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131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare
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131217 the recommender revolution : recent data for direct marketing institute ghent evening.slideshare

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Plenty of recent data from here, there and everywhere on the recommender revolution and how the Holaba model measures the recommendation power of both brands and consumers by asking The One (Simple) …

Plenty of recent data from here, there and everywhere on the recommender revolution and how the Holaba model measures the recommendation power of both brands and consumers by asking The One (Simple) Question.

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  • Hi Jo, ik heb er van genoten en ik van The Audience hetzelfde.
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  • Hey Jan,

    Ik ben vandaag nog eens door je slides gefietst. Een pak info dat mij (nog) wijzer maakt. Het aanradersfenomeen is geen hype maar vormt wel een belangrijk concept in de actuele marketingstrategie. Nogmaals dank voor je learning points en boeiende kijk tijdens je presentatie gisterenavond voor het DM Institute in de Communicatieloft.. Tot binnenkort, Jo
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  • 1. ______________________________________________________________________________  
  • 2. 10 point of views. You agree or not? ______________________________________________________________________________   1.  Distrust  of  adver0sing  is  the  most  important  reason  why  consumers  rely  more   and  more  on  recommenda0ons.     2.  Recommenders  have  a  narcissis0c  personality  disorder.  Helping  others  is  the   least  of  their  business.   3.  Brands  should  invest  more  in  their  product  and  service  delivery  instead  of   raising  their  marke0ng  budget.   4.  When  brands  measure  sa0sfac0on  and  re-­‐purchase  inten0on  among  your  own   customers,  you  know  everything  you  have  to  know.     5.  Don’t  waste  your  0me  neutralizing  the  detractors  of  a  brand  and  try  to  wake   up  the  passives.  Focus  on  the  promoters.   6.  Recommenders  win  the  baJle  on  facts/features  based  argumenta0on.  Never   on  emo0ons.  It’s  a  “ Test-­‐Aankoop”  world  we  live  in.   7.  Unless  you  day-­‐aTer-­‐day  search  for  recommenders,  make  them  happy  end   influence  them  you  will  never  succeed  in  the  new  marketplace.   8.  RFM  will  remain  thé  parameter  for  segmenta0on  of  your  marke0ng  ac0vi0es.   9.  Media  that  have  the  highest  %  of  brand  recommenders  should  be  the  prime   medium  in  which  brands  have  to  buy  space/0me.   10.  Social  media  like  TwiJer,  Facebook,  Instagram,  Pinterest  are  the  best   performing  media  for  recommenders  to  influence  their  followers.  
  • 3. I am a recommender. One of the 15% recommenders worldwide. ______________________________________________________________________________  
  • 4. Recently, on my Facebook-page I recommended Europe … while I made clear I didn’t recommend Shanghai. ______________________________________________________________________________  
  • 5. I even was an active multimedia recommender. I took pictures and screenshots. That’s probably only 1% of all citizens doing that. ______________________________________________________________________________  
  • 6. I published my recommendations on Buzzfeed to make my case stronger and get more reach. (I did it once☺) ______________________________________________________________________________  
  • 7. There we go! That’s how it looked like when it was published. ______________________________________________________________________________  
  • 8. I could follow closely the (meager) results of my recommendations. ______________________________________________________________________________  
  • 9. I am more successful to attract readers for my recommendations on Tripadvisor though. ______________________________________________________________________________  
  • 10. Are all of these other snippets also recommendations? Like mine? Not really. But some people think they are… ______________________________________________________________________________   And nobody mentions TV?
  • 11. Q&A’s in the next 15 slides. ______________________________________________________________________________   Why do brands and media get more and more interested in recommenders? Why do some consumers write recommendations? Why do all the others read their recommendations? Why are they willing to be influenced?
  • 12. Recommenders directly influence 20-50% of all purchase decisions. ______________________________________________________________________________  
  • 13. The motives of recommenders? Zocalo Research ______________________________________________________________________________  
  • 14. The  latest  data  on  influencing  power  from  …  China  (Epsilon  2013).       ______________________________________________________________________________   “Please indicate which, if any, of the following influence your decisions when deciding whether or not to purchase or sign up for a product or a service”
  • 15. “Friends & family” are the trusted source. Where is advertising …? ______________________________________________________________________________  
  • 16. Zocalo’s research ______________________________________________________________________________  
  • 17. “Friends & family” - recommendations lead. Social Media? ______________________________________________________________________________  
  • 18. Social media? ______________________________________________________________________________   https://www.custora.com/pulse/channel
  • 19. Reviews do indeed the job! ______________________________________________________________________________  
  • 20. Yelp? Trusted? (Zocalo) ______________________________________________________________________________  
  • 21. Trust? Watch out. Some reviews can be fake. Consumers are fully aware. ______________________________________________________________________________  
  • 22. The big issue: “Influencing power” and “action generating power” of paid, owned and earned media (Nielsen 2013) ______________________________________________________________________________   Earned Paid Owned
  • 23. Hence: brands permanently remix Paid, Owned and Earned Media based on their respective ROI (Zenith 2013) ______________________________________________________________________________   Earned media have most influence, are more trusted and generate more action Paid media generate more touch points, hence they are strong in building awareness. Their “Opportunity To Be Seen” is bigger. Paid media cost more than earned and owned media. Brands plan to invest less in paid media in the coming years.
  • 24. ______________________________________________________________________________   Prof.  Dr.  Gerard  Tellis   1980-­‐1990:Adver.sing  +10%  =  +2.2%  marketshare   2008:Adver.sing  +20%  =  +2.2%  marketshare   “  ….  the  authors  conduct  a  meta-­‐analysis  of  751  short-­‐term  and  402  long-­‐term  direct-­‐to-­‐ consumer  brand  adverCsing  elasCciCes  esCmated  in  56   studies  published  between  1960  and  2008.  the  study  finds  several  new   empirical  generalizaCons  about  adverCsing  elasCcity.  the  most  important   are  as  follows:  the  average  short-­‐term  adverCsing  elasCcity  is  .12,  which   is  substanCally  lower  than  the  prior  meta-­‐analyCc  mean  of  .22;  there  has  been  a  decline  in   the  adverCsing  elasCcity  over  Cme.”   Gerard  Tellis,  PhD  Michigan,  is  Professor  of  Marke.ng,  Management,  and  Organiza.on,  Neely  Chair  of  American  Enterprise,  and  Director  of   the  Center  for  Global  Innova.on,  at  the  USC  Marshall  School  of  Business.  He  is  Dis.nguished  Visitor  of  Marke.ng  Research,  Erasmus   University,  RoVerdam  and  has  been  Visi.ng  Chair  of  Marke.ng,  Strategy,  and  Innova.on  at  the  Judge  Business  School,  Cambridge  University,   UK.  Tellis  specializes  in  the  areas  of  innova.on,  adver.sing,  global  strategy,  market  entry,  new  product  growth,  promo.on,  and  pricing.  
  • 25. One of many brands’ issues: “The ROI of advertising is not what it was before”. ______________________________________________________________________________   YOY-growth +7% +3,8% +3,8% +4,6% +5,2%
  • 26. And when people talk advertising. They still talk TV. Let’s have a look. ______________________________________________________________________________  
  • 27. 18-24 Year Old in the US watching less TV. Doing what instead? ______________________________________________________________________________  
  • 28. They generate content. “User generated content”. Or they watch that UGC. (Recommendations are part of it). ______________________________________________________________________________  
  • 29. User generated content grows. Mainly on mobile. But of course, not all UGC are recommendations. ______________________________________________________________________________  
  • 30. ______________________________________________________________________________  
  • 31. ______________________________________________________________________________  
  • 32. “50% of the internet traffic today is video. Looks like 80% to 90% of internet traffic will be video in the next few years. Video for us is a place that consumers really like.” (Tim Armstrong AOL) ______________________________________________________________________________   “Google's biggest revenue driver in the future. Mark Suster of Upfront Ventures, which invests in a large YouTube content partner, suggested to the Wall Street Journal that the video platform could soon be generating $20bn in revenue.”
  • 33. VRT & Video & Instagram. Vers nieuws. Fijngesneden (1) ______________________________________________________________________________  
  • 34. VRT & Video & Instagram. Vers nieuws. Fijngesneden (2) ______________________________________________________________________________  
  • 35. VRT & Video & Instagram. Vers nieuws. Fijngesneden (3) ______________________________________________________________________________  
  • 36. Next to TV there are also media like Twitter. Let’s have a look here too… ______________________________________________________________________________  
  • 37. Tweeted online recommendations are “massive” broadcast + engagement. ______________________________________________________________________________  
  • 38. Only 3.6% of tweets are about brands. Majority of recommendations are offline! ______________________________________________________________________________  
  • 39. Offline! Still the method of recommendation. ______________________________________________________________________________  
  • 40. Males complain. Females talk. ______________________________________________________________________________  
  • 41. Industry sectors in which brands are mentioned on Twitter ______________________________________________________________________________  
  • 42. The social consumer. Buying a lot based on recommendations from other social consumers ______________________________________________________________________________  
  • 43. No comment. ______________________________________________________________________________  
  • 44. Some buy a lot online and tell it to a lot of people online too. ______________________________________________________________________________  
  • 45. What do we suggest you to do @ this ever changing marketplace? ______________________________________________________________________________   1.  Measure  your  own  recommenda0on  power  –  use  NPS  –  read   Reichheld’s  books  –  or  hire  us  for  a  while.   2.  Measure  your  compe0tors’  recommenda0on  –  use  “public,  real  0me,   con0nuous”  benchmarkers  like  Holaba.     3.  Never  stop  the  search  for  recommenders.  Also  in  your  own  database.   They  are  so  important.     4.  Iden0fy  recommenders  (Socio  –  Demo).  Don’t  rely  on  samples.   5.  Gather  insights  (why  do  they  say  what  they  say)  of  real  recommenders.   6.  Select  the  media  to  influence  these  influencers   7.  Don’t  push  too  much.  Let  them  find  you.   8.  Be  nice  and  trustworthy.    Be  human.  They  are  human  media  too.   9.  Do  all  the  above  points  permanently,  wherever  you  can.   10.  Add  the  recommenda0on  data  to  data-­‐streams  about  traffic,   conversion,  sales,  sa0sfac0on,  re-­‐purchase  intent,  rfm  models  ….   11.   Adjust  your  tac0cs  everyday.  Never  give  up.  Measure  everything.     12.  DIY!    
  • 46. Moments of truth. All 3 are crucial. ______________________________________________________________________________   1st  moment  of  truth   2nd    moment  of  truth   3rd    moment  of  truth   Buying     Consuming   Evalua.ng   Consumer   (Neutral)   Promoter><Detractor   85%  are   being   influenced   Selec.ng   shop   Selec.ng   brand   15%  are   “influencing”   others  
  • 47. Why is “recommendation measurement” so crucial? ______________________________________________________________________________   14 7
  • 48. Benchmarking. ______________________________________________________________________________  
  • 49. From  RFM  to  RRFM  to  decide  about  what  to  invest  where.   ______________________________________________________________________________   Recency,  frequency,  monetary  value  (RFM)  of  clients  are   decisive  for  investment  in  marke.ng  communica.on.   Therefore  lots  of  money  spent  (wasted)  in  this  group  of   heavy  and  recent  buyers     Light  and  non  frequent  buyers  are  oden  “neglected”     RFM  -­‐  axis  
  • 50. The recommendation power of clients becomes the decisive tool to decide on marcom-investments ______________________________________________________________________________   RFM  -­‐  axis   +RFM  &  -­‐  REC   +RFM  &  +  REC   Does  not  mean  they  all  give  posiCve   recommendaCons   Recommenda0on  axis   Frequency  and  intensity  of   recommenda2on.   -­‐  RFM  &  +  REC   -­‐  RFM  &  -­‐  REC  
  • 51. ______________________________________________________________________________  
  • 52. “If you know the enemy and know yourself, you need not fear the result of a hundred battles.” ______________________________________________________________________________   “If  you  know  the  enemy  and  know  yourself,  you  need   not  fear  the  result  of  a  hundred  baWles.  If  you  know   yourself  but  not  the  enemy,  for  every  victory  gained  you   will  also  suffer  a  defeat.  If  you  know  neither  the  enemy   nor  yourself,  you  will  succumb  in  every  baWle”   Sun  Tzu,  The  Art  of  War,   hVp://www.youtube.com/watch?v=erZ2YidTZp4.  Minute  06:45   Marke2ng  is  War.  It  s2ll  is.  
  • 53. The Holaba data. Produced by our benchmarking-tool based on the “Net Promoter System”. ______________________________________________________________________________    What  is  NPS*?   “Net  Promoter  System”  was  launched  in  2003  by  Mr.  Reichheld  of   Bain.   It  not  only  measures  the  recommendaCon  power  of  brands  but  also  of  consumers.     Consumers  can  either  be  promoters,  detractors  or  just  fence  siWers.  The  One  Ques2on  Holaba  asks   always  and  everywhere.   How  likely  is  it  that  you  will  recommend  this  brand?   Very likely The  calcula2on  of  the   recommenda2on  score  based  on   1000’s  of  consumer  scores.   10   9   Not likely 8   7   SUM  OF  9  &  10  SCORES   58,1%  of  the  scores  are  9  or  10   5   4   3   2   1   0                  SUM  OF  ALL  OF  ALL  0  TO  6  SCORES   38,1%  of  all  scores  are  in  between  0  and  6  included.   -­‐ 58,1              38,1  =      +20    NPS-­‐score  &  Holaba-­‐score  are   indicators  of  recommenda2on   power.     In  this  case  the  score  of  the  brand   is  +20   Benchmarking  to  find  out  where  all   major    compe2tors  are  on  this   scale.   6   -­‐100   +100   A  NPS-­‐score  is  a  number  in  between  -­‐100  and  +100   * Net Promoter, NPS, and Net Promoter Score are trademarks of Satmetrix Systems, Inc., Bain & Company, and Fred Reichheld.
  • 54. The  flow  of    the  ques0ons  that  produce  the  data.  Easy  and   fast  for  consumers.  S0ll  delivering  plenty  of  informa0on.     ______________________________________________________________________________   1. How likely is it that you would recommend this brand? 0   1   2   3   4   5   6   7   8   9   10   Scores  in  between   0  and  10.   2. What is your experience with this brand? None   Have  it   Had  it   Will  have  it   Could  have  it   5  experience  levels   3. Why do you give the score you give? This  is  what  I   like:  “….”   Easy  for  consumers   to  give  posi2ve   and  nega2ve  comments   This  is  what  I  don’t   like:  “…”   This  is  what  they  should   improve:  “…”  
  • 55. Day after day Holaba-users can share opinions. That way they gradually build their brand profile. ______________________________________________________________________________   Scores Scores 9-­‐10   9-­‐10   Scores    9  -­‐10   Scores 9-­‐10   Scores 9-­‐10   Scores 9-­‐10   Scores 9-­‐10   The brands these boys also recommend Scores 9-­‐10   Scores 9-­‐10   Scores 9-­‐10   Scores   9-­‐10   Scores 9-­‐10   Brand Profile of boys 18-25 yrs old, who all give a very high 9 & 10 recommendation score to brand X. Scores     0-­‐6   Scores     0-­‐6   Scores     Scores     Scores     0-­‐6   Scores     0-­‐6   0-­‐6   Scores     0-­‐6   0-­‐6   Scores     Scores     0-­‐6   Scores     0-­‐6   0-­‐6   Scores     Scores     0-­‐6   0-­‐6   Scores     0-­‐6   The brands they don’t recommend at all.
  • 56. Holaba-­‐data  are  data  our  registered  users  share  about  the  brands   they  recommend.   ______________________________________________________________________________   1.  We  know  the  brands  they  do  &  don’t  recommend       Our  users  build  profiles   brand  aYer  brand.        -­‐  all  users  build  their  personal  brand  profile.     2.  We  know  (since  they  tell  us)  their  status  with  the  brands  they  review          -­‐  actual  user        -­‐  past  user        -­‐  future/potenCal  user   3.  ATer  a  while  we  also  know  who  is  a  recommender     We  learn  who  the     recommenders  are.    or  not.                        -­‐  Why  is  that  important?     •  •  The  more  recommenders  we  iden.fy,  the  higher  the  return  on  marke.ng  investment  will   be:  only  then  brands  can  start  influencing  these  influencers  first.   Someone  who  mainly  gives  6_7_8  scores  is  probably  not  a  recommender,  but  a  fence   siVer.   °  And  of  course  we  know:   1.  2.  3.  4.  Gender  &  Age   Loca.on   Educa.on  (income?)   Network     1.  2.  3.  Size  of  their  network     Degree  of  interac.vity  –  Klout  score   SNS-­‐member  ship   5.  Phone  number  &  Email  address  (physical  address  if  needed)    
  • 57. Brands  indeed  win  more  baJles  when  they  monitor  their  own   recommenda0on  score  …  and  compare  it  to  their  compe0tor’s.   ______________________________________________________________________________   Holaba  is  the  ideal  tool  for  companies  …   –  Who  already  monitor  their  own  recommenda.on  power.     –  Who  want  to  partly  base  their  strategy  and  tac.cs  on  the  score  of   their  compe.tors.   Are  you  in  a  good   posi2on  for  the  coming   months?   EXAMPLE   Your  own  research  points  out  that  your  recommenda.on  score  is  +20     Now  you  want  to  know  how  good  or  bad  that  is,  since  an  isolated  score   shows  only  your  part  of  the  story.     Very bad ranking for “You “ 1 2 3 4 Competitor Competitor Competitor Competitor 5 6 7 8 9 Competitor Competitor Competitor Competitor Competitor 10.You (+20) Excellent ranking for “You” 1.You (+20) 2 Competitor 3 Competitor 4 Competitor 5 6 7 8 9 Competitor Competitor Competitor Competitor Competitor 10 Competitor More or less ok ranking for “You” 1 2 3 4 Competitor Competitor Competitor Competitor 2.You (+20) 6 7 8 9 Competitor Competitor Competitor Competitor 10 Competitor  When  you  know  the  score  your   clients  give,  you  also  need  to  know   the  ranking  of  you  and  your   compe2tors.     A  high  score  and  be^er  ranking  is  a   prelude  of  a  growing  market  share.  
  • 58. Two  reasons  why  the  Holaba-­‐data  are  important  for  ‘paid  media’  too.     ______________________________________________________________________________   1.  Media  that  know  which  brands  their  audience  recommend  or  not   recommend  (i.e.  brand  profiling)  have  more  arguments  to  sell  space  &  0me   to  brands.                        Selling  to  readers/viewers/listeners  who  recommend  a  brand  is  different  from  selling  to  non-­‐ recommenders.   2.  The  more  “recommenders”  a  medium  has,    the  higher  its  value:  they  can   sell  access  to  them  at  a  higher  price.                      Selling  to  the  15%  brand  recommenders  gives  brands  more  potenCal   to  generate  free  word  of  mouth.     Recommenders have a vast network and talk about brands. Fence sitters have a smaller network and talk less about brands.
  • 59. For brands that buy time & space in media, the added value of the 15% (or more) recommenders among media-audience is huge. ______________________________________________________________________________   Brands Media “Just  humans”   “Human  media”   “Just  humans”   Extra-contacts generated : brands reach consumers through media, but since not all consumers are created equal, some become “human media” and some not. When  brands  influence  the   influencers  in  your  audience,  they   influence  more  than  just  the   influencers.  
  • 60. Do ask the total market. Do ask “non-clients” too. They too judge, talk and influence. That’s why we ask them. ______________________________________________________________________________   NPS  
  • 61. Those who “want, but cannot yet” are strong recommenders too. In 2006 I recommended even Jaguar. Now …Tesla. ______________________________________________________________________________  
  • 62. 1.Comparison based on turnover/M2 ______________________________________________________________________________  
  • 63. 2. Comparison based on sold items/ticket ______________________________________________________________________________  
  • 64. 3. Comparison based on ticket price. ______________________________________________________________________________  
  • 65. 4. Comparison with objectives in annual budget ______________________________________________________________________________  
  • 66. 5. Comparision with sales in previous year ______________________________________________________________________________  
  • 67. Brandprofiles built on the Holaba-platform ______________________________________________________________________________  
  • 68. 15%  of  all  consumers  are  recommenders.     We  find  them  where  they  are  ac0ve.     ______________________________________________________________________________   “Recruitment”  will  be  most  oTen  indirect.     Through  brands’  ‘owned  media’  and  ‘paid  media’  like  yours.   •  •  •  •  On  your  newssite  (widget  on  home  page,  pop-­‐up  to  survey)     In  your  email  to  clients,  prospects,  former  clients     On  the  package   Off  -­‐  &  online  shop.  (The  best  recruitment  moment  is   immediately  ader  a  purchase)   •  •  During  phone  survey’s  through  your  call  center.   In  an  SMS.   We  target  recommenders.  They  search  and  check  review  sites  more  oTen.     –  They  are  indeed  very  ac.ve  when  they  are  in  a  pre-­‐purchasing  phase  and   search  for  second  opinions.     •  Like  they  use  Tripadvisor  or  any  other  review-­‐site  …  when  they  feel  the   need.   –  We  indeed  want  those  respondents  (15%  of  all  consumers)  who  are  pro-­‐ac.ve   and  eager  to  voice  their  opinion.  Pushing  someone  into  giving  his  opinion   doesn’t  work.  
  • 69. Paid media that support the Holaba-tool & drive traffic to it in their medium will get access to the data. Which data will they get? ______________________________________________________________________________   Which  real  users  give  high/low  scores?  On  which  brands?   Real  consumers  are  beWer  than  any  anonymous  survey   Why  do  they  give  the  scores  they  give?   How  does  your  audience  compare  -­‐on  a  brand  profile  level-­‐    with  other  users  in   our  plaworm?     What  is  the  %  of  recommenders  in  your  audience?   He gave a 10 She  gave  a  6   He gave a 10 He  gave  a  5   He  gave  a  7   He  gave  a  8   She gave a 9 She  gave  a  3   He  gave  a  10   He gave a 9
  • 70. We don’t need a lot of “samples” to have trustworthy results, representative for “the recommenders”. ______________________________________________________________________________   We  concentrate  on  those  consumers  –recommenders-­‐  who  make  the  difference.     They  influence  the  others:  recommenders  influence  directly  20-­‐50%  of  purchases.   We  don’t  survey    a  couple  of  1000  average  consumers  (representa2ve  for  10.000.000     average  consumers)       We  iden2fy  the  “born  recommenders”  (15%  of  all  consumers)  who  are  representa2ve   for  other  recommenders.   What  is  a  valuable  survey  within  a  given  period?   • 1.  Each  survey  should  have  1000-­‐1500  parCcipaCng  registered  users.     • 2.  Brands  reviewed  by  less  than  100  consumers  spontaneously,     will  not  be  included  in  the  overall  result.   Confidence level 15%  recommenders   Confidence interval Sample size needed Confidence interval All  consumers   Sample size needed Confidence interval Sample size needed Confidence interval Sample size needed Hypothetical population 100.000.000 hVp://marketresearch.about.com/od/market.research.surveys/a/Surveys-­‐Research-­‐Confidence-­‐Intervals.htm   95% 99% 4 4 600 1.040 3 3 1.067 1.849 2 2 2.401 4.160 1 1 9.306 16.638
  • 71. Brands/Media  will  get  plenty  of  data  to  improve  their  business  on   several  aspects.  Even  at  dealer-­‐level.   ______________________________________________________________________________   1.  2.  3.  4.  5.  6.  7.  Evalua0on  of  exis0ng  products    and  services   New  product  development-­‐ideas   Price  elas0city  calcula0on   Distribu0on  analysis   Marke0ng  posi0oning     Communica0on  audit   ATer-­‐sales  performance   –  8.  Dealer  performance  data   BeJer  segmenta0on     –  Up  to  the  level  of  age,  loca.on  (country,  region,  city,)  gender,     …  and  plenty  of  other  data  you  want  to  gather  in  the  private  part  
  • 72. Even  more  ac0onable  data:  discovery  and  Iden0fica0on  of   new,  interes0ng  consumers.   ______________________________________________________________________________   1.  2.  3.  4.  Discovery  of  recommenders  on  as  many  external  plaworms  as  possible   You  will  find  them  anywhere.  You  only  have  to  be  ac.ve.   Iden0fy  them.   Name,  gender,  loca.on,  brand-­‐scores  &  reviews,  brand  profile  and  many  more.   Understand  them.     Why  do  they  give  the  score  they  give.  They  intui.vely  update  your  SWOT-­‐analysis.   Based  on  individual  consumer  insights,    brands  can  target  and  personalize   their  communica.on  on  several  plasorms.   Influence  them.    “Influence  the  influencers”  because  influencers  are  media  -­‐  human  media    
  • 73. Almost finished ______________________________________________________________________________  
  • 74. The difference between a “recommended” beer and a marketing beer. ______________________________________________________________________________   http://www.ratebeer.com/
  • 75. The difference in family fortune between a “recommended” beer and a marketing beer. ______________________________________________________________________________   $11 billion € 270 million
  • 76. Appendix ______________________________________________________________________________  
  • 77. Boston Consulting Group launched BAI (december 2013) ______________________________________________________________________________  
  • 78. BCG-2. ______________________________________________________________________________  
  • 79. BCG-3. Predictive power ______________________________________________________________________________  
  • 80. BCG-4. Leaders have recommenders. Laggards detractors ______________________________________________________________________________  

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