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ORIGINS OF THE MARKETING INTELLIGENCE ENGINE

Paul Roetzer, Founder & CEO, PR 20/20

Copyright 2016 PR 20/20. All rights reserved.

@PaulRoetzer

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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.

Now imagine if machines performed the majority of those activities,

and a marketer’s primary role was to enhance rather than create.

There is a relatively untapped technology that possesses the power to change everything…

artificial intelligence

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“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

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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.)

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THE DISRUPTION OF INDUSTRIES

@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

@paulroetzer

avg  120  stops/day

what  is  the  possible  number  of  alterna!ves  for  ordering  those  stops?

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

“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

NETFLIX  uses  algorithms  to  suggest  content  and  manufacture  shows  based  on  subscriber  viewing  

habits  and  preferences.

Source:  NeUlix  Tech  Blog

75%  of  what  people  watch  on  NeSlix  is  from  some  sort  of  algorithm-­‐generated  recommenda!on

Source:  NeUlix  Tech  Blog

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.

Source: Tesla

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THE MARKETING MACHINE AGE

POTENTIAL to disrupt + the REWARD for disruption

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

90% of all data in the world has been created in the last 2 years

Source:  IBM

marketers have access to data from dozens of sources: social monitoring, media monitoring, web analytics, email, call tracking, sales, advertising, remarketing, ecommerce, mobile apps. . .

We  have  a  finite  ability  to  process  informa2on,  build  strategies,  create  content  at  scale,  and  achieve  performance  poten!al.

Algorithms, in contrast, have an almost infinite ability to process data, and deliver

predictions, recommendations and content better, faster and cheaper.

Image:  Wikimedia  Commons

@paulroetzer www.pr2020.com

And  yet  marke2ng  remains  largely  human  powered,  with  a  bit  of  

automa2on  mixed  in.

The future may be closer than you think.

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NATURAL LANGUAGE GENERATION (AKA MACHINE-ASSISTED CONTENT)

Image:  Franck  Calzada/YouTube

The AP “writes” 10x more earnings reports using AI, specifically natural language generation

@paulroetzer

Define  Founda2on  Projects

Subjective analysis Internal stakeholders 10 sections 27 profile fields 132 factors

Sample  Marke5ng  Score  Factor  Slider  Scale

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

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).  

* 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

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

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THE NEXT FRONTIER

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

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

$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

“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

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  .  .  .

Source: Social Media FrontiershKps://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.”

search,  voice  recogni!on,  language  transla!on,  robots,  driverless  cars  .  .  .

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.

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

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.”  

"Cogni2ve  technology  is  there  to  extend  and  amplify  human  exper!se,  not  replace  it.”  —  Rob  High,  Chief  Technology  Officer,  IBM  Watson

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.

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

and imagine if that was only the beginning . . .

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

“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

“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

What inevitably comes next are marketing intelligence engines

that process data and recommend actions to improve performance based on

probabilities of success.

inputs (time and money) +

outputs (projects and campaigns) +

outcomes (performance data)

“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.”

3 STEPS TO GET STARTED

#1

Evaluate repetitive, manual marketing tasks that could be intelligently automated.

#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.

#3

Consider the AI capabilities of your existing marketing technology, and explore the potential of emerging AI solutions.

Learn more at www.MarketingAIinstitute.com

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