Design   Thinking   for   Innovation   Final   Assignment     Challenge:     The   media   industry   is   under   tremendous   pressure.   Big   technology   companies   like   YouTube   and   Facebook   are   competing   for   the   same   advertising   revenue   budgets.   To   remain   viable,   media   owners   like   national   newspapers   and   public   service   broadcasters,   are   increasing   their   pace   of   digital   innovation.   I   work   for   a   UK   media   owner   and   my   team   is   responsible   for   developing   advertising   products   using   data   and   technology.     A   couple   of   years   ago,   my   team   worked   on   an   exciting   but   challenging   project   in   which   we   adopted   a   couple   of   design   thinking   tools.   The   project   involved   developing   an    algorithm   that   dynamically   assigned   website   visitors   into   audience   segments   based   on   their   purchase   propensity.   These   audiences   could   then   be   served   relevant   targeted   advertising.   Based   on   interviews   with   our   customers   (media   agencies   and   marketing   directors),   there   was   strong   interest   in   the   product.   Internally,   our   sales   team   was   keen   to   be   able   to   offer   a   sophisticated   advertising   product   that   could   command   a   higher   price.     Our   challenge   was   to   develop   a   viable   product   that   delivered   strong   marketing   performance   for   advertisers.   Our   secondary   challenge   was   to   prove   the   potential   of   the   product   in   order   to   secure   the   internal   technology   resources   to   productionise   and   scale.     Selection:     In   order   to   address   both   the   need   to   prove   product   effectiveness,   and   secure   internal   investment   -   we   decided   to   build   a   prototype   and   conduct   a   real-world   test   -   what   was   effectively   a    learning   launch .   Our   learning   launch   involved   running   live   digital   marketing   campaigns,   using   the   new   prototype.   As   Professor   Hesse   outlined   in   the   video   lecture,   we   were   keen   to   test   our   prototype   quickly   and   cheaply   to   understand   what   was   working   and   what   was   not,   before   committing   further   time   and   resources.     Application:     We   invited   two   beta   customers   (advertisers),   representing   two   different   product   types,   to   participate   in   the   learning   launch.   We   developed   a   series   of   hypotheses   that   then   needed   to   be   translated   into   the   design   of   the   test.   This   involved   setting   up   a   matrix   of   test   and   control   audience   grouping,   and   ensuring   that   the   advertising   campaign   delivery   was   set-up   correctly   to   avoid   website   users   falling   into   more   than   one   audience   group   which   would   muddy   the   results.     In   hindsight,   we   loaded   too   many   hypotheses   into   the   study.   This   necessitated   a   highly   complex   set-up   that   was   extremely   diﬃcult   for   the   campaign   operations   team   to   execute,   and   which   generated   ‘too   much’   data   which   required   a   signiﬁcant   amount   of   time   needed   to   process   the   results   at   the   end   of   the   campaign.   In   retrospect,   we   should   have   taken   a   more   iterative   approach   to   the   learning   launch   -   starting   with   the   core   hypotheses   ﬁrst,   and   then   layering   in   additional   test   criteria   in   subsequent   phases.     Insight:     As   Professor   Hesse   noted   in   the   video   lecture,   “A   learning   launch   is   not   a   failure   so   long   as   you   learn   something”   -   and   we   certainly   did   learn   a   lot   from   ours.   The   campaigns   themselves   were   successful

-   we   were   able   to   deﬁnitely   prove   that   the   product   generated   superior   advertising   effectiveness   by   driving   more   user   engagement   for   the   advertiser   (relative   to   the   control   group).   Our   beta   customers   were   pleased   and   impressed   with   the   results   of   the   study,   however   we   were   unable   to   prove   that   the   increased   effectiveness   would   have   justiﬁed   a   demand   for   the   product   at   a   higher   price   point.   That   hypothesis   was   not   factored   into   the   experiment.     Additionally,   we   were   not   able   to   deliver   on   our   secondary   challenge   -   which   was   to   prove   that   the   project   was   worth   investing   in   to   productionise   and   scale.   A   number   of   factors   contributed   to   this,   including   internal   stakeholder   politics.   In   retrospect,   adopting   more   design   thinking   tools   in   the   earlier   “What   is?”   and   “What   if?”   stages,   such   as   involving   more   stakeholder   colleagues   in   brainstorming    and    concept   developmen t (1) ,   would   have   ensured   greater   buy   in   from   the   start.     Approach:     Thankfully   because   we   kept   an   open   mind   throughout   the   learning   launch,   we   were   able   to   extract   a   myriad   of   unplanned   observations   and   learnings.   By   tuning   into   these,   we   were   able   to   avoid   conﬁrmation   bias,   and/or   potentially   terminating   the   idea   prematurely.   Instead,   the   insights   from   the   learning   launch   helped   us   to   redeﬁne   the   challenge   and   the   product   itself.      We   noted   the   deep   insights   we   were   able   to   gather   from   the   activation   of   the   product   and   knew   we   had   an   important   innovation   in   our   hands,   one   worth   sharing.   So   we   took   the   decision   to   pivot.   Instead   of   building   a   targeting   product,   we   used   the   algorithm   to   develop   an   audience   insights   tool   -   which   happened   to   be   the   ﬁrst   of   its   kind   in   our   industry.   These   insight   dashboards   became   a   success,   not   only   did   advertisers   begin   to   request   it   by   name,   it   also   won   us   an   industry   award   for   ‘Best   Use   of   Data’   that   year.     Our   primary   reﬂections   were   to   incorporate   more   design   learning   tools,   involving   more   stakeholders   and   diverse   views   from   the   start   to   ensure   we   were   correctly   deﬁning   the   challenges   and   the   opportunities.     Notes:

(1) Pablo Cervantes   and   Tommy Hall.   (2011)    Designing   for   Growth .   Columbia   Business   School

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