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SEO - The Rise of Persona Modelled Intent Driven Contextual Search

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Increasing volumes of data on users and 'users like users' via user modelling now provide search engines with clues as to what types of pages to rank for different user types, terms, in different contexts, locations & scenarios

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SEO - The Rise of Persona Modelled Intent Driven Contextual Search

  1. 1. The  Rise  of  Persona  Modelled   Contextual  Search  in  Organic   Results Presented  by:  Dawn  Anderson @dawnieando
  2. 2. Dawn Anderson • Managing  Director  -­‐ Move  It  Marketing • University  Lecturer  – Digital  marketing  &  Search • From  Manchester,  UK   • International  SEO  Consultant  – 11+  Yrs in  SEO • Pomeranian  Pooch  Lover  – Meet  Bert • Googebot Hunter  (Practice  &  Academia) • Search  Awards  Judge • Contributor • Researcher • Twitter  Chatterer  @dawnieando
  3. 3. MY SEARCH UNIVERSE IS NOT YOUR SEARCH UNIVERSE
  4. 4. THERE’S NO SUCH THING AS ‘NO 1. ON GOOGLE’ ANY MORE YOUR RANKING TOOLS ARE MOSTLY WRONG
  5. 5. MY PERSONAL SEARCH MY MOBILE SEARCH MY LOCAL & LOCATION SEARCH MY TEMPORAL SEARCH + + + + Time of Search Device Geographic Location Cookies / Search history / G+ connections 2009 EVEN  WHEN   ‘NOT  LOGGED  IN’
  6. 6. FROM AN AUDIENCE OF MANY Toward An Audience of One Assistant https://assistant.google.com/ IT’S GETTING PERSONAL TRIANGULATION   OF  DATA  POINTS Mobile data Past Search preferences Cookies
  7. 7. INTELLIGENT  CUSTOMER   RELATIONSHIP  MANAGEMENT  (CRM) EXCHANGE OF VALUE WITH PLATFORMS & SEARCH ENGINES • Location data • Device data • Cookie drop • Click data • Preferences • Histograms • Triangulation of data points • Periodic routines Increasingly refined search results and suggestions based on past behaviour, routines, location and known context / intent of search terms (beyond and including the user) D A T A S C I E N C E
  8. 8. VISIT  -­ chrome://histograms/ Histogram: AsyncDNS.AttemptCountSuccess recorded 45032 samples, mean = 1.0 (flags = 0x1) 0 O (0 = 0.0%) 1 ------------------------------------------------------------------------O (44480 = 98.8%) {0.0%} 2 -O (498 = 1.1%) {98.8%} 3 O (39 = 0.1%) {99.9%} 4 O (15 = 0.0%) {100.0%} 5 ... Histogram: AsyncDNS.ConfigChange recorded 117 samples, mean = 0.4 (flags = 0x1) 0 ------------------------------------------------------------------------O (65 = 55.6%) 1 ----------------------------------------------------------O (52 = 44.4%) {55.6%} 2 O (0 = 0.0%) {100.0%} Data  on  your  personal  search   history  being  transported Back  to  Google PERSONAL
  9. 9. 2017 2017 2017 SITUATIONAL CONTEXT & PERSONAL SEARCH 2017 RESEARCH
  10. 10. BUT  IT’S  MOSTLY   NOT  JUST  FROM   YOUR  DATA
  11. 11. BUT  ‘PEOPLE   LIKE  YOU’,  OR   ME
  12. 12. MY SEARCH UNIVERSE MIGHT BE VERY SIMILAR TO OTHERS’ SEARCH UNIVERSE’
  13. 13. People like us DEMOGRAPHICS BEHAVIOUR OF PEOPLE ‘LIKE’YOU LOCATION PEOPLE WHO CLICKED THIS ALSO CLICKED THISHISTORICAL CLICK DATA ON PERSONAL SEQUENTIAL CLICK PATTERNS OF PAST QUERY INTENT SHIFT
  14. 14. Based  on   similar  users’   collated  data   modelled
  15. 15. VISIT  -­ chrome://histograms/ Histogram: AsyncDNS.AttemptCountSuccess recorded 45032 samples, mean = 1.0 (flags = 0x1) 0 O (0 = 0.0%) 1 ------------------------------------------------------------------------O (44480 = 98.8%) {0.0%} 2 -O (498 = 1.1%) {98.8%} 3 O (39 = 0.1%) {99.9%} 4 O (15 = 0.0%) {100.0%} 5 ... Histogram: AsyncDNS.ConfigChange recorded 117 samples, mean = 0.4 (flags = 0x1) 0 ------------------------------------------------------------------------O (65 = 55.6%) 1 ----------------------------------------------------------O (52 = 44.4%) {55.6%} 2 O (0 = 0.0%) {100.0%} Collated  data  on  all  users  like   you  to  create  user  modelling  for   targetting and  relevance  in  the   future USER PROFILE MODELLING
  16. 16. TEMPORAL A long way to go with this
  17. 17. THE EXACT SAME KEYWORD / TERM MAY HAVE TOTALLY DIFFERENT MEANING AT DIFFERENT TIMES TOO
  18. 18. TEMPORAL Presentation at ESSIR2017 on work by Radinsky, K., Svore, K.M., Dumais, S.T., Shokouhi, M., Teevan, J., Bocharov, A. and Horvitz, E., 2013. Behavioral dynamics on the web: Learning, modeling, and prediction. ACM Transactions on Information Systems (TOIS), 31(3), p.16.
  19. 19. Query  Intent  Shifts Few weeks prior - ‘When’? Few days prior – ‘What to do’? During – ‘Meaning of Easter’
  20. 20. Source: McKinsey - http://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-consumer-decision-journey Linear Pre-­‐2009 CUSTOMER INFORMATION SEEKING BEHAVIOUR HAS CHANGED The  customer  journey  is   no  longer  linear  but   cyclical 2011
  21. 21. Fragmentation via Mobile The Zero Moment of Truth Is Now Many Micro- Moments I  want  to   know I  want  to   go I  want  to   buy MICRO MOMENT ==“an intent- driven moment of decision making” Google (2015)
  22. 22. 900 ‘Micro-Moments’ To Buy A Car 900 Chances to Engage Source: https://www.thinkwithgoogle.com/articles/consumer-car-buying-process-reveals-auto-marketing- opportunities.html 900 Chances to Engage We (consumers) are ALL researchers
  23. 23. Source: https://www.thinkwithgoogle.com/consumer-insights/consumer-mobile-search-buying-behavior/ We (consumers) are ALL researchers
  24. 24. Source: https://www.thinkwithgoogle.com/consumer-insights/consumer-mobile-search-buying-behavior/ It Seems We Now Want To Even Research The Irrelevant
  25. 25. Know Your Audience – GA Demographics PERSONAL Gender Age Groups Facebook Age Groups by Interest – Data Mining IMAGE SOURCE - http://blog.stephenwolfram.com/2013/04/data- science-of-the-facebook-world/
  26. 26. But  remember…  People  are  also  using  ‘on  the  go’   moments  to  research  small  things  towards  bigger  goals Almost 98% of visits are people window shopping Average ecommerce conversion +/- 2% Research Research Research Research Consider Consider Consider Consider Consider Check reviews Compare prices Buy
  27. 27. Source: http://www.cisco.com/c/en/us/solutions/collateral/service- provider/visual-networking-index-vni/complete-white-paper-c11- 481360.html Searches on mobile now exceed desktop MOBILE
  28. 28. Short Attention Span Especially On Mobile Humans average attention span has dropped to 9 seconds …Less than a goldfish Expectations  are   high.    Patience  is  low
  29. 29. Source: https://www.seroundtable.com/google-mobile-first-index-2017- 23663.html The ‘Mobile First Index’ is on its way. IT WILL COME SOON MOBILE
  30. 30. ’Being There’ Give Little Guys Advantage You can beat big brands with micro-moments Good News TEMPORAL
  31. 31. MOBILE CACHE COMPRESS CDN AMP MINIFY PRE-­FETCH PRE-­RENDER
  32. 32. Source: https://www.wired.com/2017/01/half-web-now-encrypted-makes- everyone-safer/ SAFER WEB
  33. 33. GTMetrix & Pingdom Speed Test Combine Page Speed Tests MOBILE
  34. 34. Enable  gzip compression  via  caching   plugins,  .htaccess or  via   compression  plugins MOBILE NO INTERSTITIALS ON MOBILE THANK YOU
  35. 35. Source: https://www.designbombs.com/top- wordpress-caching-plugins-compared/ CACHE PLUGINS COMPARED Comparison Research Undertaken Recently by Design Bombs MOBILE
  36. 36. Age Groups Top devices % using mobile Assisted conversions Length of time to convert No. of visits to conversion Add to basket on mobile MOBILE & LOCAL
  37. 37. REACH TEMPORAL Exploration - Ideas Not  sure  what  they  need  or   want  yet Answer questions Suggest   options Step  toward ‘bigger goals’ Low  conversion   probability Offer   ideas Decision Making Closer  to  a  conversion Refining choices Looking  for  ‘REVIEWS’ Suggest   comparisons,   calculators,   estimatorsI  want  to   know  the   ‘BEST’   moments They  want  to   ‘COMPARE’
  38. 38. Build A Desktop & Mobile Moments Map Understand Predictable Mobile Behaviour of Target Audience Be Ready When Your Audience Is On The Go Vets  in   (location) Best  x  in   (location) Where  to   buy? Dogs  &  cats   together Dog   friendly   bars  in   (location) Dog   friendly   restaurants   in   (location) “in [location]” “where?” “near” PROXIMITY PREVAILS “near me” TEMPORAL & LOCAL & MOBILE EVEN  GENERIC   TERMS   POTENTIALLY INTENT TO URL
  39. 39. TEMPORAL Google Correlate https://www.google.com/trends/correlate/ TIME  IS   OF  THE   ESSENCE WHAT   CORRELATES?
  40. 40. TEMPORAL Google Correlate https://www.google.com/trends/correlate/ TIME  IS   OF  THE   ESSENCE WHAT   CORRELATES?
  41. 41. Query Refinement – Which Way Next?? Query Refinement is a special type of ‘related search’ ‘Most next searched cluster lists’ THEY ARE NOT JUST RANDOMLY GENERATED PROBABILITY OF ’NEXT STEPS’ Remember   Semantics  and   Relationships http://delivery.acm.org/10.1145/1780000/1772776/p841-sadikov.pdf (Sadikov et al, 2010) CLUSTERING QUERY REFINEMENTS BY USER INTENT ”SHARON OSBOURNE’S DOG” PERSONAL
  42. 42. MARKOV CHAINS QUERIES DEPENDENT ON A STEP FROM THE PREVIOUS QUERY https://en.wikipedia.org/wiki/Mark ov_chain
  43. 43. LIKE RPGs CHOICES MADE DETERMINE THE NEXT CHOICES GIVEN
  44. 44. Find The Target Audience Questions – Answer The Public What Are People Asking? • Why? • How? • Which? • Where? • What? • When? Location Temporal
  45. 45. Keyword Trigger Intent & Action Cues Semantic Intent How  To  ==   Instructional Video,  images  &   instructions What  Is  /  What  Are  ==   Text  based  &  fact  filled   data  sheets Unstructured  data  like   lists  and  tabular  data on  known  ‘entities’
  46. 46. ’Be Helpful’ To Researchers SOOVLE
  47. 47. ’Be Helpful’ To Researchers SOOVLE ‘HOW TO BAKE’ SUGGESTIONS
  48. 48. Remove Ontological / Situational / Contextual Intent Fuzziness Use  Strong  Topical  Hub  Themes  And  ‘Help’  ’Hub’  ‘Hero’  ‘Local’  Content   Frameworks  too HELP HUB HERO LOCAL MAPS NAPs DIRECTIONS BRANCH NEWS BRAND TRANSACT SHOP BUY SALE CART SUPPORT KNOWLEDGE GUIDE QUESTIONS SERVICE THE BUZZ NEWS BLOGS OPINIONS ARTICLES
  49. 49. PRO TIP: Unordered & ordered lists and tables are semi-structured data Huge opportunities to get visibility in featured snippets
  50. 50. ’Be Helpful’ With Lists FEATURED SNIPPET WINS ‘HOW TO BAKE A CAKE’
  51. 51. ’Be Helpful’ With Lists FEATURED SNIPPET WINS ‘WOMEN’S DRESS SIZES’
  52. 52. Write For A 9 Year Old Common conversational text is easier for search engines To disambiguate and return in response to common conversational and voice queries. Use common words in short sentences together Users have time to read at most 28% of the words during an average visit; 20% is more likely
  53. 53. COLLABORATE TEMPORAL & PERSONAL
  54. 54. There  are  spoils  to   be  had TEMPORAL https://twitter.com/methode/status/840805504276414464
  55. 55. People do 323% better following directions with illustration than without illustrations In Search – ‘KEYWORD’ + Inspiration often == Images
  56. 56. VIDEO People spend on average 2.6x more time on pages with video than without TEMPORAL
  57. 57. TEMPORAL• Keep sentences short • Understand ‘actions’ • Put answers at the beginning to sentences and paragraphs • Most results from structured data
  58. 58. RIGHT HELP RIGHT AUDIENCE RIGHT TIME RIGHT FORMAT RIGHT PLACE RIGHT DEVICE BE   THERE ‘CONTEXTUAL SEARCH’
  59. 59. THANK YOU TWITTER - @dawnieando GOOGLE+ -+DawnAnderson888 LINKEDIN – msdawnanderson www.move-it-marketing.co.uk
  60. 60. SOURCES https://www.designbombs.com/top-wordpress-caching-plugins-compared/. 2017. 6 Best WordPress Caching Plugins Compared. [ONLINE] Available at: https://www.designbombs.com. [Accessed 18 September 2017]. Radinsky, K., Svore, K.M., Dumais, S.T., Shokouhi, M., Teevan, J., Bocharov, A. and Horvitz, E., 2013. Behavioral dynamics on the web: Learning, modeling, and prediction. ACM Transactions on Information Systems (TOIS), 31(3), p.16 Sadikov, E., Madhavan, J., Wang, L. and Halevy, A., 2010, April. Clustering query refinements by user intent. In Proceedings of the 19th international conference on World wide web (pp. 841-850). ACM Sadikov, E., Madhavan, J. and Halevy, A., Google Inc., 2013. Clustering query refinements by inferred user intent. U.S. Patent 8,423,538. https://www.wired.com/2017/01/half-web-now-encrypted-makes-everyone-safer/. 2017. Half The Web Is Now Encrypted. [ONLINE] Available at: https://www.wired.com. [Accessed 18 September 2017].

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