Why every data-scientist needs a water bottle
It has been well documented that the loneliest role in football is the goalkeeper. A role where people remember the goals you let through rather than the many saves you made. It must feel isolating, and the isolation is acutely amplified when you are staring down a penalty shootout. In a World Cup. For England. Against Colombia. For those that don’t know, England have a pretty bad history when it comes to penalties. And back in 2018 this was the exact situation that the 24 year old England goalkeeper Jordan Pickford faced.
There are few people on earth who have studied the decision-making behind penalty kicks more than London School of Economics behavioural economist Ignacio Palacios-Huerta. Of 440 penalties studied the goalkeeper only stays in the centre 3% of the time. And yet nearly 1/3 of penalty kicks go straight down the middle. Which means the law of averages indicates a goalkeeper should actually stand still every once in a while to increase their save rate. But statistics are just that. Norms. They lack human understanding. They lack context. They lack an appreciation that our human thought process is imperfect. That we are naturally flawed. That is those big moments it is better to be seen to do something, dive left or right in this case, than to stand still.
Jordan Pickford didn’t stand still that night. And he successfully helped England through to the semi-final. How? By looking at the human side of the equation. At ‘human’ data. As Gareth Southgate (England’s manager) talked to the players at the end of extra-time, the goalkeeping coach swapped Pickford’s water bottle for one with instructions. Data on which side each Colombian taker would go was written on it. And so, in the decisive moment, Pickford remained on his line ready to spring. Pickford did not guess. He knew. As Bacca struck the ball Pickford dived to his right. And he saved it.
We can all use more human data. Today, we face similar challenges in marketing and advertising. Data expertise alone is not sufficient to truly understand audiences because data by itself is nothing. It only has value if it can be turned into information and insight. In its purest form data science relies on a perfect world. However, you need to understand how people perceive. How they decide between A and B. Why we make rash decisions. Why we carry bias. Why we are naturally risk averse.
This sounds simple. But if you do not know what moves your customer psychologically, how can you expect to move them physically? Too often we look to dashboards for our intelligence. Unless that data includes human data, or the analyst is looking for human flaws it will only ever tell half the story. A dashboard can tell you what people are doing, but rarely why they are doing it.
“Many programs and services are designed not for the brains of humans but of Vulcans.”
— Rory Sutherland, Ogilvy & Mather
Applying behavior principles when reviewing data makes it imperfect. But by doing so, it actually makes it effective. Only then can you start to unify your goals and data with customer behaviour. What we know is that behavior matters. And it can completely change the rules of engagement between customers and companies. While the area of behavioural science is long established we are only just starting to see its impact in a data driven marketing world. The question is how legacy businesses look to achieve this. And the answer may lie in behavioural software linked to financial decisions. There are three aspects to this according to Greg Davies, head of behavioural finance at Oxford Risk, Digital, Design and Data.“Think of it as a Venn diagram: you have digital platforms as a mechanism for delivery of information; data that can enable you to personalise what you put in front of clients through the digital channel; and a design that makes the platform comfortable and easy to use. At the centre is the user”All parts are equal. Data isn’t the end goal, the user is. Marketing’s role is to offer real value to real people in real time. Which requires business to look beyond the data: the end user or target audience. This helps ensure precision and empathy are ingredients of equal measure. Context is still important, even if its not ‘king’ anymore. When, where and how messaging is delivered will sway consumer processing and reaction to marketing stimulus. Test and learn. And finally. The landscape is evolving rapidly. Potentially marketing is seeing the most seismic shifts in any category. As people become comfortable with new technologies such as voice or VR, they will provide new channels of influence. So consistently adapt and refine the approach. As human behavior is considered more carefully as part of business strategy, it’s important for us to think about the possible ramifications of using technology to influence human behavior so that they can be avoided, and companies can move forward ethically. We are definitely at a crossroads between behaviour and data in which those who successfully incorporate the former to their analysis will have an advantage through a better understanding of their customers.
Which is why every data scientist needs a water bottle.
This thought leadership piece was written by David Hearn, Head of Strategy, Bloomberg and member of the IAB SEA+India Analytics Committee.