeed  and  recognize  faces  in  photos  you  upload,     but  that’s  only  the  beginning  .  .  .
  49. 49. Source: Social Media Frontiers hnps://research.facebook.com/ai
  50. 50. Source: Social Media Frontiers hnps://research.facebook.com/ai “We’re  commined  to  advancing  the  field  of   machine  intelligence  and  developing  technologies   that  give  people  beer  ways  to  communicate.  In   the  long  term,  we  seek  to  understand  intelligence   and  make  intelligent  machines.”
  51. 51. The  DeepMind  team  at  Google  has  built  a  machine  that  taught  itself   how  to  play  and  win  over  49  Atari  2600  games  from  the  1980s Image:  NML32/YouTube Source:  The  New  Yorker,  ArFficial  Intelligence  Goes  To  The  Arcade
  52. 52. “It  is  programmed  to  find  a  score  rewarding,  but  is  given  no   instruc&on  in  how  to  obtain  that  reward.     ! “Its  first  moves  are  random,  made  in  ignorance  of  the   game’s  underlying  logic.  Some  are  rewarded  with  a  treat—a   score—and  some  are  not.     ! “Buried  in  the  DeepMind  code,  however,  is  an  algorithm   that  allows  the  juvenile  A.I.  to  analyze  its  previous   performance,  decipher  which  ac&ons  led  to  beer  scores,   and  change  its  future  behavior  accordingly.” Source:  The  New  Yorker,  ArFficial  Intelligence  Goes  To  The  Arcade
  53. 53. “It  is  programmed  to  find  a  score  rewarding,  but  is  given  no   instruc&on  in  how  to  obtain  that  reward.     ! “Its  first  moves  are  random,  made  in  ignorance  of  the   game’s  underlying  logic.  Some  are  rewarded  with  a  treat—a   score—and  some  are  not.     ! “Buried  in  the  DeepMind  code,  however,  is  an  algorithm   that  allows  the  juvenile  A.I.  to  analyze  its  previous   performance,  decipher  which  ac&ons  led  to  beer  scores,   and  change  its  future  behavior  accordingly.” Source:  The  New  Yorker,  ArFficial  Intelligence  Goes  To  The  Arcade
  54. 54. search,  voice  recogni&on,  language  transla&on,  robots,  driverless  cars  .  .  .
  55. 55. the  marke&ng  machine  age
  56. 56. “At  the  heart  of  all  of   these  algorithm-­‐enabled   revoluFons  on  Wall  Street   and  elsewhere,  there   exists  one  persistent  goal:   predic&on—to  be  more   exact,  predicFon  of  what   other  humans  will  do.”   @paulroetzer www.pr2020.com
  57. 57.   “Imagine  a  world  where  you  can   predict  with  above  85%  accuracy   who  will  buy,  what  they  will  buy,  how   much,  what  channel  will  reach  them,   what  message  will  resonate.”       —  Amanda  Kahlow,  6sense  founder  and  CEO Source:  VentureBeat
  58. 58. “a  predic&ve  intelligence  engine  for  markeFng  and  sales”
  59. 59. “We  then  apply  machine  learning   and  predic&ve  algorithms  to  profile   your  customers  and  predict   behaviors  such  as  likelihood  to   purchase,  churn,  and  lifeFme  value.” Source:  RetenFon  Science
  60. 60. turn  data  into  (ar&ficial)  intelligence
  61. 61. turn  data  into  (ar&ficial)  intelligence
  62. 62. Source:  NarraFve  Science
  63. 63. Source:  NarraFve  Science
  64. 64. Source:  MarkeFng  Land
  65. 65. $143.8 M $76.6 M* $36.0 M $32.4 M $36.0 M $20.0 M $15.4 M $10.8 M* $9.5 M $2.5 MSource:  Crunchbase Artificial Intelligence + Marketing $383 M
  66. 66. “We  expect  technology  spend  by  CMOs  to   increase  10x  in  10  years,  from  $12  billion  to   $120  billion,  unlocking  a  huge  opportunity  for   markeFng  technology  companies  and   opening  the  door  to  the  decade  of  the  CMO.”     ! —  Ashu  Garg,  general  partner,  FoundaFon  Capital Source:  ChiefMartec.com Image:  Tracy  Olson,  Flickr
  67. 67. 6  classes,  43  categories,  1,876  companies
  68. 68. $49  billion  in  investment  across   537  markeFng  technology  products   that  received  major  funding Source:  VentureBeat
  69. 69. consumer behavior + big data + human limitations = potential to disrupt @paulroetzer www.pr2020.com
  70. 70. capital + funding velocity + innovator advantage = reward for disruption @paulroetzer www.pr2020.com
  71. 71. potential to disrupt + reward for disruption = MARKETING @paulroetzer www.pr2020.com
  72. 72. “We’re  in  an  AI  spring.  For   our  company,  and  I  think   for  every  company,  the   revoluFon  in  data  science   will  fundamentally  change   how  we  run  our  business   because  we’re  going  to   have  computers  aiding  us   in  how  we’re  interacFng   with  our  customers.”   ! —  Marc  Benioff Source:  FortuneImage:  Wikipedia
  73. 73. acquired  by  Salesforce  in  2014  for  $390  million   ! “Salesforce.com  Inc.  has  started  working  to  integrate  ar&ficial-­‐intelligence   technology  from  acquisiFon  RelateIQ  Inc.  into  its  sozware,  seeking  to  add   predic&ve  capabili&es  that  will  help  it  compete  with  younger  startups.” Source:  Bloomberg  Business
  74. 74. is  IBM’s  Watson  the  future  of  marke&ng?
  75. 75. Image:  Wikimedia  Commons The  story  of  arFficial  intelligence  can’t  be  told  without  IBM  ,   which  possesses  an  es&mated  500  AI-­‐related  patents. Source:  Business  Insider
  76. 76. Source:  IBM
  77. 77. hnp://www.ibm.com/analyFcs/watson-­‐analyFcs/  
  78. 78. hnp://www.ibm.com/analyFcs/watson-­‐analyFcs/  
  79. 79. hnp://www.ibm.com/analyFcs/watson-­‐analyFcs/  
  80. 80. hnp://www.ibm.com/analyFcs/watson-­‐analyFcs/  
  81. 81. Image:  Wikimedia  Commons “There  is  a  science  and  an  art  to  every   profession.  Soon,  Watson  will  know  the   science  bener  than  a  human.  Humans  will   need  to  focus  on  the  art  of  their  profession— the  creaFve  elements  only  they  can  provide.   !  —  Daniel  Burrus,  author,  Burrus  Research  founder  and  CEO Source:  Wired
  82. 82. so,  what’s  possible?
  83. 83. reviewing  analy&cs   crea&ng  performance  reports  &  data  visualiza&ons   publishing  social  media  updates   planning  blog  post  topics   copywri&ng   cura&ng  content   building  strategy   alloca&ng  resources
  84. 84. Imagine  if  a  marketer’s  primary  role  was  to  curate  and  enhance     algorithm-­‐based  recommenda&ons  and  content,     rather  than  devise  them.
  85. 85. Rather  than  simply   automaFng  manual  tasks,   arFficial  intelligence  adds  a   cogniFve  layer  that  infinitely   expands  marketers’  ability   to  process  data,  idenFfy   panerns,  and  build   intelligent  strategies  and   content  faster,  cheaper  and   more  effec&vely  than   humans.
  86. 86. Algorithm-­‐based  intelligence  engine     for  all  major  markeFng  acFviFes  and  strategies.
  87. 87. Algorithm-­‐based  intelligence  engine     for  all  major  markeFng  acFviFes  and  strategies. historical  performance  data
  88. 88. Algorithm-­‐based  intelligence  engine     for  all  major  markeFng  acFviFes  and  strategies. real-­‐Fme  analy&cs
  89. 89. Algorithm-­‐based  intelligence  engine     for  all  major  markeFng  acFviFes  and  strategies. industry  and  company  benchmarks
  90. 90. Algorithm-­‐based  intelligence  engine     for  all  major  markeFng  acFviFes  and  strategies. subjecFve  human  inputs
  91. 91. Algorithm-­‐based  intelligence  engine     for  all  major  markeFng  acFviFes  and  strategies. business  and  campaign  goals
  92. 92. Algorithm-­‐based  intelligence  engine     for  all  major  markeFng  acFviFes  and  strategies. create  content
  93. 93. Algorithm-­‐based  intelligence  engine     for  all  major  markeFng  acFviFes  and  strategies. enhance  experiences
  94. 94. Algorithm-­‐based  intelligence  engine     for  all  major  markeFng  acFviFes  and  strategies. recommend  acFons
  95. 95. Algorithm-­‐based  intelligence  engine     for  all  major  markeFng  acFviFes  and  strategies. predict  outcomes
  96. 96. Algorithm-­‐based  intelligence  engine     for  all  major  markeFng  acFviFes  and  strategies. data  >  intelligence  >  ac&ons  >  outcomes
  97. 97. “The  ability  to  create  algorithms  that   imitate,  beer,  and  eventually  replace   humans  is  the  paramount  skill  of  the   next  one  hundred  years.  As  the  people   who  can  do  this  mulFply,  jobs  will   disappear,  lives  will  change,  and   industries  will  be  reborn.”     ! Christopher  Steiner,  Automate  This
  98. 98. The future may be closer than you think. @paulroetzer
  99. 99. “MarkeFng  is  now,  as  it  has   always  been,  an  art  form.   But  the  next  generaFon  of   marketers  understands  it   can  be  so  much  more.   These  innovators  are   rewriFng  what  is  possible   when  the  art  and  science   of  marke&ng  collide.” @paulroetzer www.pr2020.com
  100. 100. paul  roetzer,  @paulroetzer   ! founder  &  CEO  |  PR  20/20   author  |  The  Marke.ng  Performance  Blueprint  (Wiley,   2014)  &  The  Marke.ng  Agency  Blueprint  (Wiley,  2012)   creator  |  MarkeFng  Score  &  MarkeFng  Agency  Insider www.pr2020.com bit.ly/roetzer-­‐sxsw15

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