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Voice Search and Conversation Action Assistive Systems - Challenges & Opportunities


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We are headed to the age of assistive task driven search where the user needs help to 'do' things as well as learn things. Smart speakers, mobile phones, assistive systems and conversational search and action devices are where the buck is headed for now. Where are we at in this wave? What are the challenges? What are the opportunities right now? Here we look at some of the ways we can start to prepare our tactics and strategy to be pioneering search marketers with conversation search and conversation action.

Published in: Marketing
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Voice Search and Conversation Action Assistive Systems - Challenges & Opportunities

  1. 1. ”Voice  Search   SEO  &  Assistive   Systems...   Challenges  &   Opportunities”   “The  current  situation  for   voice  search  &  SEO  and   overcoming  challenges“ Dawn  Anderson @DawnieAndo from   @MoveItMarketing
  2. 2. WHO IS DAWN ANDERSON @dawnieando dawn.anderson@@move-it- 11+  years  SEO  &  Digital  Marketing  Consultant &  Pracademic: Contributor: Speaker  &  Trainer: Fellow  of:
  3. 3. So,  just  what  is  conversational   assistance  &  where’s  the   opportunity?
  4. 4. Evolution  of   Information Retrieval Classic   Information   Retrieval  (IR) Interactive   Information   Retrieval  (IIR) Mobile   Information   Retrieval  (MIR) Machine   Learning
  5. 5. Something’s  brewing…‘A  Call  to   Arms’  in  ‘Assistive  AI’
  6. 6. …  and  some  names Subordinate   systems Conducive   systems Decisive   systems
  7. 7. A  Trio  of  assistive  systems
  8. 8. But…  we  will  look  at  only  2  types  today…  which   are  a  hybrid  within  the  assistive  systems Provide   answers   /  search Conversation     Search Help   with   activities   /  tasks Conversation   Actions
  9. 9. Ok  Google,  Hello  Siri,   Greetings  Cortana
  10. 10. Phones,  Desktop,  Laptops  &  Smart  Speakers   &  Watches
  11. 11. Joe  Public  is  in  awe   and  wonder  at  Smart   Speakers
  12. 12. Interest  over  time  for  Google  Home  &  Amazon  Alexa
  13. 13. Stepping  out  of  the  SEO  bubble Source:  SISTRIX.  2018. Stepping  out  of  the  SEO  Bubble  -­‐ SISTRIX.  [ONLINE]  Available   at:­‐out-­‐of-­‐the-­‐seo-­‐bubble/. 26,700+  respondents
  14. 14. Why  do  people  use  voice  search? Source:  Higher  visibility  study  on  2000  people  (opportunity  sampling)
  15. 15. When  do  people  use  voice  search? Source:  Higher  visibility  study  on  2000  people  (opportunity  sampling)
  16. 16. What  do  people  do  when  using  voice  search?
  17. 17. We  are  still  on   ‘Day  One’  with   this  stuff
  18. 18. We  are  like  prospecting   gold  miners…  future  facing
  19. 19. All  signals  point  to  the  future
  20. 20. By  2020  30%  of  web   browsing  sessions  will  be   done  without  a  screen   (Gartner,  2016) 30% 70% Web  browser  sessions Without  a  screen With  a  screen
  21. 21. Search  engines  &  IR  researchers  compete  over  voice  recognition
  22. 22. How  can  we  maximise conversation  search  &   conversation  actions  opportunities?
  23. 23. Accuracy  of   results  is  more   important   than  quantity
  24. 24. Let  us  first  look   at  ‘conversation   Search’
  25. 25. In  2017  we  asked  ”How”  more  than  anything  else
  26. 26. Since  voice   search  is  mostly   on  mobile   devices   (including   phones)…  be   VERY  mobile   friendly
  27. 27. We  have  some   guidance
  28. 28. Google’s  human  quality   raters  guidelines
  29. 29. And  also  some  of  the  researchers  who  work  on   the  Conversational  Search  team  at  Google   Switzerland
  30. 30. Machine  Learning   Word2Vec  &  Concept2Vec
  31. 31. What  did   they  say?
  32. 32. Tip  1  – Meet  informational  needs…  in  the  right   context
  33. 33. What  are  the  questions? Who? What? Where? When? Why?
  34. 34. Transactional Navigational Informational A  Taxonomy   of  Web  Search   (Broder,  2002)
  35. 35. Map  Different   Question   Answering   Content  To   Informational   Needs Informational Navigational Transactional Guides,  FAQs,  Quick   Answers,  How  to,   Articles Directions,   Branch  locations,   Meet  the  Team,   Avout Case  Studies,   Product  Reviews,   Testimonials,   Product  videos,   360  images,  specs
  36. 36. Think  often  ‘On  the  Go’  location  intent  focus
  37. 37. Tip  2  – Keep  answers  short  &  get  to  the  point   early  – prosody  modifications  &  sentence  stress
  38. 38. Keep  it  brief  &  concise
  39. 39. Tip  3  – Watch    out  for  grammar,  spelling  &   pronunciation
  40. 40. Soundex,  Metaphone,  Double  Metaphone (or  similar)  Algorithms
  41. 41. Tip  4  – Watch  out  for  pesky  pronouns
  42. 42. Linguisitics are   Complicated  – Watch  out  for   anaphora  &   cataphora resolution
  43. 43. It’s  raining  pronouns  – many  types  of  pronouns
  44. 44. So  many  ways  to  misunderstand  natural  language Intensive Reciprocal Reflexive Personal Relative Indefinite Demonstrativ e Possessive Interrogative Myself Each  other Herself I Who Anything This Mine Who Himself One   another Himself You Whom Everybody That His Whom Herself Myself He Whose few Those Theirs Which Itself Ourselves She Which many These Hers What Ourselves Yourself We That none Ours Whatever Yourself They What some Yours Whichever Me Whatever Whomever Him Whoever Her Whomever Us whichever
  45. 45. Computer   programs  lose   track  of  who   is  who  easily I’m  confused…  Here…  Have   some  flowers  instead  ;P  ;P
  46. 46. They Us She He Them I You Him Her Me E.g.   Minimize   these   personal   pronouns…
  47. 47. That Those These This Time  &  Space  confuses   things  further
  48. 48. Instead…  refer  to  entities  by  name  (where   possible)
  49. 49. Dis…  Ambiguate
  50. 50. Unstructured   data  (text) Semi-­‐ structured   data Relational   databases Structured   data XML  sitemaps Ordered  lists Unordered   lists Tabular  data Data  Feeds Turn  ‘fluffy’  web  pages  into  machine-­‐ understandable  formats  – add  signals
  51. 51. Structured   data  explosion 2017 2014
  52. 52. Over  half  of  voice   search  results   hold  featured   snippets  (Dr  Pete   Myers,  Moz,   2017) Work  on  building  out  the  Knowledge  Graph
  53. 53. Tip  5  – Cover  all  bases  due  to  paraphrasing   absence
  54. 54. • Well  structured  long  form   informational  content  (where   appropriate) • Semantic  headings • Write  for  a  featured  snippet   win  (few  exceptions) • Cover  the  bases  because  of   extraction  &  compression  (no   paraphrasing)
  55. 55. Tip  6  -­‐ Avoid  Tables
  56. 56. You  may  need   a  dual  or   triple  content   strategy 2%
  57. 57. Now  let’s  look  at  ‘conversation  actions’
  58. 58. Many  subtasks  towards  a  major  task
  59. 59. Google  Assistant  – Actions,   Entities,  Dialog  flows  &  Intents
  60. 60. Many  built-­‐in  intents  &  many  ‘coming  soon’
  61. 61. Extend  Actions  on  Google  using  Machine   Learning
  62. 62. Extend  Actions   on  Google   using  Machine   Learning
  63. 63. Understand  your  customers  to  assist  with  AI Customer   Service  Data Customer   Panels Email   questions FAQs Build   Assistant  App
  64. 64. Understand  your  customers  to  assist  with  AI Perceived   Information  need Micro-­‐task Micro-­‐task Micro-­‐task Micro-­‐task Micro-­‐task Task Micro-­‐task Micro-­‐task Micro-­‐task Micro-­‐task Task Micro-­‐task Micro-­‐task Task Micro-­‐task Micro-­‐task Micro-­‐task Task Micro-­‐task Micro-­‐task Task Micro-­‐task Task We  can  identify  the   user’s  probable  top   tasks  &  subtasks Identify  their  needs  &   what  info  they  need   along  the  way
  65. 65. Paraphrase  handling  on  ‘Actions’  appears  to   be  programmable
  66. 66. Hotel  Booking   Dialogflow
  67. 67. Book  hotel   intent When  do  you   want  to  stay? dates dates How  many   nights? 3  nights 2  nights Overnight A  week Single  or   double  room? Single  room Double  room Programme your   own  expected   questions  and   answers
  68. 68. We  are  at  ’Day  One’  but  the   future  is  ’Assistive’
  69. 69. Thank  you Keep  in  touch @DawnieAndo @MoveItMarketing
  70. 70. References,  Sources  &   Further  Reading
  71. 71. References • Broder,  A.,  2002,  September.  A  taxonomy  of  web  search.  In ACM  Sigir forum (Vol.  36,  No.   2,  pp.  3-­‐10).  ACM. • Chuklin,  A.,  Severyn,  A.,  Trippas,  J.,  Alfonseca,  E.,  Silen,  H.  and  Spina,  D.,  2018.  Prosody   Modifications  for  Question-­‐Answering  in  Voice-­‐Only  Settings. arXiv preprint   arXiv:1806.03957. • HigherVisibility.  2018. How  Popular  is  Voice  Search?  |  HigherVisibility.  [ONLINE]  Available   at:­‐popular-­‐is-­‐voice-­‐search/ • Filippova,  K.,  Alfonseca,  E.,  Colmenares,  C.A.,  Kaiser,  L.  and  Vinyals,  O.,  2015.  Sentence   compression  by  deletion  with  lstms.  In Proceedings  of  the  2015  Conference  on  Empirical   Methods  in  Natural  Language  Processing (pp.  360-­‐368). • Filippova,  K.  and  Alfonseca,  E.,  2015.  Fast  k-­‐best  sentence  compression. arXiv preprint   arXiv:1510.08418. • Google  Developers.  2018. Content-­‐based  Actions   | Actions  on  Google   | Google   Developers.  [ONLINE]  Available  at:­‐ actions/.  [Accessed  18  June  2018]
  72. 72. References • Mitkov,  R.,  2014. Anaphora  resolution.  Routledge. • NLP  Department  -­‐ Stanford  University  -­‐ Imran  Q  Sayed.  2018. Issues  in  Anaphora   Resolution.  [ONLINE]  Available   at:   [Accessed  28  June  2018]. • Radlinski,  F.  and  Craswell,  N.,  2017,  March.  A  theoretical  framework  for  conversational   search.  In Proceedings  of  the  2017  Conference  on  Conference  Human  Information   Interaction  and  Retrieval (pp.  117-­‐126).  ACM. • Schalkwyk,  J.,  Beeferman,  D.,  Beaufays,  F.,  Byrne,  B.,  Chelba,  C.,  Cohen,  M.,  Kamvar,  M.   and  Strope,  B.,  2010.  “Your  word  is  my  command”:  Google  search  by  voice:  a  case  study.   In Advances  in  speech  recognition (pp.  61-­‐90).  Springer,  Boston,  MA. • SISTRIX.  2018. Stepping  out  of  the  SEO  Bubble  -­‐ SISTRIX.  [ONLINE]  Available   at:­‐out-­‐of-­‐the-­‐seo-­‐bubble/.  [Accessed  16  June   2018].
  73. 73. References • The  Stanford  Question  Answering  Dataset.  2018. The  Stanford   Question  Answering  Dataset.  [ONLINE]  Available   at:­‐explorer/.   • Trippas,  J.R.,  Spina,  D.,  Cavedon,  L.,  Joho,  H.  and  Sanderson,  M.,  2018.   Informing  the  Design  of  Spoken  Conversational  Search.