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.

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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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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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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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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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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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: hannah@vervesearch.com
     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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