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Origins of the Marketing Intelligence Engine (INBOUND 2016)

The velocity of change in the marketing industry is accelerating, but what we see today is elementary when we consider the potential of what comes next. This session provides a glimpse into the future of marketing, and the opportunities that exist for those who can harness the power of artificial intelligence and cognitive technology like IBM's Watson. They will be able to do more with less, run personalized campaigns of unprecedented complexity, and analyze massive data sets to predict outcomes. The opportunities are endless for those with the will and vision to transform the industry. Attendees will:

- Learn what the disruption of other industries can teach us about the inevitable impact artificial intelligence will have on the marketing industry.
- Discover existing marketing technologies using artificial intelligence to make marketing more efficient and effective.
- Get inspired to explore what’s possible for the future of marketing, as well as their businesses and careers.

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Origins of the Marketing Intelligence Engine (INBOUND 2016)

  1. 1. #INBOUND16 ORIGINS OF THE MARKETING INTELLIGENCE ENGINE Paul Roetzer, Founder & CEO, PR 20/20 Copyright 2016 PR 20/20. All rights reserved. @PaulRoetzer
  2. 2. #INBOUND16 Consider  how  much  !me  your  marke2ng  team  spends  .  .  . crea2ng  ad  copy   managing  digital  ad  campaigns   tes2ng  headlines,  landing  pages,  ads   scheduling/publishing  social  shares   predic2ng  opens,  clicks,  conversions   reviewing  analy!cs   wri2ng  performance  reports   recommending  strategies   alloca2ng  resources dra;ing  social  media  updates   discovering  keywords   planning  blog  post  topics   wri!ng  content   op!mizing  content   cura!ng  content   personalizing  content   automa!ng  content   building  email  workflows Copyright 2016 PR 20/20. All rights reserved.
  3. 3. Now imagine if machines performed the majority of those activities, and a marketer’s primary role was to enhance rather than create.
  4. 4. There is a relatively untapped technology that possesses the power to change everything…
  5. 5. artificial intelligence
  6. 6. #INBOUND16 “The  science  of  making  machines  smart.”     —  Demis  Hassabis,  Co-­‐Founder  &  CEO  of  DeepMind what  is  ar!ficial  intelligence? (which  in  turn  augments  human  knowledge  and  capabili5es) Source: Rolling Stone
  7. 7. #INBOUND16 a  set  of  instruc!ons  that  tells  the  machine  what  to  do.   what  is  an  algorithm? (except  with  AI  the  machine  can  create  its  own  algorithms,  determine  new  paths,   and  unlock  unlimited  poten5al  to  advance  marke5ng,  and  mankind.)
  8. 8. #INBOUND16 THE DISRUPTION OF INDUSTRIES
  9. 9. @paulroetzer www.pr2020.com 60%  of  all  trades  are  executed  by  computers     with  liKle  or  no  real-­‐2me  oversight  from  humans.   Source:  Christopher  Steiner,  Automate  This
  10. 10. @paulroetzer avg  120  stops/day
  11. 11. what  is  the  possible  number  of   alterna!ves  for  ordering  those  stops?
  12. 12. 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
  13. 13. “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   combina2ons.”   Jack  Levis     Senior  director  of  process  management,  UPS Source: Wall Street Journal
  14. 14. NETFLIX  uses  algorithms  to  suggest  content  and   manufacture  shows  based  on  subscriber  viewing   habits  and  preferences. Source:  NeUlix  Tech  Blog
  15. 15. 75%  of  what  people  watch  on  NeSlix  is  from  some   sort  of  algorithm-­‐generated  recommenda!on Source:  NeUlix  Tech  Blog
  16. 16. 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,  seVngs,   character  roles  and  actors.
  17. 17. Source: Tesla
  18. 18. #INBOUND16 THE MARKETING MACHINE AGE
  19. 19. POTENTIAL to disrupt + the REWARD for disruption
  20. 20. ExactTarget  IPO  (Mar  '12) Oracle  buys  Eloqua  (Dec  '12) SF  buys  ExactTarget  (Jun  '13) IBM  buys  Silverpop  (Apr  '14) Marketo  market  cap  (8-­‐30-­‐16) HubSpot  market  cap  (8-­‐30-­‐16) 0 5 10 15 20 25 $161.5M $871  M $2.5  B venture  funding,  mergers,  acquisi2ons  and  IPOs  have  fueled   the  marke!ng  automa!on  space   @paulroetzer www.pr2020.com $270  M $1.6  B $1.9  B
  21. 21. 90% of all data in the world has been created in the last 2 years Source:  IBM
  22. 22. marketers have access to data from dozens of sources: social monitoring, media monitoring, web analytics, email, call tracking, sales, advertising, remarketing, ecommerce, mobile apps. . .
  23. 23. We  have  a  finite  ability  to   process  informa2on,  build   strategies,  create  content   at  scale,  and  achieve   performance  poten!al.
  24. 24. Algorithms, in contrast, have an almost infinite ability to process data, and deliver predictions, recommendations and content better, faster and cheaper. Image:  Wikimedia  Commons
  25. 25. @paulroetzer www.pr2020.com And  yet  marke2ng  remains  largely   human  powered,  with  a  bit  of   automa2on  mixed  in.
  26. 26. The future may be closer than you think.
  27. 27. #INBOUND16 NATURAL LANGUAGE GENERATION (AKA MACHINE-ASSISTED CONTENT)
  28. 28. Image:  Franck  Calzada/YouTube The AP “writes” 10x more earnings reports using AI, specifically natural language generation
  29. 29. @paulroetzer
  30. 30. Define  Founda2on  Projects Subjective analysis Internal stakeholders 10 sections 27 profile fields 132 factors Sample  Marke5ng  Score  Factor  Slider  Scale
  31. 31. A  strong  marke!ng  technology  founda2on  is  cri2cal  to  driving  performance.  Core   technologies,  when  integrated,  improve  efficiencies,  maximize  produc2vity  and  ROI,  and   create  compe22ve  advantages.  The  company  should  priori2ze  CMS  (5),  CRM  (4),  email   marke2ng  (3),  marke2ng  analy2cs  (2)  and  marke2ng  automa2on  (2).   sample key finding
  32. 32. A  strong  marke!ng  technology  founda2on  is  cri2cal  to  driving   performance.  Core  technologies,  when  integrated,  improve   efficiencies,  maximize  produc2vity  and  ROI,  and  create   compe22ve  advantages.  The  company  should  priori2ze  CMS  (5),   CRM  (4),  email  marke2ng  (3),  marke2ng  analy2cs  (2)  and   marke2ng  automa2on  (2).  
  33. 33. * Requires human writers to develop and enhance templates. Using  Natural  Language  Genera!on  (aka  Machine  Assisted)*:   50  briefs  x  15  minutes  per  brief  =  12.5  hours/month The  Diff:   37.5  hours  (at  a  cost  of  $250/month  for  the  license.) Tradi!onal  Way:     50  briefs  x  1  hour  per  brief  =  50  hours/month
  34. 34. The  Benefits   More  accurate  (eliminates  human  error)   More  briefs  published  (enables  content  at  scale)   More  cost  efficient  (shi;s  2me  to  edi2ng  only)   More  engagement   More  value  crea!on  for  members   More  new  business  opportuni!es
  35. 35. #INBOUND16 THE NEXT FRONTIER
  36. 36. Private investment in the AI sector has grown from $1.7B in 2010 to $14.9B in 2014 The market for AI based analytics could grow from $8.2B to $70B by 2020. — Source: BofA Merrill Lynch: Robot Revolution — Global Robot & AI Primer
  37. 37. There are dozens of AI-powered marketing tools that you can use to predict, plan, create, optimize, personalize, promote, measure and analyze. Source: Timothy Neesom
  38. 38. $29.4 M $36.0 M $9.5 M Source:  Crunchbase Artificial Intelligence + Marketing $279+ M $80.0 M* $66.0 M $14.5 M $13.9 M $11.0 M $5.4 M $14.2 M
  39. 39. “We’re in an AI spring. For our company, and I think for every company, the revolution in data science will fundamentally change how we run our business because we’re going to have computers aiding us in how we’re interacting with our customers.” — Marc Benioff Source:  FortuneImage:  Wikipedia
  40. 40. Source: Social Media Frontiers Facebook  uses  “deep  learning,”  an  AI  subfield,  to  filter  your  Newsfeed   and  recognize  faces  in  photos  you  upload,     but  that’s  only  the  beginning  .  .  .
  41. 41. Source: Social Media Frontiers hKps://research.facebook.com/ai “We’re  commiKed  to  advancing  the  field  of  machine   intelligence  and  developing  technologies  that  give   people  beger  ways  to  communicate.  In  the  long  term,   we  seek  to  understand  intelligence  and  make  intelligent   machines.”
  42. 42. search,  voice  recogni!on,  language  transla!on,  robots,  driverless  cars  .  .  .
  43. 43. Image:  Wikimedia  CommonsSource:  Business  Insider The  story  of  ar2ficial  intelligence  can’t  be  told  without  IBM  ,  which   possesses  an  es!mated  500  AI-­‐related  patents.
  44. 44. IBM  Watson  is  a  technology  plaUorm  that  uses     natural  language  processing  and  machine  learning  to  reveal  insights     from  large  amounts  of  unstructured  data Source: IBM
  45. 45. Source: Popular Science “IBM  used  machine  learning  and  experimental  Watson  APIs,  parsing  out  the  trailers   of  100  horror  movies.  It  did  visual,  audio,  and  composi2on  analysis  of  individual   scenes.  .  .  .  Watson  was  then  fed  the  full  film,  and  it  chose  scenes  for  the  trailer.  .  .  .  A   process  that  would  normally  take  weeks  was  reduced  to  hours.”  
  46. 46. "Cogni2ve  technology  is  there  to  extend  and  amplify  human   exper!se,  not  replace  it.”   —  Rob  High,  Chief  Technology  Officer,  IBM  Watson
  47. 47. Rather  than  simply  automa2ng  manual   tasks,  ar!ficial  intelligence  adds  a  cogni!ve   layer  that  infinitely  expands  marketers’   ability  to  process  data,  iden2fy  paKerns,   predict  outcomes,  and  build  intelligent   strategies  and  content  beger,  faster  and   cheaper.
  48. 48. dra;ing  social  media  updates  *  discovering  keywords  *  planning  blog  post  topics  *  wri!ng  content  *            op!mizing  content   *  cura!ng  content  *  personalizing  content  *  automa!ng  content  *  building  email  workflows  *  crea2ng  ad  copy  *  managing   digital  ad  campaigns  *  tes2ng  headlines,  landing  pages,  ads  *  scheduling/publishing  social  shares  *  predic2ng  opens,  clicks,   conversions  *  reviewing  analy!cs  *                        wri2ng  performance  reports  *  recommending  strategies  *  alloca2ng  resources
  49. 49. and imagine if that was only the beginning . . .
  50. 50. 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,  Ar2ficial  Intelligence  Goes  To  The  Arcade
  51. 51. “It is programmed to find a score rewarding, but is given no instruction 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 actions led to better scores, and change its future behavior accordingly.” Source:  The  New  Yorker,  Ar2ficial  Intelligence  Goes  To  The  Arcade
  52. 52. “It is programmed to find a score rewarding, but is given no instruction 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 actions led to better scores, and change its future behavior accordingly.” Source:  The  New  Yorker,  Ar2ficial  Intelligence  Goes  To  The  Arcade
  53. 53. What inevitably comes next are marketing intelligence engines that process data and recommend actions to improve performance based on probabilities of success.
  54. 54. inputs (time and money) + outputs (projects and campaigns) + outcomes (performance data)
  55. 55. “The ability to create algorithms that imitate, better, and eventually replace humans is the paramount skill of the next one hundred years. As the people who can do this multiply, jobs will disappear, lives will change, and industries will be reborn.”
  56. 56. 3 STEPS TO GET STARTED
  57. 57. #1 Evaluate repetitive, manual marketing tasks that could be intelligently automated.
  58. 58. #2 Assess opportunities to get more out of your data— discover insights, predict outcomes, devise strategies, personalize content across channels, and tell stories at scale.
  59. 59. #3 Consider the AI capabilities of your existing marketing technology, and explore the potential of emerging AI solutions.
  60. 60. Learn more at www.MarketingAIinstitute.com
  61. 61. paul  roetzer   paul@pr2020.com   @paulroetzer   CEO  |  PR  20/20   creator  |  Marke2ng  Ar2ficial  Intelligence  Ins2tute   author  |  The  Marke5ng  Performance  Blueprint  (Wiley,  2014)  &  The   Marke5ng  Agency  Blueprint  (Wiley,  2012) www.pr2020.com

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