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Digi-Tech Marketing Data Strategy

Jan 22, 2015

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Page 1: Digi-Tech Marketing Data Strategy

>  Marke(ng  Data  Strategy  <  Smart  data  driven  marke-ng  

Page 2: Digi-Tech Marketing Data Strategy

>  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  

June  2010   ©  Datalicious  Pty  Ltd   2  

Page 3: Digi-Tech Marketing Data Strategy

>  Smart  data  driven  marke(ng  

June  2010   ©  Datalicious  Pty  Ltd   3  

Media  A;ribu(on  &  Modeling  

Op(mise  channel  mix,  predict  sales  

Tes(ng  &  Op(misa(on  Remove  barriers,  drive  sales  

Boos(ng  ROI  

Targeted  Direct  Marke(ng    Increase  relevance,  reduce  churn  

“Using  data  to  widen  the  funnel”  

Page 4: Digi-Tech Marketing Data Strategy

>  Wide  range  of  data  services  

June  2010   ©  Datalicious  Pty  Ltd   4  

Data  PlaIorms    Data  collec(on  and  processing    Web  analy(cs  solu(ons    Omniture,  Google  Analy(cs,  etc    Tag-­‐less  online  data  capture    End-­‐to-­‐end  data  plaIorms    IVR  and  call  center  repor(ng    Single  customer  view  

Insights  Analy(cs    Data  mining  and  modelling    Customised  dashboards    Tableau,  SpoIire,  SPSS,  etc    Media  a;ribu(on  models    Market  and  compe(tor  trends    Social  media  monitoring    Customer  profiling  

Ac(on  Campaigns    Data  usage  and  applica(on    Marke(ng  automa(on    Alterian,  SiteCore,  Inxmail,  etc    Targe(ng  and  merchandising    Internal  search  op(misa(on    CRM  strategy  and  execu(on    Tes(ng  programs    

Page 5: Digi-Tech Marketing Data Strategy

>  Clients  across  all  industries  

June  2010   ©  Datalicious  Pty  Ltd   5  

Page 6: Digi-Tech Marketing Data Strategy

>  Today  

§  Capturing  data  – Op-ons,  limita-ons,  innova-ons  

§  Genera-ng  insights  – Process,  metrics,  examples  

§  Taking  ac-on  – Media,  targe-ng,  tes-ng  

June  2010   ©  Datalicious  Pty  Ltd   6  

Page 7: Digi-Tech Marketing Data Strategy

June  2010   ©  Datalicious  Pty  Ltd   7  

Ques(ons?  Yell  out  or  tweet  @datalicious  

 

Page 8: Digi-Tech Marketing Data Strategy

June  2010   ©  Datalicious  Pty  Ltd   8  

Clive  Humby:  Data  is  the  new  oil  

Page 9: Digi-Tech Marketing Data Strategy

June  2010   ©  Datalicious  Pty  Ltd   9  

Oil  and  data  come  at  a  price  

Page 10: Digi-Tech Marketing Data Strategy

>  Google  Ngram:  Privacy    

June  2010   ©  Datalicious  Pty  Ltd   10  

Page 11: Digi-Tech Marketing Data Strategy

June  2010   ©  Datalicious  Pty  Ltd  

Collec(ng  data    for  the  sake  of  it  or  to  add  value  to  customers?  

11  

Page 12: Digi-Tech Marketing Data Strategy

Marke(ng  

Mix  

Product  

Price  

Place  

Promo(on  

Physical  Evidence  

People  

Process  

Partners  

June  2010   ©  Datalicious  Pty  Ltd   12  

Page 13: Digi-Tech Marketing Data Strategy

>  Capturing  data  

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  

June  2010   ©  Datalicious  Pty  Ltd   13  

Page 14: Digi-Tech Marketing Data Strategy

>  Digital  data  is  plen(ful  and  cheap      

June  2010   ©  Datalicious  Pty  Ltd   14  

Source:  Omniture  Summit,  MaV  Belkin,  2007  

Page 15: Digi-Tech Marketing Data Strategy

>  Digital  metric  categories  

June  2010   ©  Datalicious  Pty  Ltd   15  

Source:  Accuracy  Whitepaper  for  web  analy-cs,  Brian  CliYon,  2008  

+Social  

Page 16: Digi-Tech Marketing Data Strategy

>  What  plaIorm  to  use  

June  2010   ©  Datalicious  Pty  Ltd   16  

Time,  Control  

Soph

is-ca-o

n  

Stage  1:  Data   Stage  2:  Insights   Stage  3:  Ac(on  

Third  par-es  control  most  data,  ad  hoc  repor-ng  only,  i.e.    what  happened?  

Data  is  being  brought    in-­‐house,  shiY  towards  insights  genera-on  and  data  mining,  i.e.  why  did  it  happen?  

Data  is  fully  owned    in-­‐house,  advanced  predic-ve  modelling  and  trigger  based  marke-ng,  i.e.  what    will  happen  and    making  it  happen!  

Page 17: Digi-Tech Marketing Data Strategy

>  Governance  and  data  integrity  

June  2010   ©  Datalicious  Pty  Ltd   17  

Source:  Omniture  Summit,  MaV  Belkin,  2007  

Page 18: Digi-Tech Marketing Data Strategy

>  Tag-­‐less  data  capture  

June  2010   ©  Datalicious  Pty  Ltd   18  

Google:  “atomic  labs”      www.atomiclabs.com  

Page 19: Digi-Tech Marketing Data Strategy

>  Google  data  in  Australia    

June  2010   ©  Datalicious  Pty  Ltd   19  

Source:  hVp://www.hitwise.com/au/resources/data-­‐centre  

Page 20: Digi-Tech Marketing Data Strategy

>  Search  at  all  stages    

June  2010   ©  Datalicious  Pty  Ltd   20  

Source:  Inside  the  Mind  of  the  Searcher,  Enquiro  2004  

Page 21: Digi-Tech Marketing Data Strategy

>  Search  call  to  ac(on  for  offline    

June  2010   ©  Datalicious  Pty  Ltd   21  

Page 22: Digi-Tech Marketing Data Strategy

June  2010   ©  Datalicious  Pty  Ltd   22  

Page 23: Digi-Tech Marketing Data Strategy

>  PURLs  boos(ng  DM  response  rates  

June  2010   ©  Datalicious  Pty  Ltd   23  

Text  

Page 24: Digi-Tech Marketing Data Strategy

>  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  

June  2010   ©  Datalicious  Pty  Ltd   24  

Page 25: Digi-Tech Marketing Data Strategy

>  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  

June  2010   ©  Datalicious  Pty  Ltd   25  

Page 26: Digi-Tech Marketing Data Strategy

>  Jet  Interac(ve  phone  call  data  

June  2010   ©  Datalicious  Pty  Ltd   26  

Page 27: Digi-Tech Marketing Data Strategy

>  Bad  experience:  67%  hang  up  

June  2010   ©  Datalicious  Pty  Ltd   27  

2/3  of  callers  hang  up  the  phone  as  they  cannot  get  what  they  want  fast  enough.  

Page 28: Digi-Tech Marketing Data Strategy

>  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  

June  2010   ©  Datalicious  Pty  Ltd   28  

Calls  to  ac(on  can  help  shape  the  customer  experience  not  just  evaluate  responses  

Page 29: Digi-Tech Marketing Data Strategy

>  Cookie  based  tracking  process    

June  2010   ©  Datalicious  Pty  Ltd   29  

Source:  Google  Analy-cs,  Jus-n  Cutroni,  2007  

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?  

Page 30: Digi-Tech Marketing Data Strategy

>  Duplica(on  across  channels    

June  2010   ©  Datalicious  Pty  Ltd   30  

Banner    Ads  

Email    Blast  

Paid    Search  

Organic  Search  

$  Bid    Mgmt  

Ad    Server  

Email  PlaIorm  

Google  Analy(cs  

$  

$  

$  

Page 31: Digi-Tech Marketing Data Strategy

Central  Analy(cs  PlaIorm  

$  

$  

$  

>  De-­‐duplica(on  across  channels    

June  2010   ©  Datalicious  Pty  Ltd   31  

Banner    Ads  

Email    Blast  

Paid    Search  

Organic  Search  

$  

Page 32: Digi-Tech Marketing Data Strategy

>  Datalicious  SuperTag  

June  2010   ©  Datalicious  Pty  Ltd   32  

Ad  Sever,  Paid  Search   SuperTag   Web  

Analy-cs  

Use  the  same  business  rules  to  trigger  conversions    across  all  plaIorms  to  reduce  discrepancies  

Page 33: Digi-Tech Marketing Data Strategy

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.    

>  Unique  visitor  overes(ma(on    

June  2010   ©  Datalicious  Pty  Ltd   33  

Source:  White  Paper,  RedEye,  2007  

Page 34: Digi-Tech Marketing Data Strategy

>  Maximise  iden(fica(on  points    

20%  

40%  

60%  

80%  

100%  

120%  

140%  

160%  

0   4   8   12   16   20   24   28   32   36   40   44   48  

Weeks  

−−−  Probability  of  iden-fica-on  through  Cookies  

June  2010   34  ©  Datalicious  Pty  Ltd  

Page 35: Digi-Tech Marketing Data Strategy

>  Customer  profiling  in  ac(on    

June  2010   ©  Datalicious  Pty  Ltd   35  

Using  website  and  email  responses  to  learn  a  liVle  bite  more  about  

subscribers  at  every    touch  point  to  keep  

 refining  profiles  and  messages.  

Page 36: Digi-Tech Marketing Data Strategy

>  Online  form  best  prac(ce  

June  2010   ©  Datalicious  Pty  Ltd   36  

Maximise  data  integrity  Age  vs.  year  of  birth  Free  text  vs.  op-ons  

Use  auto-­‐complete    wherever  possible  

Page 37: Digi-Tech Marketing Data Strategy

>  Research  online,  shop  offline    

June  2010   ©  Datalicious  Pty  Ltd   37  

Source:  2008  Digital  Future  Report,  Surveying  The  Digital  Future,  Year  Seven,  USC  Annenberg  School  

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>  Offline  sales  driven  by  online  

June  2010   ©  Datalicious  Pty  Ltd   38  

Website  research  

Phone  order  

Retail  order  

Online  order  

Cookie  

Adver(sing    campaign  

Credit  check,  fulfilment  

Online  order  confirma(on  

Virtual  order  confirma(on  

Confirma(on  email  

Page 39: Digi-Tech Marketing Data Strategy

>  Summary:  Capturing  data  

§  Plenty  of  data  sources  and  planorms  §  Especially  search  is  great  free  data  source  § Maintaining  data  integrity  takes  effort  §  Cookie  technology  has  its  limita-ons  §  New  tag-­‐less  technologies  emerging  § Maximise  iden-fica-on  points  §  Offline  can  be  -ed  to  online  

June  2010   ©  Datalicious  Pty  Ltd   39  

Page 40: Digi-Tech Marketing Data Strategy

>  Genera(ng  insights  

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  

June  2010   ©  Datalicious  Pty  Ltd   40  

Page 41: Digi-Tech Marketing Data Strategy

>  Corporate  data  journey    

June  2010   ©  Datalicious  Pty  Ltd   41  

Time,  Control  

Soph

is-ca-o

n  

Stage  1  

Data  Stage  2  

Insights  Stage  3  Ac(on  

Third  par-es  control  most  data,  ad  hoc  repor-ng  only,  i.e.    what  happened?  

Data  is  being  brought    in-­‐house,  shiY  towards  insights  genera-on  and  data  mining,  i.e.  why  did  it  happen?  

Data  is  fully  owned    in-­‐house,  advanced  predic-ve  modelling  and  trigger  based  marke-ng,  i.e.  what    will  happen  and    making  it  happen!  

“Followers”  

“Leaders”  

“Laggards”  

Page 42: Digi-Tech Marketing Data Strategy

>  Process  is  key  to  success    

June  2010   ©  Datalicious  Pty  Ltd   42  

Source:  Omniture  Summit,  MaV  Belkin,  2007  

Page 43: Digi-Tech Marketing Data Strategy

Awareness   Interest   Desire   Ac(on   Sa(sfac(on  

>  AIDA  and  AIDAS  formulas    

June  2010   ©  Datalicious  Pty  Ltd   43  

Social  media  

New  media  

Old  media  

Page 44: Digi-Tech Marketing Data Strategy

Reach  (Awareness)  

Engagement  (Interest  &  Desire)  

Conversion  (Ac-on)  

+Buzz  (Sa-sfac-on)  

>  Simplified  AIDAS  funnel    

June  2010   ©  Datalicious  Pty  Ltd   44  

Page 45: Digi-Tech Marketing Data Strategy

People  reached  

People  engaged  

People  converted  

People  delighted  

>  Marke(ng  is  about  people    

June  2010   ©  Datalicious  Pty  Ltd   45  

40%   10%   1%  

Page 46: Digi-Tech Marketing Data Strategy

People  reached  

People  engaged  

People  converted  

People  delighted  

>  Addi(onal  funnel  breakdowns    

June  2010   ©  Datalicious  Pty  Ltd   46  

40%   10%   1%  

New  prospects  vs.  exis-ng  customers  

Brand  vs.  direct  response  campaign  

Page 47: Digi-Tech Marketing Data Strategy

June  2010   ©  Datalicious  Pty  Ltd   47  

New  vs.  returning  visitors  

Page 48: Digi-Tech Marketing Data Strategy

June  2010   ©  Datalicious  Pty  Ltd   48  

AU/NZ  vs.  rest  of  world  

Page 49: Digi-Tech Marketing Data Strategy

>  Poten(al  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      June  2010   ©  Datalicious  Pty  Ltd   49  

Page 50: Digi-Tech Marketing Data Strategy

>  Conversion  funnel  1.0  

June  2010  

Conversion  funnel  Product  page,  add  to  shopping  cart,  view  shopping  cart,  cart  checkout,  payment  details,  shipping  informa-on,  order  confirma-on,  etc  

Conversion  event  

Campaign  responses  

©  Datalicious  Pty  Ltd   50  

Page 51: Digi-Tech Marketing Data Strategy

>  Conversion  funnel  2.0  

June  2010  

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  

©  Datalicious  Pty  Ltd   51  

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>  Addi(onal  success  metrics  

June  2010   ©  Datalicious  Pty  Ltd   52  

Click  Through  

Add  To  Cart  

Click  Through  

Bounce  Rate  

Click  Through   $  

Click  Through  

Call  back  requests  

Store  Searches   >  ...   $  

$  

$  Cart  Checkout  

Pages  Per  Visit  

?  

Avg  Cart  Value  

Page 53: Digi-Tech Marketing Data Strategy

Exercise:  Sta(s(cal  significance  

June  2010   ©  Datalicious  Pty  Ltd   53  

Page 54: Digi-Tech Marketing Data Strategy

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  

Google  “nss  sample  size  calculator”  June  2010   ©  Datalicious  Pty  Ltd   54  

Page 55: Digi-Tech Marketing Data Strategy

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  

Google  “nss  sample  size  calculator”  June  2010   ©  Datalicious  Pty  Ltd   55  

Page 56: Digi-Tech Marketing Data Strategy

Exercise:  Metrics  framework  

June  2010   ©  Datalicious  Pty  Ltd   56  

Page 57: Digi-Tech Marketing Data Strategy

Level   Reach   Engagement   Conversion   +Buzz  

Level  1,  people  

Level  2,  strategic  

Level  3,  tac(cal  

Funnel  breakdowns  

>  Exercise:  Metrics  framework    

June  2010   ©  Datalicious  Pty  Ltd   57  

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Level   Reach   Engagement   Conversion   +Buzz  

Level  1,  people  

People  reached  

People  engaged  

People  converted  

People  delighted  

Level  2,  strategic  

Display  impressions   ?   ?   ?  

Level  3,  tac(cal  

Interac(on  rate,  etc   ?   ?   ?  

Funnel  breakdowns   Exis(ng  customers  vs.  new  prospects,  products,  etc  

>  Exercise:  Metrics  framework    

June  2010   ©  Datalicious  Pty  Ltd   58  

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>  Establishing  a  baseline  

June  2010   ©  Datalicious  Pty  Ltd   59  

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.  

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Campaign  response  data  

>  Combining  data  sources  

June  2010   ©  Datalicious  Pty  Ltd   60  

Customer  profile  data  

+   The  whole  is  greater    than  the  sum  of  its  parts  

Website  behavioural  data  

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>  Transac(ons  plus  behaviours  

June  2010   ©  Datalicious  Pty  Ltd   61  

+  one-­‐off  collec-on  of  demographical  data    age,  gender,  address,  etc  customer  lifecycle  metrics  and  key  dates  profitability,  expira(on,  etc  predic-ve  models  based  on  data  mining  

propensity  to  buy,  churn,  etc  historical  data  from  previous  transac-ons  

average  order  value,  points,  etc  

CRM  Profile  

Updated  Occasionally  

tracking  of  purchase  funnel  stage  

browsing,  checkout,  etc  tracking  of  content  preferences  

products,  brands,  features,  etc  tracking  of  external  campaign  responses  

search  terms,  referrers,  etc  tracking  of  internal  promo-on  responses  

emails,  internal  search,  etc  

Site  Behaviour  

Updated  Con(nuously  

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>  Sample  customer  level  data    

June  2010   ©  Datalicious  Pty  Ltd   62  

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Geo-­‐demographic  data  

>  Enhancing  data  sources  

June  2010   ©  Datalicious  Pty  Ltd   63  

3rd  party  data  

+   The  whole  is  greater    than  the  sum  of  its  parts  

Customer  profile  data  

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>  Geo-­‐demographic  segments  

June  2010   ©  Datalicious  Pty  Ltd   64  

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June  2010   ©  Datalicious  Pty  Ltd   65  

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>  Hitwise  Mosaic  segment  swing  

australia.com  vs.  newzealand.com   australia.com  vs.  bulafiji.com    

June  2010   ©  Datalicious  Pty  Ltd   66  

Source:  Hitwise,  2006  

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>  Single  source  of  truth  repor(ng  

June  2010   ©  Datalicious  Pty  Ltd   67  

Insights   Repor(ng  

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June  2010   ©  Datalicious  Pty  Ltd   68  

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June  2010   ©  Datalicious  Pty  Ltd  

Thinking  outside  the  box  

69  

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>  Store  locator  searches  

June  2010   ©  Datalicious  Pty  Ltd   70  

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>  Search  and  brand  strength    

June  2010   ©  Datalicious  Pty  Ltd   71  

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>  Search  and  media  planning    

June  2010   ©  Datalicious  Pty  Ltd   72  

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>  Search  driving  offline  crea(ve    

June  2010   ©  Datalicious  Pty  Ltd   73  

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>  Importance  of  calendar  events    

June  2010   ©  Datalicious  Pty  Ltd   74  

Traffic  spikes  or  other  data  anomalies  without  context  are  very  hard  to  interpret  and  can  render  data  useless  

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>  Summary:  Genera(ng  insights  

§  Right  resources  and  processes  are  key  §  Define  a  standardised  metrics  framework  § Maintain  framework  to  enable  comparison  §  Combine  data  sets  for  hidden  insights    §  Establish  a  single  (data)  source  of  truth  §  Think  outside  the  box  and  across  channels  §  Data  does  not  equal  significance  

June  2010   ©  Datalicious  Pty  Ltd   75  

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>  Taking  ac(on  

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010  

June  2010   ©  Datalicious  Pty  Ltd   76  

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>  Smart  data  driven  marke(ng  

June  2010   ©  Datalicious  Pty  Ltd   77  

Media  A;ribu(on  &  Modeling  

Op(mise  channel  mix,  predict  sales  

Tes(ng  &  Op(misa(on  Remove  barriers,  drive  sales  

Boos(ng  ROI  

Targeted  Direct  Marke(ng    Increase  relevance,  reduce  churn  

“Using  data  to  widen  the  funnel”  

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Direct  mail,    email,  etc  

Facebook  Twi;er,  etc  

>  Campaign  flow  and  calls  to  ac(on    

June  2010   ©  Datalicious  Pty  Ltd   78  

POS  kiosks,  loyalty  cards,  etc  

CRM  program  

Home  pages,  portals,  etc  

YouTube,    blog,  etc  

Paid    search  

Organic    search  

Landing  pages,  offers,  etc  

PR,  WOM,  events,  etc  

TV,  print,    radio,  etc  

C2  

C3  

=  Paid  media  

=  Viral  elements  

Call  center,    retail  stores,  etc  

=  Coupons,  surveys  

Display  ads,  affiliates,  etc  

C1  

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>  Success  a;ribu(on  models    

June  2010   ©  Datalicious  Pty  Ltd   79  

Banner    Ad  $100  

Email    Blast  

Paid    Search  $100  

Banner    Ad  $100  

Affiliate    Referral  $100  

Success  $100  

Success  $100  

Banner    Ad  

Paid    Search  

Organic  Search  $100  

Success  $100  

Last  channel  gets  all  credit  

First  channel  gets  all  credit  

All  channels  get  equal  credit  

Print    Ad  $33  

Social    Media  $33  

Paid    Search  $33  

Success  $100  

All  channels  get  par(al  credit  

Paid    Search  

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>  First  and  last  click  a;ribu(on    

June  2010   ©  Datalicious  Pty  Ltd   80  

Chart  shows  percentage  of  channel  touch  points  that  lead  to  a  conversion.  

Neither  first    nor  last-­‐click  measurement  would  provide  true  picture    

Paid/Organic  Search  

Emails/Shopping  Engines  

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Closer  

SEM  Generic  

Banner    View  

TV    Ad  

>  Full  path  to  purchase  

June  2010   ©  Datalicious  Pty  Ltd   81  

Influencer   Influencer   $  

Banner  Click   Online  

SEO  Generic  

Affiliate  Click   Offline  

SEO  Branded  

Direct    Visit  

Email  Update   Abandon  

Direct    Visit  

Social  Media  

SEO  Branded  

Introducer  

Page 82: Digi-Tech Marketing Data Strategy

>  Understanding  channel  mix  

June  2010   ©  Datalicious  Pty  Ltd   82  

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>  ClearSaleing  media  a;ribu(on  

June  2010   ©  Datalicious  Pty  Ltd   83  

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June  2010   ©  Datalicious  Pty  Ltd   84  

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The  right  message  Via  the  right  channel  To  the  right  person  At  the  right  -me  

Targe(ng  

June  2010   ©  Datalicious  Pty  Ltd   85  

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Capture  internet  traffic  Capture  50-­‐100%  of  fair  market  share  of  traffic  

Increase  consumer  engagement  Exceed  50%  of  best  compe-tor’s  engagement  rate    

Capture  qualified  leads  and  sell  Convert  10-­‐15%  to  leads  and  of  that  20%  to  sales  

Building  consumer  loyalty  Build  60%  loyalty  rate  and  40%  sales  conversion  

Increase  online  revenue  Earn  10-­‐20%  incremental  revenue  online  

>  Increase  revenue  by  10-­‐20%    

June  2010   ©  Datalicious  Pty  Ltd   86  

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>  New  consumer  decision  journey  

June  2010   ©  Datalicious  Pty  Ltd   87  

The  consumer  decision  process  is  changing  from  linear  to  circular.  

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>  New  consumer  decision  journey  

June  2010   ©  Datalicious  Pty  Ltd   88  

The  consumer  decision  process  is  changing  from  linear  to  circular.  

Change  increases  the  importance  of  experience  during  research  phase.  

Online  research    

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June  2010   ©  Datalicious  Pty  Ltd   89  

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>  Coordina(on  across  channels        

June  2010   ©  Datalicious  Pty  Ltd   90  

Off-­‐site  targe(ng  

On-­‐site  targe(ng  

Profile    targe(ng  

Genera(ng  awareness  

Crea(ng  engagement  

Maximising  revenue  

TV,  radio,  print,  outdoor,  search  marke-ng,  display  ads,  performance  networks,  affiliates,  social  media,  etc  

Retail  stores,  in-­‐store  kiosks,  call  centers,  brochures,  websites,  mobile  apps,  online  chat,  social  media,  etc  

Outbound  calls,  direct  mail,  emails,  social  media,  SMS,  mobile  apps,  etc  

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Off-­‐site  targe-ng  

On-­‐site  targe-ng  

Profile  targe-ng  

>  Combining  targe(ng  plaIorms    

June  2010   ©  Datalicious  Pty  Ltd   91  

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June  2010   ©  Datalicious  Pty  Ltd   92  

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June  2010   ©  Datalicious  Pty  Ltd   93  

Take  a  closer  look  at  our  cash  flow  solu(ons  

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June  2010   ©  Datalicious  Pty  Ltd   94  

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On-­‐site    segments  

Off-­‐site  segments  

>  Combining  technology    

June  2010   ©  Datalicious  Pty  Ltd   95  

CRM  

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>  Datalicious  SuperTag  

June  2010   ©  Datalicious  Pty  Ltd   96  

§ One  tag  for  all  sites  and  planorms  §  Hosted  internally  or  externally  §  Fast  tag  implementa-on/updates  §  Eliminates  JavaScript  caching  §  Enables  code  tes-ng  on  live  site  §  Enables  heat  map  implementa-on  §  Enables  redirects  for  A/B  tes-ng  §  Enables  network  wide  re-­‐targe-ng  §  Enables  live  chat  implementa-on  §  Plus  mul--­‐channel  media  aVribu-on  

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>  Affinity  re-­‐targe(ng  in  ac(on    

June  2010   ©  Datalicious  Pty  Ltd   97  

Different  type  of    visitors  respond  to    different  ads.  By  using  category  affinity  targe-ng,    response  rates  are    liYed  significantly    across  products.  

Message  CTR  By  Category  Affinity  

Postpay   Prepay   Broadb.   Business  

Blackberry  Bold   - - - + 5GB  Mobile  Broadband   - - + - Blackberry  Storm   + - + + 12  Month  Caps   - + - +

Google:  “vodafone  omniture  case  study”    or  h;p://bit.ly/de70b7  

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>  Ad-­‐sequencing  in  ac(on  

June  2010   ©  Datalicious  Pty  Ltd   98  

Marke-ng  is  about  telling  stories  and  

stories  are  not  sta-c  but  evolve  over  -me  

Ad-­‐sequencing  can  help  to  evolve  stories  over  -me  the    more  users  engage  with  ads  

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>  Sample  site  visitor  composi(on    

June  2010   ©  Datalicious  Pty  Ltd   99  

30%  exis(ng  customers  with  extensive  profile  including  transac-onal  history  of  which  maybe  50%  can  actually  be  iden-fied  as  individuals    

30%  new  visitors  with  no  previous  website  history  aside  from  campaign  or  referrer  data  of  which  maybe  50%  is  useful  

10%  serious  prospects  with  limited  profile  data  

30%  repeat  visitors  with  referral  data  and  some  website  history  allowing  50%  to  be  segmented  by  content  affinity  

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Exercise:  Targe(ng  matrix  

June  2010   ©  Datalicious  Pty  Ltd   100  

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Purchase  Cycle  

Segments:  Colour,  price,  product  affinity,  etc  

Media  Channels  

Data    Points  

Default,  awareness  

Research,  considera(on  

Purchase  intent  

Reten(on,  up/cross-­‐sell  

>  Exercise:  Targe(ng  matrix  

June  2010   ©  Datalicious  Pty  Ltd   101  

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Purchase  Cycle  

Segments:  Colour,  price,  product  affinity,  etc  

Media  Channels  

Data    Points  

Default,  awareness  

Have  you    seen  A?  

Have  you    seen  B?  

Display,  search,  etc   Default  

Research,  considera(on  

A  has  great    features!  

B  has  great    features!  

Search,  website,  etc  

Ad  clicks,  prod  views  

Purchase  intent  

A  delivers  great  value!  

B  delivers  great  value!  

Website,  emails,  etc  

Cart  adds,  checkouts  

Reten(on,  up/cross-­‐sell  

Why  not  buy  B?  

Why  not  buy  A?  

Direct  mails,  emails,  etc  

Email  clicks,  logins,  etc  

>  Exercise:  Targe(ng  matrix  

June  2010   ©  Datalicious  Pty  Ltd   102  

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>  Quality  content  is  key    

Avinash  Kaushik:    “The  principle  of  garbage  in,  garbage  out  applies  here.  […  what  makes  a  behaviour  

targe;ng  pla<orm  ;ck,  and  produce  results,  is  not  its  intelligence,  it  is  your  ability  to  actually  feed  it  the  right  content  which  it  can  then  target  [….  You  feed  your  BT  system  crap  and  it  will  quickly  and  efficiently  target  crap  to  your  

customers.  Faster  then  you  could    ever  have  yourself.”  

June  2010   ©  Datalicious  Pty  Ltd   103  

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>  ClickTale  tes(ng  case  study    

June  2010   ©  Datalicious  Pty  Ltd   104  

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Test   Segment   Content   KPIs   Poten(al   Results  

Test  #1A     New  prospects  

Conversion  form  A  

Next  step,  order,  etc   ?   ?  

Test  #1B   New  prospects  

Conversion  form  B  

Next  step,  order,  etc   ?   ?  

Test  #1N   New  prospects  

Conversion  form  N  

Next  step,  order,  etc   ?   ?  

?   ?   ?   ?   ?   ?  

>  Developing  a  tes(ng  matrix  

June  2010   ©  Datalicious  Pty  Ltd   105  

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>  Summary  

§  There  is  no  magic  formula  for  ROI  §  Focus  on  the  en-re  conversion  funnel  § Media  aVribu-on  is  hard  but  necessary  §  Neither  first  nor  last  click  method  works  §  Create  a  coordinated  targeted  experience  §  Content  is  always  king  no  maVer  what  §  Test,  learn  and  refine  con-nuously  

June  2010   ©  Datalicious  Pty  Ltd   106  

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June  2010   ©  Datalicious  Pty  Ltd  

Don’t  wait    for  be;er  data,  get  started  now.  

107  

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June  2010   ©  Datalicious  Pty  Ltd   108  

Contact  me  [email protected]  

 Learn  more  

blog.datalicious.com    

Follow  me  twi;er.com/datalicious  

 

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Data  >  Insights  >  Ac(on  

June  2010   ©  Datalicious  Pty  Ltd   109