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  Marke(ng	
  Data	
  Strategy	
  <	
  
       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	
  
May	
  2011	
               ©	
  Datalicious	
  Pty	
  Ltd	
         2	
  
>	
  Smart	
  data	
  driven	
  marke(ng	
  

                  Media	
  A;ribu(on	
  &	
  Modeling                      	
  

                      Op(mise	
  channel	
  mix,	
  predict	
  sales	
  

                    Targeted	
  Direct	
  Marke(ng	
  	
  
                        Increase	
  relevance,	
  reduce	
  churn	
  

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

                                 Boost	
  ROAS	
  
May	
  2011	
                         ©	
  Datalicious	
  Pty	
  Ltd	
            3	
  
>	
  Wide	
  range	
  of	
  data	
  services	
  

       Data	
                                         Insights	
                                 Ac(on	
  
       PlaIorms	
                                     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,	
  SpoIire,	
  SPSS,	
  etc	
     Alterian,	
  SiteCore,	
  Inxmail,	
  etc	
  
       	
                                             	
                                         	
  
       Tag-­‐less	
  online	
  data	
  capture	
      Media	
  a;ribu(on	
  models	
             Targe(ng	
  and	
  merchandising	
  
       	
                                             	
                                         	
  
       End-­‐to-­‐end	
  data	
  plaIorms	
           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	
  
                                                                                                 	
  




May	
  2011	
                                               ©	
  Datalicious	
  Pty	
  Ltd	
                                                     4	
  
>	
  Clients	
  across	
  all	
  industries	
  




May	
  2011	
         ©	
  Datalicious	
  Pty	
  Ltd	
     5	
  
>	
  Data	
  driven	
  marke(ng	
  
§  What	
  is	
  data	
  driven	
  marke-ng?	
  
§  Self	
  assessment:	
  Your	
  capabili-es	
  	
  
§  Strategies	
  for	
  effec-ve	
  data	
  collec-on	
  
§  Campaign	
  development	
  and	
  data	
  integrity	
  
§  Effec-ve	
  mul--­‐channel	
  campaign	
  execu-on	
  
§  Analysis	
  and	
  performance	
  measurement	
  
§  In-­‐sourcing	
  or	
  outsourcing	
  

May	
  2011	
           ©	
  Datalicious	
  Pty	
  Ltd	
      6	
  
Clive	
  Humby:	
  Data	
  is	
  the	
  new	
  oil	
  



 May	
  2011	
        ©	
  Datalicious	
  Pty	
  Ltd	
     7	
  
>	
  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	
  	
  

May	
  2011	
                       ©	
  Datalicious	
  Pty	
  Ltd	
                                                       8	
  
>	
  Corporate	
  data	
  journey	
  	
  
                 Stage	
  1	
                        Stage	
  2	
                                 	
  
                                                                                              Stage	
  3
                 Data	
                              Insights	
                               Ac(on	
  

                                                                                            Data	
  is	
  fully	
  owned	
  	
  
	
  
  Sophis-ca-on




                                                                                            in-­‐house,	
  advanced	
  
                                                     Data	
  is	
  being	
  brought	
  	
   predic-ve	
  modelling	
  
                                                     in-­‐house,	
  shi]	
  towards	
   and	
  trigger	
  based	
  
                 Third	
  par-es	
  control	
        insights	
  genera-on	
  and	
   marke-ng,	
  i.e.	
  what	
  	
  
                                                     data	
  mining,	
  i.e.	
  why	
       will	
  happen	
  and	
  	
  
                 most	
  data,	
  ad	
  hoc	
  
                                                     did	
  it	
  happen?	
                 making	
  it	
  happen!	
  
                 repor-ng	
  only,	
  i.e.	
  	
  
                 what	
  happened?	
  
                                                                Time,	
  Control   	
  

May	
  2011	
                                            ©	
  Datalicious	
  Pty	
  Ltd	
                                          9	
  
May	
  2011	
     ©	
  Datalicious	
  Pty	
  Ltd	
     10	
  
Oil	
  and	
  data	
  come	
  at	
  a	
  price	
  


May	
  2011	
       ©	
  Datalicious	
  Pty	
  Ltd	
     11	
  
>	
  Google	
  Ngram:	
  Privacy	
  	
  




May	
  2011	
        ©	
  Datalicious	
  Pty	
  Ltd	
     12	
  
Collec(ng	
  data	
  	
  
                  for	
  the	
  sake	
  of	
  it	
  
                   or	
  to	
  add	
  value	
  
                   to	
  customers?	
  

May	
  2011	
                ©	
  Datalicious	
  Pty	
  Ltd	
     13	
  
>	
  Privacy	
  vs.	
  data	
  benefits	
  policy	
  
§  Do	
  not	
  hide	
  behind	
  small	
  print	
  
§  Use	
  plain	
  English	
  in	
  your	
  privacy	
  policy	
  
§  Explain	
  exactly	
  what	
  data	
  you	
  are	
  recording	
  
§  Explain	
  why	
  you	
  are	
  recording	
  the	
  data	
  
§  Explain	
  the	
  benefits	
  for	
  the	
  consumer	
  
§  Provide	
  opt-­‐out	
  and	
  feedback	
  op-ons	
  
§  Make	
  opt-­‐outs	
  a	
  KPI	
  not	
  just	
  opt-­‐ins	
  
=	
  Data	
  benefits	
  and	
  privacy	
  policy	
  
May	
  2011	
                ©	
  Datalicious	
  Pty	
  Ltd	
       14	
  
Exercise:	
  Marke(ng	
  mix	
  


May	
  2011	
         ©	
  Datalicious	
  Pty	
  Ltd	
     15	
  
Product	
  

        Partners	
                                Price	
  




                       Marke(ng	
  
Process	
  
                       Mix              	
  
                                                              Place	
  




         People	
                              Promo(on	
  

                        Physical	
  
                         Evidence	
  
Targe(ng	
  




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

May	
  2011	
     ©	
  Datalicious	
  Pty	
  Ltd	
            17	
  
>	
  Increase	
  revenue	
  by	
  10-­‐20%	
  	
  
    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	
  

May	
  2011	
                                             ©	
  Datalicious	
  Pty	
  Ltd	
              18	
  
>	
  New	
  consumer	
  decision	
  journey	
  
 The	
  consumer	
  decision	
  process	
  is	
  changing	
  from	
  linear	
  to	
  circular.	
  




May	
  2011	
                                ©	
  Datalicious	
  Pty	
  Ltd	
                        19	
  
>	
  New	
  consumer	
  decision	
  journey	
  
 The	
  consumer	
  decision	
  process	
  is	
  changing	
  from	
  linear	
  to	
  circular.	
  




                                                                     Online	
  research	
  	
  

 Change	
  increases	
  
 the	
  importance	
  of	
  
 experience	
  during	
  
 research	
  phase.	
  
May	
  2011	
                                ©	
  Datalicious	
  Pty	
  Ltd	
                        20	
  
May	
  2011	
     ©	
  Datalicious	
  Pty	
  Ltd	
     21	
  
>	
  Coordina(on	
  across	
  channels	
  	
  	
  	
  
                  Genera(ng	
                  Crea(ng	
                                Maximising	
  
                  awareness	
                engagement	
                                revenue	
  


        TV,	
  radio,	
  print,	
     Retail	
  stores,	
  in-­‐store	
          Outbound	
  calls,	
  direct	
  
        outdoor,	
  search	
          kiosks,	
  call	
  centers,	
              mail,	
  emails,	
  social	
  
        marke-ng,	
  display	
        brochures,	
  websites,	
                  media,	
  SMS,	
  mobile	
  
        ads,	
  performance	
         mobile	
  apps,	
  online	
                apps,	
  etc	
  
        networks,	
  affiliates,	
      chat,	
  social	
  media,	
  etc	
  
        social	
  media,	
  etc	
  


                    Off-­‐site	
                   On-­‐site	
                              Profile	
  	
  
                   targe(ng	
                    targe(ng	
                               targe(ng	
  


May	
  2011	
                               ©	
  Datalicious	
  Pty	
  Ltd	
                                        22	
  
>	
  Combining	
  targe(ng	
  plaIorms	
  	
  

                                  Off-­‐site	
  
                                 targe-ng	
  




                   Profile	
                                   On-­‐site	
  
                  targe-ng	
                                 targe-ng	
  



May	
  2011	
                ©	
  Datalicious	
  Pty	
  Ltd	
                 23	
  
November	
  2010	
     ©	
  Datalicious	
  Pty	
  Ltd	
     24	
  
Take	
  a	
  closer	
  
                                                            look	
  at	
  our	
  
                                                            cash	
  flow	
  
                                                            solu(ons	
  
November	
  2010	
     ©	
  Datalicious	
  Pty	
  Ltd	
                               25	
  
>	
  Affinity	
  re-­‐targe(ng	
  in	
  ac(on	
  	
  
                                                                                                         Different	
  type	
  of	
  	
  
                                                                                                         visitors	
  respond	
  to	
  	
  
                                                                                                         different	
  ads.	
  By	
  
                                                                                                         using	
  category	
  
                                                                                                         affinity	
  targe-ng,	
  	
  
                                                                                                         response	
  rates	
  are	
  	
  
                                                                                                         li]ed	
  significantly	
  	
  
                                                                                                         across	
  products.	
  

                                                                                              CTR	
  By	
  Category	
  Affinity	
  
                                                         Message	
  
                                                                                Postpay	
        Prepay	
         Broadb.	
         Business	
  

                                               Blackberry	
  Bold	
                 -                -                -                +
        Google:	
  “vodafone	
                 5GB	
  Mobile	
  Broadband	
         -                -               +                  -
       omniture	
  case	
  study”	
  	
        Blackberry	
  Storm	
               +                 -               +                 +
      or	
  h;p://bit.ly/de70b7	
              12	
  Month	
  Caps	
                -               +                 -                +

May	
  2011	
                           ©	
  Datalicious	
  Pty	
  Ltd	
                                                                       26	
  
>	
  Ad-­‐sequencing	
  in	
  ac(on	
  
                                                                                 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	
  
May	
  2011	
                             ©	
  Datalicious	
  Pty	
  Ltd	
                                     27	
  
>	
  Prospect	
  targe(ng	
  parameters	
  	
  




May	
  2011	
      ©	
  Datalicious	
  Pty	
  Ltd	
     28	
  
November	
  2010	
     ©	
  Datalicious	
  Pty	
  Ltd	
     29	
  
>	
  Sample	
  site	
  visitor	
  composi(on	
  	
  
   30%	
  new	
  visitors	
  with	
  no	
                    30%	
  repeat	
  visitors	
  with	
  
   previous	
  website	
  history	
                          referral	
  data	
  and	
  some	
  
   aside	
  from	
  campaign	
  or	
                         website	
  history	
  allowing	
  
   referrer	
  data	
  of	
  which	
                         50%	
  to	
  be	
  segmented	
  by	
  
   maybe	
  50%	
  is	
  useful	
                            content	
  affinity	
  


   30%	
  exis(ng	
  customers	
  with	
  extensive	
                              10%	
  serious	
  
   profile	
  including	
  transac-onal	
  history	
  of	
                          prospects	
  
   which	
  maybe	
  50%	
  can	
  actually	
  be	
                                with	
  limited	
  
   iden-fied	
  as	
  individuals	
  	
                                             profile	
  data	
  

May	
  2011	
                            ©	
  Datalicious	
  Pty	
  Ltd	
                                30	
  
>	
  Search	
  call	
  to	
  ac(on	
  for	
  offline	
  	
  




May	
  2011	
           ©	
  Datalicious	
  Pty	
  Ltd	
     31	
  
May	
  2011	
     ©	
  Datalicious	
  Pty	
  Ltd	
     32	
  
>	
  PURLs	
  boos(ng	
  DM	
  response	
  rates	
  
                                                          Text	
  




May	
  2011	
        ©	
  Datalicious	
  Pty	
  Ltd	
                33	
  
>	
  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	
  

May	
  2011	
                           ©	
  Datalicious	
  Pty	
  Ltd	
               34	
  
>	
  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	
  

May	
  2011	
                          ©	
  Datalicious	
  Pty	
  Ltd	
                  35	
  
>	
  Jet	
  Interac(ve	
  phone	
  call	
  data	
  




May	
  2011	
        ©	
  Datalicious	
  Pty	
  Ltd	
     36	
  
>	
  Poten(al	
  calls	
  to	
  ac(on	
  	
  
§     Unique	
  click-­‐through	
  URLs	
                      Calls	
  to	
  ac(on	
  
§     Unique	
  vanity	
  domains	
  or	
  URLs	
              can	
  help	
  shape	
  
§     Unique	
  phone	
  numbers	
                             the	
  customer	
  
§     Unique	
  search	
  terms	
                              experience	
  not	
  
                                                                just	
  evaluate	
  
§     Unique	
  email	
  addresses	
  
                                                                responses	
  
§     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	
  

May	
  2011	
                         ©	
  Datalicious	
  Pty	
  Ltd	
                 37	
  
>	
  The	
  consumer	
  data	
  journey	
  	
  
   To	
  transac(onal	
  data	
                                               To	
  reten(on	
  messages	
  




   From	
  suspect	
  to	
               prospect	
                                        To	
  customer	
  
                     Time   	
                                                          Time   	
  




   From	
  behavioural	
  data	
                                          From	
  awareness	
  messages	
  

May	
  2011	
                        ©	
  Datalicious	
  Pty	
  Ltd	
                                       38	
  
>	
  Combining	
  data	
  sources	
  

           Website	
  behavioural	
  data	
  




             Campaign	
  response	
  data	
  
                                                           +	
                            The	
  whole	
  is	
  greater	
  	
  
                                                                                        than	
  the	
  sum	
  of	
  its	
  parts	
  




                  Customer	
  profile	
  data	
  



May	
  2011	
                                      ©	
  Datalicious	
  Pty	
  Ltd	
                                                    39	
  
>	
  Transac(ons	
  plus	
  behaviours	
  

                    CRM	
  Profile	
                                                                                 Site	
  Behaviour	
  
              one-­‐off	
  collec-on	
  of	
  demographical	
  data	
  	
                                                   tracking	
  of	
  purchase	
  funnel	
  stage	
  




                                                                                    +	
  
                  age,	
  gender,	
  address,	
  etc	
                                                                  browsing,	
  checkout,	
  etc	
  
              customer	
  lifecycle	
  metrics	
  and	
  key	
  dates	
                                                     tracking	
  of	
  content	
  preferences	
  
             profitability,	
  expira(on,	
  etc	
                                                                  products,	
  brands,	
  features,	
  etc	
  
                  predic-ve	
  models	
  based	
  on	
  data	
  mining	
                                              tracking	
  of	
  external	
  campaign	
  responses	
  
            propensity	
  to	
  buy,	
  churn,	
  etc	
                                                              search	
  terms,	
  referrers,	
  etc	
  
              historical	
  data	
  from	
  previous	
  transac-ons	
                                                 tracking	
  of	
  internal	
  promo-on	
  responses	
  
         average	
  order	
  value,	
  points,	
  etc	
                                                              emails,	
  internal	
  search,	
  etc	
  




        Updated	
  Occasionally	
                                                                                 Updated	
  Con(nuously	
  


May	
  2011	
                                                                ©	
  Datalicious	
  Pty	
  Ltd	
                                                                   40	
  
>	
  Customer	
  profiling	
  in	
  ac(on	
  	
  
                      Using	
  website	
  and	
  email	
  responses	
  
                         to	
  learn	
  a	
  lille	
  bite	
  more	
  about	
  
                                               subscribers	
  at	
  every	
  	
  
                                               touch	
  point	
  to	
  keep	
  
                                                      	
  refining	
  profiles	
  
                                                           and	
  messages.	
  




May	
  2011	
        ©	
  Datalicious	
  Pty	
  Ltd	
                          41	
  
>	
  Online	
  form	
  best	
  prac(ce	
  
                                                                       Maximise	
  data	
  integrity	
  
                                                                       Age	
  vs.	
  year	
  of	
  birth	
  
                                                                       Free	
  text	
  vs.	
  op-ons	
  




 Use	
  auto-­‐complete	
  	
  
 wherever	
  possible	
  
May	
  2011	
                     ©	
  Datalicious	
  Pty	
  Ltd	
                                        42	
  
Exercise:	
  Enriching	
  profiles	
  


May	
  2011	
     ©	
  Datalicious	
  Pty	
  Ltd	
     43	
  
>	
  Exercise:	
  Enriching	
  profiles	
  

                  CRM	
  Profile	
                                          Site	
  Behaviour	
  




                       ?	
   +	
   ?	
  
May	
  2011	
                         ©	
  Datalicious	
  Pty	
  Ltd	
                             44	
  
Exercise:	
  Customer	
  IDs	
  


May	
  2011	
             ©	
  Datalicious	
  Pty	
  Ltd	
     45	
  
>	
  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	
  

May	
  2011	
                        ©	
  Datalicious	
  Pty	
  Ltd	
                                       46	
  
>	
  Enhancing	
  data	
  sources	
  

                  Customer	
  profile	
  data	
  




                  Geo-­‐demographic	
  data	
  
                                                           +	
                            The	
  whole	
  is	
  greater	
  	
  
                                                                                        than	
  the	
  sum	
  of	
  its	
  parts	
  




                       3rd	
  party	
  data	
  



May	
  2011	
                                      ©	
  Datalicious	
  Pty	
  Ltd	
                                                    47	
  
>	
  Geo-­‐demographic	
  segments	
  




May	
  2011	
     ©	
  Datalicious	
  Pty	
  Ltd	
     48	
  
May	
  2011	
     ©	
  Datalicious	
  Pty	
  Ltd	
     49	
  
Event	
  sponsor	
  presenta(on	
  




May	
  2011	
               ©	
  Datalicious	
  Pty	
  Ltd	
     50	
  
transcape	
  
       data	
  solu,ons	
  
	
  
E-­‐commerce	
  customers	
  
Mail	
  Order	
  Catalog	
  Buyers	
  
Magazine	
  Subscribers	
  
transcape	
                                            Buyer	
  File	
  	
  
	
                                                             1	
                                 Buyer	
  File	
  	
  
            Buyer	
  File	
  	
  
                                                                                                            2	
  
                   7	
  

                                                                                                          Buyer	
  File	
  	
  
        Buyer	
  File	
  	
  
                                                                                                                   3	
  
               6	
  

                                    Buyer	
  File	
  	
                             Buyer	
  File	
  	
  
                                            5	
                                              4	
  
                                     "IMP	
  have	
  been	
  working	
  with	
  Alliance	
  Data	
  ever	
  since	
  they	
  launched	
  and	
  have	
  using	
  
                                     their	
  Australian	
  &	
  NZ	
  data	
  with	
  great	
  success	
  across	
  a	
  range	
  of	
  products"	
  

                                                                                                                    Victoria Coleman
                                                                                                                    Media Manager
                                                                                                                    International Masters Publishers
transcape	
  
	
   Selectable	
  by:	
  

    Recency	
                             Frequency	
                 Money	
  
        Recency              Count            Frequency   Count             Spend           Count
  0	
  to	
  6	
  mo.          371,012   1	
  x             734,436   Less	
  Than	
  $25     138,346
                                                                      $25	
  -­‐	
  $50       131,671
  6	
  to	
  12	
  mo.         269,457   2	
  x             206,257
                                                                      $50	
  -­‐	
  $100      324,512
  12	
  to	
  18	
  mo.        295,601   3x                 110,751   $100	
  -­‐	
  $250     329,338
  18	
  to	
  24	
  mo.        397,162   4+                 281,788   $250+                   409,365
  Total                      1,333,232   Total            1,333,232   Total                 1,333,232
transcape	
  
	
   Selectable	
  by:	
     Income	
  
                             Age	
  
                             Gender	
  
  Female	
                                Male	
  
RFM	
  Segmenta(on	
  (house	
  file)	
  

                  0-­‐6	
  mo.	
     7-­‐12	
  mo.	
            13-­‐24	
  mo.	
     25-­‐36	
  mo.	
     37mo.+	
  
   <$10	
         1.20%	
             0.70%	
                     0.50%	
              0.30%	
            0.10%	
  

$10-­‐$24	
       1.50%	
             0.90%	
                      0.70%	
              0.40%	
           0.20%	
  

$25-­‐$49	
       1.80%	
             1.20%	
                      1.00%	
               0.50%	
           0.30%	
  

$50-­‐$99	
       2.00%	
             1.70%	
                     1.20%	
               0.80%	
            0.40%	
  

$100-­‐$249	
     2.50%	
            2.10%	
                      1.50%	
              1.10%	
             0.50%	
  

  $250+	
         3.00%+	
            2.20%	
                     2.00%	
              1.40%	
             0.70%	
  
                                                         	
  


                                     450,000	
  Buyers	
  
                                      50,000	
  
Last	
  bought	
  from	
  YOU	
  
25-­‐36	
  mo.,	
  $25-­‐$49	
  
Response	
  Rate	
  =	
  0.50%	
  




                                                   transcape	
  
                                     35,000	
  
50,000	
  Buyers	
                   matches	
  




                                                   1	
  .4	
  million	
  names	
  
Last	
  bought	
  from	
  you	
                                       Response	
  Rate	
  =	
     0.50%	
  
                                                                                                  0.90%	
  
25-­‐36	
  mo.,	
  $25-­‐$49	
                                                Universe	
  =	
     50,000	
  
                                                                                                  35,000	
  
                                                                                                  20,000	
  

Have	
  also	
  bought	
  elsewhere	
  
Frequency	
  =	
  	
   1x	
  
                       1+	
  
                       3x	
  
                       2x	
  
                                                 Recency	
  
Value	
                      0-­‐12	
  mo.	
     12-­‐24	
  mo.	
             25+	
  mo.	
  

       <$25	
                      0.50%	
         0.30%	
                      0.10%	
  

    $25-­‐49	
                     0.70%	
         0.50%	
                      0.30%	
  

   $50-­‐$99	
                     0.90%	
         0.70%	
                      0.50%	
  

       $100+	
                     1.10%	
         0.90%	
                      0.70%	
  


                      Further	
  op(mise	
  your	
  house	
  file	
  segments	
  
Transac(onal	
  Data	
  
Demographic	
  
  Geographic	
  
GeoSmart Segments
Geodemographic Profile                                                                                                                                             Standard    Normalised
                                                                                             #     Description                          transcape %   Client %
                                                                                                                                                                     Index       Index
                                                                                               1   Prestige                                    1.41         5.45        387.36        6.46
                                                                                               2   High Status Urban                           1.11         0.63         56.99       -1.00
GeoSmart Groups                                                                                3   Desirable Suburban                          2.36         7.35        310.86        8.82
                                                                                               4   Affluent Family                             1.95         3.68        188.64        3.57
                                                                                               5   High Density Urban                          1.07         0.44         41.39       -1.19
                                                                  Standard    Normalised       6   Urban Bohemian                              1.25         0.32         25.28       -1.45
 #       Description                   transcape %   Client %
                                                                    Index       Index          7   Affluent Multicultural                      1.23         3.11        252.98        3.52
     1   High Status Stronger Family         15.11        34.28        226.88       44.39      8   High Status Suburban                        2.39         7.48        313.22        8.99
     2   High Status Weaker family            5.18         5.51        106.38        0.76      9   Coastal Emplty Nest & Retirement            1.92         1.77         92.26       -0.34
                                                                                              10   Desirable Urban                             1.75         4.12        235.94        4.59
     3   Mid Status Stronger Family          24.90        25.54        102.55        1.64     11   High Status Family                          2.80         0.63         22.65       -3.22
     4   Mid Status Weaker family             4.83         6.97        144.43        4.79     12   Mature Affluent Suburban                    1.06         4.82        456.31        5.57
     5   Low Status Stronger Family          25.02        10.46         41.79      -31.28     13   Aspiring Family                             2.89         3.11        107.37        0.48
     6   Low Status Weaker family             9.51        11.47        120.66        4.61     14   Mid Status Family Starter                   1.69         1.65         97.77       -0.08
                                                                                              15   Affluent Seachange                          1.64         1.20         73.50       -0.96
     7   Disadvantaged                       13.70         4.69         34.24      -16.88     16   Established Multicultural Suburban          2.70         1.90         70.43       -1.75
     8   Unclassified                         1.75         1.08         61.50       -1.44     17   Urban Lifestyle                             0.68         0.70        102.68        0.04
                                                                                              18   Mid Status Suburban                         3.01         5.32        176.69        4.90
                                                                                              19   Provincial Fringe                           2.11         0.82         39.08       -2.39
                                                                                              20   Metro Fringe                                0.87         1.90        218.14        2.02
                                                                                              21   Mid Status Urban                            1.66         4.75        285.75        5.58
                                                                                              22   Mixed Multicultural Suburban                0.87         0.13         14.49       -0.89
                                                                                              23   Mining                                      1.41         1.52        107.92        0.25
                                                                                              24   Mid Status Young Family                     3.21         1.39         43.40       -3.53
                                                                                              25   Mature Mid Status Family                    4.50         6.59        146.45        4.65
                                                                                              26   Multicultural Urban Lifestyle               0.57         0.00          0.00
                                                                                              27   University Enclaves                         0.36         0.51        139.57        0.31
                                                                                              28   Holiday Lifestyle                           0.45         0.25         56.21       -0.41
                                                                                              29   Multicultural Mixed Urban                   1.10         0.76         69.17       -0.74
                                                                                              30   Establishing Multicultural Family           1.98         0.82         41.66       -2.19
                                                                                              31   Elderly Enclaves                            1.15         0.51         44.19       -1.24
                                                                                              32   Establishing Provincial family              2.91         1.20         41.44       -3.24
                                                                                              33   New Age Lifestyle                           0.91         0.95        105.02        0.10
                                                                                              34   Mature Provincial Suburban                  4.30         1.01         23.60       -5.01
                                                                                              35   Mixed Suburban                              0.96         0.19         19.82       -1.07
                                                                                              36   Inland Rural Fringe                         1.43         3.36        234.37        3.72
                                                                                              37   Established Multicultural family            1.31         0.13          9.69       -1.16
                                                                                              38   Provincial Mixed Urban                      2.17         0.82         37.94       -2.48
                                                                                              39   Low Status Rural Fringe                     1.93         0.51         26.25       -2.25
                                                                                              40   Family Achiever                             1.91         0.51         26.59       -2.23
                                                                                              41   Old European Blue Collar                    1.03         0.95         91.95       -0.19
                                                                                              42   Established Blue Collar Suburban            3.04         6.59        217.10        7.15
                                                                                              43   Blue Collar Family                          3.10         2.60         83.72       -1.14
                                                                                              44   Provincial Blue Collar Suburban             5.65         1.77         31.43       -6.73
                                                                                              45   Middle Eastern Multicultural                0.77         0.00          0.00
                                                                                              46   Poor Mixed Urban                            1.14         0.82         72.07       -0.70
                                                                                              47   Low Status Mixed Multicultural              1.39         0.70         50.00       -1.40
                                                                                              48   Small Town Blue Collar Suburban             4.39         1.58         36.10       -5.12
                                                                                              49   Established Asian                           0.60         0.00          0.00
                                                                                              50   Mobile Holiday Accommodation                0.21         0.13         60.52       -0.17
                                                                                              51   Elderly Provincial Urban                    2.04         0.70         34.12       -2.38
                                                                                              52   Provincial Battler                          2.93         0.76         25.98       -3.43
                                                                                              53   High Density Welfare                        0.16         0.00          0.00
                                                                                              54   Suburban Welfare                            0.83         0.00          0.00
                                                                                              55   Indigenous & Remote                         1.62         1.08         66.49       -1.17
                                                                                              56   Unclassified                                0.13                       0.00
transcape	
  
       data	
  solu,ons	
  
	
  

          Thank	
  you!	
  
Exercise:	
  Targe(ng	
  matrix	
  


May	
  2011	
     ©	
  Datalicious	
  Pty	
  Ltd	
     62	
  
>	
  Exercise:	
  Targe(ng	
  matrix	
  
         Purchase	
        Segments:	
  Colour,	
  price,	
                        Media	
        Data	
  	
  
           Cycle	
           product	
  affinity,	
  etc	
                          Channels	
     Points	
  

        Default,	
  
       awareness	
  

      Research,	
  
    considera(on	
  

         Purchase	
  
          intent	
  

      Reten(on,	
  
     up/cross-­‐sell	
  


May	
  2011	
                                ©	
  Datalicious	
  Pty	
  Ltd	
                                    63	
  
>	
  Exercise:	
  Targe(ng	
  matrix	
  
         Purchase	
           Segments:	
  Colour,	
  price,	
                                 Media	
                  Data	
  	
  
           Cycle	
              product	
  affinity,	
  etc	
                                   Channels	
               Points	
  

        Default,	
           Have	
  you	
  	
              Have	
  you	
  	
                 Display,	
  
                                                                                                                      Default	
  
       awareness	
            seen	
  A?	
                   seen	
  B?	
                    search,	
  etc	
  

      Research,	
          A	
  has	
  great	
  	
        B	
  has	
  great	
  	
             Search,	
             Ad	
  clicks,	
  
    considera(on	
          features!	
                    features!	
                      website,	
  etc	
      prod	
  views	
  

         Purchase	
         A	
  delivers	
               B	
  delivers	
                     Website,	
            Cart	
  adds,	
  
          intent	
         great	
  value!	
             great	
  value!	
                   emails,	
  etc	
       checkouts	
  

      Reten(on,	
            Why	
  not	
                    Why	
  not	
                   Direct	
  mails,	
     Email	
  clicks,	
  
     up/cross-­‐sell	
       buy	
  B?	
                     buy	
  A?	
                     emails,	
  etc	
       logins,	
  etc	
  


May	
  2011	
                                          ©	
  Datalicious	
  Pty	
  Ltd	
                                                   64	
  
May	
  2011	
     ©	
  Datalicious	
  Pty	
  Ltd	
     65	
  
May	
  2011	
     ©	
  Datalicious	
  Pty	
  Ltd	
     66	
  
May	
  2011	
     ©	
  Datalicious	
  Pty	
  Ltd	
     67	
  
May	
  2011	
     ©	
  Datalicious	
  Pty	
  Ltd	
     68	
  
Exercise:	
  Marke(ng	
  automa(on	
  



May	
  2011	
     ©	
  Datalicious	
  Pty	
  Ltd	
     69	
  
May	
  2011	
     ©	
  Datalicious	
  Pty	
  Ltd	
     70	
  
>	
  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.”	
  
May	
  2011	
                            ©	
  Datalicious	
  Pty	
  Ltd	
                    71	
  
Plan	
  to	
  fail	
  …	
  
    May	
  2011	
             ©	
  Datalicious	
  Pty	
  Ltd	
     72	
  
>	
  Develop	
  a	
  tes(ng	
  matrix	
  
           Test	
     Segment	
     Content	
                      KPIs	
     Poten(al	
     Results	
  




May	
  2011	
                           ©	
  Datalicious	
  Pty	
  Ltd	
                                   73	
  
>	
  Develop	
  a	
  tes(ng	
  matrix	
  
           Test	
           Segment	
        Content	
                       KPIs	
     Poten(al	
     Results	
  

                              New	
       Conversion	
   Next	
  step,	
  
      Test	
  #1A	
  	
  
                            prospects	
     form	
  A	
   order,	
  etc	
                   ?	
           ?	
  
                              New	
       Conversion	
   Next	
  step,	
  
      Test	
  #1B	
  
                            prospects	
     form	
  B	
   order,	
  etc	
                   ?	
           ?	
  
                              New	
       Conversion	
   Next	
  step,	
  
      Test	
  #1N	
  
                            prospects	
     form	
  N	
   order,	
  etc	
                   ?	
           ?	
  

             ?	
                ?	
              ?	
                            ?	
         ?	
           ?	
  
May	
  2011	
                                     ©	
  Datalicious	
  Pty	
  Ltd	
                                   74	
  
>	
  AIDA	
  and	
  AIDAS	
  formulas	
  	
  
   Old	
  media	
  

   New	
  media	
  



     Awareness	
         Interest	
             Desire	
                     Ac(on	
     Sa(sfac(on	
  




   Social	
  media	
  




May	
  2011	
                           ©	
  Datalicious	
  Pty	
  Ltd	
                                  75	
  
>	
  Simplified	
  AIDAS	
  funnel	
  	
  



              Reach	
            Engagement	
                                      Conversion	
             +Buzz	
  
            (Awareness)   	
     (Interest	
  &	
  Desire)	
                                (Ac-on)	
     (Sa-sfac-on)	
  




May	
  2011	
                                          ©	
  Datalicious	
  Pty	
  Ltd	
                                      76	
  
>	
  Marke(ng	
  is	
  about	
  people	
  	
  



             People	
                People	
                              People	
                 People	
  
            reached	
     40%	
     engaged	
       10%	
                 converted	
     1%	
     delighted	
  




May	
  2011	
                                ©	
  Datalicious	
  Pty	
  Ltd	
                                      77	
  
>	
  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	
  




May	
  2011	
                                 ©	
  Datalicious	
  Pty	
  Ltd	
                                      78	
  
New	
  vs.	
  returning	
  visitors	
  




May	
  2011	
                    ©	
  Datalicious	
  Pty	
  Ltd	
     79	
  
AU/NZ	
  vs.	
  rest	
  of	
  world	
  




May	
  2011	
                      ©	
  Datalicious	
  Pty	
  Ltd	
     80	
  
>	
  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	
  
	
  	
  
May	
  2011	
                            ©	
  Datalicious	
  Pty	
  Ltd	
           81	
  
>	
  Developing	
  a	
  metrics	
  framework	
  	
  

             Level	
        Reach	
      Engagement	
                        Conversion	
     +Buzz	
  

          Level	
  1,	
  
          people	
  

          Level	
  2,	
  
         strategic	
  

          Level	
  3,	
  
          tac(cal	
  

        Funnel	
  
     breakdowns	
  


May	
  2011	
                           ©	
  Datalicious	
  Pty	
  Ltd	
                                  82	
  
>	
  Developing	
  a	
  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	
  


May	
  2011	
                                    ©	
  Datalicious	
  Pty	
  Ltd	
                                      83	
  
>	
  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.	
  




May	
  2011	
                               ©	
  Datalicious	
  Pty	
  Ltd	
     84	
  
>	
  Importance	
  of	
  calendar	
  events	
  	
  




    Traffic	
  spikes	
  or	
  other	
  data	
  anomalies	
  without	
  context	
  are	
  
       very	
  hard	
  to	
  interpret	
  and	
  can	
  render	
  data	
  useless	
  
May	
  2011	
                         ©	
  Datalicious	
  Pty	
  Ltd	
                 85	
  
>	
  Out-­‐sourcing	
  or	
  in-­‐sourcing?	
  
                                                                 Year	
  1	
                             Year	
  2	
                                 	
  
                                                                                                                                                  Year	
  3
                                                                 PlaIorms	
                              Training	
                               Support	
  
	
  
  Degree	
  of	
  in-­‐house	
  control	
  and	
  sophis-ca-on




                                                                                                                                                  Reduce	
  vendor	
  reliance	
  
                                                                                                                                                  to	
  absolute	
  minimum	
  
                                                                                                         Start	
  taking	
  control	
  of	
       but	
  consider	
  the	
  value	
  
                                                                                                         technology	
  and	
  data,	
             of	
  support	
  agreements	
  
                                                                                                         shi]	
  vendor	
  focus	
  to	
          for	
  both	
  maintenance	
  
                                                                 Engage	
  third	
  par-es	
  
                                                                                                         enhancements	
  and	
  the	
             as	
  well	
  as	
  updates	
  on	
  
                                                                 with	
  more	
  experience	
  
                                                                                                         provision	
  of	
  training	
  	
        market	
  innova-ons	
  and	
  
                                                                 to	
  get	
  started	
  and	
  to	
  
                                                                 implement	
  technology	
               for	
  internal	
  resources	
           new	
  features.	
  

                                                                                                                    Time,	
  Control   	
  

May	
  2011	
                                                                                                ©	
  Datalicious	
  Pty	
  Ltd	
                                        86	
  
Contact	
  me	
  
                  cbartens@datalicious.com	
  
                             	
  
                       Learn	
  more	
  
                     blog.datalicious.com	
  
                               	
  
                        Follow	
  me	
  
                   twi;er.com/datalicious	
  
                             	
  
May	
  2011	
               ©	
  Datalicious	
  Pty	
  Ltd	
     87	
  
Data	
  >	
  Insights	
  >	
  Ac(on	
  

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ADMA Marketing Data Strategy

  • 1. >  Marke(ng  Data  Strategy  <   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   May  2011   ©  Datalicious  Pty  Ltd   2  
  • 3. >  Smart  data  driven  marke(ng   Media  A;ribu(on  &  Modeling   Op(mise  channel  mix,  predict  sales   Targeted  Direct  Marke(ng     Increase  relevance,  reduce  churn   Tes(ng  &  Op(misa(on   Remove  barriers,  drive  sales   Boost  ROAS   May  2011   ©  Datalicious  Pty  Ltd   3  
  • 4. >  Wide  range  of  data  services   Data   Insights   Ac(on   PlaIorms   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,  SpoIire,  SPSS,  etc   Alterian,  SiteCore,  Inxmail,  etc         Tag-­‐less  online  data  capture   Media  a;ribu(on  models   Targe(ng  and  merchandising         End-­‐to-­‐end  data  plaIorms   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     May  2011   ©  Datalicious  Pty  Ltd   4  
  • 5. >  Clients  across  all  industries   May  2011   ©  Datalicious  Pty  Ltd   5  
  • 6. >  Data  driven  marke(ng   §  What  is  data  driven  marke-ng?   §  Self  assessment:  Your  capabili-es     §  Strategies  for  effec-ve  data  collec-on   §  Campaign  development  and  data  integrity   §  Effec-ve  mul--­‐channel  campaign  execu-on   §  Analysis  and  performance  measurement   §  In-­‐sourcing  or  outsourcing   May  2011   ©  Datalicious  Pty  Ltd   6  
  • 7. Clive  Humby:  Data  is  the  new  oil   May  2011   ©  Datalicious  Pty  Ltd   7  
  • 8. >  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     May  2011   ©  Datalicious  Pty  Ltd   8  
  • 9. >  Corporate  data  journey     Stage  1   Stage  2     Stage  3 Data   Insights   Ac(on   Data  is  fully  owned       Sophis-ca-on in-­‐house,  advanced   Data  is  being  brought     predic-ve  modelling   in-­‐house,  shi]  towards   and  trigger  based   Third  par-es  control   insights  genera-on  and   marke-ng,  i.e.  what     data  mining,  i.e.  why   will  happen  and     most  data,  ad  hoc   did  it  happen?   making  it  happen!   repor-ng  only,  i.e.     what  happened?   Time,  Control   May  2011   ©  Datalicious  Pty  Ltd   9  
  • 10. May  2011   ©  Datalicious  Pty  Ltd   10  
  • 11. Oil  and  data  come  at  a  price   May  2011   ©  Datalicious  Pty  Ltd   11  
  • 12. >  Google  Ngram:  Privacy     May  2011   ©  Datalicious  Pty  Ltd   12  
  • 13. Collec(ng  data     for  the  sake  of  it   or  to  add  value   to  customers?   May  2011   ©  Datalicious  Pty  Ltd   13  
  • 14. >  Privacy  vs.  data  benefits  policy   §  Do  not  hide  behind  small  print   §  Use  plain  English  in  your  privacy  policy   §  Explain  exactly  what  data  you  are  recording   §  Explain  why  you  are  recording  the  data   §  Explain  the  benefits  for  the  consumer   §  Provide  opt-­‐out  and  feedback  op-ons   §  Make  opt-­‐outs  a  KPI  not  just  opt-­‐ins   =  Data  benefits  and  privacy  policy   May  2011   ©  Datalicious  Pty  Ltd   14  
  • 15. Exercise:  Marke(ng  mix   May  2011   ©  Datalicious  Pty  Ltd   15  
  • 16. Product   Partners   Price   Marke(ng   Process   Mix   Place   People   Promo(on   Physical   Evidence  
  • 17. Targe(ng   The  right  message   Via  the  right  channel   To  the  right  person   At  the  right  -me   May  2011   ©  Datalicious  Pty  Ltd   17  
  • 18. >  Increase  revenue  by  10-­‐20%     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   May  2011   ©  Datalicious  Pty  Ltd   18  
  • 19. >  New  consumer  decision  journey   The  consumer  decision  process  is  changing  from  linear  to  circular.   May  2011   ©  Datalicious  Pty  Ltd   19  
  • 20. >  New  consumer  decision  journey   The  consumer  decision  process  is  changing  from  linear  to  circular.   Online  research     Change  increases   the  importance  of   experience  during   research  phase.   May  2011   ©  Datalicious  Pty  Ltd   20  
  • 21. May  2011   ©  Datalicious  Pty  Ltd   21  
  • 22. >  Coordina(on  across  channels         Genera(ng   Crea(ng   Maximising   awareness   engagement   revenue   TV,  radio,  print,   Retail  stores,  in-­‐store   Outbound  calls,  direct   outdoor,  search   kiosks,  call  centers,   mail,  emails,  social   marke-ng,  display   brochures,  websites,   media,  SMS,  mobile   ads,  performance   mobile  apps,  online   apps,  etc   networks,  affiliates,   chat,  social  media,  etc   social  media,  etc   Off-­‐site   On-­‐site   Profile     targe(ng   targe(ng   targe(ng   May  2011   ©  Datalicious  Pty  Ltd   22  
  • 23. >  Combining  targe(ng  plaIorms     Off-­‐site   targe-ng   Profile   On-­‐site   targe-ng   targe-ng   May  2011   ©  Datalicious  Pty  Ltd   23  
  • 24. November  2010   ©  Datalicious  Pty  Ltd   24  
  • 25. Take  a  closer   look  at  our   cash  flow   solu(ons   November  2010   ©  Datalicious  Pty  Ltd   25  
  • 26. >  Affinity  re-­‐targe(ng  in  ac(on     Different  type  of     visitors  respond  to     different  ads.  By   using  category   affinity  targe-ng,     response  rates  are     li]ed  significantly     across  products.   CTR  By  Category  Affinity   Message   Postpay   Prepay   Broadb.   Business   Blackberry  Bold   - - - + Google:  “vodafone   5GB  Mobile  Broadband   - - + - omniture  case  study”     Blackberry  Storm   + - + + or  h;p://bit.ly/de70b7   12  Month  Caps   - + - + May  2011   ©  Datalicious  Pty  Ltd   26  
  • 27. >  Ad-­‐sequencing  in  ac(on   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   May  2011   ©  Datalicious  Pty  Ltd   27  
  • 28. >  Prospect  targe(ng  parameters     May  2011   ©  Datalicious  Pty  Ltd   28  
  • 29. November  2010   ©  Datalicious  Pty  Ltd   29  
  • 30. >  Sample  site  visitor  composi(on     30%  new  visitors  with  no   30%  repeat  visitors  with   previous  website  history   referral  data  and  some   aside  from  campaign  or   website  history  allowing   referrer  data  of  which   50%  to  be  segmented  by   maybe  50%  is  useful   content  affinity   30%  exis(ng  customers  with  extensive   10%  serious   profile  including  transac-onal  history  of   prospects   which  maybe  50%  can  actually  be   with  limited   iden-fied  as  individuals     profile  data   May  2011   ©  Datalicious  Pty  Ltd   30  
  • 31. >  Search  call  to  ac(on  for  offline     May  2011   ©  Datalicious  Pty  Ltd   31  
  • 32. May  2011   ©  Datalicious  Pty  Ltd   32  
  • 33. >  PURLs  boos(ng  DM  response  rates   Text   May  2011   ©  Datalicious  Pty  Ltd   33  
  • 34. >  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   May  2011   ©  Datalicious  Pty  Ltd   34  
  • 35. >  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   May  2011   ©  Datalicious  Pty  Ltd   35  
  • 36. >  Jet  Interac(ve  phone  call  data   May  2011   ©  Datalicious  Pty  Ltd   36  
  • 37. >  Poten(al  calls  to  ac(on     §  Unique  click-­‐through  URLs   Calls  to  ac(on   §  Unique  vanity  domains  or  URLs   can  help  shape   §  Unique  phone  numbers   the  customer   §  Unique  search  terms   experience  not   just  evaluate   §  Unique  email  addresses   responses   §  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   May  2011   ©  Datalicious  Pty  Ltd   37  
  • 38. >  The  consumer  data  journey     To  transac(onal  data   To  reten(on  messages   From  suspect  to   prospect   To  customer   Time   Time   From  behavioural  data   From  awareness  messages   May  2011   ©  Datalicious  Pty  Ltd   38  
  • 39. >  Combining  data  sources   Website  behavioural  data   Campaign  response  data   +   The  whole  is  greater     than  the  sum  of  its  parts   Customer  profile  data   May  2011   ©  Datalicious  Pty  Ltd   39  
  • 40. >  Transac(ons  plus  behaviours   CRM  Profile   Site  Behaviour   one-­‐off  collec-on  of  demographical  data     tracking  of  purchase  funnel  stage   +   age,  gender,  address,  etc   browsing,  checkout,  etc   customer  lifecycle  metrics  and  key  dates   tracking  of  content  preferences   profitability,  expira(on,  etc   products,  brands,  features,  etc   predic-ve  models  based  on  data  mining   tracking  of  external  campaign  responses   propensity  to  buy,  churn,  etc   search  terms,  referrers,  etc   historical  data  from  previous  transac-ons   tracking  of  internal  promo-on  responses   average  order  value,  points,  etc   emails,  internal  search,  etc   Updated  Occasionally   Updated  Con(nuously   May  2011   ©  Datalicious  Pty  Ltd   40  
  • 41. >  Customer  profiling  in  ac(on     Using  website  and  email  responses   to  learn  a  lille  bite  more  about   subscribers  at  every     touch  point  to  keep    refining  profiles   and  messages.   May  2011   ©  Datalicious  Pty  Ltd   41  
  • 42. >  Online  form  best  prac(ce   Maximise  data  integrity   Age  vs.  year  of  birth   Free  text  vs.  op-ons   Use  auto-­‐complete     wherever  possible   May  2011   ©  Datalicious  Pty  Ltd   42  
  • 43. Exercise:  Enriching  profiles   May  2011   ©  Datalicious  Pty  Ltd   43  
  • 44. >  Exercise:  Enriching  profiles   CRM  Profile   Site  Behaviour   ?   +   ?   May  2011   ©  Datalicious  Pty  Ltd   44  
  • 45. Exercise:  Customer  IDs   May  2011   ©  Datalicious  Pty  Ltd   45  
  • 46. >  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   May  2011   ©  Datalicious  Pty  Ltd   46  
  • 47. >  Enhancing  data  sources   Customer  profile  data   Geo-­‐demographic  data   +   The  whole  is  greater     than  the  sum  of  its  parts   3rd  party  data   May  2011   ©  Datalicious  Pty  Ltd   47  
  • 48. >  Geo-­‐demographic  segments   May  2011   ©  Datalicious  Pty  Ltd   48  
  • 49. May  2011   ©  Datalicious  Pty  Ltd   49  
  • 50. Event  sponsor  presenta(on   May  2011   ©  Datalicious  Pty  Ltd   50  
  • 51. transcape   data  solu,ons    
  • 52. E-­‐commerce  customers   Mail  Order  Catalog  Buyers   Magazine  Subscribers  
  • 53. transcape   Buyer  File       1   Buyer  File     Buyer  File     2   7   Buyer  File     Buyer  File     3   6   Buyer  File     Buyer  File     5   4   "IMP  have  been  working  with  Alliance  Data  ever  since  they  launched  and  have  using   their  Australian  &  NZ  data  with  great  success  across  a  range  of  products"   Victoria Coleman Media Manager International Masters Publishers
  • 54. transcape     Selectable  by:   Recency   Frequency   Money   Recency Count Frequency Count Spend Count 0  to  6  mo. 371,012 1  x 734,436 Less  Than  $25 138,346 $25  -­‐  $50 131,671 6  to  12  mo. 269,457 2  x 206,257 $50  -­‐  $100 324,512 12  to  18  mo. 295,601 3x 110,751 $100  -­‐  $250 329,338 18  to  24  mo. 397,162 4+ 281,788 $250+ 409,365 Total 1,333,232 Total 1,333,232 Total 1,333,232
  • 55. transcape     Selectable  by:   Income   Age   Gender   Female   Male  
  • 56. RFM  Segmenta(on  (house  file)   0-­‐6  mo.   7-­‐12  mo.   13-­‐24  mo.   25-­‐36  mo.   37mo.+   <$10   1.20%   0.70%   0.50%   0.30%   0.10%   $10-­‐$24   1.50%   0.90%   0.70%   0.40%   0.20%   $25-­‐$49   1.80%   1.20%   1.00%   0.50%   0.30%   $50-­‐$99   2.00%   1.70%   1.20%   0.80%   0.40%   $100-­‐$249   2.50%   2.10%   1.50%   1.10%   0.50%   $250+   3.00%+   2.20%   2.00%   1.40%   0.70%     450,000  Buyers   50,000  
  • 57. Last  bought  from  YOU   25-­‐36  mo.,  $25-­‐$49   Response  Rate  =  0.50%   transcape   35,000   50,000  Buyers   matches   1  .4  million  names  
  • 58. Last  bought  from  you   Response  Rate  =   0.50%   0.90%   25-­‐36  mo.,  $25-­‐$49   Universe  =   50,000   35,000   20,000   Have  also  bought  elsewhere   Frequency  =     1x   1+   3x   2x   Recency   Value   0-­‐12  mo.   12-­‐24  mo.   25+  mo.   <$25   0.50%   0.30%   0.10%   $25-­‐49   0.70%   0.50%   0.30%   $50-­‐$99   0.90%   0.70%   0.50%   $100+   1.10%   0.90%   0.70%   Further  op(mise  your  house  file  segments  
  • 60. GeoSmart Segments Geodemographic Profile Standard Normalised # Description transcape % Client % Index Index 1 Prestige 1.41 5.45 387.36 6.46 2 High Status Urban 1.11 0.63 56.99 -1.00 GeoSmart Groups 3 Desirable Suburban 2.36 7.35 310.86 8.82 4 Affluent Family 1.95 3.68 188.64 3.57 5 High Density Urban 1.07 0.44 41.39 -1.19 Standard Normalised 6 Urban Bohemian 1.25 0.32 25.28 -1.45 # Description transcape % Client % Index Index 7 Affluent Multicultural 1.23 3.11 252.98 3.52 1 High Status Stronger Family 15.11 34.28 226.88 44.39 8 High Status Suburban 2.39 7.48 313.22 8.99 2 High Status Weaker family 5.18 5.51 106.38 0.76 9 Coastal Emplty Nest & Retirement 1.92 1.77 92.26 -0.34 10 Desirable Urban 1.75 4.12 235.94 4.59 3 Mid Status Stronger Family 24.90 25.54 102.55 1.64 11 High Status Family 2.80 0.63 22.65 -3.22 4 Mid Status Weaker family 4.83 6.97 144.43 4.79 12 Mature Affluent Suburban 1.06 4.82 456.31 5.57 5 Low Status Stronger Family 25.02 10.46 41.79 -31.28 13 Aspiring Family 2.89 3.11 107.37 0.48 6 Low Status Weaker family 9.51 11.47 120.66 4.61 14 Mid Status Family Starter 1.69 1.65 97.77 -0.08 15 Affluent Seachange 1.64 1.20 73.50 -0.96 7 Disadvantaged 13.70 4.69 34.24 -16.88 16 Established Multicultural Suburban 2.70 1.90 70.43 -1.75 8 Unclassified 1.75 1.08 61.50 -1.44 17 Urban Lifestyle 0.68 0.70 102.68 0.04 18 Mid Status Suburban 3.01 5.32 176.69 4.90 19 Provincial Fringe 2.11 0.82 39.08 -2.39 20 Metro Fringe 0.87 1.90 218.14 2.02 21 Mid Status Urban 1.66 4.75 285.75 5.58 22 Mixed Multicultural Suburban 0.87 0.13 14.49 -0.89 23 Mining 1.41 1.52 107.92 0.25 24 Mid Status Young Family 3.21 1.39 43.40 -3.53 25 Mature Mid Status Family 4.50 6.59 146.45 4.65 26 Multicultural Urban Lifestyle 0.57 0.00 0.00 27 University Enclaves 0.36 0.51 139.57 0.31 28 Holiday Lifestyle 0.45 0.25 56.21 -0.41 29 Multicultural Mixed Urban 1.10 0.76 69.17 -0.74 30 Establishing Multicultural Family 1.98 0.82 41.66 -2.19 31 Elderly Enclaves 1.15 0.51 44.19 -1.24 32 Establishing Provincial family 2.91 1.20 41.44 -3.24 33 New Age Lifestyle 0.91 0.95 105.02 0.10 34 Mature Provincial Suburban 4.30 1.01 23.60 -5.01 35 Mixed Suburban 0.96 0.19 19.82 -1.07 36 Inland Rural Fringe 1.43 3.36 234.37 3.72 37 Established Multicultural family 1.31 0.13 9.69 -1.16 38 Provincial Mixed Urban 2.17 0.82 37.94 -2.48 39 Low Status Rural Fringe 1.93 0.51 26.25 -2.25 40 Family Achiever 1.91 0.51 26.59 -2.23 41 Old European Blue Collar 1.03 0.95 91.95 -0.19 42 Established Blue Collar Suburban 3.04 6.59 217.10 7.15 43 Blue Collar Family 3.10 2.60 83.72 -1.14 44 Provincial Blue Collar Suburban 5.65 1.77 31.43 -6.73 45 Middle Eastern Multicultural 0.77 0.00 0.00 46 Poor Mixed Urban 1.14 0.82 72.07 -0.70 47 Low Status Mixed Multicultural 1.39 0.70 50.00 -1.40 48 Small Town Blue Collar Suburban 4.39 1.58 36.10 -5.12 49 Established Asian 0.60 0.00 0.00 50 Mobile Holiday Accommodation 0.21 0.13 60.52 -0.17 51 Elderly Provincial Urban 2.04 0.70 34.12 -2.38 52 Provincial Battler 2.93 0.76 25.98 -3.43 53 High Density Welfare 0.16 0.00 0.00 54 Suburban Welfare 0.83 0.00 0.00 55 Indigenous & Remote 1.62 1.08 66.49 -1.17 56 Unclassified 0.13 0.00
  • 61. transcape   data  solu,ons     Thank  you!  
  • 62. Exercise:  Targe(ng  matrix   May  2011   ©  Datalicious  Pty  Ltd   62  
  • 63. >  Exercise:  Targe(ng  matrix   Purchase   Segments:  Colour,  price,   Media   Data     Cycle   product  affinity,  etc   Channels   Points   Default,   awareness   Research,   considera(on   Purchase   intent   Reten(on,   up/cross-­‐sell   May  2011   ©  Datalicious  Pty  Ltd   63  
  • 64. >  Exercise:  Targe(ng  matrix   Purchase   Segments:  Colour,  price,   Media   Data     Cycle   product  affinity,  etc   Channels   Points   Default,   Have  you     Have  you     Display,   Default   awareness   seen  A?   seen  B?   search,  etc   Research,   A  has  great     B  has  great     Search,   Ad  clicks,   considera(on   features!   features!   website,  etc   prod  views   Purchase   A  delivers   B  delivers   Website,   Cart  adds,   intent   great  value!   great  value!   emails,  etc   checkouts   Reten(on,   Why  not   Why  not   Direct  mails,   Email  clicks,   up/cross-­‐sell   buy  B?   buy  A?   emails,  etc   logins,  etc   May  2011   ©  Datalicious  Pty  Ltd   64  
  • 65. May  2011   ©  Datalicious  Pty  Ltd   65  
  • 66. May  2011   ©  Datalicious  Pty  Ltd   66  
  • 67. May  2011   ©  Datalicious  Pty  Ltd   67  
  • 68. May  2011   ©  Datalicious  Pty  Ltd   68  
  • 69. Exercise:  Marke(ng  automa(on   May  2011   ©  Datalicious  Pty  Ltd   69  
  • 70. May  2011   ©  Datalicious  Pty  Ltd   70  
  • 71. >  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.”   May  2011   ©  Datalicious  Pty  Ltd   71  
  • 72. Plan  to  fail  …   May  2011   ©  Datalicious  Pty  Ltd   72  
  • 73. >  Develop  a  tes(ng  matrix   Test   Segment   Content   KPIs   Poten(al   Results   May  2011   ©  Datalicious  Pty  Ltd   73  
  • 74. >  Develop  a  tes(ng  matrix   Test   Segment   Content   KPIs   Poten(al   Results   New   Conversion   Next  step,   Test  #1A     prospects   form  A   order,  etc   ?   ?   New   Conversion   Next  step,   Test  #1B   prospects   form  B   order,  etc   ?   ?   New   Conversion   Next  step,   Test  #1N   prospects   form  N   order,  etc   ?   ?   ?   ?   ?   ?   ?   ?   May  2011   ©  Datalicious  Pty  Ltd   74  
  • 75. >  AIDA  and  AIDAS  formulas     Old  media   New  media   Awareness   Interest   Desire   Ac(on   Sa(sfac(on   Social  media   May  2011   ©  Datalicious  Pty  Ltd   75  
  • 76. >  Simplified  AIDAS  funnel     Reach   Engagement   Conversion   +Buzz   (Awareness)   (Interest  &  Desire)   (Ac-on)   (Sa-sfac-on)   May  2011   ©  Datalicious  Pty  Ltd   76  
  • 77. >  Marke(ng  is  about  people     People   People   People   People   reached   40%   engaged   10%   converted   1%   delighted   May  2011   ©  Datalicious  Pty  Ltd   77  
  • 78. >  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   May  2011   ©  Datalicious  Pty  Ltd   78  
  • 79. New  vs.  returning  visitors   May  2011   ©  Datalicious  Pty  Ltd   79  
  • 80. AU/NZ  vs.  rest  of  world   May  2011   ©  Datalicious  Pty  Ltd   80  
  • 81. >  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       May  2011   ©  Datalicious  Pty  Ltd   81  
  • 82. >  Developing  a  metrics  framework     Level   Reach   Engagement   Conversion   +Buzz   Level  1,   people   Level  2,   strategic   Level  3,   tac(cal   Funnel   breakdowns   May  2011   ©  Datalicious  Pty  Ltd   82  
  • 83. >  Developing  a  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   May  2011   ©  Datalicious  Pty  Ltd   83  
  • 84. >  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.   May  2011   ©  Datalicious  Pty  Ltd   84  
  • 85. >  Importance  of  calendar  events     Traffic  spikes  or  other  data  anomalies  without  context  are   very  hard  to  interpret  and  can  render  data  useless   May  2011   ©  Datalicious  Pty  Ltd   85  
  • 86. >  Out-­‐sourcing  or  in-­‐sourcing?   Year  1   Year  2     Year  3 PlaIorms   Training   Support     Degree  of  in-­‐house  control  and  sophis-ca-on Reduce  vendor  reliance   to  absolute  minimum   Start  taking  control  of   but  consider  the  value   technology  and  data,   of  support  agreements   shi]  vendor  focus  to   for  both  maintenance   Engage  third  par-es   enhancements  and  the   as  well  as  updates  on   with  more  experience   provision  of  training     market  innova-ons  and   to  get  started  and  to   implement  technology   for  internal  resources   new  features.   Time,  Control   May  2011   ©  Datalicious  Pty  Ltd   86  
  • 87. Contact  me   cbartens@datalicious.com     Learn  more   blog.datalicious.com     Follow  me   twi;er.com/datalicious     May  2011   ©  Datalicious  Pty  Ltd   87  
  • 88. Data  >  Insights  >  Ac(on