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

Publishing.


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 Publishing .