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CommBank Analytics

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Datalicious
Datalicious

Datalicious is a data analytics agency founded in late 2007 that has grown to provide 360-degree data services to clients across industries. It combines analysts and developers with carefully selected partners to turn data into actionable insights and execute smart, data-driven marketing campaigns. The document discusses Datalicious' history and services, which include data platforms, analytics, insights, and campaigns to help clients optimize their marketing strategies.

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>  CommBank  Analy-cs  <   Smart  data  driven  marke-ng  
>  Short  but  sharp  history   §  Datalicious  was  founded  late  2007   §  Strong  Omniture  web  analy-cs  history   §  Now  360  data  agency  with  specialist  team   §  Combina-on  of  analysts  and  developers   §  Carefully  selected  best  of  breed  partners   §  Driving  industry  best  prac-ce  (ADMA)   §  Turning  data  into  ac-onable  insights   §  Execu-ng  smart  data  driven  campaigns   April  2011   ©  Datalicious  Pty  Ltd   2  
>  Clients  across  all  industries   April  2011   ©  Datalicious  Pty  Ltd   3  
>  Wide  range  of  data  services   Data   Insights   Ac-on   PlaAorms   Analy-cs   Campaigns         Data  collec-on  and  processing   Data  mining  and  modelling   Data  usage  and  applica-on         Web  analy-cs  solu-ons   Customised  dashboards   Marke-ng  automa-on         Omniture,  Google  Analy-cs,  etc   Tableau,  SpoAire,  SPSS,  etc   Alterian,  SiteCore,  Inxmail,  etc         Tag-­‐less  online  data  capture   Media  aMribu-on  models   Targe-ng  and  merchandising         End-­‐to-­‐end  data  plaAorms   Market  and  compe-tor  trends   Internal  search  op-misa-on         IVR  and  call  center  repor-ng   Social  media  monitoring   CRM  strategy  and  execu-on         Single  customer  view   Customer  profiling   Tes-ng  programs     April  2011   ©  Datalicious  Pty  Ltd   4  
>  Smart  data  driven  marke-ng     Metrics  Framework Metrics  Framework Media  AMribu-on Benchmarking  and  trending   Benchmarking  and  trending     Op-mise  channel  mix   Targe-ng     Increase  relevance   Tes-ng   Improve  usability     $$$   April  2011   ©  Datalicious  Pty  Ltd   5  
>  Workshop  brief   §  Defining  a  metrics  framework   –  What  to  report  on,  when  and  why?   –  Matching  strategic  and  tac-cal  goals  to  metrics   –  Covering  all  major  categories  of  business  goals   §  Finding  and  developing  the  right  data   –  Data  sources  across  channels  and  goals   –  Meaningful  trends  vs.  100%  accurate  data   –  Human  and  technological  limita-ons   §  Campaign  flow  and  media  aZribu-on   –  Designing  a  campaign  flow  including  metrics   –  Media  aZribu-on  in  a  mul--­‐channel  environment   April  2011   ©  Datalicious  Pty  Ltd   6  
101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010   >  Metrics  framework   April  2011   ©  Datalicious  Pty  Ltd   7  
>  AIDA  and  AIDAS  formulas     Old  media   New  media   Awareness   Interest   Desire   Ac-on   Sa-sfac-on   Social  media   April  2011   ©  Datalicious  Pty  Ltd   8  
>  Importance  of  social  media     Search   Company   Promo-on   Consumer   WOM,  blogs,  reviews,   ra-ngs,  communi-es,   social  networks,  photo   sharing,  video  sharing   April  2011   ©  Datalicious  Pty  Ltd   9  
>  Social  as  the  new  search     April  2011   ©  Datalicious  Pty  Ltd   10  
>  Simplified  AIDAS  funnel     Reach   Engagement   Conversion   +Buzz   (Awareness)   (Interest  &  Desire)   (Ac-on)   (Sa-sfac-on)   April  2011   ©  Datalicious  Pty  Ltd   11  
>  Marke-ng  is  about  people     People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted   April  2011   ©  Datalicious  Pty  Ltd   12  
>  Addi-onal  funnel  breakdowns     Brand  vs.  direct  response  campaign   People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted   New  prospects  vs.  exis-ng  customers   April  2011   ©  Datalicious  Pty  Ltd   13  
New  vs.  returning  visitors   April  2011   ©  Datalicious  Pty  Ltd   14  
AU/NZ  vs.  rest  of  world   April  2011   ©  Datalicious  Pty  Ltd   15  
Prospect  vs.  customer   High  vs.  low  value   Product  affinity   Post  code,  age,  sex,  etc   April  2011   ©  Datalicious  Pty  Ltd   16  
Exercise:  Funnel  breakdowns   April  2011   ©  Datalicious  Pty  Ltd   17  
>  Exercise:  Funnel  breakdowns     §  List  poten-ally  insighaul  funnel  breakdowns   –  Brand  vs.  direct  response  campaign   –  New  prospects  vs.  exis-ng  customers   –  Baseline  vs.  incremental  conversions   –  Compe--ve  ac-vity,  i.e.  none,  a  lot,  etc   –  Segments,  i.e.  age,  loca-on,  influence,  etc   –  Channels,  i.e.  search,  display,  social,  etc   –  Campaigns,  i.e.  this/last  week,  month,  year,  etc   –  Products  and  brands,  i.e.  iphone,  htc,  etc   –  Offers,  i.e.  free  minutes,  free  handset,  etc   –  Devices,  i.e.  home,  office,  mobile,  tablet,  etc   April  2011   ©  Datalicious  Pty  Ltd   18  
>  Geo-­‐demographic  segments   April  2011   ©  Datalicious  Pty  Ltd   19  
>  Rela-ve  or  calculated  metrics     §  Bounce  rate   §  Conversion  rate   §  Cost  per  acquisi-on   §  Pages  views  per  visit   §  Product  views  per  visit   §  Cart  abandonment  rate   §  Average  order  value   April  2011   ©  Datalicious  Pty  Ltd   20  
Exercise:  Conversion  metrics   April  2011   ©  Datalicious  Pty  Ltd   21  
>  Exercise:  Conversion  metrics     §  Key  conversion  metrics  differ  by  category   –  Commerce   –  Lead  genera-on   –  Content  publishing   –  Customer  service   April  2011   ©  Datalicious  Pty  Ltd   22  
>  Exercise:  Conversion  metrics     April  2011   ©  Datalicious  Pty  Ltd   23   Source:  Omniture  Summit,  MaZ  Belkin,  2007  
>  Conversion  funnel  1.0     Campaign  responses   Conversion  funnel   Product  page,  add  to  shopping  cart,  view  shopping  cart,   cart  checkout,  payment  details,  shipping  informa-on,   order  confirma-on,  etc   Conversion  event   April  2011   ©  Datalicious  Pty  Ltd   24  
>  Conversion  funnel  2.0     Campaign  responses  (inbound  spokes)   Offline  campaigns,  banner  ads,  email  marke-ng,     referrals,  organic  search,  paid  search,     internal  promo-ons,  etc       Landing  page  (hub)       Success  events  (outbound  spokes)   Bounce  rate,  add  to  cart,  cart  checkout,  confirmed  order,     call  back  request,  registra-on,  product  comparison,     product  review,  forward  to  friend,  etc   April  2011   ©  Datalicious  Pty  Ltd   25  
>  Addi-onal  success  metrics     Click   Through   $   Click   Add  To     Cart   Through   Cart   Checkout   ?   $   Click   Page   Page     Product     Through   Bounce   Views   Views   $   Click   Call  back   Store   Through   request   Search   ?   $   April  2011   ©  Datalicious  Pty  Ltd   26  
>  Conversion  funnel  design   Visits   Visits       Product  Views   Non-­‐Bounces*       Cart  Adds   Engagements**       Checkouts   Leads**       Conversions   Conversions         *  Non-­‐bounce  event   **  Serialised  events,   i.e.  once  per  visit     April  2011   ©  Datalicious  Pty  Ltd   27  
Exercise:  Conversion  funnel   April  2011   ©  Datalicious  Pty  Ltd   28  
>  Exercise:  Conversion  funnel   April  2011   ©  Datalicious  Pty  Ltd   29  
>  Measuring  social  media     Sen-ment   Influence   Reach   April  2011   ©  Datalicious  Pty  Ltd   30  
Exercise:  Metrics  framework   April  2011   ©  Datalicious  Pty  Ltd   31  
>  Exercise:  Metrics  framework     Level   Reach   Engagement   Conversion   +Buzz   Level  1,   people   Level  2,   strategic   Level  3,   tac-cal   Funnel   breakdowns   April  2011   ©  Datalicious  Pty  Ltd   32  
>  Exercise:  Metrics  framework     Level   Reach   Engagement   Conversion   +Buzz   Level  1   People   People   People   People   People   reached   engaged   converted   delighted   Level  2   Display   Strategic   impressions   ?   ?   ?   Level  3   Interac-on   Tac-cal   rate,  etc   ?   ?   ?   Funnel   Exis-ng  customers  vs.  new  prospects,  products,  etc   Breakdowns   April  2011   ©  Datalicious  Pty  Ltd   33  
>  ROI,  ROMI,  BE,  etc     R−I R  Revenue   = ROI   I  Investment     I   ROI  Return  on    investment     IR − MI IR  Incremental    revenue   = ROMI   MI MI    Marke-ng    investment   ROMI  Return  on   IR − MI  marke-ng    investment   = ROMI + BE   BE  Brand  equity   MI April  2011   ©  Datalicious  Pty  Ltd   34  
>  Success:  ROMI  +  BE     IR − MI = ROMI + BE MI §  Establish  incremental  revenue  (IR)   –  Requires  baseline  revenue  to  calculate  addi-onal     revenue  as  well  as  revenue  from  cost  savings   §  Establish  marke-ng  investment  (MI)   –  Requires  all  costs  across  technology,  content,  data     and  resources  plus  promo-ons  and  discounts   §  Establish  brand  equity  contribu-on  (BE)   –  Requires  addi-onal  sok  metrics  to  evaluate  subscriber   percep-ons,  experience,  altudes  and  word  of  mouth     April  2011   ©  Datalicious  Pty  Ltd   35  
>  Establishing  a  baseline   Switch  all  adver-sing  off  for  a  period   of  -me  (unlikely)  or  establish  a  smaller   control  group  that  is  representa-ve  of   the  en-re  popula-on  (i.e.  search  term,   geography,  etc)  and  switch  off  selected   channels  one  at  a  -me  to  minimise   impact  on  overall  conversions.   April  2011   ©  Datalicious  Pty  Ltd   36  
>  Process  is  key  to  success     April  2011   ©  Datalicious  Pty  Ltd   37   Source:  Omniture  Summit,  MaZ  Belkin,  2007  
>  Summary  and  ac-on  items     §  Defining  a  metrics  framework   –  Develop  standardised  metrics  framework   –  Define  addi-onal  funnel  breakdowns   –  Establish  baseline  and  incremental   –  Define  addi-onal  success  metrics   –  Define  conversion  funnels   April  2011   ©  Datalicious  Pty  Ltd   38  
>  Recommended  resources     §  200501  WAA  Key  Metrics  &  KPIs   §  200708  WAA  Analy-cs  Defini-ons  Volume  1   §  200612  Omniture  Effec-ve  Measurement   §  200804  Omniture  Calculated  Metrics  White  Paper   §  200702  Omniture  Effec-ve  Segmenta-on  Guide   §  200810  Ronnestam  Online  Adver-sing  And  AIDAS   §  201004  Al-meter  Social  Marke-ng  Analy-cs   §  201008  CSR  Customer  Sa-sfac-on  Vs  Delight   §  Google  “Enquiro  Search  Engine  Results  2010  PDF”   §  Google  “Razorfish  Ac-onable  Analy-cs  Report  PDF”   §  Google  “Forrester  Interac-ve  Marke-ng  Metrics  PDF”   April  2011   ©  Datalicious  Pty  Ltd   39  
101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010   >  Data  sources     April  2011   ©  Datalicious  Pty  Ltd   40  
>  Major  data  categories   Campaign  data   TV,  print,  call  center,  search,   web  analy-cs,  ad  serving,  etc       Campaigns   Customers   Customer  data   Direct  mail,  call  center,  web   analy-cs,  emails,  surveys,  etc       Consumer  data   Geo-­‐demographics,  search,   Compe-tors   Consumers   social,  3rd  party  research,  etc       Compe-tor  data   Search,  social,  ad  spend,  3rd   party  research,  news,  etc     April  2011   ©  Datalicious  Pty  Ltd   41  
>  Digital  data  is  plen-ful  and  cheap       April  2011   ©  Datalicious  Pty  Ltd   42   Source:  Omniture  Summit,  MaZ  Belkin,  2007  
>  Mul-ple  metrics  data  sources   Media  and  search  data   Website,  call  center  and  retail  data   People   People   People   People   reached   engaged   converted   delighted   Quan-ta-ve  and  qualita-ve  research  data   Social  media  data   Social  media   April  2011   ©  Datalicious  Pty  Ltd   43  
>  Reach  and  channel  overlap     TV/Print     audience   Banner   Search   audience   audience   April  2011   ©  Datalicious  Pty  Ltd   44  
>  Es-ma-ng  reach  and  overlap     §  Apply  average  unique  visitor  count  per  recorded   unique  user  names  to  all  unique  visitor  figures  in   Google  Analy-cs,  Omniture,  etc.   §  Apply  ra-o  of  total  banner  impressions  to  unique   banner  impressions  from  ad  server  to  paid  and   organic  search  impressions  in  Google  AdWords  and   Google  Webmaster  Tools.   §  Compare  Google  Keyword  Tool  impressions  for  a   specific  search  term  to  reach  for  the  same  term  in   Google  Ad  Planner.   §  Or  just  add  the  reach  figures  for  all  channels  up  …   April  2011   ©  Datalicious  Pty  Ltd   45  
>  Google  data  in  Australia     Source:  hZp://www.hitwise.com/au/resources/data-­‐centre   April  2011   ©  Datalicious  Pty  Ltd   46  
>  Search  at  all  stages     April  2011   ©  Datalicious  Pty  Ltd   47   Source:  Inside  the  Mind  of  the  Searcher,  Enquiro  2004  
>  Search  and  brand  strength     April  2011   ©  Datalicious  Pty  Ltd   48  
>  Search  and  the  product  lifecycle     Nokia  N-­‐Series   Apple  iPhone   April  2011   ©  Datalicious  Pty  Ltd   49  
>  Search  and  media  planning     April  2011   ©  Datalicious  Pty  Ltd   50  
>  Search  driving  offline  crea-ve     April  2011   ©  Datalicious  Pty  Ltd   51  
Exercise:  Search  insights   April  2011   ©  Datalicious  Pty  Ltd   52  
>  Exercise:  Search  insights     §  Iden-fy  key  category  search  terms   –  Data  from  Google  AdWords  Keyword  Tool   –  Search  for  “google  keyword  tool”   –  Wordle  and  IBM  Many  Eyes  for  visualiza-ons   –  Search  for  “wordle  word  clouds”  and  “ibm  many  eyes”   §  Iden-fy  search  term  trends  and  compe-tors   –  Google  Trends  and  Google  Search  Insights   –  Search  for  “google  trends”  and  “google  search  insights”   §  Search  and  media  planning   –  DoubleClick  Ad  Planner  by  Google   –  Search  for  “google  ad  planner”   April  2011   ©  Datalicious  Pty  Ltd   53  
>  Cookie  based  tracking  process     What  if:  Someone  deletes  their  cookies?  Or  uses  a  device   that  does  not  support  JavaScript?  Or  uses  two  computers   (work  vs.  home)?  Or  two  people  use  the  same  computer?   April  2011   ©  Datalicious  Pty  Ltd   54   Source:  Google  Analy-cs,  Jus-n  Cutroni,  2007  
>  Unique  visitor  overes-ma-on     The  study  examined     data  from  two  of     the  UK’s  busiest     ecommerce     websites,  ASDA   and  William  Hill.     Given  that  more     than  half  of  all  page     impressions  on  these     sites  are  from  logged-­‐in     users,  they  provided  a  robust     sample  to  compare  IP-­‐based  and  cookie-­‐based  analysis  against.   The  results  were  staggering,  for  example  an  IP-­‐based  approach   overes-mated  visitors  by  up  to  7.6  -mes  whilst  a  cookie-­‐based   approach  overes-mated  visitors  by  up  to  2.3  -mes.     April  2011   ©  Datalicious  Pty  Ltd   55   Source:  White  Paper,  RedEye,  2007  
>  Maximise  iden-fica-on  points     160%   140%   120%   100%   80%   60%   −−−  Probability  of  iden-fica-on  through  Cookies   40%   20%   0   4   8   12   16   20   24   28   32   36   40   44   48   Weeks   April  2011   ©  Datalicious  Pty  Ltd   56  
>  Maximise  iden-fica-on  points   Mobile   Home   Work   Online   Phone   Branch   April  2011   ©  Datalicious  Pty  Ltd   57  
>  Combining  data  sources   Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  data   April  2011   ©  Datalicious  Pty  Ltd   58  
>  Duplica-on  across  channels     Paid     Bid     Search   Mgmt   $   Banner     Ad     Ads   Server   $   Email     Email   Blast   PlaAorm   $   Organic   Google   Search   Analy-cs   $   April  2011   ©  Datalicious  Pty  Ltd   59  
>  Cookie  expira-on  impact   Paid     Bid     Search   Mgmt   $   Banner     Banner     Ad     Ad  Click   Ad  View   Server   $   Email     Email   Expira-on   Blast   PlaAorm   $   Organic   Google   Search   Analy-cs   $   April  2011   ©  Datalicious  Pty  Ltd   60  
>  CBA  repor-ng  plaAorms   April  2011   ©  Datalicious  Pty  Ltd   61  
>  De-­‐duplica-on  across  channels     Paid     Search   $   Banner     Ads   $   Central   Analy-cs   PlaAorm   Email     Blast   $   Organic   Search   $   April  2011   ©  Datalicious  Pty  Ltd   62  
De-­‐duplica-on  across  channels   April  2011   ©  Datalicious  Pty  Ltd   63  
Exercise:  Duplica-on  impact   April  2011   ©  Datalicious  Pty  Ltd   64  
>  Exercise:  Duplica-on  impact     §  Double-­‐coun-ng  of  conversions  across  channels  can   have  a  significant  impact  on  key  metrics,  especially  CPA   §  Example:  Display  ads  and  paid  search   –  Total  media  budget  of  $10,000  of  which  50%  is  spend  on  paid   search  and  50%  on  display  ads   –  Total  of  100  conversions  across  both  channels  with  a  channel   overlap  of  50%,  i.e.  both  channels  claim  100%  of  conversions   based  on  their  own  repor-ng  but  once  de-­‐duplicated  they   each  only  contributed  50%  of  conversions   –  What  are  the  ini-al  CPA  values  and  what  is  the  true  CPA?   §  Solu-on:  $50  ini-al  CPA  and  $100  true  CPA   –  $5,000  /  100  =  $50  ini-al  CPA  and  $5,000  /  50  =  $100  true   CPA  (which  represents  a  100%  increase)   April  2011   ©  Datalicious  Pty  Ltd   65  
>  Single  source  of  truth  repor-ng   Insights   Repor-ng   April  2011   ©  Datalicious  Pty  Ltd   66  
April  2011   ©  Datalicious  Pty  Ltd   67  
April  2011   ©  Datalicious  Pty  Ltd   68  
Google:  “visualisa-on  methods”   April  2011   ©  Datalicious  Pty  Ltd   69  
Exercise:  Sta-s-cal  significance   April  2011   ©  Datalicious  Pty  Ltd   70  
How  many  survey  responses  do  you  need     if  you  have  10,000  customers?   How  many  email  opens  do  you  need  to  test  2  subject  lines   if  your  subscriber  base  is  50,000?   How  many  orders  do  you  need  to  test  6  banner  execu-ons     if  you  serve  1,000,000  banners   April  2011   ©  Datalicious  Pty  Ltd   71   Google  “nss  sample  size  calculator”  
How  many  survey  responses  do  you  need     if  you  have  10,000  customers?   369  for  each  ques-on  or  369  complete  responses   How  many  email  opens  do  you  need  to  test  2  subject  lines   if  your  subscriber  base  is  50,000?  And  email  sends?   381  per  subject  line  or  381  x  2  =  762  email  opens   How  many  orders  do  you  need  to  test  6  banner  execu-ons     if  you  serve  1,000,000  banners?   383  sales  per  banner  execu-on  or  383  x  6  =  2,298  sales   April  2011   ©  Datalicious  Pty  Ltd   72   Google  “nss  sample  size  calculator”  
>  Addi-onal  success  metrics     Click   Through   $   Click   Add  To     Cart   Through   Cart   Checkout   ?   $   Click   Page   Page     Product     Through   Bounce   Views   Views   $   Click   Call  back   Store   Through   request   Search   ?   $   April  2011   ©  Datalicious  Pty  Ltd   73  
>  Combining  data  sources   Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  data   April  2011   ©  Datalicious  Pty  Ltd   74  
>  Behaviours  plus  transac-ons     Site  Behaviour   CRM  Profile   tracking  of  purchase  funnel  stage   one-­‐off  collec-on  of  demographical  data     +   browsing,  checkout,  etc   age,  gender,  address,  etc   tracking  of  content  preferences   customer  lifecycle  metrics  and  key  dates   products,  brands,  features,  etc   profitability,  expira-on,  etc   tracking  of  external  campaign  responses   predic-ve  models  based  on  data  mining   search  terms,  referrers,  etc   propensity  to  buy,  churn,  etc   tracking  of  internal  promo-on  responses   historical  data  from  previous  transac-ons   emails,  internal  search,  etc   average  order  value,  points,  etc   Updated  Con-nuously   Updated  Occasionally   April  2011   ©  Datalicious  Pty  Ltd   75  
Exercise:  Customer  IDs   April  2011   ©  Datalicious  Pty  Ltd   76  
>  Exercise:  Customer  IDs   To  transac-onal  data   To  reten-on  messages   From  suspect  to   prospect   To  customer   Time   Time   From  behavioural  data   From  awareness  messages   April  2011   ©  Datalicious  Pty  Ltd   77  
>  Sample  customer  level  data     April  2011   ©  Datalicious  Pty  Ltd   78  
>  Atomic  labs  tag-­‐less  analy-cs   §  Single  point  of  data   capture  and  processing   §  Real-­‐-me  queries  to   enrich  website  data     §  Mul-ple  data  export   op-ons  for  web  analy-cs   §  Enriching  single-­‐customer   view  website  behaviour   April  2011   ©  Datalicious  Pty  Ltd   79  
April  2011   ©  Datalicious  Pty  Ltd   80  
Sen-ment  analysis:  People  vs.  machine   April  2011   ©  Datalicious  Pty  Ltd   81  
>  Al-meter  social  analy-cs     Social  Marke-ng   Analy-cs  is  the   discipline  that  helps   companies  measure,   assess  and  explain  the   performance  of  social   media  ini-a-ves  in  the   context  of  specific   business  objec-ves.   April  2011   ©  Datalicious  Pty  Ltd   82  
>  Importance  of  calendar  events     Traffic  spikes  or  other  data  anomalies  without  context  are   very  hard  to  interpret  and  can  render  data  useless   April  2011   ©  Datalicious  Pty  Ltd   83  
Calendar  events  to  add  context   April  2011   ©  Datalicious  Pty  Ltd   84  
>  Summary  and  ac-on  items     §  Finding  and  developing  the  right  data   –  Ensure  de-­‐duplica-on  via  central  analy-cs   –  Check  reports  for  sta-s-cal  significance   –  Check  data  sources  and  their  accuracy   –  Combine  data  sources  across  channels   –  Start  popula-ng  a  calendar  of  events   April  2011   ©  Datalicious  Pty  Ltd   85  
>  Recommended  resources     §  200311  UK  RedEye  Cookie  Case  Study   §  200807  Kaushik  Tracking  Offline  Conversion   §  200904  Kaushik  Standard  Metrics  Revisited   §  201002  Kaushik  8  Compe--ve  Intelligence  Data  Sources   §  201005  Google  Ad  Planner  Data  Wrong  By  Up  To  20%   §  201005  MPI  How  Sta-s-cally  Valid  Is  Your  Survey   §  201009  Google  Analy-cs  How  To  Tag  Links   §  200903  Coremetrics  Conversion  Benchmarks  By  Industry   §  200906  WOM  Online  The  People  Vs  Machines  Debate   §  201007  WSJ  The  Web's  New  Gold  Mine  Your  Secrets   §  201008  Adver-singAge  Are  Marketers  Really  Spying  On  You   April  2011   ©  Datalicious  Pty  Ltd   86  
101011010010010010101111010010010101010100001011111001010101 010100101011001100010100101001101101001101001010100111001010 010010101001001010010100100101001111101010100101001001001010   >  Media  aMribu-on   April  2011   ©  Datalicious  Pty  Ltd   87  
>  Campaign  flow  and  calls  to  ac-on     =  Paid  media   Organic     PR,  WOM,   search   events,  etc   =  Viral  elements   =  Coupons,  surveys   YouTube,     Home  pages,   Paid     TV,  print,     blog,  etc   portals,  etc   search   radio,  etc   Direct  mail,     Landing  pages,   Display  ads,   email,  etc   offers,  etc   affiliates,  etc   C1   C2   CRM   Facebook   program   TwiMer,  etc   C3   POS  kiosks,   Call  center,     loyalty  cards,  etc   retail  stores,  etc   April  2011   ©  Datalicious  Pty  Ltd   88  
Exercise:  Campaign  flow   April  2011   ©  Datalicious  Pty  Ltd   89  
>  Reach  and  channel  overlap     TV/Print     audience   Banner   Search   audience   audience   April  2011   ©  Datalicious  Pty  Ltd   90  
>  Ad  server  exposure  test   Banner   TV/Print   Search   Impression   Response   Response   $   Banner   Search   Direct   Impression   Response   Response   $   Users  are   segmented   before  1st   ad  is  even   Exposed  group:  90%  of  users  get  branded  message   served     Control  group:  10%  of  users  get  non-­‐branded  message   Banner   Search   Direct   Impression   Response   Response   $   April  2011   ©  Datalicious  Pty  Ltd   91  
>  Indirect  display  impact     April  2011   ©  Datalicious  Pty  Ltd   92  
>  Indirect  display  impact     April  2011   ©  Datalicious  Pty  Ltd   93  
April  2011   ©  Datalicious  Pty  Ltd   94  
>  Indirect  display  impact     April  2011   ©  Datalicious  Pty  Ltd   95  
>  Success  aMribu-on  models     Banner     Paid     Organic   Success   Last  channel   Search   Ad   Search   $100   $100   gets  all  credit   Banner     Paid     Email     Success   First  channel   Ad   $100   Search   Blast   $100   gets  all  credit   Paid     Banner     Affiliate     Success   All  channels  get   Search   Ad   Referral   $100   $100   $100   $100   equal  credit   Print     Social     Paid     Success   All  channels  get   Ad   Media   Search   $33   $33   $33   $100   par-al  credit   April  2011   ©  Datalicious  Pty  Ltd   96  
>  First  and  last  click  aMribu-on     Chart  shows   percentage  of   channel  touch   points  that  lead   Paid/Organic  Search   to  a  conversion.   Neither  first     Emails/Shopping  Engines   nor  last-­‐click   measurement   would  provide   true  picture     April  2011   ©  Datalicious  Pty  Ltd   97  
>  CBA  first  and  last  touch  reports   April  2011   ©  Datalicious  Pty  Ltd   98  
Adobe  campaign  stack  does  not  include   organic  channels  or  banner  impressions   and  does  not  expire  on  any  event,  i.e.   con-nues  as  long  as  the  cookie  is  present.   April  2011   ©  Datalicious  Pty  Ltd   99  
>  Full  path  to  purchase   Introducer   Influencer   Influencer   Closer   $   SEM   Banner   Direct     SEO   Online   Generic   Click   Visit   Branded   Banner     SEO   Affiliate   Social   Offline   View   Generic   Click   Media   TV     SEO   Direct     Email   Abandon   Ad   Branded   Visit   Update   April  2011   ©  Datalicious  Pty  Ltd   100  
>  Search  call  to  ac-on  for  offline     April  2011   ©  Datalicious  Pty  Ltd   101  
April  2011   ©  Datalicious  Pty  Ltd   102  
>  PURLs  boos-ng  DM  response  rates   Text   April  2011   ©  Datalicious  Pty  Ltd   103  
>  Poten-al  calls  to  ac-on     §  Unique  click-­‐through  URLs   §  Unique  vanity  domains  or  URLs   §  Unique  phone  numbers   §  Unique  search  terms   §  Unique  email  addresses   §  Unique  personal  URLs  (PURLs)   §  Unique  SMS  numbers,  QR  codes   §  Unique  promo-onal  codes,  vouchers   §  Geographic  loca-on  (Facebook,  FourSquare)   §  Plus  regression  analysis  of  cause  and  effect   April  2011   ©  Datalicious  Pty  Ltd   104  
>  Unique  phone  numbers   §  1  unique  phone  number     –  Phone  number  is  considered  part  of  the  brand   –  Media  origin  of  calls  cannot  be  established   –  Added  value  of  website  interac-on  unknown   §  2-­‐10  unique  phone  numbers   –  Different  numbers  for  different  media  channels   –  Exclusive  number(s)  reserved  for  website  use   –  Call  origin  data  more  granular  but  not  perfect   –  Difficult  to  rotate  and  pause  numbers   April  2011   ©  Datalicious  Pty  Ltd   105  
>  Unique  phone  numbers   §  10+  unique  phone  numbers   –  Different  numbers  for  different  media  channels   –  Different  numbers  for  different  product  categories   –  Different  numbers  for  different  conversion  steps   –  Call  origin  becoming  useful  to  shape  call  script   –  Feasible  to  pause  numbers  to  improve  integrity   §  100+  unique  phone  numbers   –  Different  numbers  for  different  website  visitors   –  Call  origin  and  -me  stamp  enable  individual  match   –  Call  conversions  matched  back  to  search  terms   April  2011   ©  Datalicious  Pty  Ltd   106  
>  Jet  Interac-ve  phone  call  data   April  2011   ©  Datalicious  Pty  Ltd   107  
>  Full  path  to  purchase   Introducer   Influencer   Influencer   Closer   $   SEM   Banner   Direct     SEO   Online   Generic   Click   Visit   Branded   Banner     SEO   Affiliate   Social   Offline   View   Generic   Click   Media   TV     SEO   Direct     Email   Abandon   Ad   Branded   Visit   Update   April  2011   ©  Datalicious  Pty  Ltd   108  
>  Research  online,  shop  offline     April  2011   ©  Datalicious  Pty  Ltd   109   Source:  2008  Digital  Future  Report,  Surveying  The  Digital  Future,  Year  Seven,  USC  Annenberg  School  
>  Cross-­‐channel  impact   April  2011   ©  Datalicious  Pty  Ltd   110  
>  Offline  sales  driven  by  online   Adver-sing     Phone   Credit  check,   campaign   order   fulfilment   Retail   Confirma-on   order   email   Website   Online   Online  order   Virtual  order   research   order   confirma-on   confirma-on   Cookie   April  2011   ©  Datalicious  Pty  Ltd   111  
Exercise:  Offline  conversions   April  2011   ©  Datalicious  Pty  Ltd   112  
>  Exercise:  Offline  conversions     §  Email  click-­‐through  aker  purchase   §  First  online  login  aker  purchase   §  Unique  website  or  visitor  phone  number   §  Call  back  request  or  online  chat   §  Unique  website  promo-on  code   §  Unique  printable  vouchers   §  Store  locator  searches   §  Make  an  appointment  online   April  2011   ©  Datalicious  Pty  Ltd   113  
>  Single  source  of  truth  repor-ng   Insights   Repor-ng   April  2011   ©  Datalicious  Pty  Ltd   114  
>  Where  to  collect  the  data     Ad  Server   Web  Analy-cs   Banner  impressions   Referral  visits   Banner  clicks   Social  media  visits   +   Organic  search  visits   Paid  search  clicks   Paid  search  visits   Email  visits,  etc   Lacking  organic  visits   Lacking  ad  impressions   More  granular  &  complex   Less  granular  &  complex   April  2011   ©  Datalicious  Pty  Ltd   115  
>  Raw  aMribu-on  data   Web  Analy-cs   AFFILIATE  >  SEO  >  $$$   SEM  >  SOCIAL  >  EMAIL  >  DIRECT  >  $$$     Ad  Server   01/01/2011  12:00  AD  IMPRESSION   01/01/2011  12:05  SEO   07/01/2011  17:00  EMAIL   08/01/2011  15:00  $$$     April  2011   ©  Datalicious  Pty  Ltd   116  
>  Combine  purchase  paths   Mobile   Home   Work   Tablet   Media   Etc   April  2011   ©  Datalicious  Pty  Ltd   117  
>  Combining  data  sources   April  2011   ©  Datalicious  Pty  Ltd   118  
>  Understanding  channel  mix   April  2011   ©  Datalicious  Pty  Ltd   119  
April  2011   ©  Datalicious  Pty  Ltd   120  
>  Website  entry  survey     De-­‐duped  Campaign  Report   Greatest  Influencer  on  Branded  Search  /  STS   }   Channel   %  of  Conversions   Channel   %  of  Influence   Straight  to  Site   27%   Word  of  Mouth   32%   SEO  Branded   15%   Blogging  &  Social  Media   24%   SEM  Branded   9%   Newspaper  Adver-sing   9%   SEO  Generic   7%   Display  Adver-sing   14%   SEM  Generic   14%   Email  Marke-ng   7%   Display  Adver-sing   7%   Retail  Promo-ons   14%   Affiliate  Marke-ng   9%   Referrals   5%   Conversions  aZributed  to  search  terms   Email  Marke-ng   7%   that  contain  brand  keywords  and  direct   website  visits  are  most  likely  not  the   origina-ng  channel  that  generated  the   awareness  and  as  such  conversion   credits  should  be  re-­‐allocated.     April  2011   ©  Datalicious  Pty  Ltd   121  
>  Adjus-ng  for  offline  impact   -­‐5   -­‐15   -­‐10   +5   +15   +10   April  2011   ©  Datalicious  Pty  Ltd   122  
April  2011   ©  Datalicious  Pty  Ltd   123  
April  2011   ©  Datalicious  Pty  Ltd   124  
>  ClearSaleing  media  aMribu-on   April  2011   ©  Datalicious  Pty  Ltd   125  
>  Success  aMribu-on  models     Introducer   Influencer   Influencer   Closer   $   Even     25%   25%   25%   25%   AMrib.   Exclusion   33%   33%   33%   0%   AMrib.   PaMern   30%   20%   20%   30%   AMrib.   April  2011   ©  Datalicious  Pty  Ltd   126  
>  Path  across  different  segments   Introducer   Influencer   Influencer   Closer   $   Product     Channel  1   Channel  2   Channel  3   Channel  4   A  vs.  B   New   Channel  1   Channel  2   Channel  3   Channel  4   prospects   Exis-ng   Channel  1   Channel  2   Channel  3   Product  4   customers   April  2011   ©  Datalicious  Pty  Ltd   127  
Exercise:  AMribu-on  model   April  2011   ©  Datalicious  Pty  Ltd   128  
>  Exercise:  AMribu-on  models     Introducer   Influencer   Influencer   Closer   $   Even     25%   25%   25%   25%   AMrib.   Exclusion   33%   33%   33%   0%   AMrib.   ?   ?   ?   ?   Custom   AMrib.   April  2011   ©  Datalicious  Pty  Ltd   129  
>  Common  aMribu-on  models   §  Allocate  more  conversion  credits  to  more   recent  touch  points  for  brands  with  a  strong   baseline  to  s-mulate  repeat  purchases     §  Allocate  more  conversion  credits  to  more   recent  touch  points  for  brands  with  a  direct   response  focus   §  Allocate  more  conversion  credits  to  ini-a-ng   touch  points  for  new  and  expensive  brands  and   products  to  insert  them  into  the  mindset   April  2011   ©  Datalicious  Pty  Ltd   130  
>  Media  aMribu-on  phases     §  Phase  1:  De-­‐duplica-on   –  Conversion  de-­‐duplica-on  across  all  channels   –  Requires  one  central  repor-ng  plaaorm   –  Limited  to  first/last  click  aZribu-on   §  Phase  2:  Direct  response  pathing   –  Response  pathing  across  paid  and  organic  channels   –  Only  covers  clicks  and  not  mere  banner  views   –  Can  be  enabled  in  Google  Analy-cs  and  Omniture   §  Phase  3:  Full  purchase  path   –  Direct  response  tracking  including  banner  exposure   –  Google  Analy-cs  and  Omniture  data  collec-on  limited   –  Easier  to  import  addi-onal  channels  into  ad  server   April  2011   ©  Datalicious  Pty  Ltd   131  
>  Summary  and  ac-on  items     §  Campaign  flow  and  media  aZribu-on   –  Draw  campaign  flow  for  your  company   –  Check  plaaorm  cookie  expira-on  periods   –  Enable  pathing  of  direct  campaign  responses   –  Inves-gate  addi-onal  pathing  op-ons   –  Inves-gate  how  to  track  offline  conversions   April  2011   ©  Datalicious  Pty  Ltd   132  
>  Recommended  resources     §  200812  ComScore  How  Online  Adver-sing  Works   §  200905  iProspect  Research  Study  Search  And  Display   §  200904  ClearSaleing  American  AZribu-on  Index   §  201003  Datalicious  Tying  Offline  Sales  To  Online  Media   §  Google:  “Forrester  Campaign  AZribu-on  Framework  PDF”   April  2011   ©  Datalicious  Pty  Ltd   133  
Contact  us   insights@datalicious.com     Learn  more   blog.datalicious.com     Follow  us   twiMer.com/datalicious     April  2011   ©  Datalicious  Pty  Ltd   134  
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CommBank Analytics

  • 1. >  CommBank  Analy-cs  <   Smart  data  driven  marke-ng  
  • 2. >  Short  but  sharp  history   §  Datalicious  was  founded  late  2007   §  Strong  Omniture  web  analy-cs  history   §  Now  360  data  agency  with  specialist  team   §  Combina-on  of  analysts  and  developers   §  Carefully  selected  best  of  breed  partners   §  Driving  industry  best  prac-ce  (ADMA)   §  Turning  data  into  ac-onable  insights   §  Execu-ng  smart  data  driven  campaigns   April  2011   ©  Datalicious  Pty  Ltd   2  
  • 3. >  Clients  across  all  industries   April  2011   ©  Datalicious  Pty  Ltd   3  
  • 4. >  Wide  range  of  data  services   Data   Insights   Ac-on   PlaAorms   Analy-cs   Campaigns         Data  collec-on  and  processing   Data  mining  and  modelling   Data  usage  and  applica-on         Web  analy-cs  solu-ons   Customised  dashboards   Marke-ng  automa-on         Omniture,  Google  Analy-cs,  etc   Tableau,  SpoAire,  SPSS,  etc   Alterian,  SiteCore,  Inxmail,  etc         Tag-­‐less  online  data  capture   Media  aMribu-on  models   Targe-ng  and  merchandising         End-­‐to-­‐end  data  plaAorms   Market  and  compe-tor  trends   Internal  search  op-misa-on         IVR  and  call  center  repor-ng   Social  media  monitoring   CRM  strategy  and  execu-on         Single  customer  view   Customer  profiling   Tes-ng  programs     April  2011   ©  Datalicious  Pty  Ltd   4  
  • 5. >  Smart  data  driven  marke-ng     Metrics  Framework Metrics  Framework Media  AMribu-on Benchmarking  and  trending   Benchmarking  and  trending     Op-mise  channel  mix   Targe-ng     Increase  relevance   Tes-ng   Improve  usability     $$$   April  2011   ©  Datalicious  Pty  Ltd   5  
  • 6. >  Workshop  brief   §  Defining  a  metrics  framework   –  What  to  report  on,  when  and  why?   –  Matching  strategic  and  tac-cal  goals  to  metrics   –  Covering  all  major  categories  of  business  goals   §  Finding  and  developing  the  right  data   –  Data  sources  across  channels  and  goals   –  Meaningful  trends  vs.  100%  accurate  data   –  Human  and  technological  limita-ons   §  Campaign  flow  and  media  aZribu-on   –  Designing  a  campaign  flow  including  metrics   –  Media  aZribu-on  in  a  mul--­‐channel  environment   April  2011   ©  Datalicious  Pty  Ltd   6  
  • 8. >  AIDA  and  AIDAS  formulas     Old  media   New  media   Awareness   Interest   Desire   Ac-on   Sa-sfac-on   Social  media   April  2011   ©  Datalicious  Pty  Ltd   8  
  • 9. >  Importance  of  social  media     Search   Company   Promo-on   Consumer   WOM,  blogs,  reviews,   ra-ngs,  communi-es,   social  networks,  photo   sharing,  video  sharing   April  2011   ©  Datalicious  Pty  Ltd   9  
  • 10. >  Social  as  the  new  search     April  2011   ©  Datalicious  Pty  Ltd   10  
  • 11. >  Simplified  AIDAS  funnel     Reach   Engagement   Conversion   +Buzz   (Awareness)   (Interest  &  Desire)   (Ac-on)   (Sa-sfac-on)   April  2011   ©  Datalicious  Pty  Ltd   11  
  • 12. >  Marke-ng  is  about  people     People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted   April  2011   ©  Datalicious  Pty  Ltd   12  
  • 13. >  Addi-onal  funnel  breakdowns     Brand  vs.  direct  response  campaign   People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted   New  prospects  vs.  exis-ng  customers   April  2011   ©  Datalicious  Pty  Ltd   13  
  • 14. New  vs.  returning  visitors   April  2011   ©  Datalicious  Pty  Ltd   14  
  • 15. AU/NZ  vs.  rest  of  world   April  2011   ©  Datalicious  Pty  Ltd   15  
  • 16. Prospect  vs.  customer   High  vs.  low  value   Product  affinity   Post  code,  age,  sex,  etc   April  2011   ©  Datalicious  Pty  Ltd   16  
  • 17. Exercise:  Funnel  breakdowns   April  2011   ©  Datalicious  Pty  Ltd   17  
  • 18. >  Exercise:  Funnel  breakdowns     §  List  poten-ally  insighaul  funnel  breakdowns   –  Brand  vs.  direct  response  campaign   –  New  prospects  vs.  exis-ng  customers   –  Baseline  vs.  incremental  conversions   –  Compe--ve  ac-vity,  i.e.  none,  a  lot,  etc   –  Segments,  i.e.  age,  loca-on,  influence,  etc   –  Channels,  i.e.  search,  display,  social,  etc   –  Campaigns,  i.e.  this/last  week,  month,  year,  etc   –  Products  and  brands,  i.e.  iphone,  htc,  etc   –  Offers,  i.e.  free  minutes,  free  handset,  etc   –  Devices,  i.e.  home,  office,  mobile,  tablet,  etc   April  2011   ©  Datalicious  Pty  Ltd   18  
  • 19. >  Geo-­‐demographic  segments   April  2011   ©  Datalicious  Pty  Ltd   19  
  • 20. >  Rela-ve  or  calculated  metrics     §  Bounce  rate   §  Conversion  rate   §  Cost  per  acquisi-on   §  Pages  views  per  visit   §  Product  views  per  visit   §  Cart  abandonment  rate   §  Average  order  value   April  2011   ©  Datalicious  Pty  Ltd   20  
  • 21. Exercise:  Conversion  metrics   April  2011   ©  Datalicious  Pty  Ltd   21  
  • 22. >  Exercise:  Conversion  metrics     §  Key  conversion  metrics  differ  by  category   –  Commerce   –  Lead  genera-on   –  Content  publishing   –  Customer  service   April  2011   ©  Datalicious  Pty  Ltd   22  
  • 23. >  Exercise:  Conversion  metrics     April  2011   ©  Datalicious  Pty  Ltd   23   Source:  Omniture  Summit,  MaZ  Belkin,  2007  
  • 24. >  Conversion  funnel  1.0     Campaign  responses   Conversion  funnel   Product  page,  add  to  shopping  cart,  view  shopping  cart,   cart  checkout,  payment  details,  shipping  informa-on,   order  confirma-on,  etc   Conversion  event   April  2011   ©  Datalicious  Pty  Ltd   24  
  • 25. >  Conversion  funnel  2.0     Campaign  responses  (inbound  spokes)   Offline  campaigns,  banner  ads,  email  marke-ng,     referrals,  organic  search,  paid  search,     internal  promo-ons,  etc       Landing  page  (hub)       Success  events  (outbound  spokes)   Bounce  rate,  add  to  cart,  cart  checkout,  confirmed  order,     call  back  request,  registra-on,  product  comparison,     product  review,  forward  to  friend,  etc   April  2011   ©  Datalicious  Pty  Ltd   25  
  • 26. >  Addi-onal  success  metrics     Click   Through   $   Click   Add  To     Cart   Through   Cart   Checkout   ?   $   Click   Page   Page     Product     Through   Bounce   Views   Views   $   Click   Call  back   Store   Through   request   Search   ?   $   April  2011   ©  Datalicious  Pty  Ltd   26  
  • 27. >  Conversion  funnel  design   Visits   Visits       Product  Views   Non-­‐Bounces*       Cart  Adds   Engagements**       Checkouts   Leads**       Conversions   Conversions         *  Non-­‐bounce  event   **  Serialised  events,   i.e.  once  per  visit     April  2011   ©  Datalicious  Pty  Ltd   27  
  • 28. Exercise:  Conversion  funnel   April  2011   ©  Datalicious  Pty  Ltd   28  
  • 29. >  Exercise:  Conversion  funnel   April  2011   ©  Datalicious  Pty  Ltd   29  
  • 30. >  Measuring  social  media     Sen-ment   Influence   Reach   April  2011   ©  Datalicious  Pty  Ltd   30  
  • 31. Exercise:  Metrics  framework   April  2011   ©  Datalicious  Pty  Ltd   31  
  • 32. >  Exercise:  Metrics  framework     Level   Reach   Engagement   Conversion   +Buzz   Level  1,   people   Level  2,   strategic   Level  3,   tac-cal   Funnel   breakdowns   April  2011   ©  Datalicious  Pty  Ltd   32  
  • 33. >  Exercise:  Metrics  framework     Level   Reach   Engagement   Conversion   +Buzz   Level  1   People   People   People   People   People   reached   engaged   converted   delighted   Level  2   Display   Strategic   impressions   ?   ?   ?   Level  3   Interac-on   Tac-cal   rate,  etc   ?   ?   ?   Funnel   Exis-ng  customers  vs.  new  prospects,  products,  etc   Breakdowns   April  2011   ©  Datalicious  Pty  Ltd   33  
  • 34. >  ROI,  ROMI,  BE,  etc     R−I R  Revenue   = ROI   I  Investment     I   ROI  Return  on    investment     IR − MI IR  Incremental    revenue   = ROMI   MI MI    Marke-ng    investment   ROMI  Return  on   IR − MI  marke-ng    investment   = ROMI + BE   BE  Brand  equity   MI April  2011   ©  Datalicious  Pty  Ltd   34  
  • 35. >  Success:  ROMI  +  BE     IR − MI = ROMI + BE MI §  Establish  incremental  revenue  (IR)   –  Requires  baseline  revenue  to  calculate  addi-onal     revenue  as  well  as  revenue  from  cost  savings   §  Establish  marke-ng  investment  (MI)   –  Requires  all  costs  across  technology,  content,  data     and  resources  plus  promo-ons  and  discounts   §  Establish  brand  equity  contribu-on  (BE)   –  Requires  addi-onal  sok  metrics  to  evaluate  subscriber   percep-ons,  experience,  altudes  and  word  of  mouth     April  2011   ©  Datalicious  Pty  Ltd   35  
  • 36. >  Establishing  a  baseline   Switch  all  adver-sing  off  for  a  period   of  -me  (unlikely)  or  establish  a  smaller   control  group  that  is  representa-ve  of   the  en-re  popula-on  (i.e.  search  term,   geography,  etc)  and  switch  off  selected   channels  one  at  a  -me  to  minimise   impact  on  overall  conversions.   April  2011   ©  Datalicious  Pty  Ltd   36  
  • 37. >  Process  is  key  to  success     April  2011   ©  Datalicious  Pty  Ltd   37   Source:  Omniture  Summit,  MaZ  Belkin,  2007  
  • 38. >  Summary  and  ac-on  items     §  Defining  a  metrics  framework   –  Develop  standardised  metrics  framework   –  Define  addi-onal  funnel  breakdowns   –  Establish  baseline  and  incremental   –  Define  addi-onal  success  metrics   –  Define  conversion  funnels   April  2011   ©  Datalicious  Pty  Ltd   38  
  • 39. >  Recommended  resources     §  200501  WAA  Key  Metrics  &  KPIs   §  200708  WAA  Analy-cs  Defini-ons  Volume  1   §  200612  Omniture  Effec-ve  Measurement   §  200804  Omniture  Calculated  Metrics  White  Paper   §  200702  Omniture  Effec-ve  Segmenta-on  Guide   §  200810  Ronnestam  Online  Adver-sing  And  AIDAS   §  201004  Al-meter  Social  Marke-ng  Analy-cs   §  201008  CSR  Customer  Sa-sfac-on  Vs  Delight   §  Google  “Enquiro  Search  Engine  Results  2010  PDF”   §  Google  “Razorfish  Ac-onable  Analy-cs  Report  PDF”   §  Google  “Forrester  Interac-ve  Marke-ng  Metrics  PDF”   April  2011   ©  Datalicious  Pty  Ltd   39  
  • 41. >  Major  data  categories   Campaign  data   TV,  print,  call  center,  search,   web  analy-cs,  ad  serving,  etc       Campaigns   Customers   Customer  data   Direct  mail,  call  center,  web   analy-cs,  emails,  surveys,  etc       Consumer  data   Geo-­‐demographics,  search,   Compe-tors   Consumers   social,  3rd  party  research,  etc       Compe-tor  data   Search,  social,  ad  spend,  3rd   party  research,  news,  etc     April  2011   ©  Datalicious  Pty  Ltd   41  
  • 42. >  Digital  data  is  plen-ful  and  cheap       April  2011   ©  Datalicious  Pty  Ltd   42   Source:  Omniture  Summit,  MaZ  Belkin,  2007  
  • 43. >  Mul-ple  metrics  data  sources   Media  and  search  data   Website,  call  center  and  retail  data   People   People   People   People   reached   engaged   converted   delighted   Quan-ta-ve  and  qualita-ve  research  data   Social  media  data   Social  media   April  2011   ©  Datalicious  Pty  Ltd   43  
  • 44. >  Reach  and  channel  overlap     TV/Print     audience   Banner   Search   audience   audience   April  2011   ©  Datalicious  Pty  Ltd   44  
  • 45. >  Es-ma-ng  reach  and  overlap     §  Apply  average  unique  visitor  count  per  recorded   unique  user  names  to  all  unique  visitor  figures  in   Google  Analy-cs,  Omniture,  etc.   §  Apply  ra-o  of  total  banner  impressions  to  unique   banner  impressions  from  ad  server  to  paid  and   organic  search  impressions  in  Google  AdWords  and   Google  Webmaster  Tools.   §  Compare  Google  Keyword  Tool  impressions  for  a   specific  search  term  to  reach  for  the  same  term  in   Google  Ad  Planner.   §  Or  just  add  the  reach  figures  for  all  channels  up  …   April  2011   ©  Datalicious  Pty  Ltd   45  
  • 46. >  Google  data  in  Australia     Source:  hZp://www.hitwise.com/au/resources/data-­‐centre   April  2011   ©  Datalicious  Pty  Ltd   46  
  • 47. >  Search  at  all  stages     April  2011   ©  Datalicious  Pty  Ltd   47   Source:  Inside  the  Mind  of  the  Searcher,  Enquiro  2004  
  • 48. >  Search  and  brand  strength     April  2011   ©  Datalicious  Pty  Ltd   48  
  • 49. >  Search  and  the  product  lifecycle     Nokia  N-­‐Series   Apple  iPhone   April  2011   ©  Datalicious  Pty  Ltd   49  
  • 50. >  Search  and  media  planning     April  2011   ©  Datalicious  Pty  Ltd   50  
  • 51. >  Search  driving  offline  crea-ve     April  2011   ©  Datalicious  Pty  Ltd   51  
  • 52. Exercise:  Search  insights   April  2011   ©  Datalicious  Pty  Ltd   52  
  • 53. >  Exercise:  Search  insights     §  Iden-fy  key  category  search  terms   –  Data  from  Google  AdWords  Keyword  Tool   –  Search  for  “google  keyword  tool”   –  Wordle  and  IBM  Many  Eyes  for  visualiza-ons   –  Search  for  “wordle  word  clouds”  and  “ibm  many  eyes”   §  Iden-fy  search  term  trends  and  compe-tors   –  Google  Trends  and  Google  Search  Insights   –  Search  for  “google  trends”  and  “google  search  insights”   §  Search  and  media  planning   –  DoubleClick  Ad  Planner  by  Google   –  Search  for  “google  ad  planner”   April  2011   ©  Datalicious  Pty  Ltd   53  
  • 54. >  Cookie  based  tracking  process     What  if:  Someone  deletes  their  cookies?  Or  uses  a  device   that  does  not  support  JavaScript?  Or  uses  two  computers   (work  vs.  home)?  Or  two  people  use  the  same  computer?   April  2011   ©  Datalicious  Pty  Ltd   54   Source:  Google  Analy-cs,  Jus-n  Cutroni,  2007  
  • 55. >  Unique  visitor  overes-ma-on     The  study  examined     data  from  two  of     the  UK’s  busiest     ecommerce     websites,  ASDA   and  William  Hill.     Given  that  more     than  half  of  all  page     impressions  on  these     sites  are  from  logged-­‐in     users,  they  provided  a  robust     sample  to  compare  IP-­‐based  and  cookie-­‐based  analysis  against.   The  results  were  staggering,  for  example  an  IP-­‐based  approach   overes-mated  visitors  by  up  to  7.6  -mes  whilst  a  cookie-­‐based   approach  overes-mated  visitors  by  up  to  2.3  -mes.     April  2011   ©  Datalicious  Pty  Ltd   55   Source:  White  Paper,  RedEye,  2007  
  • 56. >  Maximise  iden-fica-on  points     160%   140%   120%   100%   80%   60%   −−−  Probability  of  iden-fica-on  through  Cookies   40%   20%   0   4   8   12   16   20   24   28   32   36   40   44   48   Weeks   April  2011   ©  Datalicious  Pty  Ltd   56  
  • 57. >  Maximise  iden-fica-on  points   Mobile   Home   Work   Online   Phone   Branch   April  2011   ©  Datalicious  Pty  Ltd   57  
  • 58. >  Combining  data  sources   Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  data   April  2011   ©  Datalicious  Pty  Ltd   58  
  • 59. >  Duplica-on  across  channels     Paid     Bid     Search   Mgmt   $   Banner     Ad     Ads   Server   $   Email     Email   Blast   PlaAorm   $   Organic   Google   Search   Analy-cs   $   April  2011   ©  Datalicious  Pty  Ltd   59  
  • 60. >  Cookie  expira-on  impact   Paid     Bid     Search   Mgmt   $   Banner     Banner     Ad     Ad  Click   Ad  View   Server   $   Email     Email   Expira-on   Blast   PlaAorm   $   Organic   Google   Search   Analy-cs   $   April  2011   ©  Datalicious  Pty  Ltd   60  
  • 61. >  CBA  repor-ng  plaAorms   April  2011   ©  Datalicious  Pty  Ltd   61  
  • 62. >  De-­‐duplica-on  across  channels     Paid     Search   $   Banner     Ads   $   Central   Analy-cs   PlaAorm   Email     Blast   $   Organic   Search   $   April  2011   ©  Datalicious  Pty  Ltd   62  
  • 63. De-­‐duplica-on  across  channels   April  2011   ©  Datalicious  Pty  Ltd   63  
  • 64. Exercise:  Duplica-on  impact   April  2011   ©  Datalicious  Pty  Ltd   64  
  • 65. >  Exercise:  Duplica-on  impact     §  Double-­‐coun-ng  of  conversions  across  channels  can   have  a  significant  impact  on  key  metrics,  especially  CPA   §  Example:  Display  ads  and  paid  search   –  Total  media  budget  of  $10,000  of  which  50%  is  spend  on  paid   search  and  50%  on  display  ads   –  Total  of  100  conversions  across  both  channels  with  a  channel   overlap  of  50%,  i.e.  both  channels  claim  100%  of  conversions   based  on  their  own  repor-ng  but  once  de-­‐duplicated  they   each  only  contributed  50%  of  conversions   –  What  are  the  ini-al  CPA  values  and  what  is  the  true  CPA?   §  Solu-on:  $50  ini-al  CPA  and  $100  true  CPA   –  $5,000  /  100  =  $50  ini-al  CPA  and  $5,000  /  50  =  $100  true   CPA  (which  represents  a  100%  increase)   April  2011   ©  Datalicious  Pty  Ltd   65  
  • 66. >  Single  source  of  truth  repor-ng   Insights   Repor-ng   April  2011   ©  Datalicious  Pty  Ltd   66  
  • 67. April  2011   ©  Datalicious  Pty  Ltd   67  
  • 68. April  2011   ©  Datalicious  Pty  Ltd   68  
  • 69. Google:  “visualisa-on  methods”   April  2011   ©  Datalicious  Pty  Ltd   69  
  • 70. Exercise:  Sta-s-cal  significance   April  2011   ©  Datalicious  Pty  Ltd   70  
  • 71. How  many  survey  responses  do  you  need     if  you  have  10,000  customers?   How  many  email  opens  do  you  need  to  test  2  subject  lines   if  your  subscriber  base  is  50,000?   How  many  orders  do  you  need  to  test  6  banner  execu-ons     if  you  serve  1,000,000  banners   April  2011   ©  Datalicious  Pty  Ltd   71   Google  “nss  sample  size  calculator”  
  • 72. How  many  survey  responses  do  you  need     if  you  have  10,000  customers?   369  for  each  ques-on  or  369  complete  responses   How  many  email  opens  do  you  need  to  test  2  subject  lines   if  your  subscriber  base  is  50,000?  And  email  sends?   381  per  subject  line  or  381  x  2  =  762  email  opens   How  many  orders  do  you  need  to  test  6  banner  execu-ons     if  you  serve  1,000,000  banners?   383  sales  per  banner  execu-on  or  383  x  6  =  2,298  sales   April  2011   ©  Datalicious  Pty  Ltd   72   Google  “nss  sample  size  calculator”  
  • 73. >  Addi-onal  success  metrics     Click   Through   $   Click   Add  To     Cart   Through   Cart   Checkout   ?   $   Click   Page   Page     Product     Through   Bounce   Views   Views   $   Click   Call  back   Store   Through   request   Search   ?   $   April  2011   ©  Datalicious  Pty  Ltd   73  
  • 74. >  Combining  data  sources   Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  data   April  2011   ©  Datalicious  Pty  Ltd   74  
  • 75. >  Behaviours  plus  transac-ons     Site  Behaviour   CRM  Profile   tracking  of  purchase  funnel  stage   one-­‐off  collec-on  of  demographical  data     +   browsing,  checkout,  etc   age,  gender,  address,  etc   tracking  of  content  preferences   customer  lifecycle  metrics  and  key  dates   products,  brands,  features,  etc   profitability,  expira-on,  etc   tracking  of  external  campaign  responses   predic-ve  models  based  on  data  mining   search  terms,  referrers,  etc   propensity  to  buy,  churn,  etc   tracking  of  internal  promo-on  responses   historical  data  from  previous  transac-ons   emails,  internal  search,  etc   average  order  value,  points,  etc   Updated  Con-nuously   Updated  Occasionally   April  2011   ©  Datalicious  Pty  Ltd   75  
  • 76. Exercise:  Customer  IDs   April  2011   ©  Datalicious  Pty  Ltd   76  
  • 77. >  Exercise:  Customer  IDs   To  transac-onal  data   To  reten-on  messages   From  suspect  to   prospect   To  customer   Time   Time   From  behavioural  data   From  awareness  messages   April  2011   ©  Datalicious  Pty  Ltd   77  
  • 78. >  Sample  customer  level  data     April  2011   ©  Datalicious  Pty  Ltd   78  
  • 79. >  Atomic  labs  tag-­‐less  analy-cs   §  Single  point  of  data   capture  and  processing   §  Real-­‐-me  queries  to   enrich  website  data     §  Mul-ple  data  export   op-ons  for  web  analy-cs   §  Enriching  single-­‐customer   view  website  behaviour   April  2011   ©  Datalicious  Pty  Ltd   79  
  • 80. April  2011   ©  Datalicious  Pty  Ltd   80  
  • 81. Sen-ment  analysis:  People  vs.  machine   April  2011   ©  Datalicious  Pty  Ltd   81  
  • 82. >  Al-meter  social  analy-cs     Social  Marke-ng   Analy-cs  is  the   discipline  that  helps   companies  measure,   assess  and  explain  the   performance  of  social   media  ini-a-ves  in  the   context  of  specific   business  objec-ves.   April  2011   ©  Datalicious  Pty  Ltd   82  
  • 83. >  Importance  of  calendar  events     Traffic  spikes  or  other  data  anomalies  without  context  are   very  hard  to  interpret  and  can  render  data  useless   April  2011   ©  Datalicious  Pty  Ltd   83  
  • 84. Calendar  events  to  add  context   April  2011   ©  Datalicious  Pty  Ltd   84  
  • 85. >  Summary  and  ac-on  items     §  Finding  and  developing  the  right  data   –  Ensure  de-­‐duplica-on  via  central  analy-cs   –  Check  reports  for  sta-s-cal  significance   –  Check  data  sources  and  their  accuracy   –  Combine  data  sources  across  channels   –  Start  popula-ng  a  calendar  of  events   April  2011   ©  Datalicious  Pty  Ltd   85  
  • 86. >  Recommended  resources     §  200311  UK  RedEye  Cookie  Case  Study   §  200807  Kaushik  Tracking  Offline  Conversion   §  200904  Kaushik  Standard  Metrics  Revisited   §  201002  Kaushik  8  Compe--ve  Intelligence  Data  Sources   §  201005  Google  Ad  Planner  Data  Wrong  By  Up  To  20%   §  201005  MPI  How  Sta-s-cally  Valid  Is  Your  Survey   §  201009  Google  Analy-cs  How  To  Tag  Links   §  200903  Coremetrics  Conversion  Benchmarks  By  Industry   §  200906  WOM  Online  The  People  Vs  Machines  Debate   §  201007  WSJ  The  Web's  New  Gold  Mine  Your  Secrets   §  201008  Adver-singAge  Are  Marketers  Really  Spying  On  You   April  2011   ©  Datalicious  Pty  Ltd   86  
  • 88. >  Campaign  flow  and  calls  to  ac-on     =  Paid  media   Organic     PR,  WOM,   search   events,  etc   =  Viral  elements   =  Coupons,  surveys   YouTube,     Home  pages,   Paid     TV,  print,     blog,  etc   portals,  etc   search   radio,  etc   Direct  mail,     Landing  pages,   Display  ads,   email,  etc   offers,  etc   affiliates,  etc   C1   C2   CRM   Facebook   program   TwiMer,  etc   C3   POS  kiosks,   Call  center,     loyalty  cards,  etc   retail  stores,  etc   April  2011   ©  Datalicious  Pty  Ltd   88  
  • 89. Exercise:  Campaign  flow   April  2011   ©  Datalicious  Pty  Ltd   89  
  • 90. >  Reach  and  channel  overlap     TV/Print     audience   Banner   Search   audience   audience   April  2011   ©  Datalicious  Pty  Ltd   90  
  • 91. >  Ad  server  exposure  test   Banner   TV/Print   Search   Impression   Response   Response   $   Banner   Search   Direct   Impression   Response   Response   $   Users  are   segmented   before  1st   ad  is  even   Exposed  group:  90%  of  users  get  branded  message   served     Control  group:  10%  of  users  get  non-­‐branded  message   Banner   Search   Direct   Impression   Response   Response   $   April  2011   ©  Datalicious  Pty  Ltd   91  
  • 92. >  Indirect  display  impact     April  2011   ©  Datalicious  Pty  Ltd   92  
  • 93. >  Indirect  display  impact     April  2011   ©  Datalicious  Pty  Ltd   93  
  • 94. April  2011   ©  Datalicious  Pty  Ltd   94  
  • 95. >  Indirect  display  impact     April  2011   ©  Datalicious  Pty  Ltd   95  
  • 96. >  Success  aMribu-on  models     Banner     Paid     Organic   Success   Last  channel   Search   Ad   Search   $100   $100   gets  all  credit   Banner     Paid     Email     Success   First  channel   Ad   $100   Search   Blast   $100   gets  all  credit   Paid     Banner     Affiliate     Success   All  channels  get   Search   Ad   Referral   $100   $100   $100   $100   equal  credit   Print     Social     Paid     Success   All  channels  get   Ad   Media   Search   $33   $33   $33   $100   par-al  credit   April  2011   ©  Datalicious  Pty  Ltd   96  
  • 97. >  First  and  last  click  aMribu-on     Chart  shows   percentage  of   channel  touch   points  that  lead   Paid/Organic  Search   to  a  conversion.   Neither  first     Emails/Shopping  Engines   nor  last-­‐click   measurement   would  provide   true  picture     April  2011   ©  Datalicious  Pty  Ltd   97  
  • 98. >  CBA  first  and  last  touch  reports   April  2011   ©  Datalicious  Pty  Ltd   98  
  • 99. Adobe  campaign  stack  does  not  include   organic  channels  or  banner  impressions   and  does  not  expire  on  any  event,  i.e.   con-nues  as  long  as  the  cookie  is  present.   April  2011   ©  Datalicious  Pty  Ltd   99  
  • 100. >  Full  path  to  purchase   Introducer   Influencer   Influencer   Closer   $   SEM   Banner   Direct     SEO   Online   Generic   Click   Visit   Branded   Banner     SEO   Affiliate   Social   Offline   View   Generic   Click   Media   TV     SEO   Direct     Email   Abandon   Ad   Branded   Visit   Update   April  2011   ©  Datalicious  Pty  Ltd   100  
  • 101. >  Search  call  to  ac-on  for  offline     April  2011   ©  Datalicious  Pty  Ltd   101  
  • 102. April  2011   ©  Datalicious  Pty  Ltd   102  
  • 103. >  PURLs  boos-ng  DM  response  rates   Text   April  2011   ©  Datalicious  Pty  Ltd   103  
  • 104. >  Poten-al  calls  to  ac-on     §  Unique  click-­‐through  URLs   §  Unique  vanity  domains  or  URLs   §  Unique  phone  numbers   §  Unique  search  terms   §  Unique  email  addresses   §  Unique  personal  URLs  (PURLs)   §  Unique  SMS  numbers,  QR  codes   §  Unique  promo-onal  codes,  vouchers   §  Geographic  loca-on  (Facebook,  FourSquare)   §  Plus  regression  analysis  of  cause  and  effect   April  2011   ©  Datalicious  Pty  Ltd   104  
  • 105. >  Unique  phone  numbers   §  1  unique  phone  number     –  Phone  number  is  considered  part  of  the  brand   –  Media  origin  of  calls  cannot  be  established   –  Added  value  of  website  interac-on  unknown   §  2-­‐10  unique  phone  numbers   –  Different  numbers  for  different  media  channels   –  Exclusive  number(s)  reserved  for  website  use   –  Call  origin  data  more  granular  but  not  perfect   –  Difficult  to  rotate  and  pause  numbers   April  2011   ©  Datalicious  Pty  Ltd   105  
  • 106. >  Unique  phone  numbers   §  10+  unique  phone  numbers   –  Different  numbers  for  different  media  channels   –  Different  numbers  for  different  product  categories   –  Different  numbers  for  different  conversion  steps   –  Call  origin  becoming  useful  to  shape  call  script   –  Feasible  to  pause  numbers  to  improve  integrity   §  100+  unique  phone  numbers   –  Different  numbers  for  different  website  visitors   –  Call  origin  and  -me  stamp  enable  individual  match   –  Call  conversions  matched  back  to  search  terms   April  2011   ©  Datalicious  Pty  Ltd   106  
  • 107. >  Jet  Interac-ve  phone  call  data   April  2011   ©  Datalicious  Pty  Ltd   107  
  • 108. >  Full  path  to  purchase   Introducer   Influencer   Influencer   Closer   $   SEM   Banner   Direct     SEO   Online   Generic   Click   Visit   Branded   Banner     SEO   Affiliate   Social   Offline   View   Generic   Click   Media   TV     SEO   Direct     Email   Abandon   Ad   Branded   Visit   Update   April  2011   ©  Datalicious  Pty  Ltd   108  
  • 109. >  Research  online,  shop  offline     April  2011   ©  Datalicious  Pty  Ltd   109   Source:  2008  Digital  Future  Report,  Surveying  The  Digital  Future,  Year  Seven,  USC  Annenberg  School  
  • 110. >  Cross-­‐channel  impact   April  2011   ©  Datalicious  Pty  Ltd   110  
  • 111. >  Offline  sales  driven  by  online   Adver-sing     Phone   Credit  check,   campaign   order   fulfilment   Retail   Confirma-on   order   email   Website   Online   Online  order   Virtual  order   research   order   confirma-on   confirma-on   Cookie   April  2011   ©  Datalicious  Pty  Ltd   111  
  • 112. Exercise:  Offline  conversions   April  2011   ©  Datalicious  Pty  Ltd   112  
  • 113. >  Exercise:  Offline  conversions     §  Email  click-­‐through  aker  purchase   §  First  online  login  aker  purchase   §  Unique  website  or  visitor  phone  number   §  Call  back  request  or  online  chat   §  Unique  website  promo-on  code   §  Unique  printable  vouchers   §  Store  locator  searches   §  Make  an  appointment  online   April  2011   ©  Datalicious  Pty  Ltd   113  
  • 114. >  Single  source  of  truth  repor-ng   Insights   Repor-ng   April  2011   ©  Datalicious  Pty  Ltd   114  
  • 115. >  Where  to  collect  the  data     Ad  Server   Web  Analy-cs   Banner  impressions   Referral  visits   Banner  clicks   Social  media  visits   +   Organic  search  visits   Paid  search  clicks   Paid  search  visits   Email  visits,  etc   Lacking  organic  visits   Lacking  ad  impressions   More  granular  &  complex   Less  granular  &  complex   April  2011   ©  Datalicious  Pty  Ltd   115  
  • 116. >  Raw  aMribu-on  data   Web  Analy-cs   AFFILIATE  >  SEO  >  $$$   SEM  >  SOCIAL  >  EMAIL  >  DIRECT  >  $$$     Ad  Server   01/01/2011  12:00  AD  IMPRESSION   01/01/2011  12:05  SEO   07/01/2011  17:00  EMAIL   08/01/2011  15:00  $$$     April  2011   ©  Datalicious  Pty  Ltd   116  
  • 117. >  Combine  purchase  paths   Mobile   Home   Work   Tablet   Media   Etc   April  2011   ©  Datalicious  Pty  Ltd   117  
  • 118. >  Combining  data  sources   April  2011   ©  Datalicious  Pty  Ltd   118  
  • 119. >  Understanding  channel  mix   April  2011   ©  Datalicious  Pty  Ltd   119  
  • 120. April  2011   ©  Datalicious  Pty  Ltd   120  
  • 121. >  Website  entry  survey     De-­‐duped  Campaign  Report   Greatest  Influencer  on  Branded  Search  /  STS   }   Channel   %  of  Conversions   Channel   %  of  Influence   Straight  to  Site   27%   Word  of  Mouth   32%   SEO  Branded   15%   Blogging  &  Social  Media   24%   SEM  Branded   9%   Newspaper  Adver-sing   9%   SEO  Generic   7%   Display  Adver-sing   14%   SEM  Generic   14%   Email  Marke-ng   7%   Display  Adver-sing   7%   Retail  Promo-ons   14%   Affiliate  Marke-ng   9%   Referrals   5%   Conversions  aZributed  to  search  terms   Email  Marke-ng   7%   that  contain  brand  keywords  and  direct   website  visits  are  most  likely  not  the   origina-ng  channel  that  generated  the   awareness  and  as  such  conversion   credits  should  be  re-­‐allocated.     April  2011   ©  Datalicious  Pty  Ltd   121  
  • 122. >  Adjus-ng  for  offline  impact   -­‐5   -­‐15   -­‐10   +5   +15   +10   April  2011   ©  Datalicious  Pty  Ltd   122  
  • 123. April  2011   ©  Datalicious  Pty  Ltd   123  
  • 124. April  2011   ©  Datalicious  Pty  Ltd   124  
  • 125. >  ClearSaleing  media  aMribu-on   April  2011   ©  Datalicious  Pty  Ltd   125  
  • 126. >  Success  aMribu-on  models     Introducer   Influencer   Influencer   Closer   $   Even     25%   25%   25%   25%   AMrib.   Exclusion   33%   33%   33%   0%   AMrib.   PaMern   30%   20%   20%   30%   AMrib.   April  2011   ©  Datalicious  Pty  Ltd   126  
  • 127. >  Path  across  different  segments   Introducer   Influencer   Influencer   Closer   $   Product     Channel  1   Channel  2   Channel  3   Channel  4   A  vs.  B   New   Channel  1   Channel  2   Channel  3   Channel  4   prospects   Exis-ng   Channel  1   Channel  2   Channel  3   Product  4   customers   April  2011   ©  Datalicious  Pty  Ltd   127  
  • 128. Exercise:  AMribu-on  model   April  2011   ©  Datalicious  Pty  Ltd   128  
  • 129. >  Exercise:  AMribu-on  models     Introducer   Influencer   Influencer   Closer   $   Even     25%   25%   25%   25%   AMrib.   Exclusion   33%   33%   33%   0%   AMrib.   ?   ?   ?   ?   Custom   AMrib.   April  2011   ©  Datalicious  Pty  Ltd   129  
  • 130. >  Common  aMribu-on  models   §  Allocate  more  conversion  credits  to  more   recent  touch  points  for  brands  with  a  strong   baseline  to  s-mulate  repeat  purchases     §  Allocate  more  conversion  credits  to  more   recent  touch  points  for  brands  with  a  direct   response  focus   §  Allocate  more  conversion  credits  to  ini-a-ng   touch  points  for  new  and  expensive  brands  and   products  to  insert  them  into  the  mindset   April  2011   ©  Datalicious  Pty  Ltd   130  
  • 131. >  Media  aMribu-on  phases     §  Phase  1:  De-­‐duplica-on   –  Conversion  de-­‐duplica-on  across  all  channels   –  Requires  one  central  repor-ng  plaaorm   –  Limited  to  first/last  click  aZribu-on   §  Phase  2:  Direct  response  pathing   –  Response  pathing  across  paid  and  organic  channels   –  Only  covers  clicks  and  not  mere  banner  views   –  Can  be  enabled  in  Google  Analy-cs  and  Omniture   §  Phase  3:  Full  purchase  path   –  Direct  response  tracking  including  banner  exposure   –  Google  Analy-cs  and  Omniture  data  collec-on  limited   –  Easier  to  import  addi-onal  channels  into  ad  server   April  2011   ©  Datalicious  Pty  Ltd   131  
  • 132. >  Summary  and  ac-on  items     §  Campaign  flow  and  media  aZribu-on   –  Draw  campaign  flow  for  your  company   –  Check  plaaorm  cookie  expira-on  periods   –  Enable  pathing  of  direct  campaign  responses   –  Inves-gate  addi-onal  pathing  op-ons   –  Inves-gate  how  to  track  offline  conversions   April  2011   ©  Datalicious  Pty  Ltd   132  
  • 133. >  Recommended  resources     §  200812  ComScore  How  Online  Adver-sing  Works   §  200905  iProspect  Research  Study  Search  And  Display   §  200904  ClearSaleing  American  AZribu-on  Index   §  201003  Datalicious  Tying  Offline  Sales  To  Online  Media   §  Google:  “Forrester  Campaign  AZribu-on  Framework  PDF”   April  2011   ©  Datalicious  Pty  Ltd   133  
  • 134. Contact  us   insights@datalicious.com     Learn  more   blog.datalicious.com     Follow  us   twiMer.com/datalicious     April  2011   ©  Datalicious  Pty  Ltd   134  
  • 135. Data  >  Insights  >  Ac-on