Tweet
Tweet

Slide 1

Slide 1 text

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

Slide 2

Slide 2 text

@hannah_bo_banna hello there!

Slide 3

Slide 3 text

bad at photos, pretty good at other stuff

Slide 4

Slide 4 text

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

Slide 5

Slide 5 text

@hannah_bo_banna huh?

Slide 6

Slide 6 text

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

Slide 7

Slide 7 text

links from over 120 sites

Slide 8

Slide 8 text

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

Slide 9

Slide 9 text

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

Slide 10

Slide 10 text

No content

Slide 11

Slide 11 text

No content

Slide 12

Slide 12 text

No content

Slide 13

Slide 13 text

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

Slide 14

Slide 14 text

No content

Slide 15

Slide 15 text

No content

Slide 16

Slide 16 text

No content

Slide 17

Slide 17 text

links from over 150 sites

Slide 18

Slide 18 text

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

Slide 19

Slide 19 text

No content

Slide 20

Slide 20 text

links from over 200 sites

Slide 21

Slide 21 text

@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

Slide 22

Slide 22 text

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

Slide 23

Slide 23 text

@hannah_bo_banna to gain links & coverage for our clients

Slide 24

Slide 24 text

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

Slide 25

Slide 25 text

@hannah_bo_banna over time this translates into stronger organic rankings

Slide 26

Slide 26 text

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

Slide 27

Slide 27 text

so, what’s this talk all about?

Slide 28

Slide 28 text

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

Slide 29

Slide 29 text

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

Slide 30

Slide 30 text

No content

Slide 31

Slide 31 text

@hannah_bo_banna then I was like, wait a minute…

Slide 32

Slide 32 text

@hannah_bo_banna am I really good at this?

Slide 33

Slide 33 text

@hannah_bo_banna when it comes to memory humans are hellishly unreliable

Slide 34

Slide 34 text

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

Slide 35

Slide 35 text

@hannah_bo_banna I feel like this is pretty common

Slide 36

Slide 36 text

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

Slide 37

Slide 37 text

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

Slide 38

Slide 38 text

@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

Slide 39

Slide 39 text

@hannah_bo_banna however

Slide 40

Slide 40 text

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

Slide 41

Slide 41 text

we reassemble it

Slide 42

Slide 42 text

@hannah_bo_banna additionally

Slide 43

Slide 43 text

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

Slide 44

Slide 44 text

@hannah_bo_banna what does this mean?

Slide 45

Slide 45 text

@hannah_bo_banna let me give you an example:

Slide 46

Slide 46 text

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

Slide 47

Slide 47 text

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

Slide 48

Slide 48 text

@hannah_bo_banna I’m going back in time…

Slide 49

Slide 49 text

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

Slide 50

Slide 50 text

@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)

Slide 51

Slide 51 text

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

Slide 52

Slide 52 text

@hannah_bo_banna then this pops in my brain

Slide 53

Slide 53 text

links from over 120 sites

Slide 54

Slide 54 text

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

Slide 55

Slide 55 text

@hannah_bo_banna then this pops in my brain

Slide 56

Slide 56 text

links from over 120 sites

Slide 57

Slide 57 text

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

Slide 58

Slide 58 text

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

Slide 59

Slide 59 text

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

Slide 60

Slide 60 text

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

Slide 61

Slide 61 text

No content

Slide 62

Slide 62 text

No content

Slide 63

Slide 63 text

No content

Slide 64

Slide 64 text

No content

Slide 65

Slide 65 text

No content

Slide 66

Slide 66 text

@hannah_bo_banna remember this?

Slide 67

Slide 67 text

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

Slide 68

Slide 68 text

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

Slide 69

Slide 69 text

@hannah_bo_banna I am supposed to be good at it

Slide 70

Slide 70 text

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

Slide 71

Slide 71 text

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

Slide 72

Slide 72 text

@hannah_bo_banna or reconstruct, or reassemble my memory…

Slide 73

Slide 73 text

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

Slide 74

Slide 74 text

{insert your own expletive here}

Slide 75

Slide 75 text

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

Slide 76

Slide 76 text

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

Slide 77

Slide 77 text

@hannah_bo_banna how did I do this?

Slide 78

Slide 78 text

@hannah_bo_banna this stuff isn’t strictly binary

Slide 79

Slide 79 text

@hannah_bo_banna so I created a scale:

Slide 80

Slide 80 text

@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

Slide 81

Slide 81 text

@hannah_bo_banna huh?

Slide 82

Slide 82 text

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

Slide 83

Slide 83 text

@hannah_bo_banna scores range from 0-500 points

Slide 84

Slide 84 text

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

Slide 85

Slide 85 text

@hannah_bo_banna here are a few examples of scores…

Slide 86

Slide 86 text

this link on the Guardian scores 456 points

Slide 87

Slide 87 text

this link on Fast Company scores 284 points

Slide 88

Slide 88 text

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

Slide 89

Slide 89 text

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

Slide 90

Slide 90 text

@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

Slide 91

Slide 91 text

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

Slide 92

Slide 92 text

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

Slide 93

Slide 93 text

@hannah_bo_banna I consider 3 things:

Slide 94

Slide 94 text

@hannah_bo_banna resonance

Slide 95

Slide 95 text

@hannah_bo_banna resonance noun the power to evoke emotion

Slide 96

Slide 96 text

@hannah_bo_banna initially I might consider this at a topic level

Slide 97

Slide 97 text

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

Slide 98

Slide 98 text

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

Slide 99

Slide 99 text

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

Slide 100

Slide 100 text

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

Slide 101

Slide 101 text

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

Slide 102

Slide 102 text

@hannah_bo_banna breadth of appeal

Slide 103

Slide 103 text

@hannah_bo_banna how many publications are likely to cover this?

Slide 104

Slide 104 text

@hannah_bo_banna can we sell it in to different verticals?

Slide 105

Slide 105 text

@hannah_bo_banna different countries?

Slide 106

Slide 106 text

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

Slide 107

Slide 107 text

@hannah_bo_banna past experience

Slide 108

Slide 108 text

@hannah_bo_banna how have I seen similar pieces perform before?

Slide 109

Slide 109 text

@hannah_bo_banna so…

Slide 110

Slide 110 text

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

Slide 111

Slide 111 text

@hannah_bo_banna fast forward around 8 months

Slide 112

Slide 112 text

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

Slide 113

Slide 113 text

@hannah_bo_banna how good are your predictions?

Slide 114

Slide 114 text

No content

Slide 115

Slide 115 text

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

Slide 116

Slide 116 text

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

Slide 117

Slide 117 text

@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

Slide 118

Slide 118 text

@hannah_bo_banna ok, let’s do this…

Slide 119

Slide 119 text

@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?

Slide 120

Slide 120 text

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

Slide 121

Slide 121 text

@hannah_bo_banna really not very good L

Slide 122

Slide 122 text

@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

Slide 123

Slide 123 text

@hannah_bo_banna how did I do?

Slide 124

Slide 124 text

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

Slide 125

Slide 125 text

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

Slide 126

Slide 126 text

{insert your own expletive here}

Slide 127

Slide 127 text

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

Slide 128

Slide 128 text

@hannah_bo_banna how often am I a bit wrong?

Slide 129

Slide 129 text

@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

Slide 130

Slide 130 text

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

Slide 131

Slide 131 text

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

Slide 132

Slide 132 text

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

Slide 133

Slide 133 text

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

Slide 134

Slide 134 text

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

Slide 135

Slide 135 text

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)

Slide 136

Slide 136 text

@hannah_bo_banna & how often am I very wrong?

Slide 137

Slide 137 text

@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

Slide 138

Slide 138 text

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

Slide 139

Slide 139 text

@hannah_bo_banna quick recap?

Slide 140

Slide 140 text

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

Slide 141

Slide 141 text

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

Slide 142

Slide 142 text

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

Slide 143

Slide 143 text

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

Slide 144

Slide 144 text

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

Slide 145

Slide 145 text

@hannah_bo_banna eeek, let’s move on

Slide 146

Slide 146 text

@hannah_bo_banna how good am I at predicting a winner?

Slide 147

Slide 147 text

@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

Slide 148

Slide 148 text

@hannah_bo_banna actually I did a bit better here J

Slide 149

Slide 149 text

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

Slide 150

Slide 150 text

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

Slide 151

Slide 151 text

@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)

Slide 152

Slide 152 text

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

Slide 153

Slide 153 text

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

Slide 154

Slide 154 text

@hannah_bo_banna quick recap?

Slide 155

Slide 155 text

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

Slide 156

Slide 156 text

@hannah_bo_banna I am simultaneously more right AND more wrong

Slide 157

Slide 157 text

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

Slide 158

Slide 158 text

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

Slide 159

Slide 159 text

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

Slide 160

Slide 160 text

@hannah_bo_banna so what was my problem?

Slide 161

Slide 161 text

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

Slide 162

Slide 162 text

@hannah_bo_banna resonance

Slide 163

Slide 163 text

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

Slide 164

Slide 164 text

@hannah_bo_banna hmmmm

Slide 165

Slide 165 text

as a topic I guess it’s somewhat resonant…

Slide 166

Slide 166 text

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

Slide 167

Slide 167 text

@hannah_bo_banna but I thought we had a bigger problem

Slide 168

Slide 168 text

@hannah_bo_banna breadth of appeal

Slide 169

Slide 169 text

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

Slide 170

Slide 170 text

@hannah_bo_banna past experience

Slide 171

Slide 171 text

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

Slide 172

Slide 172 text

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

Slide 173

Slide 173 text

I think I was hoping for a result like this…

Slide 174

Slide 174 text

or maybe this…

Slide 175

Slide 175 text

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

Slide 176

Slide 176 text

the client wasn’t convinced about this one either…

Slide 177

Slide 177 text

@hannah_bo_banna so what’s going on here?

Slide 178

Slide 178 text

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

Slide 179

Slide 179 text

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

Slide 180

Slide 180 text

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

Slide 181

Slide 181 text

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

Slide 182

Slide 182 text

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

Slide 183

Slide 183 text

@hannah_bo_banna but actually, the piece is closer to this

Slide 184

Slide 184 text

No content

Slide 185

Slide 185 text

this tells you exactly what you would expect…

Slide 186

Slide 186 text

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

Slide 187

Slide 187 text

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

Slide 188

Slide 188 text

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

Slide 189

Slide 189 text

No content

Slide 190

Slide 190 text

No content

Slide 191

Slide 191 text

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

Slide 192

Slide 192 text

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

Slide 193

Slide 193 text

topical angles…

Slide 194

Slide 194 text

niche angles…

Slide 195

Slide 195 text

@hannah_bo_banna & many, many local angles

Slide 196

Slide 196 text

No content

Slide 197

Slide 197 text

No content

Slide 198

Slide 198 text

No content

Slide 199

Slide 199 text

No content

Slide 200

Slide 200 text

No content

Slide 201

Slide 201 text

No content

Slide 202

Slide 202 text

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

Slide 203

Slide 203 text

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

Slide 204

Slide 204 text

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

Slide 205

Slide 205 text

@hannah_bo_banna & I wondered what else might affect it

Slide 206

Slide 206 text

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

Slide 207

Slide 207 text

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

Slide 208

Slide 208 text

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

Slide 209

Slide 209 text

@hannah_bo_banna but actually…

Slide 210

Slide 210 text

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

Slide 211

Slide 211 text

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

Slide 212

Slide 212 text

@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

Slide 213

Slide 213 text

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

Slide 214

Slide 214 text

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

Slide 215

Slide 215 text

this really surprised me

Slide 216

Slide 216 text

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

Slide 217

Slide 217 text

@hannah_bo_banna I recognise this might be tricky to parse

Slide 218

Slide 218 text

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

Slide 219

Slide 219 text

@hannah_bo_banna high prediction ≠ love

Slide 220

Slide 220 text

@hannah_bo_banna here’s an example:

Slide 221

Slide 221 text

this piece reveals the most congested roads across the UK

Slide 222

Slide 222 text

Prediction: A

Slide 223

Slide 223 text

@hannah_bo_banna how did I come up with this?

Slide 224

Slide 224 text

@hannah_bo_banna motoring journalists can be tough to pitch…

Slide 225

Slide 225 text

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

Slide 226

Slide 226 text

@hannah_bo_banna but there are some exceptions:

Slide 227

Slide 227 text

No content

Slide 228

Slide 228 text

No content

Slide 229

Slide 229 text

No content

Slide 230

Slide 230 text

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

Slide 231

Slide 231 text

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

Slide 232

Slide 232 text

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

Slide 233

Slide 233 text

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

Slide 234

Slide 234 text

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

Slide 235

Slide 235 text

@hannah_bo_banna it really does

Slide 236

Slide 236 text

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

Slide 237

Slide 237 text

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

Slide 238

Slide 238 text

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

Slide 239

Slide 239 text

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

Slide 240

Slide 240 text

@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)

Slide 241

Slide 241 text

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

Slide 242

Slide 242 text

@hannah_bo_banna what do I think is going on here?

Slide 243

Slide 243 text

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

Slide 244

Slide 244 text

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

Slide 245

Slide 245 text

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

Slide 246

Slide 246 text

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

Slide 247

Slide 247 text

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

Slide 248

Slide 248 text

@hannah_bo_banna what else did I learn?

Slide 249

Slide 249 text

@hannah_bo_banna I may not be predicting the future

Slide 250

Slide 250 text

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

Slide 251

Slide 251 text

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

Slide 252

Slide 252 text

@hannah_bo_banna but that’s not the whole story

Slide 253

Slide 253 text

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

Slide 254

Slide 254 text

No content

Slide 255

Slide 255 text

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

Slide 256

Slide 256 text

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

Slide 257

Slide 257 text

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

Slide 258

Slide 258 text

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

Slide 259

Slide 259 text

@hannah_bo_banna what if my prediction was different?

Slide 260

Slide 260 text

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

Slide 261

Slide 261 text

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?

Slide 262

Slide 262 text

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…

Slide 263

Slide 263 text

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

Slide 264

Slide 264 text

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

Slide 265

Slide 265 text

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

Slide 266

Slide 266 text

{insert your own expletive here}

Slide 267

Slide 267 text

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

Slide 268

Slide 268 text

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

Slide 269

Slide 269 text

@hannah_bo_banna #1

Slide 270

Slide 270 text

@hannah_bo_banna start recording your own predictions now

Slide 271

Slide 271 text

@hannah_bo_banna actually record them

Slide 272

Slide 272 text

@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

Slide 273

Slide 273 text

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

Slide 274

Slide 274 text

@hannah_bo_banna your memory is unreliable

Slide 275

Slide 275 text

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

Slide 276

Slide 276 text

@hannah_bo_banna we reassemble it

Slide 277

Slide 277 text

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

Slide 278

Slide 278 text

@hannah_bo_banna as such…

Slide 279

Slide 279 text

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

Slide 280

Slide 280 text

@hannah_bo_banna this is actually a really powerful thing to learn

Slide 281

Slide 281 text

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

Slide 282

Slide 282 text

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

Slide 283

Slide 283 text

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

Slide 284

Slide 284 text

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

Slide 285

Slide 285 text

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

Slide 286

Slide 286 text

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

Slide 287

Slide 287 text

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

Slide 288

Slide 288 text

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

Slide 289

Slide 289 text

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

Slide 290

Slide 290 text

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

Slide 291

Slide 291 text

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

Slide 292

Slide 292 text

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

Slide 293

Slide 293 text

@hannah_bo_banna you might not be predicting the future

Slide 294

Slide 294 text

@hannah_bo_banna you might actually be affecting it

Slide 295

Slide 295 text

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

Slide 296

Slide 296 text

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

Slide 297

Slide 297 text

@hannah_bo_banna ultimately…

Slide 298

Slide 298 text

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

Slide 299

Slide 299 text

@hannah_bo_banna give people the opportunity to try stuff out

Slide 300

Slide 300 text

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

Slide 301

Slide 301 text

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

Slide 302

Slide 302 text

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

Slide 303

Slide 303 text

@hannah_bo_banna but mainly because…

Slide 304

Slide 304 text

@hannah_bo_banna you know a lot less than you think

Slide 305

Slide 305 text

@hannah_bo_banna looking back…

Slide 306

Slide 306 text

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

Slide 307

Slide 307 text

@hannah_bo_banna but I let them try out those things anyway

Slide 308

Slide 308 text

@hannah_bo_banna & you know what?

Slide 309

Slide 309 text

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

Slide 310

Slide 310 text

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

Slide 311

Slide 311 text

good luck out there x

Slide 312

Slide 312 text

@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

Slide 313

Slide 313 text

@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