Mapping the Brand Graph: a study of the O2 audience on Twitter [UPDATED]

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The objective of the O2 Brand Graph pilot was to mine social media data in a way that would allow us to connect it to audience studies.

This presentation is an initial exploration of how we can use social media to augment a segmentation model with real-time data. Instead of tracking contents by keywords (“horizontal” tracking – any content mentioning specific keywords and keyphrases), we looked into mining social media contents and behaviours by audiences (“vertical” tracking – any content generated from a set of sources, regardless of the features of the content).

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  • We wanted to identify subcommunities within the O2 audience on Twitter.BecauseTwitter is an interest graph, we assumed that following someone implied sharing the interest of the followed user.Therefore a subcommunity would be identified by a high concentration of horizontal connections within the graph.
  • To get this information we had to map:58k users following O2;Who was following each of the 58k users;Who else in the graph any of the users was following other than O2 or the primary O2 follower.For the sake of this exercise we looked at a sample of 1000 users.We selected the top users with less than 2000 followers. We then mapped their connection to O2.We then mapped who was following them.Finally we mapped how the primary and secondary followers were connected to each other user in the graph.We ended up plotting a graph of 1 million nodes, 1 million primary connections and 574278 horizontal connections within the graph.
  • The blue links represent how primary and secondary followers are connected to each other within the graph.
  • By looking at the density of the connections we could then identify hubs within the audience, points of high concentration of similar interests.
  • Once we knew where the hubs were we than isolated then and looked into the clusters.We identifies 10 users with the highest number of connections to other primary and secondary followers within the O2 social graph.
  • We then went on profiling the clusters.
  • It is possible to use social media to study audiences > not just opinions or topics.With the O2 Brand Graph - map and understand the audience of a brand online - mining social media in a different way / we could do a similar exercise for axe, dove 
  • Low Klout scores (1-10) – it’s about my passions & interestsHigh Klout scores (51-60) – it’s professionalSame = light blueMusicLondon (though higher Klout is a bit more London-centric), also “UK”Fan, love (although there’s a lot more love from low-Klout users, who tell you more about themselves + their interests)Red = difference: the high Klout guys talk about their professions a lot more.“Marketing” is in bothBut “blogger”, “journalist”, “editor” and “writer” are high Klout onlyAs our “social media”, “digital”, “director”Green = difference: the low Klout followers tell you about themselvesMarriedFamilyChildrenStudentIt’s also low-Klout people who talk about “technology” and “mobile” as interests – high Klout talk “digital” (from job titles).
  • Love……my … family / friends / job / music / life / hubby / kids / dog…to … travel / have fun / laugh / talk…music … and fashion/ films / photography / music…the … arsenal / toon[love football lower down the list]Like “Love”, “fan” is one of the most common words in user profiles (active Twitter users).@O2 followers are “fans of”……the Wanted (boyband)…the Arsenal…the WWE…The Only Way Is Essex…the great outdoors…the F1…MUSIC (lots of music fans)…all things funny / digital / apple / tech / motorsport / Japanese….F1…Football…LFC / Liverpool FC…Cricket…Arsenal….Totenham Hotspur
  • Mapping the Brand Graph: a study of the O2 audience on Twitter [UPDATED]

    1. 1. Digital  Shoreditch  2012  #ds12  Brand  Day,  London,  30th  May,  2012     THE  BRAND  GRAPH   Dynamic  audience  mapping  through  social  media  data   Francesco  D’Orazio,  Chief  Innova9on  Officer  &  Head  of  FACE  Labs,  FACE,  @abc3d   Jess  Owens,  Senior  Analyst,  FACE,  @hautepop  
    2. 2. Brands  have  been  amassing  online  fan  bases  for  10  years  now  
    3. 3. Brands  as  Egomaniacs  
    4. 4. We  have  been  asking  the  wrong  ques9on  
    5. 5. “We  talk  of  the  rela9onships  consumers  have  with  our  brands  as  if  they  were  primary  but  the  data  points  to  things  being  otherwise.  Consumers’  most  valuable  rela9onships  are  not  with  brands  but  with  other  consumers”                          Mark  Earls  
    6. 6. As  a  result,  we  don’t  really  know  who  we  are  talking  to…  
    7. 7. Social  data  allow  us  to  learn  about  the  rela9onships  and  interac9ons  between  consumers  and  how  brands  can  fit  into  that  equa9on     Cosmic 140 © Information Architects, Inc. 2010
    8. 8. Social  data  allows  us  to  see  how  interac9ons  play  out  at  individual  level  (microscope)  
    9. 9. Social  data  allow  us  to  see  how  interac9ons  play  out  at  network  level  (the  macroscope)  
    10. 10. The  telescope   helped  us   understand  the   infinitely  great.     The  microscope   helped  understand   the  infinitely  small.     Today  we  are   confronted  with  another  infinite:  the   infinitely  complex.              
    11. 11. Social  science.    Computa9onal   Social  data.    Social  Science   Computa9on.              
    12. 12. © Alexandre Farto aka Vhils 2010 But we are only just scratching ! the surface > CONTENT
    13. 13. Currently  we  look  at  social  media     like  a  butcher  looks  at  a  carcass  Cu[ng  by  keywords   Aggrega9ng   by  channels   Weigh9ng  by   influence  
    14. 14. © Alexandre Farto aka Vhils 2010 Missing out on CONTEXT > stories, social spaces, physical spaces… BEHAVIOUR > activities, purchase, consumption…
    15. 15. The  code  behind  a  tweet  
    16. 16. Social  Media  vs  Social  Data   Social media is the set of applications and platforms allowing Social data is the people to participate in collective information online social activities produced by millions of people as they actively participate in online social activities. 17
    17. 17. Cra]ing  quality  insights  requires    custom  data,  algorithms  and  analysis            Off                  the            s  h  e  l  f      approaches    a    r    e                                 not                        good        enough  
    18. 18. Dimensions  of  social  data   19© Altimeter Group
    19. 19. Beyond  Keywords…   By  keywords   By  audience   Share  of  Voice   Share  of  Mind  
    20. 20. What  we  tracked  58,339 public profilesall @O2 followers3,120,371 tweets (nov 2011)122,220 tweets/day (avg)
    21. 21. What  we  mined   Profiles   Social  Graph   Interest  Graph  
    22. 22. What  we  found  Demographics   Informa9on  Flows  Interests   Influence  Dynamics  Behaviours   A[tudes  towards   topics,  brands  etc.  
    23. 23. Introducing  the  Brand  Graph   +   Social  Graph   Interest  Graph   “…the  correla9on  of  the  network  of  people  who  are  connected  to   the  brand  (Social  Graph)  and  the  network  of  interests,  topics  and   ac9vi9es  of  that  group  of  people  (Interest  Graph).”              
    24. 24. @O2  followers  (magenta  nodes)  Followers  of  the  @O2  followers  (magenta  nodes)  Mutual  connec9ons  within  the  graph  (blue  links)  
    25. 25. A  sample  of  the  top  1000  @O2  followers  with  less  than  2000  followers  generated  1  million  nodes  (primary  and  secondary  followers)  and  600K  horizontal  connec9ons  within  the  graph  
    26. 26. Mutual  connec9ons  within  the  graph  iden9fy  shared  interests,  and  therefore  clusters  
    27. 27. One  cluster  in  detail  
    28. 28. A  dynamic  map  of  the  brand’s  audience  
    29. 29. What  defines  them?  
    30. 30. Profile-­‐mining  by  Influence  level   Influen9al   Casual  
    31. 31. When  did  they  join  Twiger?  #signups   6,000   5,000   4,000   3,000   2,000   1,000   0   Jul  06   Jan  09   Nov  11  
    32. 32. How  o]en  do  they  tweet?  #tweets   10,000   9,000   8,000   7,000   6,000   5,000   4,000   3,000   2,000   1,000   0   0   10   20   30   40   50   60   70   80   90   100   percen9les  
    33. 33. Where  are  they  twee9ng  from?   No[ngham     21,415   Edinburgh     22,833   Belfast     24,169   Leeds     26,702   Glasgow     35,150   Series1   Liverpool     40,161   Birmingham     41,265  Greater  London     79,643   Manchester     84,055   London     342,630  
    34. 34. When  do  they  tweet?   1   2   3   4   5   x   6   7   8   x   9   10   11   12   13   14   15   16   x   17   18   19   20   x   21   22   23   24   0   50000   100000   150000   200000   250000  
    35. 35. 400   Following  users  350   1,400   1,200   1,000  300   800   600   400  250   200   0  200   0   20   40   60   80   100  150  100   50   0   1   10   100   1,000   10,000   100,000   #followers  
    36. 36. #  Followers  users   1200   700.00   600.00   1000   500.00   400.00   300.00   800   200.00   100.00   .00   600   0   20   40   60   80   100   400   200   0   1   10   100   1,000   10,000   100,000   1,000,000   followers   10,000,000  
    37. 37. How  influen9al  are  they?  users   2500   2000   1500   1000   500   0   0   10   20   30   40   50   60   70   Klout  score   80  
    38. 38. What  do  they  talk  about?  
    39. 39. Most  shared  domains   0   1000   2000   3000   4000   5000   6000   7000   twiger.com   facebook.com   youtube.com   foursquare.com   t.co   twitpic.com   yfrog.com   twitlonger.com   bbc.co.uk   instagr.am  twigascope.com   Social  networks   amazon.com   Twiger  add-­‐ons   getglue.com   amazon.co.uk   News  sites   guardian.co.uk   Shopping   lockerz.com   paper.li   Social  aggregators   digitalspy.co.uk   Other  /  compe99ons   reuters.com   skyvegas.com  
    40. 40. Top  50  domains  by  category  Social  media   Social  media   News   Social   Shopping   Other  channels   tools   aggregators  Twiger  (#1)   T.co  (#5)   BBC  (#9)   Lockerz  (#16)   Amazon.com   SkyVegas  compe99on   (#12)   (#20)  Facebook  (#2)   Twitpic  (#6)   Guardian  (#15)   Paper.li  (#17)   Amazon.co.uk   Comps  at  PickMeUp   (#14)   Magazine  (#25)  YouTube  (#3)   Yfrog  (#7)   DigitalSpy  (#18)   Raptr  (#21)   WooCompare   UK  Movember  (#32)   (#30)  FourSquare   Twitlonger  (#8)   Reuters  (#19)   Redgage  (#22)   XmasElves.co.uk   Nuvear9cles  content  (#4)   (#35)   farm  (#32)  Instagram   Twigascope  (#11)   The  Sun  (#23)   Clct.me  (#36)   iTunes  (#39)   Forum.ShopTo.net  (#10)   compe99on  (#37)  GetGlue  (#13)   Tweetdeck  (#24)   TheNextWeb   PhotoZZ  (#46)   Laura  Ashley   SocialAble  social  media   (#28)   (#41)   agency  (#39)  Flickr  (#28)   Ow.ly  (#26)   Engadget  (#29)   MailLife.co.uk   Look  magazine  comps   (#48)   (#43)  G+  (#40)   Bit.ly  (#34)   Mashable  (#33)   Dawsons.co.uk   Jeff  Bullas  social  media   (#49)   guru  (#45)   TweetAdder   Recombu  (#47)   (#42)   Twibbon  (#44)   Telegraph  (#50)  
    41. 41. Top  adverts  discussed  
    42. 42. @O2  followers  seem  largely  indifferent  to  the  UK  Top  10  box  office  films   Johnny  English  Reborn   69   The  Lion  King   743   A  mixture  of  movie   Real  Steel   27   chager  and  dvd  release,   The  Three  Musketeers   26   including  RT  compe99on   at  Play.com   Tinker  Tailor  Soldier  Spy   105   Footloose   45   Dolphin  Tale   4   Midnight  in  Paris   26   Dont  be  Afraid  of  the  Dark   20   What theyʼre actually talking about:" Drive   78   Twilight:  Breaking  Dawn   3865   Dr  Who   2352   Tin9n   1374  
    43. 43. How  does  @O2  fit  into  this  landscape?   Mentions of @O2" @O2 follower activity"Total: 3,120,371 contents in a month from @O2ʼs followers"Of this, 7,523 included “@O2” – thatʼs 0.24%!@O2ʼs followerstweet @O2 approx 350 times per day
Overall @O2 sees 1330 mentions/day"
    44. 44. What’s  coming  next?  Dynamic  Segments   Audience   APIs  
    45. 45. THE  BRAND  GRAPH  Dynamic  audience  mapping  through  social  media  data  Francesco  D’Orazio,  CIO,  FACE,  @abc3d  Jess  Owens,  Senior  Analyst,  FACE,  @hautepop  

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