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Creativity, Crystal Balls & Eating Ground Glass

Hannah Smith
October 15, 2018

Creativity, Crystal Balls & Eating Ground Glass

Over the past eighteen months, Hannah has been responsible for launching more than 100 creative pieces which have generated over 5,300 pieces of linked coverage.

Towards the end of last year, someone asked her “How good are you at predicting the success (or otherwise) of a piece?” She realised she didn’t have a great answer, so this year she resolved to make a prediction about how each piece would perform ahead of launch.

In this session she’ll be sharing what she got right, what she got wrong, and what she learned along the way.

Hannah Smith

October 15, 2018
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Transcript

  1. @hannah_bo_banna
    creativity,
    crystal balls
    &
    eating ground glass
    @hannah_bo_banna

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  2. @hannah_bo_banna
    hello there!

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  3. bad at photos, pretty good at other stuff

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  4. @hannah_bo_banna
    my team’s job is to make things
    that journalists want to write about
    & people want to share

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  5. @hannah_bo_banna
    huh?

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  6. this was created for PartyCasino,
    we analysed forty years of box office data, to see which actors made the most profitable films

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  7. links from over 120 sites

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  8. this was created for Lenstore,
    it’s the world’s first gigapixel time lapse panorama of London’s skyline

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  9. @hannah_bo_banna
    because it’s a gigapixel photo
    you can zoom in wherever you want

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  13. @hannah_bo_banna
    & because it’s a time lapse
    you can also see how London
    looks across a full day

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  17. links from over 150 sites

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  18. this was created for GoCompare,
    it’s a tribute to the modernist buildings we’ve lost to the wrecking ball

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  20. links from over 200 sites

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  21. @hannah_bo_banna
    over the past 18 months,
    I’ve been responsible for launching more than
    100 creative pieces
    which have generated over
    5,300 pieces of linked coverage

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  22. @hannah_bo_banna
    incidentally,
    you know why do we do this, right?

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  23. @hannah_bo_banna
    to gain links & coverage
    for our clients

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  24. @hannah_bo_banna
    links from highly authoritative sites,
    increase the authority of our clients’ sites

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  25. @hannah_bo_banna
    over time this translates into
    stronger organic rankings

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  26. stronger organic rankings = more money
    (for the vast majority of websites)

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  27. so, what’s this talk all about?

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  28. @hannah_bo_banna
    towards the end of last year
    someone asked me:

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  29. @hannah_bo_banna
    “how good are you at predicting
    whether or not a piece will be successful?”

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  31. @hannah_bo_banna
    then I was like, wait a minute…

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  32. @hannah_bo_banna
    am I really good at this?

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  33. @hannah_bo_banna
    when it comes to memory
    humans are hellishly unreliable

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  34. @hannah_bo_banna
    ever put something in a safe place,
    & then forgotten where that safe place is?

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  35. @hannah_bo_banna
    I feel like this is pretty common

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  36. @hannah_bo_banna
    & most people would accept they have
    a poor memory for that sort of thing

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  37. @hannah_bo_banna
    but how good are you at remembering
    past events?

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  38. @hannah_bo_banna
    most people think that
    they’re able to accurately recall
    things that happened in the past
    & how they were feeling at the time

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  39. @hannah_bo_banna
    however

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  40. @hannah_bo_banna
    neuroscientists have discovered that
    each time we remember something,
    we reconstruct the event

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  41. we reassemble it

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  42. @hannah_bo_banna
    additionally

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  43. @hannah_bo_banna
    psychologists have noted that
    we suppress memories that are painful or
    damaging to our self-esteem

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  44. @hannah_bo_banna
    what does this mean?

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  45. @hannah_bo_banna
    let me give you an example:

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  46. let’s imagine you’ve asked me whether or not
    I thought that piece would be a success

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  47. @hannah_bo_banna
    right now my brain’s doing
    something like this…

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  48. @hannah_bo_banna
    I’m going back in time…

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  49. @hannah_bo_banna
    we’d have been working on it
    through September 2017

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  50. @hannah_bo_banna
    there was a bunch of other stuff
    going on at the same time
    (we were selling the company, I did BrightonSEO, new client pitches)

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  51. @hannah_bo_banna
    I’m thinking about the challenges we had
    producing the piece

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  52. @hannah_bo_banna
    then this pops in my brain

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  53. links from over 120 sites

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  54. @hannah_bo_banna
    I’m trying to remember how I felt about the
    piece right before it launched

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  55. @hannah_bo_banna
    then this pops in my brain

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  56. links from over 120 sites

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  57. @hannah_bo_banna
    I think that I felt the data
    revealed some great stories…

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  58. @hannah_bo_banna
    who’s the most profitable actor in Hollywood?
    Tom Cruise? Brad Pitt? The Rock?

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  59. actually, it’s this guy, in the blue vest

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  60. @hannah_bo_banna
    but am I just reconstructing my memory
    based on what happened post-launch?

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  65. View Slide

  66. @hannah_bo_banna
    remember this?

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  67. @hannah_bo_banna
    psychologists have noted that
    we suppress memories that are painful or
    damaging to our self-esteem

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  68. @hannah_bo_banna
    judging the likelihood of success
    for creative pieces
    is essentially what I’m paid to do

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  69. @hannah_bo_banna
    I am supposed to be good at it

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  70. @hannah_bo_banna
    it would be damaging to my
    self-esteem to recall
    instances where I was wrong

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  71. @hannah_bo_banna
    so it’s conceivable that I would suppress
    memories like that

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  72. @hannah_bo_banna
    or reconstruct,
    or reassemble my memory…

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  73. @hannah_bo_banna
    to create a version of history
    where I always thought
    this campaign was a great idea

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  74. {insert your own expletive here}

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  75. @hannah_bo_banna
    so I figured I ought to find out
    how good or otherwise
    I really am at predicting this stuff

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  76. @hannah_bo_banna
    so at the beginning of this year
    I resolved to make a prediction about
    how each piece would perform

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  77. @hannah_bo_banna
    how did I do this?

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  78. @hannah_bo_banna
    this stuff isn’t strictly binary

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  79. @hannah_bo_banna
    so I created a scale:

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  80. @hannah_bo_banna
    Scoring Band LinkScore points
    A 10,000+
    B 5,000 to 9,999
    C 2,000 to 4,999
    D 1,000 – 1,999
    E less than 1,000

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  81. @hannah_bo_banna
    huh?

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  82. this is how we measure the ‘value’ of a link
    https://www.vervesearch.com/linkscore/

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  83. @hannah_bo_banna
    scores range from 0-500 points

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  84. @hannah_bo_banna
    scores are calculated based on a range
    of factors including:
    site authority, language,
    follow vs nofollow & more

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  85. @hannah_bo_banna
    here are a few examples of scores…

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  86. this link on the Guardian scores 456 points

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  87. this link on Fast Company scores 284 points

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  88. this link on the Independent is nofollow so it scores 42 points

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  89. @hannah_bo_banna
    to help you understand what this looks like in
    terms of numbers of links

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  90. @hannah_bo_banna
    Scoring Band LinkScore points Equivalent number of links
    A 10,000+ more than 100
    B 5,000 to 9,999 50-99
    C 2,000 to 4,999 20-49
    D 1,000 – 1,999 10-19
    E less than 1,000 less than 10
    this is just an indicator – an average link is not 100 points

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  91. @hannah_bo_banna
    so I began to make predictions
    about all of our campaigns ahead of launch

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  92. @hannah_bo_banna
    wait…
    how do you make predictions like this?

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  93. @hannah_bo_banna
    I consider 3 things:

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  94. @hannah_bo_banna
    resonance

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  95. @hannah_bo_banna
    resonance
    noun
    the power to evoke emotion

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  96. @hannah_bo_banna
    initially I might consider this at a topic level

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  97. 35x articles are written about AI vs RPA
    & these articles get 8x more engagement

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  98. @hannah_bo_banna
    but it’s really important to think about this at a
    human level too

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  99. @hannah_bo_banna
    how many people are likely to care about this?

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  100. @hannah_bo_banna
    how many people are likely to be touched in
    some way by this?

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  101. @hannah_bo_banna
    more on resonance here:
    bit.ly/feels-2016
    &
    bit.ly/time-machine-2016

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  102. @hannah_bo_banna
    breadth of appeal

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  103. @hannah_bo_banna
    how many publications are likely
    to cover this?

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  104. @hannah_bo_banna
    can we sell it in to different verticals?

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  105. @hannah_bo_banna
    different countries?

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  106. @hannah_bo_banna
    can we use the piece to tell a variety of stories?

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  107. @hannah_bo_banna
    past experience

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  108. @hannah_bo_banna
    how have I seen similar pieces
    perform before?

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  109. @hannah_bo_banna
    so…

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  110. @hannah_bo_banna
    in January I began to make predictions
    about all of our campaigns ahead of launch

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  111. @hannah_bo_banna
    fast forward around 8 months

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  112. @hannah_bo_banna
    & I’m pitching this talk to Will…

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  113. @hannah_bo_banna
    how good are your
    predictions?

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  115. @hannah_bo_banna
    back then I had not yet
    checked my own numbers

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  116. @hannah_bo_banna
    & this rather lovely quote
    would prove to be more true
    than I’d have liked …

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  117. @hannah_bo_banna
    “she who lives by the crystal ball
    soon learns to eat ground glass…”
    ~Edgar R. Fiedler, The Three Rs of Economic Forecasting: Irrational, Irrelevant and Irreverent, 1977

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  118. @hannah_bo_banna
    ok, let’s do this…

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  119. @hannah_bo_banna
    this talk is in 3 parts:
    - how good am I at predicting the future accurately?
    - what seems to affect my judgement?
    - what do I think I’ve learned?

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  120. @hannah_bo_banna
    how good am I
    at predicting the future accurately?

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  121. @hannah_bo_banna
    really not very good L

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  122. @hannah_bo_banna
    Scoring Band LinkScore points Equivalent number of links
    A 10,000+ more than 100
    B 5,000 to 9,999 50-99
    C 2,000 to 4,999 20-49
    D 1,000 – 1,999 10-19
    E less than 1,000 less than 10
    as a reminder – these are the scoring bands – I predict a band for each campaign

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  123. @hannah_bo_banna
    how did I do?

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  124. @hannah_bo_banna
    I correctly predicted the
    LinkScore band for just 47% of campaigns

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  125. @hannah_bo_banna
    which means I am wrong more often
    than I am right

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  126. {insert your own expletive here}

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  127. @hannah_bo_banna
    ok, so I’m wrong a lot…
    but how wrong?

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  128. @hannah_bo_banna
    how often am I a bit wrong?

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  129. @hannah_bo_banna
    Scoring Band LinkScore points Equivalent number of links
    A 10,000+ more than 100
    B 5,000 to 9,999 50-99
    C 2,000 to 4,999 20-49
    D 1,000 – 1,999 10-19
    E less than 1,000 less than 10
    a bit wrong = wrong by one scoring band

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  130. @hannah_bo_banna
    for around 33% of campaigns
    my prediction was out by one scoring band

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  131. @hannah_bo_banna
    here are some examples of where
    I was a bit wrong

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  132. we created this for GoCompare Travel Insurance,
    to highlight a variety of languages which are in danger of extinction

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  133. I thought this campaign would do better than it actually did…
    Prediction: band B (5,000-9,999 points) Actual: band C (4,957 points & 48 links)
    Prediction: B
    Actual: C

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  134. we created this for IG Index,
    it compares the salaries of various world leaders to GDP, population & average earnings

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  135. Prediction: C
    Actual: B
    I thought this campaign would do worse than it actually did…
    Prediction: band C (2,000-4,999 points) Actual: band B (8,844 points & 74 links)

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  136. @hannah_bo_banna
    & how often am I very wrong?

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  137. @hannah_bo_banna
    Scoring Band LinkScore points Equivalent number of links
    A 10,000+ more than 100
    B 5,000 to 9,999 50-99
    C 2,000 to 4,999 20-49
    D 1,000 – 1,999 10-19
    E less than 1,000 less than 10
    very wrong = wrong by two or more scoring bands

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  138. @hannah_bo_banna
    for around 20% of campaigns
    my prediction was out
    by two or more scoring bands

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  139. @hannah_bo_banna
    quick recap?

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  140. 47% 33% 20%
    0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
    All Campaigns
    Correct A Bit Wrong Very Wrong

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  141. @hannah_bo_banna
    dear reader,
    this does not make me feel good

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  142. @hannah_bo_banna
    I’ve been doing this stuff
    for close to ten years now

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  143. @hannah_bo_banna
    I really feel like I ought to
    be more right than this

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  144. 47% 33% 20%
    0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
    All Campaigns
    Correct A Bit Wrong Very Wrong

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  145. @hannah_bo_banna
    eeek, let’s move on

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  146. @hannah_bo_banna
    how good am I at predicting a winner?

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  147. @hannah_bo_banna
    Scoring Band LinkScore points Equivalent number of links
    A 10,000+ more than 100
    B 5,000 to 9,999 50-99
    this is just an indicator – an average link is not 100 points

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  148. @hannah_bo_banna
    actually I did a bit better here J

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  149. @hannah_bo_banna
    I correctly predicted the
    LinkScore band for 75% of these campaigns

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  150. @hannah_bo_banna
    which means I am right more often
    than I am wrong J

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  151. @hannah_bo_banna
    but for the remaining 25%
    I wasn’t a bit wrong, I was very wrong*
    (*very wrong means I was out by two or more scoring bands)

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  152. @hannah_bo_banna
    so I was very wrong about
    one in four of our best performing campaigns

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  153. 75% 25%
    0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
    Best Performing
    Correct A Bit Wrong Very Wrong

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  154. @hannah_bo_banna
    quick recap?

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  155. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
    Best Performing
    All Campaigns
    Correct A Bit Wrong Very Wrong

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  156. @hannah_bo_banna
    I am simultaneously
    more right AND more wrong

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  157. @hannah_bo_banna
    I thought you might be interested to see one
    of the campaigns I was very wrong about

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  158. we created this for GoCompare,
    using 20 years of IMDb data, we calculated the most filmed locations on the planet

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  159. I predicted less than 1,000 points;
    the campaign actually generated over 29,000 points (392 links)
    Prediction: E
    Actual: A

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  160. @hannah_bo_banna
    so what was my problem?

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  161. @hannah_bo_banna
    remember I said I consider 3 things when
    making predictions?
    (I’ll look at each in turn)

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  162. @hannah_bo_banna
    resonance

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  163. @hannah_bo_banna
    have you ever wondered what the most filmed
    locations on earth are?

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  164. @hannah_bo_banna
    hmmmm

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  165. as a topic I guess it’s somewhat resonant…

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  166. @hannah_bo_banna
    In short, I’m not 100% sold

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  167. @hannah_bo_banna
    but I thought we had a bigger problem

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  168. @hannah_bo_banna
    breadth of appeal

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  169. I didn’t feel that the results were surprising,
    and, as such, I felt that the piece would have limited appeal

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  170. @hannah_bo_banna
    past experience

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  171. @hannah_bo_banna
    but I think it some pretty lazy thinking
    about past experiences
    that really led me awry

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  172. I didn’t feel that the results were surprising,
    and, as such, I felt that the piece would have limited appeal

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  173. I think I was hoping for a result like this…

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  174. or maybe this…

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  175. I was so concerned about this, that when we reviewed the data
    I suggested we drop the campaign

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  176. the client wasn’t convinced about this one either…

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  177. @hannah_bo_banna
    so what’s going on here?

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  178. @hannah_bo_banna
    when you create a lot of pieces
    (& we really do)

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  179. @hannah_bo_banna
    you start to come up with a sort of shorthand

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  180. @hannah_bo_banna
    which can be either helpful or harmful
    depending on how lazy your thinking is

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  181. in my head, this was ‘Director’s Cut’ for filming locations

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  182. @hannah_bo_banna
    & perhaps you can kind of see where I was
    going with that

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  183. @hannah_bo_banna
    but actually, the piece is closer to this

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  184. View Slide

  185. this tells you exactly what you would expect…

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  186. @hannah_bo_banna
    but it’s compelling nevertheless,
    because we’ve proved it to be true

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  187. this piece is not ‘Director’s Cut’ for filming locations
    it’s closer to ‘Unicorn League’ for filming locations

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  188. the results aren’t surprising… but journalists covered it because we’ve proved it

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  189. View Slide

  190. View Slide

  191. @hannah_bo_banna
    but that wasn’t the only direction
    I was wrong in…

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  192. @hannah_bo_banna
    the piece had huge breadth of appeal
    (which somehow I just missed)

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  193. topical angles…

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  194. niche angles…

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  195. @hannah_bo_banna
    & many, many local angles

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  196. View Slide

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  199. View Slide

  200. View Slide

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  202. @hannah_bo_banna
    I’m very glad my team talked me round J

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  203. 29,611 points (392 links)
    we’ve generated coverage from this piece, every single month, for the past nine months…

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  204. @hannah_bo_banna
    so we’ve seen that lazy thinking,
    or shorthand like
    ‘Director’s Cut’ for film locations
    affects my judgement

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  205. @hannah_bo_banna
    & I wondered what else
    might affect it

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  206. @hannah_bo_banna
    if the campaign is my idea
    how accurate are my predictions?

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  207. @hannah_bo_banna
    when something is your idea,
    there’s a danger of falling in love with it

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  208. @hannah_bo_banna
    so I wondered if maybe my predictions for my
    own ideas were wildly optimistic

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  209. @hannah_bo_banna
    but actually…

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  210. @hannah_bo_banna
    my predictions for my own campaign ideas
    were 78% accurate

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  211. @hannah_bo_banna
    & where I was wrong,
    it was only by one scoring band

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  212. @hannah_bo_banna
    LinkScore points Equivalent number of links
    10,000+ more than 100
    5,000 to 9,999 50-99
    2,000 to 4,999 20-49
    1,000 – 1,999 10-19
    less than 1,000 less than 10
    this is just an indicator – an average link is not 100 points

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  213. @hannah_bo_banna
    my predictions about my own ideas
    are the most accurate

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  214. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
    My Ideas
    Best Performing
    All Campaigns
    Correct A Bit Wrong Very Wrong

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  215. this really surprised me

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  216. @hannah_bo_banna
    I then wondered if whether or not
    I ‘loved’ the idea
    had an impact on my predictions

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  217. @hannah_bo_banna
    I recognise this might be tricky to parse

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  218. @hannah_bo_banna
    I make predictions based on how well I think
    each campaign will do

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  219. @hannah_bo_banna
    high prediction ≠ love

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  220. @hannah_bo_banna
    here’s an example:

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  221. this piece reveals the most congested roads across the UK

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  222. Prediction: A

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  223. @hannah_bo_banna
    how did I come up with this?

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  224. @hannah_bo_banna
    motoring journalists can be tough to pitch…

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  225. @hannah_bo_banna
    unless it’s got an engine, wheels,
    & goes vroom,
    frequently, they’re not interested

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  226. @hannah_bo_banna
    but there are some exceptions:

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  227. View Slide

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  230. @hannah_bo_banna
    this is a resonant topic
    because pretty much everyone
    gets annoyed about being stuck in traffic

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  231. @hannah_bo_banna
    it has reasonable breadth of appeal because we
    can go to both UK nationals & regionals

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  232. @hannah_bo_banna
    & I feel confident because I’ve seen other
    similar stuff do well before

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  233. Prediction: A
    Love it? Nope
    Actual: band A (over 16,000 points & 317 links)

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  234. @hannah_bo_banna
    if I ‘love’ the idea,
    does it impact my predictions?

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  235. @hannah_bo_banna
    it really does

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  236. @hannah_bo_banna
    if I love the idea
    my predictions are only accurate
    36% of the time

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  237. @hannah_bo_banna
    also, if I love the idea my predictions
    are always high as opposed to low

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  238. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
    Love
    My Ideas
    Best Performing
    All Campaigns
    Correct A Bit Wrong Very Wrong

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  239. @hannah_bo_banna
    what if I don’t love the idea?

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  240. @hannah_bo_banna
    If I don’t love the idea
    my predictions are accurate
    71% of the time
    (& when I’m wrong I’m only a bit wrong)

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  241. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
    Don't Love
    Love
    My Ideas
    Best Performing
    All Campaigns
    Correct A Bit Wrong Very Wrong

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  242. @hannah_bo_banna
    what do I think is going on here?

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  243. @hannah_bo_banna
    I’m pretty good at making
    accurate predictions for my own ideas
    & ideas I don’t love

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  244. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
    Don't Love
    My Ideas
    Correct A Bit Wrong Very Wrong

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  245. @hannah_bo_banna
    but I’m pretty terrible at making
    accurate predictions for
    ideas I love

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  246. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
    Love
    Don't Love
    My Ideas
    Correct A Bit Wrong Very Wrong

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  247. @hannah_bo_banna
    apparently I don’t fall in love with my own ideas,
    but I do fall in love with other peoples

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  248. @hannah_bo_banna
    what else did I learn?

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  249. @hannah_bo_banna
    I may not be predicting the future

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  250. @hannah_bo_banna
    I’m concerned I might actually be affecting it

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  251. the campaign generated 9,790 points (200 links)
    Prediction: B
    Actual: B

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  252. @hannah_bo_banna
    but that’s not the whole story

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  253. 0
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    April May
    Demolishing Modernism – Cumulative Points by Month

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  254. View Slide

  255. 0
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    Demolishing Modernism – Cumulative Points by Month

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  256. 0
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    Demolishing Modernism – Cumulative Points by Month

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  257. @hannah_bo_banna
    I don’t think I did this deliberately
    (in order to make my predictions correct)

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  258. @hannah_bo_banna
    but clearly my own belief or bias
    affected the outcome here

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  259. @hannah_bo_banna
    what if my prediction was different?

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  260. I predicted 5,000 – 9,999 points;
    but what if I’d have predicted 1,000 – 1,999 points?
    Prediction: D?
    Actual: D?

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  261. 0
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    Demolishing Modernism – Cumulative Points by Month
    would I have been ok with stopping here?

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  262. 0
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    we never would have got here…

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  263. @hannah_bo_banna
    I deliberately did not share my predictions
    with my team

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  264. @hannah_bo_banna
    they didn’t even know I was making predictions
    (they do now)

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  265. @hannah_bo_banna
    but I wonder if I have been
    unconsciously influencing them nevertheless

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  266. {insert your own expletive here}

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  267. @hannah_bo_banna
    ok, let’s wrap this thing up…

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  268. @hannah_bo_banna
    I’d like to leave you with a few thoughts

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  269. @hannah_bo_banna
    #1

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  270. @hannah_bo_banna
    start recording your own predictions now

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  271. @hannah_bo_banna
    actually record them

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  272. @hannah_bo_banna
    when I came to run the numbers on my own
    predictions I was shocked by some of the
    numbers I’d written down

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  273. I was certain I had scored this & many other creative campaigns higher

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  274. @hannah_bo_banna
    your memory is unreliable

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  275. @hannah_bo_banna
    each time we remember something,
    we reconstruct the event

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  276. @hannah_bo_banna
    we reassemble it

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  277. @hannah_bo_banna
    & we suppress memories that are painful or
    damaging to our self-esteem

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  278. @hannah_bo_banna
    as such…

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  279. @hannah_bo_banna
    your judgement might be considerably worse
    than you think it is

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  280. @hannah_bo_banna
    this is actually a really powerful thing to learn

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  281. @hannah_bo_banna
    over time if you can recognise the situations in
    which your judgements are bad

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  282. @hannah_bo_banna
    you might be able to adapt
    & make better decisions

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  283. @hannah_bo_banna
    it is really good for me to know that:

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  284. @hannah_bo_banna
    I’m pretty good at spotting
    a ‘great’ campaign

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  285. 75% 25%
    0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
    Best Performing
    Correct A Bit Wrong Very Wrong

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  286. @hannah_bo_banna
    I’m pretty good at making
    accurate predictions for my own ideas
    & ideas I don’t love

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  287. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
    Don't Love
    My Ideas
    Correct A Bit Wrong Very Wrong

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  288. @hannah_bo_banna
    & it’s really important that I know that…

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  289. @hannah_bo_banna
    I’m pretty terrible at making
    accurate predictions for
    ideas I love

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  290. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
    Love
    Don't Love
    My Ideas
    Correct A Bit Wrong Very Wrong

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  291. @hannah_bo_banna
    I don’t fall in love with my own ideas, but I do
    fall in love with other peoples

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  292. @hannah_bo_banna
    if you do decide to make predictions like this,
    exercise a little caution…

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  293. @hannah_bo_banna
    you might not be
    predicting the future

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  294. @hannah_bo_banna
    you might actually be affecting it

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  295. @hannah_bo_banna
    your own belief or bias
    may affect the outcome
    (which might be good, or really, really, really bad)

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  296. I predicted 5,000 – 9,999 points;
    but what if I’d have predicted 1,000 – 1,999 points?
    Prediction: D?
    Actual: D?

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  297. @hannah_bo_banna
    ultimately…

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  298. @hannah_bo_banna
    getting things right,
    is more important
    than being right

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  299. @hannah_bo_banna
    give people the opportunity
    to try stuff out

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  300. @hannah_bo_banna
    even if you’re not convinced
    that stuff will work

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  301. @hannah_bo_banna
    firstly because if things do go wrong
    people will actually learn something

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  302. @hannah_bo_banna
    if you’re not allowed to make mistakes,
    you can’t learn anything

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  303. @hannah_bo_banna
    but mainly because…

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  304. @hannah_bo_banna
    you know a lot less than you think

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  305. @hannah_bo_banna
    looking back…

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  306. @hannah_bo_banna
    there have been countless times
    members of my team have tried things
    I was *certain* would not work

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  307. @hannah_bo_banna
    but I let them try out
    those things anyway

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  308. @hannah_bo_banna
    & you know what?

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  309. @hannah_bo_banna
    stopping them from trying those things
    would have been a massive error

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  310. @hannah_bo_banna
    because a whole bunch of those things
    worked out just great

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  311. good luck out there x

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  312. @hannah_bo_banna
    Hannah Smith
    Head of Creative
    Verve Search
    send questions and/or pictures of cats to: [email protected]
    tweet things @hannah_bo_banna

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  313. @hannah_bo_banna
    Big love to the team at Verve who helped me put this together J
    Images:
    fortune: http://askbobarcana.blogspot.com/2013/02/the-golden-wheel-fortune-teller.html
    money: http://fortune.com/2017/01/19/scent-of-money/
    good: https://www.youtube.com/watch?v=eHT5KNoWjzY
    lego: https://www.looper.com/40131/trolls-director-reassemble-lego-movie-sequel/
    jesus titty christ: http://www.sickchirpse.com/illusions-that-will-blow-your-mind/
    calvin & hobbes: http://inverseintuition.org/phi_2010/areas/cavecliff.html
    danbo - https://www.flickr.com/photos/nomadic_lass/6889892777/in/set-72157629144987013
    unicorn - http://queenofheartsonthesleeve.tumblr.com/post/58736423727/ok-not-much-but-here-it-is-3
    Credits

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