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never believed Trump was capable of succeeding in his presidential bid, the unbiased analysis of the AI was monitoring countless attitudinal responses to the campaigns. Raincock explains that “when all the human beings, all the experts, all the pollsters were saying no way, it won’t happen – the AI was still predicting the opposite.”
How Did Pollsters Get It So Wrong?
What sets the AI apart from traditional pollsters is the removal of the human element – and therefore the removal of bias. The AI has no preconceptions or prejudgements, and that creates a distinction. When traditional pollsters questions (eg. how do you plan to vote?), whereas the AI delved much deeper, analysing not what people were willing to admit to pollsters, but rather every post a person put out, and their emotional connection to that.
“It stops being about how you will
vote, and it starts being about how a certain candidate or election issue makes you feel. It extrapolates that into what it expects that feeling to predict. It’s a more nuanced understanding than a straightforward answer to a traditional poll,” explains Raincock. Essentially, by using machines rather than human analysis, you completely leave out the innate human bias. “With pollsters and political
commentators, there was a part of them that just couldn’t see how human beings could vote for someone who was saying and doing the things that Donald Trump was saying and doing. And so there was a biased that was connected to their already preconceived ideas as to what was acceptable and what might be deemed to be acceptable by the voting public. And we think that’s
ultimately what it comes down to - it’s precisely because it isn’t a human being that it can predict human beings better.”
What Does This Mean For Marketers?
Raincock believes that the creation of such a capable AI is the beginning of a new wave of consumer analysis. “When we looked at the analysis after 41/50 states and 4/5 swing states. There’s always going to be margin for error in these things but we’re seeing applications for marketing,” she says. “We’re already using AI in some ways in marketing, but beyond that there’s been all sorts of applications for understanding and interpreting data in a more meaningful way, creating more is that because it can predict behaviour and analyse on a massive scale – taking a massive sample, bigger than is humanly possible.” To give an impression of the scale of data the AI can process – it would have taken 2000 researchers 30 years to analyse what Havas analysed in around a month for the US election. With this scale of understanding applied to brand penetration, purchasing power, buying decisions and where buying power lies, the application for marketers can be astronomical. “Mainly the reason why it’s a bit of
an unknown is because people don’t understand it. They start thinking you’re talking unstructured data and dark web – all of these things that are And actually it’s a lot simpler than that. All you’re doing is layering it up with
that you need to understand, and it goes away and understands it for you,” explains Raincock.
EagleAI visualisations of it's election analysis based on extensive data
havaslondon.co.uk
53 issue 29 winter 2016
Words: Georgia Sanders Illustration: Egor Keon
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