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 . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: 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 .
### Output: 