Creativity, Crystal Balls & Eating Ground Glass

2c022eec0c5b8c58dd0b48d1c42863e2?s=47 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.

2c022eec0c5b8c58dd0b48d1c42863e2?s=128

Hannah Smith

October 15, 2018
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Transcript

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

  2. @hannah_bo_banna hello there!

  3. bad at photos, pretty good at other stuff

  4. @hannah_bo_banna my team’s job is to make things that journalists

    want to write about & people want to share
  5. @hannah_bo_banna huh?

  6. this was created for PartyCasino, we analysed forty years of

    box office data, to see which actors made the most profitable films
  7. links from over 120 sites

  8. this was created for Lenstore, it’s the world’s first gigapixel

    time lapse panorama of London’s skyline
  9. @hannah_bo_banna because it’s a gigapixel photo you can zoom in

    wherever you want
  10. None
  11. None
  12. None
  13. @hannah_bo_banna & because it’s a time lapse you can also

    see how London looks across a full day
  14. None
  15. None
  16. None
  17. links from over 150 sites

  18. this was created for GoCompare, it’s a tribute to the

    modernist buildings we’ve lost to the wrecking ball
  19. None
  20. links from over 200 sites

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

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

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

    our clients’ sites
  25. @hannah_bo_banna over time this translates into stronger organic rankings

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

    of websites)
  27. so, what’s this talk all about?

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

  29. @hannah_bo_banna “how good are you at predicting whether or not

    a piece will be successful?”
  30. None
  31. @hannah_bo_banna then I was like, wait a minute…

  32. @hannah_bo_banna am I really good at this?

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

  34. @hannah_bo_banna ever put something in a safe place, & then

    forgotten where that safe place is?
  35. @hannah_bo_banna I feel like this is pretty common

  36. @hannah_bo_banna & most people would accept they have a poor

    memory for that sort of thing
  37. @hannah_bo_banna but how good are you at remembering past events?

  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
  39. @hannah_bo_banna however

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

    we reconstruct the event
  41. we reassemble it

  42. @hannah_bo_banna additionally

  43. @hannah_bo_banna psychologists have noted that we suppress memories that are

    painful or damaging to our self-esteem
  44. @hannah_bo_banna what does this mean?

  45. @hannah_bo_banna let me give you an example:

  46. let’s imagine you’ve asked me whether or not I thought

    that piece would be a success
  47. @hannah_bo_banna right now my brain’s doing something like this…

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

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

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

    piece
  52. @hannah_bo_banna then this pops in my brain

  53. links from over 120 sites

  54. @hannah_bo_banna I’m trying to remember how I felt about the

    piece right before it launched
  55. @hannah_bo_banna then this pops in my brain

  56. links from over 120 sites

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

    great stories…
  58. @hannah_bo_banna who’s the most profitable actor in Hollywood? Tom Cruise?

    Brad Pitt? The Rock?
  59. actually, it’s this guy, in the blue vest

  60. @hannah_bo_banna but am I just reconstructing my memory based on

    what happened post-launch?
  61. None
  62. None
  63. None
  64. None
  65. None
  66. @hannah_bo_banna remember this?

  67. @hannah_bo_banna psychologists have noted that we suppress memories that are

    painful or damaging to our self-esteem
  68. @hannah_bo_banna judging the likelihood of success for creative pieces is

    essentially what I’m paid to do
  69. @hannah_bo_banna I am supposed to be good at it

  70. @hannah_bo_banna it would be damaging to my self-esteem to recall

    instances where I was wrong
  71. @hannah_bo_banna so it’s conceivable that I would suppress memories like

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

  73. @hannah_bo_banna to create a version of history where I always

    thought this campaign was a great idea
  74. {insert your own expletive here}

  75. @hannah_bo_banna so I figured I ought to find out how

    good or otherwise I really am at predicting this stuff
  76. @hannah_bo_banna so at the beginning of this year I resolved

    to make a prediction about how each piece would perform
  77. @hannah_bo_banna how did I do this?

  78. @hannah_bo_banna this stuff isn’t strictly binary

  79. @hannah_bo_banna so I created a scale:

  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
  81. @hannah_bo_banna huh?

  82. this is how we measure the ‘value’ of a link

    https://www.vervesearch.com/linkscore/
  83. @hannah_bo_banna scores range from 0-500 points

  84. @hannah_bo_banna scores are calculated based on a range of factors

    including: site authority, language, follow vs nofollow & more
  85. @hannah_bo_banna here are a few examples of scores…

  86. this link on the Guardian scores 456 points

  87. this link on Fast Company scores 284 points

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

    42 points
  89. @hannah_bo_banna to help you understand what this looks like in

    terms of numbers of links
  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
  91. @hannah_bo_banna so I began to make predictions about all of

    our campaigns ahead of launch
  92. @hannah_bo_banna wait… how do you make predictions like this?

  93. @hannah_bo_banna I consider 3 things:

  94. @hannah_bo_banna resonance

  95. @hannah_bo_banna resonance noun the power to evoke emotion

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

  97. 35x articles are written about AI vs RPA & these

    articles get 8x more engagement
  98. @hannah_bo_banna but it’s really important to think about this at

    a human level too
  99. @hannah_bo_banna how many people are likely to care about this?

  100. @hannah_bo_banna how many people are likely to be touched in

    some way by this?
  101. @hannah_bo_banna more on resonance here: bit.ly/feels-2016 & bit.ly/time-machine-2016

  102. @hannah_bo_banna breadth of appeal

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

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

  105. @hannah_bo_banna different countries?

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

    of stories?
  107. @hannah_bo_banna past experience

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

  109. @hannah_bo_banna so…

  110. @hannah_bo_banna in January I began to make predictions about all

    of our campaigns ahead of launch
  111. @hannah_bo_banna fast forward around 8 months

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

  113. @hannah_bo_banna how good are your predictions?

  114. None
  115. @hannah_bo_banna back then I had not yet checked my own

    numbers
  116. @hannah_bo_banna & this rather lovely quote would prove to be

    more true than I’d have liked …
  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
  118. @hannah_bo_banna ok, let’s do this…

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

  121. @hannah_bo_banna really not very good L

  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
  123. @hannah_bo_banna how did I do?

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

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

    am right
  126. {insert your own expletive here}

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

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

  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
  130. @hannah_bo_banna for around 33% of campaigns my prediction was out

    by one scoring band
  131. @hannah_bo_banna here are some examples of where I was a

    bit wrong
  132. we created this for GoCompare Travel Insurance, to highlight a

    variety of languages which are in danger of extinction
  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
  134. we created this for IG Index, it compares the salaries

    of various world leaders to GDP, population & average earnings
  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)
  136. @hannah_bo_banna & how often am I very wrong?

  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
  138. @hannah_bo_banna for around 20% of campaigns my prediction was out

    by two or more scoring bands
  139. @hannah_bo_banna quick recap?

  140. 47% 33% 20% 0% 10% 20% 30% 40% 50% 60%

    70% 80% 90% 100% All Campaigns Correct A Bit Wrong Very Wrong
  141. @hannah_bo_banna dear reader, this does not make me feel good

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

    years now
  143. @hannah_bo_banna I really feel like I ought to be more

    right than this
  144. 47% 33% 20% 0% 10% 20% 30% 40% 50% 60%

    70% 80% 90% 100% All Campaigns Correct A Bit Wrong Very Wrong
  145. @hannah_bo_banna eeek, let’s move on

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

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

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

    these campaigns
  150. @hannah_bo_banna which means I am right more often than I

    am wrong J
  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)
  152. @hannah_bo_banna so I was very wrong about one in four

    of our best performing campaigns
  153. 75% 25% 0% 10% 20% 30% 40% 50% 60% 70%

    80% 90% 100% Best Performing Correct A Bit Wrong Very Wrong
  154. @hannah_bo_banna quick recap?

  155. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

    100% Best Performing All Campaigns Correct A Bit Wrong Very Wrong
  156. @hannah_bo_banna I am simultaneously more right AND more wrong

  157. @hannah_bo_banna I thought you might be interested to see one

    of the campaigns I was very wrong about
  158. we created this for GoCompare, using 20 years of IMDb

    data, we calculated the most filmed locations on the planet
  159. I predicted less than 1,000 points; the campaign actually generated

    over 29,000 points (392 links) Prediction: E Actual: A
  160. @hannah_bo_banna so what was my problem?

  161. @hannah_bo_banna remember I said I consider 3 things when making

    predictions? (I’ll look at each in turn)
  162. @hannah_bo_banna resonance

  163. @hannah_bo_banna have you ever wondered what the most filmed locations

    on earth are?
  164. @hannah_bo_banna hmmmm

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

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

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

  168. @hannah_bo_banna breadth of appeal

  169. I didn’t feel that the results were surprising, and, as

    such, I felt that the piece would have limited appeal
  170. @hannah_bo_banna past experience

  171. @hannah_bo_banna but I think it some pretty lazy thinking about

    past experiences that really led me awry
  172. I didn’t feel that the results were surprising, and, as

    such, I felt that the piece would have limited appeal
  173. I think I was hoping for a result like this…

  174. or maybe this…

  175. I was so concerned about this, that when we reviewed

    the data I suggested we drop the campaign
  176. the client wasn’t convinced about this one either…

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

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

    really do)
  179. @hannah_bo_banna you start to come up with a sort of

    shorthand
  180. @hannah_bo_banna which can be either helpful or harmful depending on

    how lazy your thinking is
  181. in my head, this was ‘Director’s Cut’ for filming locations

  182. @hannah_bo_banna & perhaps you can kind of see where I

    was going with that
  183. @hannah_bo_banna but actually, the piece is closer to this

  184. None
  185. this tells you exactly what you would expect…

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

    be true
  187. this piece is not ‘Director’s Cut’ for filming locations it’s

    closer to ‘Unicorn League’ for filming locations
  188. the results aren’t surprising… but journalists covered it because we’ve

    proved it
  189. None
  190. None
  191. @hannah_bo_banna but that wasn’t the only direction I was wrong

    in…
  192. @hannah_bo_banna the piece had huge breadth of appeal (which somehow

    I just missed)
  193. topical angles…

  194. niche angles…

  195. @hannah_bo_banna & many, many local angles

  196. None
  197. None
  198. None
  199. None
  200. None
  201. None
  202. @hannah_bo_banna I’m very glad my team talked me round J

  203. 29,611 points (392 links) we’ve generated coverage from this piece,

    every single month, for the past nine months…
  204. @hannah_bo_banna so we’ve seen that lazy thinking, or shorthand like

    ‘Director’s Cut’ for film locations affects my judgement
  205. @hannah_bo_banna & I wondered what else might affect it

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

    my predictions?
  207. @hannah_bo_banna when something is your idea, there’s a danger of

    falling in love with it
  208. @hannah_bo_banna so I wondered if maybe my predictions for my

    own ideas were wildly optimistic
  209. @hannah_bo_banna but actually…

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

    accurate
  211. @hannah_bo_banna & where I was wrong, it was only by

    one scoring band
  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
  213. @hannah_bo_banna my predictions about my own ideas are the most

    accurate
  214. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

    100% My Ideas Best Performing All Campaigns Correct A Bit Wrong Very Wrong
  215. this really surprised me

  216. @hannah_bo_banna I then wondered if whether or not I ‘loved’

    the idea had an impact on my predictions
  217. @hannah_bo_banna I recognise this might be tricky to parse

  218. @hannah_bo_banna I make predictions based on how well I think

    each campaign will do
  219. @hannah_bo_banna high prediction ≠ love

  220. @hannah_bo_banna here’s an example:

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

  222. Prediction: A

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

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

  225. @hannah_bo_banna unless it’s got an engine, wheels, & goes vroom,

    frequently, they’re not interested
  226. @hannah_bo_banna but there are some exceptions:

  227. None
  228. None
  229. None
  230. @hannah_bo_banna this is a resonant topic because pretty much everyone

    gets annoyed about being stuck in traffic
  231. @hannah_bo_banna it has reasonable breadth of appeal because we can

    go to both UK nationals & regionals
  232. @hannah_bo_banna & I feel confident because I’ve seen other similar

    stuff do well before
  233. Prediction: A Love it? Nope Actual: band A (over 16,000

    points & 317 links)
  234. @hannah_bo_banna if I ‘love’ the idea, does it impact my

    predictions?
  235. @hannah_bo_banna it really does

  236. @hannah_bo_banna if I love the idea my predictions are only

    accurate 36% of the time
  237. @hannah_bo_banna also, if I love the idea my predictions are

    always high as opposed to low
  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
  239. @hannah_bo_banna what if I don’t love the idea?

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

  243. @hannah_bo_banna I’m pretty good at making accurate predictions for my

    own ideas & ideas I don’t love
  244. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

    100% Don't Love My Ideas Correct A Bit Wrong Very Wrong
  245. @hannah_bo_banna but I’m pretty terrible at making accurate predictions for

    ideas I love
  246. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

    100% Love Don't Love My Ideas Correct A Bit Wrong Very Wrong
  247. @hannah_bo_banna apparently I don’t fall in love with my own

    ideas, but I do fall in love with other peoples
  248. @hannah_bo_banna what else did I learn?

  249. @hannah_bo_banna I may not be predicting the future

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

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

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

  253. 0 1000 2000 3000 4000 5000 6000 7000 8000 9000

    10000 April May Demolishing Modernism – Cumulative Points by Month
  254. None
  255. 0 1000 2000 3000 4000 5000 6000 7000 8000 9000

    10000 April May June Demolishing Modernism – Cumulative Points by Month
  256. 0 1000 2000 3000 4000 5000 6000 7000 8000 9000

    10000 April May June July August Demolishing Modernism – Cumulative Points by Month
  257. @hannah_bo_banna I don’t think I did this deliberately (in order

    to make my predictions correct)
  258. @hannah_bo_banna but clearly my own belief or bias affected the

    outcome here
  259. @hannah_bo_banna what if my prediction was different?

  260. I predicted 5,000 – 9,999 points; but what if I’d

    have predicted 1,000 – 1,999 points? Prediction: D? Actual: D?
  261. 0 1000 2000 3000 4000 5000 6000 7000 8000 9000

    10000 April May Demolishing Modernism – Cumulative Points by Month would I have been ok with stopping here?
  262. 0 1000 2000 3000 4000 5000 6000 7000 8000 9000

    10000 April May June July August Demolishing Modernism – Cumulative Points by Month we never would have got here…
  263. @hannah_bo_banna I deliberately did not share my predictions with my

    team
  264. @hannah_bo_banna they didn’t even know I was making predictions (they

    do now)
  265. @hannah_bo_banna but I wonder if I have been unconsciously influencing

    them nevertheless
  266. {insert your own expletive here}

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

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

  269. @hannah_bo_banna #1

  270. @hannah_bo_banna start recording your own predictions now

  271. @hannah_bo_banna actually record them

  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
  273. I was certain I had scored this & many other

    creative campaigns higher
  274. @hannah_bo_banna your memory is unreliable

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

  276. @hannah_bo_banna we reassemble it

  277. @hannah_bo_banna & we suppress memories that are painful or damaging

    to our self-esteem
  278. @hannah_bo_banna as such…

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

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

  281. @hannah_bo_banna over time if you can recognise the situations in

    which your judgements are bad
  282. @hannah_bo_banna you might be able to adapt & make better

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

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

  285. 75% 25% 0% 10% 20% 30% 40% 50% 60% 70%

    80% 90% 100% Best Performing Correct A Bit Wrong Very Wrong
  286. @hannah_bo_banna I’m pretty good at making accurate predictions for my

    own ideas & ideas I don’t love
  287. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

    100% Don't Love My Ideas Correct A Bit Wrong Very Wrong
  288. @hannah_bo_banna & it’s really important that I know that…

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

    I love
  290. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

    100% Love Don't Love My Ideas Correct A Bit Wrong Very Wrong
  291. @hannah_bo_banna I don’t fall in love with my own ideas,

    but I do fall in love with other peoples
  292. @hannah_bo_banna if you do decide to make predictions like this,

    exercise a little caution…
  293. @hannah_bo_banna you might not be predicting the future

  294. @hannah_bo_banna you might actually be affecting it

  295. @hannah_bo_banna your own belief or bias may affect the outcome

    (which might be good, or really, really, really bad)
  296. I predicted 5,000 – 9,999 points; but what if I’d

    have predicted 1,000 – 1,999 points? Prediction: D? Actual: D?
  297. @hannah_bo_banna ultimately…

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

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

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

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

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

    learn anything
  303. @hannah_bo_banna but mainly because…

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

  305. @hannah_bo_banna looking back…

  306. @hannah_bo_banna there have been countless times members of my team

    have tried things I was *certain* would not work
  307. @hannah_bo_banna but I let them try out those things anyway

  308. @hannah_bo_banna & you know what?

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

    a massive error
  310. @hannah_bo_banna because a whole bunch of those things worked out

    just great
  311. good luck out there x

  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
  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